Adaptive multi-resolution Modularity for detecting communities in networks

NASA Astrophysics Data System (ADS)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

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.

Similarity between community structures of different online social networks and its impact on underlying community detection

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2015-03-01

As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

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

The best of both worlds: A combined approach for analyzing microalgal diversity via metabarcoding and morphology-based methods

PubMed Central

Kahlert, Maria; Fink, Patrick

2017-01-01

An increasing number of studies use next generation sequencing (NGS) to analyze complex communities, but is the method sensitive enough when it comes to identification and quantification of species? We compared NGS with morphology-based identification methods in an analysis of microalgal (periphyton) communities. We conducted a mesocosm experiment in which we allowed two benthic grazer species to feed upon benthic biofilms, which resulted in altered periphyton communities. Morphology-based identification and 454 (Roche) pyrosequencing of the V4 region in the small ribosomal unit (18S) rDNA gene were used to investigate the community change caused by grazing. Both the NGS-based data and the morphology-based method detected a marked shift in the biofilm composition, though the two methods varied strongly in their abilities to detect and quantify specific taxa, and neither method was able to detect all species in the biofilms. For quantitative analysis, we therefore recommend using both metabarcoding and microscopic identification when assessing the community composition of eukaryotic microorganisms. PMID:28234997

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

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

Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit

PubMed Central

Schaub, Michael T.; Delvenne, Jean-Charles; Yaliraki, Sophia N.; Barahona, Mauricio

2012-01-01

In recent years, there has been a surge of interest in community detection algorithms for complex networks. A variety of computational heuristics, some with a long history, have been proposed for the identification of communities or, alternatively, of good graph partitions. In most cases, the algorithms maximize a particular objective function, thereby finding the ‘right’ split into communities. Although a thorough comparison of algorithms is still lacking, there has been an effort to design benchmarks, i.e., random graph models with known community structure against which algorithms can be evaluated. However, popular community detection methods and benchmarks normally assume an implicit notion of community based on clique-like subgraphs, a form of community structure that is not always characteristic of real networks. Specifically, networks that emerge from geometric constraints can have natural non clique-like substructures with large effective diameters, which can be interpreted as long-range communities. In this work, we show that long-range communities escape detection by popular methods, which are blinded by a restricted ‘field-of-view’ limit, an intrinsic upper scale on the communities they can detect. The field-of-view limit means that long-range communities tend to be overpartitioned. We show how by adopting a dynamical perspective towards community detection [1], [2], in which the evolution of a Markov process on the graph is used as a zooming lens over the structure of the network at all scales, one can detect both clique- or non clique-like communities without imposing an upper scale to the detection. Consequently, the performance of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of equally good results in real networks with local, sparser connectivity. We illustrate our ideas with constructive examples and through the analysis of real-world networks from imaging, protein structures and the power grid, where a multiscale structure of non clique-like communities is revealed. PMID:22384178

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.

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.

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.

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.

Workshop on the Detection, Classification, Localization and Density Estimation of Marine Mammals Using Passive Acoustics - 2015

DTIC Science & Technology

2015-09-30

together the research community working on marine mammal acoustics to discuss detection, classification, localization and density estimation methods...and Density Estimation of Marine Mammals Using Passive Acoustics - 2015 John A. Hildebrand Scripps Institution of Oceanography UCSD La Jolla...dclde LONG-TERM GOALS The goal of this project was to bring together the community of researchers working on methods for detection

COMPARISON OF MEMBRANE FILTER, MULTIPLE-FERMENTATION-TUBE, AND PRESENCE-ABSENCE TECHNIQUES FOR DETECTING TOTAL COLIFORMS IN SMALL COMMUNITY WATER SYSTEMS

EPA Science Inventory

Methods for detecting total coliform bacteria in drinking water were compared using 1483 different drinking water samples from 15 small community water systems in Vermont and New Hampshire. The methods included the membrane filter (MF) technique, a ten tube fermentation tube tech...

Game theory and extremal optimization for community detection in complex dynamic networks.

PubMed

Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca

2014-01-01

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

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.

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

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

Tripartite community structure in social bookmarking data

NASA Astrophysics Data System (ADS)

Neubauer, Nicolas; Obermayer, Klaus

2011-12-01

Community detection is a branch of network analysis concerned with identifying strongly connected subnetworks. Social bookmarking sites aggregate datasets of often hundreds of millions of triples (document, user, and tag), which, when interpreted as edges of a graph, give rise to special networks called 3-partite, 3-uniform hypergraphs. We identify challenges and opportunities of generalizing community detection and in particular modularity optimization to these structures. Two methods for community detection are introduced that preserve the hypergraph's special structure to different degrees. Their performance is compared on synthetic datasets, showing the benefits of structure preservation. Furthermore, a tool for interactive exploration of the community detection results is introduced and applied to examples from real datasets. We find additional evidence for the importance of structure preservation and, more generally, demonstrate how tripartite community detection can help understand the structure of social bookmarking data.

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

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.

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.

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

PubMed

Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

2018-03-15

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Overlapping community detection in weighted networks via a Bayesian approach

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Maximal Neighbor Similarity Reveals Real Communities in Networks

PubMed Central

Žalik, Krista Rizman

2015-01-01

An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence. PMID:26680448

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.

Behavior Based Social Dimensions Extraction for Multi-Label Classification

PubMed Central

Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

2016-01-01

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

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.

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

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.

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.

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 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

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.

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.

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

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

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.

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

NASA Astrophysics Data System (ADS)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

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.

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.

Foliar fungi of Betula pendula: impact of tree species mixtures and assessment methods

PubMed Central

Nguyen, Diem; Boberg, Johanna; Cleary, Michelle; Bruelheide, Helge; Hönig, Lydia; Koricheva, Julia; Stenlid, Jan

2017-01-01

Foliar fungi of silver birch (Betula pendula) in an experimental Finnish forest were investigated across a gradient of tree species richness using molecular high-throughput sequencing and visual macroscopic assessment. We hypothesized that the molecular approach detects more fungal taxa than visual assessment, and that there is a relationship among the most common fungal taxa detected by both techniques. Furthermore, we hypothesized that the fungal community composition, diversity, and distribution patterns are affected by changes in tree diversity. Sequencing revealed greater diversity of fungi on birch leaves than the visual assessment method. One species showed a linear relationship between the methods. Species-specific variation in fungal community composition could be partially explained by tree diversity, though overall fungal diversity was not affected by tree diversity. Analysis of specific fungal taxa indicated tree diversity effects at the local neighbourhood scale, where the proportion of birch among neighbouring trees varied, but not at the plot scale. In conclusion, both methods may be used to determine tree diversity effects on the foliar fungal community. However, high-throughput sequencing provided higher resolution of the fungal community, while the visual macroscopic assessment detected functionally active fungal species. PMID:28150710

Comparison of DNA preservation methods for environmental bacterial community samples

USGS Publications Warehouse

Gray, Michael A.; Pratte, Zoe A.; Kellogg, Christina A.

2013-01-01

Field collections of environmental samples, for example corals, for molecular microbial analyses present distinct challenges. The lack of laboratory facilities in remote locations is common, and preservation of microbial community DNA for later study is critical. A particular challenge is keeping samples frozen in transit. Five nucleic acid preservation methods that do not require cold storage were compared for effectiveness over time and ease of use. Mixed microbial communities of known composition were created and preserved by DNAgard™, RNAlater®, DMSO–EDTA–salt (DESS), FTA® cards, and FTA Elute® cards. Automated ribosomal intergenic spacer analysis and clone libraries were used to detect specific changes in the faux communities over weeks and months of storage. A previously known bias in FTA® cards that results in lower recovery of pure cultures of Gram-positive bacteria was also detected in mixed community samples. There appears to be a uniform bias across all five preservation methods against microorganisms with high G + C DNA. Overall, the liquid-based preservatives (DNAgard™, RNAlater®, and DESS) outperformed the card-based methods. No single liquid method clearly outperformed the others, leaving method choice to be based on experimental design, field facilities, shipping constraints, and allowable cost.

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

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 DRIED BLOOD SPOT METHOD TO EVALUATE CHOLINESTERASE ACTIVITY IN YOUNG CHILDREN

EPA Science Inventory

Field methods are needed to detect and monitor anticholinesterase pesticide exposure of young children. Twenty children, aged 11-18 months, living in an agricultural community along the US/Mexico border were enrolled in a pilot study investigating methods to detect pesticide expo...

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.

Bayesian Community Detection in the Space of Group-Level Functional Differences

PubMed Central

Venkataraman, Archana; Yang, Daniel Y.-J.; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder. PMID:26955022

Bayesian Community Detection in the Space of Group-Level Functional Differences.

PubMed

Venkataraman, Archana; Yang, Daniel Y-J; Pelphrey, Kevin A; Duncan, James S

2016-08-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

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

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.

Detection of hazardous cavities with combined geophysical methods

NASA Astrophysics Data System (ADS)

Hegymegi, Cs.; Nyari, Zs.; Pattantyus-Abraham, M.

2003-04-01

Unknown near-surface cavities often cause problems for municipal communities all over the world. This is the situation in Hungary in many towns and villages, too. Inhabitants and owners of real estates (houses, cottages, lands) are responsible for the safety and stability of their properties. The safety of public sites belongs to the local municipal community. Both (the owner and the community) are interested in preventing accidents. Near-surface cavities (unknown caves or earlier built and forgotten cellars) usually can be easily detected by surface geophysical methods. Traditional and recently developed measuring techniques in seismics, geoelectrics and georadar are suitable for economical investigation of hazardous, potentially collapsing cavities, prior to excavation and reinforcement. This poster will show some example for detection of cellars and caves being dangerous for civil population because of possible collapse under public sites (road, yard, playground, agricultural territory, etc.). The applied and presented methods are ground penetrating radar, seismic surface tomography and analysis of single traces, geoelectric 2D and 3D resistivity profiling. Technology and processing procedure will be presented.

Comparison of DNA preservation methods for environmental bacterial community samples.

PubMed

Gray, Michael A; Pratte, Zoe A; Kellogg, Christina A

2013-02-01

Field collections of environmental samples, for example corals, for molecular microbial analyses present distinct challenges. The lack of laboratory facilities in remote locations is common, and preservation of microbial community DNA for later study is critical. A particular challenge is keeping samples frozen in transit. Five nucleic acid preservation methods that do not require cold storage were compared for effectiveness over time and ease of use. Mixed microbial communities of known composition were created and preserved by DNAgard(™), RNAlater(®), DMSO-EDTA-salt (DESS), FTA(®) cards, and FTA Elute(®) cards. Automated ribosomal intergenic spacer analysis and clone libraries were used to detect specific changes in the faux communities over weeks and months of storage. A previously known bias in FTA(®) cards that results in lower recovery of pure cultures of Gram-positive bacteria was also detected in mixed community samples. There appears to be a uniform bias across all five preservation methods against microorganisms with high G + C DNA. Overall, the liquid-based preservatives (DNAgard(™), RNAlater(®), and DESS) outperformed the card-based methods. No single liquid method clearly outperformed the others, leaving method choice to be based on experimental design, field facilities, shipping constraints, and allowable cost. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Mobile acoustic transects miss rare bat species: implications of survey method and spatio-temporal sampling for monitoring bats

PubMed Central

Wallrichs, Megan A.; Ober, Holly K.; McCleery, Robert A.

2017-01-01

Due to increasing threats facing bats, long-term monitoring protocols are needed to inform conservation strategies. Effective monitoring should be easily repeatable while capturing spatio-temporal variation. Mobile acoustic driving transect surveys (‘mobile transects’) have been touted as a robust, cost-effective method to monitor bats; however, it is not clear how well mobile transects represent dynamic bat communities, especially when used as the sole survey approach. To assist biologists who must select a single survey method due to resource limitations, we assessed the effectiveness of three acoustic survey methods at detecting species richness in a vast protected area (Everglades National Park): (1) mobile transects, (2) stationary surveys that were strategically located by sources of open water and (3) stationary surveys that were replicated spatially across the landscape. We found that mobile transects underrepresented bat species richness compared to stationary surveys across all major vegetation communities and in two distinct seasons (dry/cool and wet/warm). Most critically, mobile transects failed to detect three rare bat species, one of which is federally endangered. Spatially replicated stationary surveys did not estimate higher species richness than strategically located stationary surveys, but increased the rate at which species were detected in one vegetation community. The survey strategy that detected maximum species richness and the highest mean nightly species richness with minimal effort was a strategically located stationary detector in each of two major vegetation communities during the wet/warm season. PMID:29134138

Mobile acoustic transects miss rare bat species: implications of survey method and spatio-temporal sampling for monitoring bats.

PubMed

Braun de Torrez, Elizabeth C; Wallrichs, Megan A; Ober, Holly K; McCleery, Robert A

2017-01-01

Due to increasing threats facing bats, long-term monitoring protocols are needed to inform conservation strategies. Effective monitoring should be easily repeatable while capturing spatio-temporal variation. Mobile acoustic driving transect surveys ('mobile transects') have been touted as a robust, cost-effective method to monitor bats; however, it is not clear how well mobile transects represent dynamic bat communities, especially when used as the sole survey approach. To assist biologists who must select a single survey method due to resource limitations, we assessed the effectiveness of three acoustic survey methods at detecting species richness in a vast protected area (Everglades National Park): (1) mobile transects, (2) stationary surveys that were strategically located by sources of open water and (3) stationary surveys that were replicated spatially across the landscape. We found that mobile transects underrepresented bat species richness compared to stationary surveys across all major vegetation communities and in two distinct seasons (dry/cool and wet/warm). Most critically, mobile transects failed to detect three rare bat species, one of which is federally endangered. Spatially replicated stationary surveys did not estimate higher species richness than strategically located stationary surveys, but increased the rate at which species were detected in one vegetation community. The survey strategy that detected maximum species richness and the highest mean nightly species richness with minimal effort was a strategically located stationary detector in each of two major vegetation communities during the wet/warm season.

Decoding communities in networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Decoding communities in networks.

PubMed

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

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.

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

Complete graph model for community detection

NASA Astrophysics Data System (ADS)

Sun, Peng Gang; Sun, Xiya

2017-04-01

Community detection brings plenty of considerable problems, which has attracted more attention for many years. This paper develops a new framework, which tries to measure the interior and the exterior of a community based on a same metric, complete graph model. In particular, the exterior is modeled as a complete bipartite. We partition a network into subnetworks by maximizing the difference between the interior and the exterior of the subnetworks. In addition, we compare our approach with some state of the art methods on computer-generated networks based on the LFR benchmark as well as real-world networks. The experimental results indicate that our approach obtains better results for community detection, is capable of splitting irregular networks and achieves perfect results on the karate network and the dolphin network.

Algorithm for parametric community detection in networks.

PubMed

Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

2012-07-01

Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is moderate; (iv) the heuristic version of the proposed parametric method, viewed as a modularity maximization tool, gives better results than the CNM heuristic for large instances.

A METHOD TO DETECT VIABLE HELICOBACTER PYLORI BACTERIA IN GROUNDWATER

EPA Science Inventory

The inability to detect the presence of viable Helicobacter pylori bacteria in environmental waters has hindered the public health community in assessing the role water may playin the transmission of this pathogen. This work describes a cultural enrichment method coupled with an...

Monitoring of oil hydrocarbons pollution in the Sea of Japan, based on detection of marker genes in microbial communities

NASA Astrophysics Data System (ADS)

Kim, A. V.; Buzoleva, L. S.; Bogatyrenko, E. A.; Zemskaya, T. I.; Mamaeva, E. V.

2018-01-01

By means of molecular biology techniques, metabolic potential of microbial communities within the regions of inshore water areas in the Sea of Japan with various anthropogenic load was explored. Presence of functional genes, responsible for oil hydrocarbons destruction, for microbial communities within the regions of inshore water areas in the Sea of Japan was first researched. In total microbial DNA from water mass in the regions with chronic anthropogenic pollution, the genes, responsible for oxidation of broad range of n-alkanes and polycyclic aromatic hydrocarbons, were found. Detection of marker genes in the background water area (in the Vostok Bay) was ever indicating ecological deterioration within this territory. Thereby, it was demonstrated, that molecular genetic methods, aimed at marker gene detection in total bacterial DNA from environment objects, proved themselves to be more effective technique for identification of oil hydrocarbons water pollution, in comparison with trivial culturable methods.

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.

Detecting spatial regimes in ecosystems | Science Inventory ...

EPA Pesticide Factsheets

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory based method, on both terrestrial and aquatic animal data (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning

Scalable Static and Dynamic Community Detection Using Grappolo

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

Halappanavar, Mahantesh; Lu, Hao; Kalyanaraman, Anantharaman

Graph clustering, popularly known as community detection, is a fundamental kernel for several applications of relevance to the Defense Advanced Research Projects Agency’s (DARPA) Hierarchical Identify Verify Exploit (HIVE) Pro- gram. Clusters or communities represent natural divisions within a network that are densely connected within a cluster and sparsely connected to the rest of the network. The need to compute clustering on large scale data necessitates the development of efficient algorithms that can exploit modern architectures that are fundamentally parallel in nature. How- ever, due to their irregular and inherently sequential nature, many of the current algorithms for community detectionmore » are challenging to parallelize. In response to the HIVE Graph Challenge, we present several parallelization heuristics for fast community detection using the Louvain method as the serial template. We implement all the heuristics in a software library called Grappolo. Using the inputs from the HIVE Challenge, we demonstrate superior performance and high quality solutions based on four parallelization heuristics. We use Grappolo on static graphs as the first step towards community detection on streaming graphs.« less

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

Sensitivity and accuracy of DNA based methods used to describe aquatic communities for early detection of invasive fish species

EPA Science Inventory

For biomonitoring efforts aimed at early detection of aquatic invasive species (AIS), the ability to detect rare individuals is key and requires accurate species level identification to maintain a low occurrence probability of non-detection errors (failure to detect a present spe...

Detecting and analyzing research communities in longitudinal scientific networks.

PubMed

Leone Sciabolazza, Valerio; Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

2017-01-01

A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

Detecting and analyzing research communities in longitudinal scientific networks

PubMed Central

Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

2017-01-01

A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes. PMID:28797047

The relationship between species detection probability and local extinction probability

USGS Publications Warehouse

Alpizar-Jara, R.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Pollock, K.H.; Rosenberry, C.S.

2004-01-01

In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow detection probabilities that are < 1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species detection probability covaries negatively with local probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction probability performed fairly well and provided no basis for preferring one to the other.

Community detection in networks with unequal groups.

PubMed

Zhang, Pan; Moore, Cristopher; Newman, M E J

2016-01-01

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

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.

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.

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

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.

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.

Detecting spatial regimes in ecosystems

USGS Publications Warehouse

Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

2017-01-01

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

Weighted community detection and data clustering using message passing

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Yanchen; Zhang, Pan

2018-03-01

Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

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.

Community detection in networks: A user guide

NASA Astrophysics Data System (ADS)

Fortunato, Santo; Hric, Darko

2016-11-01

Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

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

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.

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.

Microarray-Based Analysis of Subnanogram Quantities of Microbial Community DNAs by Using Whole-Community Genome Amplification†

PubMed Central

Wu, Liyou; Liu, Xueduan; Schadt, Christopher W.; Zhou, Jizhong

2006-01-01

Microarray technology provides the opportunity to identify thousands of microbial genes or populations simultaneously, but low microbial biomass often prevents application of this technology to many natural microbial communities. We developed a whole-community genome amplification-assisted microarray detection approach based on multiple displacement amplification. The representativeness of amplification was evaluated using several types of microarrays and quantitative indexes. Representative detection of individual genes or genomes was obtained with 1 to 100 ng DNA from individual or mixed genomes, in equal or unequal abundance, and with 1 to 500 ng community DNAs from groundwater. Lower concentrations of DNA (as low as 10 fg) could be detected, but the lower template concentrations affected the representativeness of amplification. Robust quantitative detection was also observed by significant linear relationships between signal intensities and initial DNA concentrations ranging from (i) 0.04 to 125 ng (r2 = 0.65 to 0.99) for DNA from pure cultures as detected by whole-genome open reading frame arrays, (ii) 0.1 to 1,000 ng (r2 = 0.91) for genomic DNA using community genome arrays, and (iii) 0.01 to 250 ng (r2 = 0.96 to 0.98) for community DNAs from ethanol-amended groundwater using 50-mer functional gene arrays. This method allowed us to investigate the oligotrophic microbial communities in groundwater contaminated with uranium and other metals. The results indicated that microorganisms containing genes involved in contaminant degradation and immobilization are present in these communities, that their spatial distribution is heterogeneous, and that microbial diversity is greatly reduced in the highly contaminated environment. PMID:16820490

Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data.

PubMed

Nordberg, Maj-Liz; Evertson, Joakim

2003-12-01

Vegetation cover-change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Satellite sensors like Landsat TM offer the advantages of wide spatial coverage while providing land-cover information. This facilitates the monitoring of surface processes. This study discusses change detection in mountainous dry-heath communities in Jämtland County, Sweden, using satellite data. Landsat-5 TM and Landsat-7 ETM+ data from 1984, 1994 and 2000, respectively, were used. Different change detection methods were compared after the images had been radiometrically normalized, georeferenced and corrected for topographic effects. For detection of the classes change--no change the NDVI image differencing method was the most accurate with an overall accuracy of 94% (K = 0.87). Additional change information was extracted from an alternative method called NDVI regression analysis and vegetation change in 3 categories within mountainous dry-heath communities were detected. By applying a fuzzy set thresholding technique the overall accuracy was improved from of 65% (K = 0.45) to 74% (K = 0.59). The methods used generate a change product showing the location of changed areas in sensitive mountainous heath communities, and it also indicates the extent of the change (high, moderate and unchanged vegetation cover decrease). A total of 17% of the dry and extremely dry-heath vegetation within the study area has changed between 1984 and 2000. On average 4% of the studied heath communities have been classified as high change, i.e. have experienced "high vegetation cover decrease" during the period. The results show that the low alpine zone of the southern part of the study area shows the highest amount of "high vegetation cover decrease". The results also show that the main change occurred between 1994 and 2000.

Workshop targets development of geodetic transient detection methods: 2009 SCEC Annual Meeting: Workshop on transient anomalous strain detection; Palm Springs, California, 12-13 September 2009

USGS Publications Warehouse

Murray-Moraleda, Jessica R.; Lohman, Rowena

2010-01-01

The Southern California Earthquake Center (SCEC) is a community of researchers at institutions worldwide working to improve understanding of earthquakes and mitigate earthquake risk. One of SCEC's priority objectives is to “develop a geodetic network processing system that will detect anomalous strain transients.” Given the growing number of continuously recording geodetic networks consisting of hundreds of stations, an automated means for systematically searching data for transient signals, especially in near real time, is critical for network operations, hazard monitoring, and event response. The SCEC Transient Detection Test Exercise began in 2008 to foster an active community of researchers working on this problem, explore promising methods, and combine effective approaches in novel ways. A workshop was held in California to assess what has been learned thus far and discuss areas of focus as the project moves forward.

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

Sensitivity and accuracy of high-throughput metabarcoding methods used to describe aquatic communities for early detection of invasve fish species

EPA Science Inventory

For early detection biomonitoring of aquatic invasive species, sensitivity to rare individuals and accurate, high-resolution taxonomic classification are critical to minimize Type I and II detection errors. Given the great expense and effort associated with morphological identifi...

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.

Rising Rates of Carbapenem-Resistant Enterobacteriaceae in Community Hospitals: A Mixed-Methods Review of Epidemiology and Microbiology Practices in a Network of Community Hospitals in the Southeastern United States

PubMed Central

Thaden, Joshua T.; Lewis, Sarah S.; Hazen, Kevin C.; Huslage, Kirk; Fowler, Vance G.; Moehring, Rebekah W.; Chen, Luke F.; Jones, Constance D.; Moore, Zack S.; Sexton, Daniel J.; Anderson, Deverick J.

2014-01-01

OBJECTIVE Describe the epidemiology of carbapenem-resistant Enterobacteriaceae (CRE) and examine the effect of lower carbapenem breakpoints on CRE detection. DESIGN Retrospective cohort. SETTING Inpatient care at community hospitals. PATIENTS All patients with CRE-positive cultures were included. METHODS CRE isolated from 25 community hospitals were prospectively entered into a centralized database from January 2008 through December 2012. Microbiology laboratory practices were assessed using questionnaires. RESULTS A total of 305 CRE isolates were detected at 16 hospitals (64%). Patients with CRE had symptomatic infection in 180 cases (59%) and asymptomatic colonization in the remainder (125 cases; 41%). Klebsiella pneumoniae (277 isolates; 91%) was the most prevalent species. The majority of cases were healthcare associated (288 cases; 94%). The rate of CRE detection increased more than fivefold from 2008 (0.26 cases per 100,000 patient-days) to 2012 (1.4 cases per 100,000 patient-days; incidence rate ratio (IRR), 5.3 [95% confidence interval (CI), 1.22–22.7]; P = .01). Only 5 hospitals (20%) had adopted the 2010 Clinical and Laboratory Standards Institute (CLSI) carbapenem breakpoints. The 5 hospitals that adopted the lower carbapenem breakpoints were more likely to detect CRE after implementation of breakpoints than before (4.1 vs 0.5 cases per 100,000 patient-days; P < .001; IRR, 8.1 [95% CI, 2.7–24.6]). Hospitals that implemented the lower carbapenem breakpoints were more likely to detect CRE than were hospitals that did not (3.3 vs 1.1 cases per 100,000 patientdays; P = .01). CONCLUSIONS The rate of CRE detection increased fivefold in community hospitals in the southeastern United States from 2008 to 2012. Despite this, our estimates are likely underestimates of the true rate of CRE detection, given the low adoption of the carbapenem breakpoints recommended in the 2010 CLSI guidelines. PMID:25026612

Potential applications of next generation DNA sequencing of 16S rRNA gene amplicons in microbial water quality monitoring

PubMed Central

Vierheilig, J.; Savio, D.; Ley, R. E.; Mach, R. L.; Farnleitner, A. H.

2016-01-01

The applicability of next generation DNA sequencing (NGS) methods for water quality assessment has so far not been broadly investigated. This study set out to evaluate the potential of an NGS-based approach in a complex catchment with importance for drinking water abstraction. In this multicompartment investigation, total bacterial communities in water, faeces, soil, and sediment samples were investigated by 454 pyrosequencing of bacterial 16S rRNA gene amplicons to assess the capabilities of this NGS method for (i) the development and evaluation of environmental molecular diagnostics, (ii) direct screening of the bulk bacterial communities, and (iii) the detection of faecal pollution in water. Results indicate that NGS methods can highlight potential target populations for diagnostics and will prove useful for the evaluation of existing and the development of novel DNA-based detection methods in the field of water microbiology. The used approach allowed unveiling of dominant bacterial populations but failed to detect populations with low abundances such as faecal indicators in surface waters. In combination with metadata, NGS data will also allow the identification of drivers of bacterial community composition during water treatment and distribution, highlighting the power of this approach for monitoring of bacterial regrowth and contamination in technical systems. PMID:26606090

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.

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

[Diagnostics and antimicrobial therapy of severe community-acquired pneumonia].

PubMed

Sinopalnikov, A I; Zaitsev, A A

2015-04-01

In the current paper authors presented the latest information concerning etiology of severe community-acquired pneumonia. Most cases are caused by a relatively small number ofpathogenic bacterial and viral natures. The frequency of detection of various pathogens of severe community-acquired pneumonia may vary greatly depending on the region, season and clinical profile of patients, availability of relevant risk factors. Authors presented clinical characteristics of severe community-acquired pneumonia and comparative evaluation of a number of scales to assess the risk of adverse outcome of the disease. Diagnosis of severe community-acquired pneumonia includes the following: collecting of epidemiological history, identification of pneumonia, detection of sepsis and identification of multiple organ dysfunction syndrome, detection of acute respiratory failure, assessment of comorbidity. Authors gave recommendations concerning evaluation of the clinical manifestations of the disease, the use of instrumental and laboratory methods for diagnosis of severe community-acquired pneumonia. To select the mode of antimicrobial therapy is most important local monitoring antimicrobial resistance of pathogens. The main criteria for the effectiveness of treatment are to reduce body temperature, severe intoxication, respiratory and organ failure.

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.

Multiway spectral community detection in networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Newman, M. E. J.

2015-11-01

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited, by and large, to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.

Urinary tract infections in women with urogynaecological symptoms.

PubMed

Lakeman, Marielle M E; Roovers, Jan-Paul W R

2016-02-01

Urinary tract infections are common in the field of urogynaecology. Women with persistent urinary symptoms seem more likely to have bacteriuria despite negative cultures. In this review, we will give an overview of the recent insights on the relationship between urinary tract infection and persistent urinary symptoms and possible new therapeutic options. Recently published articles evaluated the prevalence of low-count bacteriuria (≥10 CFU/ml) or intracellular bacterial communities in women with overactive bladder symptoms (OAB). Differences in urinary microbioma observed in women with and without OAB symptoms were evaluated. In the light of these findings, current screening strategies were discussed and alternative screening methods for bacteriuria developed. Low-count bacteriuria (≥10 CFU/ml) seems to be more prevalent in women with OAB. Also intracellular bacterial communities are more commonly detected in these women. The microbioma found in women with urinary symptoms appeared to differ from healthy controls. The current screening methods might be insufficient as they are targeted at detecting uropathogenic Escherichia coli, mostly using a detection threshold of at least 10 CFU/ml and failing to detect intracellular bacterial communities. Studies evaluating the efficacy of treating women with low-count bacteriuria are limited but promising.

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.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015.

PubMed

Greene, Sharon K; Peterson, Eric R; Kapell, Deborah; Fine, Annie D; Kulldorff, Martin

2016-10-01

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space-time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

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

Detecting insect pollinator declines on regional and global scales

USGS Publications Warehouse

Lubuhn, Gretchen; Droege, Sam; Connor, Edward F.; Gemmill-Herren, Barbara; Potts, Simon G.; Minckley, Robert L.; Griswold, Terry; Jean, Robert; Kula, Emanuel; Roubik, David W.; Cane, Jim; Wright, Karen W.; Frankie, Gordon; Parker, Frank

2013-01-01

Recently there has been considerable concern about declines in bee communities in agricultural and natural habitats. The value of pollination to agriculture, provided primarily by bees, is >$200 billion/year worldwide, and in natural ecosystems it is thought to be even greater. However, no monitoring program exists to accurately detect declines in abundance of insect pollinators; thus, it is difficult to quantify the status of bee communities or estimate the extent of declines. We used data from 11 multiyear studies of bee communities to devise a program to monitor pollinators at regional, national, or international scales. In these studies, 7 different methods for sampling bees were used and bees were sampled on 3 different continents. We estimated that a monitoring program with 200-250 sampling locations each sampled twice over 5 years would provide sufficient power to detect small (2-5%) annual declines in the number of species and in total abundance and would cost U.S.$2,000,000. To detect declines as small as 1% annually over the same period would require >300 sampling locations. Given the role of pollinators in food security and ecosystem function, we recommend establishment of integrated regional and international monitoring programs to detect changes in pollinator communities.

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.

Community detection for fluorescent lifetime microscopy image segmentation

NASA Astrophysics Data System (ADS)

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Achilefu, Samuel; Nussinov, Zohar

2014-03-01

Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes.1 In the current paper, we further explore this method in FLT images of ex vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1:25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

Bacterial community analysis of swine manure treated with autothermal thermophilic aerobic digestion.

PubMed

Han, Il; Congeevaram, Shankar; Ki, Dong-Won; Oh, Byoung-Taek; Park, Joonhong

2011-02-01

Due to the environmental problems associated with disposal of livestock sludge, many stabilization studies emphasizing on the sludge volume reduction were performed. However, little is known about the microbial risk present in sludge and its stabilized products. This study microbiologically explored the effects of anaerobic lagoon fermentation (ALF) and autothermal thermophilic aerobic digestion (ATAD) on pathogen-related risk of raw swine manure by using culture-independent 16S rDNA cloning and sequencing methods. In raw swine manure, clones closely related to pathogens such as Dialister pneumosintes, Erysipelothrix rhusiopathiae, Succinivibrioan dextrinosolvens, and Schineria sp. were detected. Meanwhile, in the mesophilic ALF-treated swine manure, bacterial community clones closely related to pathogens such as Schineria sp. and Succinivibrio dextrinosolvens were still detected. Interestingly, the ATAD treatment resulted in no detection of clones closely related to pathogens in the stabilized thermophilic bacterial community, with the predominance of novel Clostridia class populations. These findings support the superiority of ATAD in selectively reducing potential human and animal pathogens compared to ALF, which is a typical manure stabilization method used in livestock farms.

Purpose-Driven Communities in Multiplex Networks: Thresholding User-Engaged Layer Aggregation

DTIC Science & Technology

2016-06-01

dark networks is a non-trivial yet useful task. Because terrorists work hard to hide their relationships/network, analysts have an incomplete picture...them identify meaningful terrorist communities. This thesis introduces a general-purpose algorithm for community detection in multiplex dark networks...aggregation, dark networks, conductance, cluster adequacy, mod- ularity, Louvain method, shortest path interdiction 15. NUMBER OF PAGES 155 16. PRICE CODE

A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network

NASA Astrophysics Data System (ADS)

Qin, Li; Li, Bing; Pan, Wei-Feng; Peng, Tao

The number of online software services based on SaaS paradigm is increasing. However, users usually find it hard to get the exact software services they need. At present, tags are widely used to annotate specific software services and also to facilitate the searching of them. Currently these tags are arbitrary and ambiguous since mostly of them are generated manually by service developers. This paper proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity between the terms, we construct a software similarity network where nodes represent software services, edges denote the similarity relationship between software services, and the weights of the edges are the similarity degrees. The hierarchical clustering algorithm is used for community detection in this software similarity network. At the final stage, tags are mined for each of the communities and stored as ontology.

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.

Community-owned resource persons for malaria vector control: enabling factors and challenges in an operational programme in Dar es Salaam, United Republic of Tanzania

PubMed Central

2011-01-01

Background Community participation in vector control and health services in general is of great interest to public health practitioners in developing countries, but remains complex and poorly understood. The Urban Malaria Control Program (UMCP) in Dar es Salaam, United Republic of Tanzania, implements larval control of malaria vector mosquitoes. The UMCP delegates responsibility for routine mosquito control and surveillance to community-owned resource persons (CORPs), recruited from within local communities via the elected local government. Methods A mixed method, cross-sectional survey assessed the ability of CORPs to detect mosquito breeding sites and larvae, and investigated demographic characteristics of the CORPs, their reasons for participating in the UMCP, and their work performance. Detection coverage was estimated as the proportion of wet habitats found by the investigator which had been reported by CORP. Detection sensitivity was estimated as the proportion of wet habitats found by the CORPS which the investigator found to contain Anopheles larvae that were also reported to be occupied by the CORP. Results The CORPs themselves perceived their role as professional rather than voluntary, with participation being a de facto form of employment. Habitat detection coverage was lower among CORPs that were recruited through the program administrative staff, compared to CORPs recruited by local government officials or health committees (Odds Ratio = 0.660, 95% confidence interval = [0.438, 0.995], P = 0.047). Staff living within their areas of responsibility had > 70% higher detection sensitivity for both Anopheline (P = 0.016) and Culicine (P = 0.012): positive habitats compared to those living outside those same areas. Discussion and conclusions Improved employment conditions as well as involving the local health committees in recruiting individual program staff, communication and community engagement skills are required to optimize achieving effective community participation, particularly to improve access to fenced compounds. A simpler, more direct, less extensive community-based surveillance system in the hands of a few, less burdened, better paid and maintained program personnel may improve performance and data quality. PMID:21955856

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.

Effects of Environmental Toxicants on Metabolic Activity of Natural Microbial Communities

PubMed Central

Barnhart, Carole L. H.; Vestal, J. Robie

1983-01-01

Two methods of measuring microbial activity were used to study the effects of toxicants on natural microbial communities. The methods were compared for suitability for toxicity testing, sensitivity, and adaptability to field applications. This study included measurements of the incorporation of 14C-labeled acetate into microbial lipids and microbial glucosidase activity. Activities were measured per unit biomass, determined as lipid phosphate. The effects of various organic and inorganic toxicants on various natural microbial communities were studied. Both methods were useful in detecting toxicity, and their comparative sensitivities varied with the system studied. In one system, the methods showed approximately the same sensitivities in testing the effects of metals, but the acetate incorporation method was more sensitive in detecting the toxicity of organic compounds. The incorporation method was used to study the effects of a point source of pollution on the microbiota of a receiving stream. Toxic doses were found to be two orders of magnitude higher in sediments than in water taken from the same site, indicating chelation or adsorption of the toxicant by the sediment. The microbiota taken from below a point source outfall was 2 to 100 times more resistant to the toxicants tested than was that taken from above the outfall. Downstream filtrates in most cases had an inhibitory effect on the natural microbiota taken from above the pollution source. The microbial methods were compared with commonly used bioassay methods, using higher organisms, and were found to be similar in ability to detect comparative toxicities of compounds, but were less sensitive than methods which use standard media because of the influences of environmental factors. PMID:16346432

A Natural View of Microbial Biodiversity within Hot Spring Cyanobacterial Mat Communities

PubMed Central

Ward, David M.; Ferris, Michael J.; Nold, Stephen C.; Bateson, Mary M.

1998-01-01

This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats. PMID:9841675

A natural view of microbial biodiversity within hot spring cyanobacterial mat communities

NASA Technical Reports Server (NTRS)

Ward, D. M.; Ferris, M. J.; Nold, S. C.; Bateson, M. M.

1998-01-01

This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats.

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.

Stochastic fluctuations and the detectability limit of network communities.

PubMed

Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

2013-12-01

We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

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.

Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation.

PubMed

Chan, Yvonne L; Schanzenbach, David; Hickerson, Michael J

2014-09-01

Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

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.

Using occupancy modeling to compare traditional versus DNA metabarcoding methods for characterizing zooplankton biodiversity

EPA Science Inventory

DNA metabarcoding tools could increase our ability to detect changes in zooplankton communities and to detect invasive zooplankton taxa while they are still rare. Nonetheless, the use of DNA-metabarcoding for characterizing zooplankton biodiversity in the Great Lakes has not bee...

Community Detection for Correlation Matrices

NASA Astrophysics Data System (ADS)

MacMahon, Mel; Garlaschelli, Diego

2015-04-01

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with "hard" cores and "soft" peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy; detect "soft stocks" that alternate between communities; and discuss implications for portfolio optimization and risk management.

Identification of ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards.

PubMed

Roubeix, Vincent; Danis, Pierre-Alain; Feret, Thibaut; Baudoin, Jean-Marc

2016-04-01

In aquatic ecosystems, the identification of ecological thresholds may be useful for managers as it can help to diagnose ecosystem health and to identify key levers to enable the success of preservation and restoration measures. A recent statistical method, gradient forest, based on random forests, was used to detect thresholds of phytoplankton community change in lakes along different environmental gradients. It performs exploratory analyses of multivariate biological and environmental data to estimate the location and importance of community thresholds along gradients. The method was applied to a data set of 224 French lakes which were characterized by 29 environmental variables and the mean abundances of 196 phytoplankton species. Results showed the high importance of geographic variables for the prediction of species abundances at the scale of the study. A second analysis was performed on a subset of lakes defined by geographic thresholds and presenting a higher biological homogeneity. Community thresholds were identified for the most important physico-chemical variables including water transparency, total phosphorus, ammonia, nitrates, and dissolved organic carbon. Gradient forest appeared as a powerful method at a first exploratory step, to detect ecological thresholds at large spatial scale. The thresholds that were identified here must be reinforced by the separate analysis of other aquatic communities and may be used then to set protective environmental standards after consideration of natural variability among lakes.

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.

Accounting for Incomplete Species Detection in Fish Community Monitoring

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

McManamay, Ryan A; Orth, Dr. Donald J; Jager, Yetta

2013-01-01

Riverine fish assemblages are heterogeneous and very difficult to characterize with a one-size-fits-all approach to sampling. Furthermore, detecting changes in fish assemblages over time requires accounting for variation in sampling designs. We present a modeling approach that permits heterogeneous sampling by accounting for site and sampling covariates (including method) in a model-based framework for estimation (versus a sampling-based framework). We snorkeled during three surveys and electrofished during a single survey in suite of delineated habitats stratified by reach types. We developed single-species occupancy models to determine covariates influencing patch occupancy and species detection probabilities whereas community occupancy models estimated speciesmore » richness in light of incomplete detections. For most species, information-theoretic criteria showed higher support for models that included patch size and reach as covariates of occupancy. In addition, models including patch size and sampling method as covariates of detection probabilities also had higher support. Detection probability estimates for snorkeling surveys were higher for larger non-benthic species whereas electrofishing was more effective at detecting smaller benthic species. The number of sites and sampling occasions required to accurately estimate occupancy varied among fish species. For rare benthic species, our results suggested that higher number of occasions, and especially the addition of electrofishing, may be required to improve detection probabilities and obtain accurate occupancy estimates. Community models suggested that richness was 41% higher than the number of species actually observed and the addition of an electrofishing survey increased estimated richness by 13%. These results can be useful to future fish assemblage monitoring efforts by informing sampling designs, such as site selection (e.g. stratifying based on patch size) and determining effort required (e.g. number of sites versus occasions).« less

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Zakajsek, James J.

1994-01-01

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

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.

Community Detection on the GPU

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

Naim, Md; Manne, Fredrik; Halappanavar, Mahantesh

We present and evaluate a new GPU algorithm based on the Louvain method for community detection. Our algorithm is the first for this problem that parallelizes the access to individual edges. In this way we can fine tune the load balance when processing networks with nodes of highly varying degrees. This is achieved by scaling the number of threads assigned to each node according to its degree. Extensive experiments show that we obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order ofmore » magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.« less

Estimating biodiversity of fungi in activated sludge communities using culture-independent methods.

PubMed

Evans, Tegan N; Seviour, Robert J

2012-05-01

Fungal diversity of communities in several activated sludge plants treating different influent wastes was determined by comparative sequence analyses of their 18S rRNA genes. Methods for DNA extraction and choice of primers for PCR amplification were both optimised using denaturing gradient gel electrophoresis profile patterns. Phylogenetic analysis revealed that the levels of fungal biodiversity in some communities, like those treating paper pulp wastes, were low, and most of the fungi detected in all communities examined were novel uncultured representatives of the major fungal subdivisions, in particular, the newly described clade Cryptomycota. The fungal populations in activated sludge revealed by these culture-independent methods were markedly different to those based on culture-dependent data. Members of the genera Penicillium, Cladosporium, Aspergillus and Mucor, which have been commonly identified in mixed liquor, were not identified in any of these plant communities. Non-fungal eukaryotic 18S rRNA genes were also amplified with the primer sets used. This is the first report where culture-independent methods have been applied to flocculated activated sludge biomass samples to estimate fungal community composition and, as expected, the data obtained gave a markedly different view of their population biodiversity compared to that based on culture-dependent methods.

Expanding Larval Fish DNA Metabarcoding to All the Great Lakes

EPA Science Inventory

Fish larvae represent a largely untapped community for detecting and monitoring breeding non-native species, mainly due to the difficulty of identifying larvae to species through morphological methods. Molecular genetic methods offer means to identify larval specimens to species ...

Analysis of microbial community composition in a lab-scale membrane distillation bioreactor

PubMed Central

Zhang, Q; Shuwen, G; Zhang, J; Fane, AG; Kjelleberg, S; Rice, SA; McDougald, D

2015-01-01

Aims Membrane distillation bioreactors (MDBR) have potential for industrial applications where wastewater is hot or waste heat is available, but the role of micro-organisms in MDBRs has never been determined, and thus was the purpose of this study. Methods and Results Microbial communities were characterized by bacterial and archaeal 16S and eukaryotic 18S rRNA gene tag-encoded pyrosequencing of DNA obtained from sludge. Taxonomy-independent analysis revealed that bacterial communities had a relatively low richness and diversity, and community composition strongly correlated with conductivity, total nitrogen and bound extracellular polymeric substances (EPS). Taxonomy-dependent analysis revealed that Rubrobacter and Caldalkalibacillus were abundant members of the bacterial community, but no archaea were detected. Eukaryotic communities had a relatively high richness and diversity, and both changes in community composition and abundance of the dominant genus, Candida, correlated with bound EPS. Conclusions Thermophilic MDBR communities were comprised of a low diversity bacterial community and a highly diverse eukaryotic community with no archea detected. Communities exhibited low resilience to changes in operational parameters. Specifically, retenatate nutrient composition and concentration was strongly correlated with the dominant species. Significance and Impact of the Study This study provides an understanding of microbial community diversity in an MDBR, which is fundamental to the optimization of reactor performance. PMID:25604265

Development of a scalable mental healthcare plan for a rural district in Ethiopia

PubMed Central

Fekadu, Abebaw; Hanlon, Charlotte; Medhin, Girmay; Alem, Atalay; Selamu, Medhin; Giorgis, Tedla W.; Shibre, Teshome; Teferra, Solomon; Tegegn, Teketel; Breuer, Erica; Patel, Vikram; Tomlinson, Mark; Thornicroft, Graham; Prince, Martin; Lund, Crick

2016-01-01

Background Developing evidence for the implementation and scaling up of mental healthcare in low- and middle-income countries (LMIC) like Ethiopia is an urgent priority. Aims To outline a mental healthcare plan (MHCP), as a scalable template for the implementation of mental healthcare in rural Ethiopia. Method A mixed methods approach was used to develop the MHCP for the three levels of the district health system (community, health facility and healthcare organisation). Results The community packages were community case detection, community reintegration and community inclusion. The facility packages included capacity building, decision support and staff well-being. Organisational packages were programme management, supervision and sustainability. Conclusions The MHCP focused on improving demand and access at the community level, inclusive care at the facility level and sustainability at the organisation level. The MHCP represented an essential framework for the provision of integrated care and may be a useful template for similar LMIC. PMID:26447174

Evaluation of microbial diversity in the pilot-scale beer brewing process by culture-dependent and culture-independent method.

PubMed

Takahashi, M; Kita, Y; Kusaka, K; Mizuno, A; Goto-Yamamoto, N

2015-02-01

In the brewing industry, microbial management is very important for stabilizing the quality of the product. We investigated the detailed microbial community of beer during fermentation and maturation, to manage beer microbiology in more detail. We brewed a beer (all-malt) and two beerlike beverages (half- and low-malt) in pilot-scale fermentation and investigated the microbial community of them using a next-generation sequencer (454 GS FLX titanium), quantitative PCR, flow cytometry and a culture-dependent method. From 28 to 88 genera of bacteria and from 9 to 38 genera of eukaryotic micro-organisms were detected in each sample. Almost all micro-organisms died out during the boiling process. However, bacteria belonging to the genera Acidovorax, Bacillus, Brevundimonas, Caulobacter, Chryseobacterium, Methylobacterium, Paenibacillus, Polaromonas, Pseudomonas, Ralstonia, Sphingomonas, Stenotrophomonas, Tepidimonas and Tissierella were detected at the early and middle stage of fermentation, even though their cell densities were low (below approx. 10(3) cells ml(-1) ) and they were not almost detected at the end of fermentation. We revealed that the microbial community of beer during fermentation and maturation is very diverse and several bacteria possibly survive during fermentation. In this study, we revealed the detailed microbial communities of beer using next-generation sequencing. Some of the micro-organisms detected in this study were found in beer brewing process for the first time. Additionally, the possibility of growth of several bacteria at the early and middle stage of fermentation was suggested. © 2014 The Society for Applied Microbiology.

Microbial rRNA: rDNA gene ratios may be unexpectedly low due to extracellular DNA preservation in soils

USDA-ARS?s Scientific Manuscript database

We tested a method of estimating the activity of detectable individual bacterial and archaeal OTUs within a community by calculating ratios of absolute 16S rRNA to rDNA copy numbers. We investigated phylogenetically coherent patterns of activity among soil prokaryotes in non-growing soil communitie...

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

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.

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.

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

Detection and Composition of Bacterial Communities in Waters using RNA-based Methods

EPA Science Inventory

In recent years, microbial water quality assessments have shifted from solely relying on pure culture-based methods to monitoring bacterial groups of interest using molecular assays such as PCR and qPCR. Furthermore, coupling next generation sequencing technologies with ribosomal...

How many fish? Comparison of two underwater visual sampling methods for monitoring fish communities

PubMed Central

Sini, Maria; Vatikiotis, Konstantinos; Katsoupis, Christos

2018-01-01

Background Underwater visual surveys (UVSs) for monitoring fish communities are preferred over fishing surveys in certain habitats, such as rocky or coral reefs and seagrass beds and are the standard monitoring tool in many cases, especially in protected areas. However, despite their wide application there are potential biases, mainly due to imperfect detectability and the behavioral responses of fish to the observers. Methods The performance of two methods of UVSs were compared to test whether they give similar results in terms of fish population density, occupancy, species richness, and community composition. Distance sampling (line transects) and plot sampling (strip transects) were conducted at 31 rocky reef sites in the Aegean Sea (Greece) using SCUBA diving. Results Line transects generated significantly higher values of occupancy, species richness, and total fish density compared to strip transects. For most species, density estimates differed significantly between the two sampling methods. For secretive species and species avoiding the observers, the line transect method yielded higher estimates, as it accounted for imperfect detectability and utilized a larger survey area compared to the strip transect method. On the other hand, large-scale spatial patterns of species composition were similar for both methods. Discussion Overall, both methods presented a number of advantages and limitations, which should be considered in survey design. Line transects appear to be more suitable for surveying secretive species, while strip transects should be preferred at high fish densities and for species of high mobility. PMID:29942703

Feasibility of portable sleep monitors to detect obstructive sleep apnea (OSA) in a vulnerable urban population.

PubMed

Nickerson, Jillian; Lee, Euny; Nedelman, Michael; Aurora, R Nisha; Krieger, Ana; Horowitz, Carol R

2015-01-01

Portable sleep monitors may offer a convenient method to expand detection of obstructive sleep apnea (OSA), yet few studies have evaluated this technology in vulnerable populations. We therefore aimed to assess the feasibility and acceptability of portable sleep monitors for detection of OSA in a prediabetic, urban minority population. We recruited a convenience sample of participants at their 12-month follow-up for a community-partnered, peer-led lifestyle intervention aimed to prevent diabetes in prediabetic and overweight patients in this prospective mixed-methods pilot study. All participants wore portable sleep monitors overnight at home. We qualitatively explored perceptions about OSA and portable monitors in a subset of participants. We tested 72 people, predominantly non-White, female, Spanish speaking, uninsured, and of low income. Use of portable sleep monitors was feasible: 100% of the monitors were returned and all participants received results. We detected OSA in 49% (defined as an Apnea-Hypopnea Index [AHI] >5) and moderate-severe OSA in 14% (AHI >15) requiring treatment in 14%. In 21 qualitative interviews, participants supported increased use of portable sleep monitors in their community, were appropriately concerned that OSA could cause progression to diabetes, and thought weight loss could prevent or improve OSA. Portable sleep monitors may represent a feasible method for detecting OSA in high-risk urban minority populations. © Copyright 2015 by the American Board of Family Medicine.

The Rise of China in the International Trade Network: A Community Core Detection Approach

PubMed Central

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995–2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism. PMID:25136895

The rise of China in the International Trade Network: a community core detection approach.

PubMed

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

Leveraging disjoint communities for detecting overlapping community structure

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy

2015-05-01

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks.

PubMed

Rodrigues, Ramon Gouveia; das Dores, Rafael Marques; Camilo-Junior, Celso G; Rosa, Thierson Couto

2016-01-01

Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression, for instance. Thus, monitoring the mood of the patients is an important part of their treatment. Many cancer patients are users of online social networks and many of them take part in cancer virtual communities where they exchange messages commenting about their treatment or giving support to other patients in the community. Most of these communities are of public access and thus are useful sources of information about the mood of patients. Based on that, Sentiment Analysis methods can be useful to automatically detect positive or negative mood of cancer patients by analyzing their messages in these online communities. The objective of this work is to present a Sentiment Analysis tool, named SentiHealth-Cancer (SHC-pt), that improves the detection of emotional state of patients in Brazilian online cancer communities, by inspecting their posts written in Portuguese language. The SHC-pt is a sentiment analysis tool which is tailored specifically to detect positive, negative or neutral messages of patients in online communities of cancer patients. We conducted a comparative study of the proposed method with a set of general-purpose sentiment analysis tools adapted to this context. Different collections of posts were obtained from two cancer communities in Facebook. Additionally, the posts were analyzed by sentiment analysis tools that support the Portuguese language (Semantria and SentiStrength) and by the tool SHC-pt, developed based on the method proposed in this paper called SentiHealth. Moreover, as a second alternative to analyze the texts in Portuguese, the collected texts were automatically translated into English, and submitted to sentiment analysis tools that do not support the Portuguese language (AlchemyAPI and Textalytics) and also to Semantria and SentiStrength, using the English option of these tools. Six experiments were conducted with some variations and different origins of the collected posts. The results were measured using the following metrics: precision, recall, F1-measure and accuracy The proposed tool SHC-pt reached the best averages for accuracy and F1-measure (harmonic mean between recall and precision) in the three sentiment classes addressed (positive, negative and neutral) in all experimental settings. Moreover, the worst accuracy value (58%) achieved by SHC-pt in any experiment is 11.53% better than the greatest accuracy (52%) presented by other addressed tools. Finally, the worst average F1 (48.46%) reached by SHC-pt in any experiment is 4.14% better than the greatest average F1 (46.53%) achieved by other addressed tools. Thus, even when we compare the SHC-pt results in complex scenario versus others in easier scenario the SHC-pt is better. This paper presents two contributions. First, it proposes the method SentiHealth to detect the mood of cancer patients that are also users of communities of patients in online social networks. Second, it presents an instantiated tool from the method, called SentiHealth-Cancer (SHC-pt), dedicated to automatically analyze posts in communities of cancer patients, based on SentiHealth. This context-tailored tool outperformed other general-purpose sentiment analysis tools at least in the cancer context. This suggests that the SentiHealth method could be instantiated as other disease-based tools during future works, for instance SentiHealth-HIV, SentiHealth-Stroke and SentiHealth-Sclerosis. Copyright © 2015. Published by Elsevier Ireland Ltd.

PMA-PhyloChip DNA Microarray to Elucidate Viable Microbial Community Structure

NASA Technical Reports Server (NTRS)

Venkateswaran, Kasthuri J.; Stam, Christina N.; Andersen, Gary L.; DeSantis, Todd

2011-01-01

Since the Viking missions in the mid-1970s, traditional culture-based methods have been used for microbial enumeration by various NASA programs. Viable microbes are of particular concern for spacecraft cleanliness, for forward contamination of extraterrestrial bodies (proliferation of microbes), and for crew health/safety (viable pathogenic microbes). However, a "true" estimation of viable microbial population and differentiation from their dead cells using the most sensitive molecular methods is a challenge, because of the stability of DNA from dead cells. The goal of this research is to evaluate a rapid and sensitive microbial detection concept that will selectively estimate viable microbes. Nucleic acid amplification approaches such as the polymerase chain reaction (PCR) have shown promise for reducing time to detection for a wide range of applications. The proposed method is based on the use of a fluorescent DNA intercalating agent, propidium monoazide (PMA), which can only penetrate the membrane of dead cells. The PMA-quenched reaction mixtures can be screened, where only the DNA from live cells will be available for subsequent PCR reaction and microarray detection, and be identified as part of the viable microbial community. An additional advantage of the proposed rapid method is that it will detect viable microbes and differentiate from dead cells in only a few hours, as opposed to less comprehensive culture-based assays, which take days to complete. This novel combination approach is called the PMA-Microarray method. DNA intercalating agents such as PMA have previously been used to selectively distinguish between viable and dead bacterial cells. Once in the cell, the dye intercalates with the DNA and, upon photolysis under visible light, produces stable DNA adducts. DNA cross-linked in this way is unavailable for PCR. Environmental samples suspected of containing a mixture of live and dead microbial cells/spores will be treated with PMA, and then incubated in the dark. Thereafter, the sample is exposed to visible light for five minutes, so that the DNA from dead cells will be cross-linked. Following this PMA treatment step, the sample is concentrated by centrifugation and washed (to remove excessive PMA) before DNA is extracted. The 16S rRNA gene fragments will be amplified by PCR to screen the total microbial community using PhyloChip DNA microarray analysis. This approach will detect only the viable microbial community since the PMA intercalated DNA from dead cells would be unavailable for PCR amplification. The total detection time including PCR reaction for low biomass samples will be a few hours. Numerous markets may use this technology. The food industry uses spore detection to validate new alternative food processing technologies, sterility, and quality. Pharmaceutical and medical equipment companies also detect spores as a marker for sterility. This system can be used for validating sterilization processes, water treatment systems, and in various public health and homeland security applications.

Novel microbiological and spatial statistical methods to improve strength of epidemiological evidence in a community-wide waterborne outbreak.

PubMed

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9-16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak.

Improving Access to Eye Care among Persons at High-Risk of Glaucoma in Philadelphia — Design and Methodology: The Philadelphia Glaucoma Detection and Treatment Project

PubMed Central

Myers, Jonathan S.; Henderer, Jeffrey; Crews, John E.; Saaddine, Jinan B.; Molineaux, Jeanne; Johnson, Deiana; Sembhi, Harjeet; Stratford, Shayla; Suleiman, Ayman; Pizzi, Laura; Spaeth, George L.; Katz, L. Jay

2016-01-01

Purpose The Wills Eye Glaucoma Research Center initiated a 2-year demonstration project to develop and implement a community-based intervention to improve detection and management of glaucoma in Philadelphia. Methods The glaucoma detection examination consisted of: ocular, medical, and family history; visual acuity testing; corneal pachymetry; biomicroscopy of the anterior segment; intraocular pressure (IOP) measurement; gonioscopy; funduscopy; automated visual field testing; and fundus-color photography. Treatment included laser surgery and/or IOP-lowering medication. A cost analysis was conducted to understand resource requirements. Outcome measures included; prevalence of glaucoma-related pathology and other eye diseases among high-risk populations; the impact of educational workshops on level of knowledge about glaucoma (assessed by pre- and post-test evaluation); and patient satisfaction of the glaucoma detection examinations in the community (assessed by satisfaction survey). Treatment outcome measures were change in IOP at 4–6 weeks and 4–6 months following selective laser trabeculoplasty treatment, deepening of the anterior chamber angle following laser-peripheral iridotomy treatment, and rate of adherence to recommended follow-up examinations. Cost outcomes included total program costs, cost per case of glaucoma detected, and cost per case of ocular disease detected. Results This project enrolled 1649 participants (African Americans aged 50+ years, adults 60+ years and individuals with a family history of glaucoma). A total of 1074 individuals attended a glaucoma educational workshop and 1508 scheduled glaucoma detection examination appointments in the community setting. Conclusions The Philadelphia Glaucoma Detection and Treatment Project aimed to improve access and use of eye care and to provide a model for a targeted community-based glaucoma program. PMID:26950056

“How Did We Get Here?”: Topic Drift in Online Health Discussions

PubMed Central

Hartzler, Andrea L; Huh, Jina; Hsieh, Gary; McDonald, David W; Pratt, Wanda

2016-01-01

Background Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic—topic drift—can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts. Objective Our goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift. Methods We manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members’ reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads’ first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard. Results Our qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively. Conclusions Our analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members’ experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty. PMID:27806924

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

PubMed Central

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

2014-01-01

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

In-Field Implementation of a Recombinant Factor C Assay for the Detection of Lipopolysaccharide as a Biomarker of Extant Life within Glacial Environments

PubMed Central

Barnett, Megan J.; Wadham, Jemma L.; Jackson, Miriam; Cullen, David C.

2012-01-01

The discovery over the past two decades of viable microbial communities within glaciers has promoted interest in the role of glaciers and ice sheets (the cryosphere) as contributors to subglacial erosion, global biodiversity, and in regulating global biogeochemical cycles. In situ or in-field detection and characterisation of microbial communities is becoming recognised as an important approach to improve our understanding of such communities. Within this context we demonstrate, for the first time, the ability to detect Gram-negative bacteria in glacial field-environments (including subglacial environments) via the detection of lipopolysaccharide (LPS); an important component of Gram-negative bacterial cell walls. In-field measurements were performed using the recently commercialised PyroGene® recombinant Factor C (rFC) endotoxin detection system and used in conjunction with a handheld fluorometer to measure the fluorescent endpoint of the assay. Twenty-seven glacial samples were collected from the surface, bed and terminus of a low-biomass Arctic valley glacier (Engabreen, Northern Norway), and were analysed in a field laboratory using the rFC assay. Sixteen of these samples returned positive LPS detection. This work demonstrates that LPS detection via rFC assay is a viable in-field method and is expected to be a useful proxy for microbial cell concentrations in low biomass environments. PMID:25585634

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

Cotton, T. E. Anne; Fitter, Alastair H.; Miller, R. Michael

Understanding the natural dynamics of arbuscular mycorrhizal (AM) fungi and their response to global environmental change is essential for the prediction of future plant growth and ecosystem functions. We investigated the long-term temporal dynamics and effect of elevated atmospheric carbon dioxide (CO 2) and ozone (O 3) concentrations on AM fungal communities. Molecular methods were used to characterize the AM fungal communities of soybean ( Glycine max) grown under elevated and ambient atmospheric concentrations of both CO 2 and O 3 within a free air concentration enrichment experiment in three growing seasons over 5 yr. Elevated CO 2 altered themore » community composition of AM fungi, increasing the ratio of Glomeraceae to Gigasporaceae. By contrast, no effect of elevated O 3 on AM fungal communities was detected. However, the greatest compositional differences detected were between years, suggesting that, at least in the short term, large-scale interannual temporal dynamics are stronger mediators than atmospheric CO 2 concentrations of AM fungal communities. We conclude that, although atmospheric change may significantly alter AM fungal communities, this effect may be masked by the influences of natural changes and successional patterns through time. We suggest that changes in carbon availability are important determinants of the community dynamics of AM fungi.« less

Remote camera-trap methods and analyses reveal impacts of rangeland management on Namibian carnivore communities

USGS Publications Warehouse

Kauffman, M.J.; Sanjayan, M.; Lowenstein, J.; Nelson, A.; Jeo, R.M.; Crooks, K.R.

2007-01-01

Assessing the abundance and distribution of mammalian carnivores is vital for understanding their ecology and providing for their long-term conservation. Because of the difficulty of trapping and handling carnivores many studies have relied on abundance indices that may not accurately reflect real abundance and distribution patterns. We developed statistical analyses that detect spatial correlation in visitation data from combined scent station and camera-trap surveys, and we illustrate how to use such data to make inferences about changes in carnivore assemblages. As a case study we compared the carnivore communities of adjacent communal and freehold rangelands in central Namibia. We used an index of overdispersion to test for repeat visits to individual camera-trap scent stations and a bootstrap simulation to test for correlations in visits to camera neighbourhoods. After distilling our presence-absence data to the most defensible spatial scale, we assessed overall carnivore visitation using logistic regression. Our analyses confirmed the expected pattern of a depauparate fauna on the communal rangelands compared to the freehold rangelands. Additionally, the species that were not detected on communal sites were the larger-bodied carnivores. By modelling these rare visits as a Poisson process we illustrate a method of inferring whether or not such patterns are because of local extinction of species or are simply a result of low sample effort. Our Namibian case study indicates that these field methods and analyses can detect meaningful differences in the carnivore communities brought about by anthropogenic influences. ?? 2007 FFI.

X-Graphs: Language and Algorithms for Heterogeneous Graph Streams

DTIC Science & Technology

2017-09-01

INTRODUCTION 1 3 METHODS , ASUMPTIONS, AND PROCEDURES 2 Software Abstractions for Graph Analytic Applications 2 High performance Platforms for Graph Processing...data is stored in a distributed file system. 3 METHODS , ASUMPTIONS, AND PROCEDURES Software Abstractions for Graph Analytic Applications To...implementations of novel methods for networks analysis: several methods for detection of overlapping communities, personalized PageRank, node embeddings into a d

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

PubMed

Park, Albert; Hartzler, Andrea L; Huh, Jina; McDonald, David W; Pratt, Wanda

2015-08-31

The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap's mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text.

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

PubMed

Akram, Usman M; Khan, Shoab A

2012-10-01

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

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

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

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.

Fungi in the future: Interannual variation and effects of atmospheric change on arbuscular mycorrhizal fungal communities

DOE PAGES

Cotton, T. E. Anne; Fitter, Alastair H.; Miller, R. Michael; ...

2015-01-05

Understanding the natural dynamics of arbuscular mycorrhizal (AM) fungi and their response to global environmental change is essential for the prediction of future plant growth and ecosystem functions. We investigated the long-term temporal dynamics and effect of elevated atmospheric carbon dioxide (CO 2) and ozone (O 3) concentrations on AM fungal communities. Molecular methods were used to characterize the AM fungal communities of soybean ( Glycine max) grown under elevated and ambient atmospheric concentrations of both CO 2 and O 3 within a free air concentration enrichment experiment in three growing seasons over 5 yr. Elevated CO 2 altered themore » community composition of AM fungi, increasing the ratio of Glomeraceae to Gigasporaceae. By contrast, no effect of elevated O 3 on AM fungal communities was detected. However, the greatest compositional differences detected were between years, suggesting that, at least in the short term, large-scale interannual temporal dynamics are stronger mediators than atmospheric CO 2 concentrations of AM fungal communities. We conclude that, although atmospheric change may significantly alter AM fungal communities, this effect may be masked by the influences of natural changes and successional patterns through time. We suggest that changes in carbon availability are important determinants of the community dynamics of AM fungi.« less

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.

Effect of Pesticide Inoculation, Duration of Composting, and Degradation Time on the Content of Compost Fatty Acids, Quantified Using Two Methods ▿ †

PubMed Central

Cardinali, Alessandra; Otto, Stefan; Vischetti, Costantino; Brown, Colin; Zanin, Giuseppe

2010-01-01

Compost biobeds can promote biodegradation of pesticides. The microbial community structure changes during the composting process, and simple methods can potentially be used to follow these changes. In this study the microbial identification (MIDI) and ester-linked (EL) procedures were used to determine the composition of fatty acid methyl esters (FAMEs) in composts aged 3 and 12 months, inoculated with 3 recalcitrant pesticides (azoxystrobin, chlorotoluron, and epoxyconazole and a coapplication of all three) after 0, 56, and 125 days of degradation. Pesticide persistence was high, and after 125 days the residue was 22 to 70% of the applied amount depending mostly on the composting age. Seventy-one FAMEs belonging to nine groups were detected. The EL method provided three times as many detections as did the MIDI method and was more sensitive for all FAME groups except alcohol. Thirty-six and five FAMEs were unique to the EL and MIDI methods, respectively. The extraction method was of importance. The EL method provided a higher number of detections for 57 FAMEs, and the MIDI method provided a higher number for 9 FAMEs, while the two methods were equal for 5 FAMEs; thus, the EL method provided a more uniform overall FAME profile. Effects of the other factors were not always clear. Inoculation with pesticide did not influence the FAME profile with the MIDI method, while it influenced cyclopropane and monounsaturated content with the EL method. Composting age and degradation time had an effect on some groups of FAMEs, and this effect was greater with the EL method. The use of some FAMEs as biomarkers to follow microbial community succession was likely influenced by the type of compost and other factors. PMID:20693445

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.

Multitask assessment of roads and vehicles network (MARVN)

NASA Astrophysics Data System (ADS)

Yang, Fang; Yi, Meng; Cai, Yiran; Blasch, Erik; Sullivan, Nichole; Sheaff, Carolyn; Chen, Genshe; Ling, Haibin

2018-05-01

Vehicle detection in wide area motion imagery (WAMI) has drawn increasing attention from the computer vision research community in recent decades. In this paper, we present a new architecture for vehicle detection on road using multi-task network, which is able to detect and segment vehicles, estimate their pose, and meanwhile yield road isolation for a given region. The multi-task network consists of three components: 1) vehicle detection, 2) vehicle and road segmentation, and 3) detection screening. Segmentation and detection components share the same backbone network and are trained jointly in an end-to-end way. Unlike background subtraction or frame differencing based methods, the proposed Multitask Assessment of Roads and Vehicles Network (MARVN) method can detect vehicles which are slowing down, stopped, and/or partially occluded in a single image. In addition, the method can eliminate the detections which are located at outside road using yielded road segmentation so as to decrease the false positive rate. As few WAMI datasets have road mask and vehicles bounding box anotations, we extract 512 frames from WPAFB 2009 dataset and carefully refine the original annotations. The resulting dataset is thus named as WAMI512. We extensively compare the proposed method with state-of-the-art methods on WAMI512 dataset, and demonstrate superior performance in terms of efficiency and accuracy.

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.

Comparison of the Diversity of Basidiomycetes from Dead Wood of the Manchurian fir (Abies holophylla) as Evaluated by Fruiting Body Collection, Mycelial Isolation, and 454 Sequencing.

PubMed

Jang, Yeongseon; Jang, Seokyoon; Min, Mihee; Hong, Joo-Hyun; Lee, Hanbyul; Lee, Hwanhwi; Lim, Young Woon; Kim, Jae-Jin

2015-10-01

In this study, three different methods (fruiting body collection, mycelial isolation, and 454 sequencing) were implemented to determine the diversity of wood-inhabiting basidiomycetes from dead Manchurian fir (Abies holophylla). The three methods recovered similar species richness (26 species from fruiting bodies, 32 species from mycelia, and 32 species from 454 sequencing), but Fisher's alpha, Shannon-Wiener, Simpson's diversity indices of fungal communities indicated fruiting body collection and mycelial isolation displayed higher diversity compared with 454 sequencing. In total, 75 wood-inhabiting basidiomycetes were detected. The most frequently observed species were Heterobasidion orientale (fruiting body collection), Bjerkandera adusta (mycelial isolation), and Trichaptum fusco-violaceum (454 sequencing). Only two species, Hymenochaete yasudae and Hypochnicium karstenii, were detected by all three methods. This result indicated that Manchurian fir harbors a diverse basidiomycetous fungal community and for complete estimation of fungal diversity, multiple methods should be used. Further studies are required to understand their ecology in the context of forest ecosystems.

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.

Evaluation of next generation sequencing for the analysis of Eimeria communities in wildlife.

PubMed

Vermeulen, Elke T; Lott, Matthew J; Eldridge, Mark D B; Power, Michelle L

2016-05-01

Next-generation sequencing (NGS) techniques are well-established for studying bacterial communities but not yet for microbial eukaryotes. Parasite communities remain poorly studied, due in part to the lack of reliable and accessible molecular methods to analyse eukaryotic communities. We aimed to develop and evaluate a methodology to analyse communities of the protozoan parasite Eimeria from populations of the Australian marsupial Petrogale penicillata (brush-tailed rock-wallaby) using NGS. An oocyst purification method for small sample sizes and polymerase chain reaction (PCR) protocol for the 18S rRNA locus targeting Eimeria was developed and optimised prior to sequencing on the Illumina MiSeq platform. A data analysis approach was developed by modifying methods from bacterial metagenomics and utilising existing Eimeria sequences in GenBank. Operational taxonomic unit (OTU) assignment at a high similarity threshold (97%) was more accurate at assigning Eimeria contigs into Eimeria OTUs but at a lower threshold (95%) there was greater resolution between OTU consensus sequences. The assessment of two amplification PCR methods prior to Illumina MiSeq, single and nested PCR, determined that single PCR was more sensitive to Eimeria as more Eimeria OTUs were detected in single amplicons. We have developed a simple and cost-effective approach to a data analysis pipeline for community analysis of eukaryotic organisms using Eimeria communities as a model. The pipeline provides a basis for evaluation using other eukaryotic organisms and potential for diverse community analysis studies. Copyright © 2016 Elsevier B.V. All rights reserved.

Screening and monitoring microbial xenobiotics' biodegradation by rapid, inexpensive and easy to perform microplate UV-absorbance measurements.

PubMed

Herzog, Bastian; Lemmer, Hilde; Horn, Harald; Müller, Elisabeth

2014-02-22

Evaluation of xenobiotics biodegradation potential, shown here for benzotriazoles (corrosion inhibitors) and sulfamethoxazole (sulfonamide antibiotic) by microbial communities and/or pure cultures normally requires time intensive and money consuming LC/GC methods that are, in case of laboratory setups, not always needed. The usage of high concentrations to apply a high selective pressure on the microbial communities/pure cultures in laboratory setups, a simple UV-absorbance measurement (UV-AM) was developed and validated for screening a large number of setups, requiring almost no preparation and significantly less time and money compared to LC/GC methods. This rapid and easy to use method was evaluated by comparing its measured values to LC-UV and GC-MS/MS results. Furthermore, its application for monitoring and screening unknown activated sludge communities (ASC) and mixed pure cultures has been tested and approved to detect biodegradation of benzotriazole (BTri), 4- and 5-tolyltriazole (4-TTri, 5-TTri) as well as SMX. In laboratory setups, xenobiotics concentrations above 1.0 mg L(-1) without any enrichment or preparation could be detected after optimization of the method. As UV-AM does not require much preparatory work and can be conducted in 96 or even 384 well plate formats, the number of possible parallel setups and screening efficiency was significantly increased while analytic and laboratory costs were reduced to a minimum.

Screening and monitoring microbial xenobiotics’ biodegradation by rapid, inexpensive and easy to perform microplate UV-absorbance measurements

PubMed Central

2014-01-01

Background Evaluation of xenobiotics biodegradation potential, shown here for benzotriazoles (corrosion inhibitors) and sulfamethoxazole (sulfonamide antibiotic) by microbial communities and/or pure cultures normally requires time intensive and money consuming LC/GC methods that are, in case of laboratory setups, not always needed. Results The usage of high concentrations to apply a high selective pressure on the microbial communities/pure cultures in laboratory setups, a simple UV-absorbance measurement (UV-AM) was developed and validated for screening a large number of setups, requiring almost no preparation and significantly less time and money compared to LC/GC methods. This rapid and easy to use method was evaluated by comparing its measured values to LC-UV and GC-MS/MS results. Furthermore, its application for monitoring and screening unknown activated sludge communities (ASC) and mixed pure cultures has been tested and approved to detect biodegradation of benzotriazole (BTri), 4- and 5-tolyltriazole (4-TTri, 5-TTri) as well as SMX. Conclusions In laboratory setups, xenobiotics concentrations above 1.0 mg L-1 without any enrichment or preparation could be detected after optimization of the method. As UV-AM does not require much preparatory work and can be conducted in 96 or even 384 well plate formats, the number of possible parallel setups and screening efficiency was significantly increased while analytic and laboratory costs were reduced to a minimum. PMID:24558966

A Persistent and Diverse Airway Microbiota Present during Chronic Obstructive Pulmonary Disease Exacerbations

PubMed Central

Huang, Yvonne J.; Kim, Eugenia; Cox, Michael J.; Brodie, Eoin L.; Brown, Ron; Wiener-Kronish, Jeanine P.

2010-01-01

Abstract Acute exacerbations of chronic obstructive pulmonary disease (COPD) are a major source of morbidity and contribute significantly to healthcare costs. Although bacterial infections are implicated in nearly 50% of exacerbations, only a handful of pathogens have been consistently identified in COPD airways, primarily by culture-based methods, and the bacterial microbiota in acute exacerbations remains largely uncharacterized. The aim of this study was to comprehensively profile airway bacterial communities using a culture-independent microarray, the 16S rRNA PhyloChip, of a cohort of COPD patients requiring ventilatory support and antibiotic therapy for exacerbation-related respiratory failure. PhyloChip analysis revealed the presence of over 1,200 bacterial taxa representing 140 distinct families, many previously undetected in airway diseases; bacterial community composition was strongly influenced by the duration of intubation. A core community of 75 taxa was detected in all patients, many of which are known pathogens. Bacterial community diversity in COPD airways is substantially greater than previously recognized and includes a number of potential pathogens detected in the setting of antibiotic exposure. Comprehensive assessment of the COPD airway microbiota using high-throughput, culture-independent methods may prove key to understanding the relationships between airway bacterial colonization, acute exacerbation, and clinical outcomes in this and other chronic inflammatory airway diseases. PMID:20141328

Molecular profiling of fungal communities in moisture damaged buildings before and after remediation--a comparison of culture-dependent and culture-independent methods.

PubMed

Pitkäranta, Miia; Meklin, Teija; Hyvärinen, Anne; Nevalainen, Aino; Paulin, Lars; Auvinen, Petri; Lignell, Ulla; Rintala, Helena

2011-10-21

Indoor microbial contamination due to excess moisture is an important contributor to human illness in both residential and occupational settings. However, the census of microorganisms in the indoor environment is limited by the use of selective, culture-based detection techniques. By using clone library sequencing of full-length internal transcribed spacer region combined with quantitative polymerase chain reaction (qPCR) for 69 fungal species or assay groups and cultivation, we have been able to generate a more comprehensive description of the total indoor mycoflora. Using this suite of methods, we assessed the impact of moisture damage on the fungal community composition of settled dust and building material samples (n = 8 and 16, correspondingly). Water-damaged buildings (n = 2) were examined pre- and post- remediation, and compared with undamaged reference buildings (n = 2). Culture-dependent and independent methods were consistent in the dominant fungal taxa in dust, but sequencing revealed a five to ten times higher diversity at the genus level than culture or qPCR. Previously unknown, verified fungal phylotypes were detected in dust, accounting for 12% of all diversity. Fungal diversity, especially within classes Dothideomycetes and Agaricomycetes tended to be higher in the water damaged buildings. Fungal phylotypes detected in building materials were present in dust samples, but their proportion of total fungi was similar for damaged and reference buildings. The quantitative correlation between clone library phylotype frequencies and qPCR counts was moderate (r = 0.59, p < 0.01). We examined a small number of target buildings and found indications of elevated fungal diversity associated with water damage. Some of the fungi in dust were attributable to building growth, but more information on the material-associated communities is needed in order to understand the dynamics of microbial communities between building structures and dust. The sequencing-based method proved indispensable for describing the true fungal diversity in indoor environments. However, making conclusions concerning the effect of building conditions on building mycobiota using this methodology was complicated by the wide natural diversity in the dust samples, the incomplete knowledge of material-associated fungi fungi and the semiquantitative nature of sequencing based methods.

Developing strategies for detection of gene doping.

PubMed

Baoutina, Anna; Alexander, Ian E; Rasko, John E J; Emslie, Kerry R

2008-01-01

It is feared that the use of gene transfer technology to enhance athletic performance, the practice that has received the term 'gene doping', may soon become a real threat to the world of sport. As recognised by the anti-doping community, gene doping, like doping in any form, undermines principles of fair play in sport and most importantly, involves major health risks to athletes who partake in gene doping. One attraction of gene doping for such athletes and their entourage lies in the apparent difficulty of detecting its use. Since the realisation of the threat of gene doping to sport in 2001, the anti-doping community and scientists from different disciplines concerned with potential misuse of gene therapy technologies for performance enhancement have focused extensive efforts on developing robust methods for gene doping detection which could be used by the World Anti-Doping Agency to monitor athletes and would meet the requirements of a legally defensible test. Here we review the approaches and technologies which are being evaluated for the detection of gene doping, as well as for monitoring the efficacy of legitimate gene therapy, in relation to the detection target, the type of sample required for analysis and detection methods. We examine the accumulated knowledge on responses of the body, at both cellular and systemic levels, to gene transfer and evaluate strategies for gene doping detection based on current knowledge of gene technology, immunology, transcriptomics, proteomics, biochemistry and physiology. (c) 2008 John Wiley & Sons, Ltd.

Noninvasive Medical Diagnostics & Treatment Using Ultrasonics

NASA Technical Reports Server (NTRS)

Bar-Cohen, Y.; Siegel, R.; Grandia, W.

1998-01-01

In parallel to the industrial application of NDE to flaw detection and material property determination, the medical community has succesfully adapted such methods to the noninvasaive diagnostics and treatment of many conditions and disorders of the human body.

Novel Microbiological and Spatial Statistical Methods to Improve Strength of Epidemiological Evidence in a Community-Wide Waterborne Outbreak

PubMed Central

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W.; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9–16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak. PMID:25147923

Detecting microbial dysbiosis associated with Pediatric Crohn’s disease despite the high variability of the gut microbiota

PubMed Central

Wang, Feng; Kaplan, Jess L.; Gold, Benjamin D.; Bhasin, Manoj K.; Ward, Naomi L.; Kellermayer, Richard; Kirschner, Barbara S.; Heyman, Melvin B.; Dowd, Scot E.; Cox, Stephen B.; Dogan, Haluk; Steven, Blaire; Ferry, George D.; Cohen, Stanley A.; Baldassano, Robert N.; Moran, Christopher J.; Garnett, Elizabeth A.; Drake, Lauren; Otu, Hasan H.; Mirny, Leonid A.; Libermann, Towia A.; Winter, Harland S.; Korolev, Kirill

2016-01-01

SUMMARY The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environment are highly variable. We developed a set of techniques to address this challenge based on population dynamics and information theory. These methods identified additional bacterial taxa associated with pediatric Crohn's disease and could detect significant changes in microbial communities with fewer samples than previous statistical approaches. We also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples and found that the ecological niche of a microbe predicts its role in Crohn’s disease. Bacteria typically residing in the lumen of healthy patients decrease in disease while bacteria typically residing on the mucosa of healthy patients increase in disease. Our results also show that the associations with Crohn’s disease are evolutionarily conserved and provide a mutual-information-based method to visualize dysbiosis. PMID:26804920

A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

PubMed

de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

2014-01-01

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

Experimental and environmental factors affect spurious detection of ecological thresholds

USGS Publications Warehouse

Daily, Jonathan P.; Hitt, Nathaniel P.; Smith, David; Snyder, Craig D.

2012-01-01

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.

Network immunization under limited budget using graph spectra

NASA Astrophysics Data System (ADS)

Zahedi, R.; Khansari, M.

2016-03-01

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.

Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park.

PubMed

Wendelberger, Kristie S; Gann, Daniel; Richards, Jennifer H

2018-03-09

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata . Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species' habitats. Our results also offer a method to monitor vegetation change in other threatened habitats.

Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park

PubMed Central

Richards, Jennifer H.

2018-01-01

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats. PMID:29522476

mRNA-Based Parallel Detection of Active Methanotroph Populations by Use of a Diagnostic Microarray

PubMed Central

Bodrossy, Levente; Stralis-Pavese, Nancy; Konrad-Köszler, Marianne; Weilharter, Alexandra; Reichenauer, Thomas G.; Schöfer, David; Sessitsch, Angela

2006-01-01

A method was developed for the mRNA-based application of microbial diagnostic microarrays to detect active microbial populations. DNA- and mRNA-based analyses of environmental samples were compared and confirmed via quantitative PCR. Results indicated that mRNA-based microarray analyses may provide additional information on the composition and functioning of microbial communities. PMID:16461725

Determination of glucosinolates in rapeseed and Thlaspi caerulescens plants by liquid chromatography-atmospheric pressure chemical ionization mass spectrometry.

PubMed

Tolrà, R P; Alonso, R; Poschenrieder, C; Barceló, D; Barceló, J

2000-08-11

Liquid chromatography-atmospheric pressure chemical ionization mass spectrometry was used to identify glucosinolates in plant extracts. Optimization of the analytical conditions and the determination of the method detection limit was performed using commercial 2-propenylglucosinolate (sinigrin). Optimal values for the following parameters were determined: nebulization pressure, gas temperature, flux of drying gas, capillar voltage, corona current and fragmentor conditions. The method detection limit for sinigrin was 2.85 ng. For validation of the method the glucosinolates in reference material (rapeseed) from the Community Bureau of Reference Materials (BCR) were analyzed. The method was applied for the determination of glucosinolates in Thlaspi caerulescens plants.

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

PubMed Central

Oates, Lawrence G.; Read, Harry W.; Gutknecht, Jessica L. M.; Duncan, David S.; Balser, Teri B.; Jackson, Randall D.

2017-01-01

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition. An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step. PMID:28745639

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.

Community Circulation Patterns of Oral Polio Vaccine Serotypes 1, 2, and 3 After Mexican National Immunization Weeks

PubMed Central

Troy, Stephanie B.; Ferreyra-Reyes, Leticia; Huang, ChunHong; Sarnquist, Clea; Canizales-Quintero, Sergio; Nelson, Christine; Báez-Saldaña, Renata; Holubar, Marisa; Ferreira-Guerrero, Elizabeth; García-García, Lourdes; Maldonado, Yvonne A.

2014-01-01

Background. With wild poliovirus nearing eradication, preventing circulating vaccine-derived poliovirus (cVDPV) by understanding oral polio vaccine (OPV) community circulation is increasingly important. Mexico, where OPV is given only during biannual national immunization weeks (NIWs) but where children receive inactivated polio vaccine (IPV) as part of their primary regimen, provides a natural setting to study OPV community circulation. Methods. In total, 216 children and household contacts in Veracruz, Mexico, were enrolled, and monthly stool samples and questionnaires collected for 1 year; 2501 stool samples underwent RNA extraction, reverse transcription, and real-time polymerase chain reaction (PCR) to detect OPV serotypes 1, 2, and 3. Results. OPV was detected up to 7 months after an NIW, but not at 8 months. In total, 35% of samples collected from children vaccinated the prior month, but only 4% of other samples, contained OPV. Although each serotype was detected in similar proportions among OPV strains shed as a result of direct vaccination, 87% of OPV acquired through community spread was serotype 2 (P < .0001). Conclusions. Serotype 2 circulates longer and is transmitted more readily than serotypes 1 or 3 after NIWs in a Mexican community primarily vaccinated with IPV. This may be part of the reason why most isolated cVDPV has been serotype 2. PMID:24367038

The multifaceted resources and microevolution of the successful human and animal pathogen methicillin-resistant Staphylococcus aureus

PubMed Central

Figueiredo, Agnes Marie Sá; Ferreira, Fabienne Antunes

2014-01-01

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most important bacterial pathogens based on its incidence and the severity of its associated infections. In addition, severe MRSA infections can occur in hospitalised patients or healthy individuals from the community. Studies have shown the infiltration of MRSA isolates of community origin into hospitals and variants of hospital-associated MRSA have caused infections in the community. These rapid epidemiological changes represent a challenge for the molecular characterisation of such bacteria as a hospital or community-acquired pathogen. To efficiently control the spread of MRSA, it is important to promptly detect the mecA gene, which is the determinant of methicillin resistance, using a polymerase chain reaction-based test or other rapidly and accurate methods that detect the mecA product penicillin-binding protein (PBP)2a or PBP2’. The recent emergence of MRSA isolates that harbour a mecA allotype, i.e., the mecC gene, infecting animals and humans has raised an additional and significant issue regarding MRSA laboratory detection. Antimicrobial drugs for MRSA therapy are becoming depleted and vancomycin is still the main choice in many cases. In this review, we present an overview of MRSA infections in community and healthcare settings with focus on recent changes in the global epidemiology, with special reference to the MRSA picture in Brazil. PMID:24789555

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.

A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance

PubMed Central

Sindato, Calvin; Mwabukusi, Mpoki; Teesdale, Scott; Olsen, Jennifer

2017-01-01

Background We describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa. Objective The objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations. Methods An EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members. Results Within the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts. Conclusions These initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels. PMID:29254916

Community organizing and community health: piloting an innovative approach to community engagement applied to an early intervention project in south London

PubMed Central

Bolton, Matthew; Moore, Imogen; Ferreira, Ana; Day, Crispin; Bolton, Derek

2016-01-01

Background The importance of community engagement in health is widely recognized, and key themes in UK National Institute for Health and Clinical Excellence (NICE) recommendations for enhancing community engagement are co-production and community control. This study reports an innovative approach to community engagement using the community-organizing methodology, applied in an intervention of social support to increase social capital, reduce stress and improve well-being in mothers who were pregnant and/or with infants aged 0–2 years. Methods Professional community organizers in Citizens-UK worked with local member civic institutions in south London to facilitate social support to a group of 15 new mothers. Acceptability of the programme, adherence to principles of co-production and community control, and changes in the outcomes of interest were assessed quantitatively in a quasi-experimental design. Results The programme was found to be feasible and acceptable to participating mothers, and perceived by them to involve co-production and community control. There were no detected changes in subjective well-being, but there were important reductions in distress on a standard self-report measure (GHQ-12). There were increases in social capital of a circumscribed kind associated with the project. Conclusions Community organizing provides a promising model and method of facilitating community engagement in health. PMID:25724610

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.

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.

The importance of accounting for imperfect detection when estimating functional and phylogenetic community structure.

PubMed

Si, Xingfeng; Cadotte, Marc W; Zhao, Yuhao; Zhou, Haonan; Zeng, Di; Li, Jiaqi; Jin, Tinghao; Ren, Peng; Wang, Yanping; Ding, Ping; Tingley, Morgan W

2018-06-26

Incorporating imperfect detection when estimating species richness has become commonplace in the past decade. However, the question of how imperfect detection of species affects estimates of functional and phylogenetic community structure remains untested. We used long-term counts of breeding bird species that were detected at least once on islands in a land-bridge island system, and employed multi-species occupancy models to assess the effects of imperfect detection of species on estimates of bird diversity and community structure by incorporating species traits and phylogenies. Our results showed that taxonomic, functional, and phylogenetic diversity were all underestimated significantly as a result of species' imperfect detection, with taxonomic diversity showing the greatest bias. The functional and phylogenetic structure calculated from observed communities were both more clustered than those from the detection-corrected communities due to missed distinct species. The discrepancy between observed and estimated diversity differed according to the measure of biodiversity employed. Our study demonstrates the importance of accounting for species' imperfect detection in biodiversity studies, especially for functional and phylogenetic community ecology, and when attempting to infer community assembly processes. With datasets that allow for detection-corrected community structure, we can better estimate diversity and infer the underlying mechanisms that structure community assembly, and thus make reliable management decisions for the conservation of biodiversity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Application of clone library analysis and real-time PCR for comparison of microbial communities in a low-grade copper sulfide ore bioheap leachate.

PubMed

Bowei, Chen; Xingyu, Liu; Wenyan, Liu; Jiankang, Wen

2009-11-01

The microbial communities of leachate from a bioleaching heap located in China were analyzed using the 16S rRNA gene clone library and real-time quantitative PCR. Both methods showed that Leptospirillum spp. were the dominant bacteria, and Ferroplasma acidiphilum were the only archaea detected in the leachate. Clone library results indicated that nine operational taxonomic units (OTUs) were obtained, which fell into four divisions, the Nitrospirae (74%), the gamma-Proteobacteria (14%), the Actinobacteria (6%) and the Euryarchaeota (6%). The results obtained by real-time PCR in some ways were the same as clone library analysis. Furthermore, Sulfobacillus spp., detected only by real-time PCR, suggests that real-time PCR was a reliable technology to study the microbial communities in bioleaching environments. It is a useful tool to assist clone library analysis, to further understand microbial consortia and to have comprehensive and exact microbiological information about bioleaching environments. Finally, the interactions among the microorganisms detected in the leachate were summarized according to the characteristics of these species.

The ground truth about metadata and community detection in networks.

PubMed

Peel, Leto; Larremore, Daniel B; Clauset, Aaron

2017-05-01

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

Needs and Problems of Posbindu Program: Community Health Volunteers Perspective

NASA Astrophysics Data System (ADS)

Putri, S. T.; Andriyani, S.

2018-01-01

Posbindu is a form of public participation to conduct early detection and monitoring of risk factors for non-communicable diseases(NCD), and where it was carried out in as an integrated manner, routine and periodic event. This paper aims to investigates the needs and problems on Posbindu Program based on community health volunteers(CHVs) perspective. This study used descriptive qualitative method by open ended questions. Content analysis using to explicating the result. There are 3 theme finding about elderly needs in Posbindu; medical care, support group community, and health education. We found four theme problems which in Posbindu program: low motivation from elderly, Inadequate of facilities, physical disability, failed communication. To be effective in Posbindu program, all the stakeholders have reached consensus on the Posbindu program as elderly need. CHVs need given wide knowledge about early detection, daily care, control disease continuously so that the elderly keep feeling the advantages of coming to the Posbindu.

Detection of Mycoplasma pneumoniae by real-time PCR.

PubMed

Winchell, Jonas M; Mitchell, Stephanie L

2013-01-01

Mycoplasma pneumoniae is a significant cause of respiratory disease, accounting for approximately 20% of cases of community-acquired pneumonia. Although several diagnostic methods exist to detect M. pneumoniae in respiratory specimens, real-time PCR has emerged as a significant improvement for the rapid diagnosis of this pathogen. The method described herein details the procedure for the detection of M. pneumoniae by real-time PCR (qPCR). The qPCR assay described can be performed with three targets specific for M. pneumoniae (Mp181, Mp3, and Mp7) and one marker for the detection of the RNaseP gene found in human nucleic acid as an internal control reaction. Recent studies have demonstrated the ability of this procedure to reliably identify this agent and facilitate the timely recognition of an outbreak.

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Guidelines for the detection of Trichinella larvae at the slaughterhouse in a quality assurance system.

PubMed

Rossi, Patrizia; Pozio, Edoardo

2008-01-01

The European Community Regulation (EC) No. 2075/2005 lays down specific rules on official controls for the detection of Trichinella in fresh meat for human consumption, recommending the pooled-sample digestion method as the reference method. The aim of this document is to provide specific guidance to implement an appropriate Trichinella digestion method by a laboratory accredited according to the ISO/IEC 17025:2005 international standard, and performing microbiological testing following the EA-04/10:2002 international guideline. Technical requirements for the correct implementation of the method, such as the personnel competence, specific equipments and reagents, validation of the method, reference materials, sampling, quality assurance of results and quality control of performance are provided, pointing out the critical control points for the correct implementation of the digestion method.

Liquid Chromatography with Absorbance Detection and with Isotope-Dilution Mass Spectrometry for Determination of Isoflavones in Soy Standard Reference Materials

PubMed Central

Phillips, Melissa M.; Bedner, Mary; Gradl, Manuela; Burdette, Carolyn Q.; Nelson, Michael A.; Yen, James H.; Sander, Lane C.; Rimmer, Catherine A.

2017-01-01

Two independent analytical approaches, based on liquid chromatography with absorbance detection and liquid chromatography with mass spectrometric detection, have been developed for determination of isoflavones in soy materials. These two methods yield comparable results for a variety of soy-based foods and dietary supplements. Four Standard Reference Materials (SRMs) have been produced by the National Institute of Standards and Technology to assist the food and dietary supplement community in method validation and have been assigned values for isoflavone content using both methods. These SRMs include SRM 3234 Soy Flour, SRM 3236 Soy Protein Isolate, SRM 3237 Soy Protein Concentrate, and SRM 3238 Soy-Containing Solid Oral Dosage Form. A fifth material, SRM 3235 Soy Milk, was evaluated using the methods and found to be inhomogeneous for isoflavones and unsuitable for value assignment. PMID:27832301

Monitoring of the microbial communities involved in the soy sauce manufacturing process by PCR-denaturing gradient gel electrophoresis.

PubMed

Tanaka, Yasushi; Watanabe, Jun; Mogi, Yoshinobu

2012-08-01

Soy sauce is a traditional seasoning produced through the fermentation of soybeans and wheat using microbes. In this study, the microbial communities involved in the soy sauce manufacturing process were analyzed by PCR-Denaturing Gradient Gel Electrophoresis (PCR-DGGE). The bacterial DGGE profile indicated that the bacterial microbes in the koji were Weissella cibaria (Weissella confusa, Weissella kimchii, Weissella salipiscis, Lactobacillus fermentum, Lactobacillus plantarum, Lactobacillus iners, or Streptococcus thermophilus), Staphylococcus gallinarum (or Staphylococcus xylosus), and Staphylococcus kloosii. In addition to these bacteria, Tetragenococcus halophilus was also detected in the mash during lactic acid fermentation. The fungal DGGE profile indicated that the fungal microbes in the koji were not only Aspergillus oryzae but also several yeasts. In the mash, Zygosaccharomyces rouxii appeared in the early fermentation stage, Candida etchellsii (or Candida nodaensis) and Candida versatilis were detected at the middle fermentation stage, and Candida etchellsii was detected at the mature fermentation stage. These results suggest that the microbial communities present during the soy sauce manufacturing process change drastically throughout its production. This is the first report to reveal the microbial communities involved in the soy sauce manufacturing process using a culture-independent method. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

BridgeRank: A novel fast centrality measure based on local structure of the network

NASA Astrophysics Data System (ADS)

Salavati, Chiman; Abdollahpouri, Alireza; Manbari, Zhaleh

2018-04-01

Ranking nodes in complex networks have become an important task in many application domains. In a complex network, influential nodes are those that have the most spreading ability. Thus, identifying influential nodes based on their spreading ability is a fundamental task in different applications such as viral marketing. One of the most important centrality measures to ranking nodes is closeness centrality which is efficient but suffers from high computational complexity O(n3) . This paper tries to improve closeness centrality by utilizing the local structure of nodes and presents a new ranking algorithm, called BridgeRank centrality. The proposed method computes local centrality value for each node. For this purpose, at first, communities are detected and the relationship between communities is completely ignored. Then, by applying a centrality in each community, only one best critical node from each community is extracted. Finally, the nodes are ranked based on computing the sum of the shortest path length of nodes to obtained critical nodes. We have also modified the proposed method by weighting the original BridgeRank and selecting several nodes from each community based on the density of that community. Our method can find the best nodes with high spread ability and low time complexity, which make it applicable to large-scale networks. To evaluate the performance of the proposed method, we use the SIR diffusion model. Finally, experiments on real and artificial networks show that our method is able to identify influential nodes so efficiently, and achieves better performance compared to other recent methods.

a Comparison of Empirical and Inteligent Methods for Dust Detection Using Modis Satellite Data

NASA Astrophysics Data System (ADS)

Shahrisvand, M.; Akhoondzadeh, M.

2013-09-01

Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (Aerosol Optical Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to assess the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (Aerosol Optical Thickness) product of NASA.

Multi-Detection Events, Probability Density Functions, and Reduced Location Area

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

Eslinger, Paul W.; Schrom, Brian T.

2016-03-01

Abstract Several efforts have been made in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) community to assess the benefits of combining detections of radionuclides to improve the location estimates available from atmospheric transport modeling (ATM) backtrack calculations. We present a Bayesian estimation approach rather than a simple dilution field of regard approach to allow xenon detections and non-detections to be combined mathematically. This system represents one possible probabilistic approach to radionuclide event formation. Application of this method to a recent interesting radionuclide event shows a substantial reduction in the location uncertainty of that event.

Cultivation-Independent Characterization of Methylobacterium Populations in the Plant Phyllosphere by Automated Ribosomal Intergenic Spacer Analysis▿ †

PubMed Central

Knief, Claudia; Frances, Lisa; Cantet, Franck; Vorholt, Julia A.

2008-01-01

Bacteria of the genus Methylobacterium are widespread in the environment, but their ecological role in ecosystems, such as the plant phyllosphere, is not very well understood. To gain better insight into the distribution of different Methylobacterium species in diverse ecosystems, a rapid and specific cultivation-independent method for detection of these organisms and analysis of their community structure is needed. Therefore, 16S rRNA gene-targeted primers specific for this genus were designed and evaluated. These primers were used in PCR in combination with a reverse primer that binds to the tRNAAla gene, which is located upstream of the 23S rRNA gene in the 16S-23S intergenic spacer (IGS). PCR products that were of different lengths were obtained due to the length heterogeneity of the IGS of different Methylobacterium species. This length variation allowed generation of fingerprints of Methylobacterium communities in environmental samples by automated ribosomal intergenic spacer analysis. The Methylobacterium communities on leaves of different plant species in a natural field were compared using this method. The new method allows rapid comparisons of Methylobacterium communities and is thus a useful tool to study Methylobacterium communities in different ecosystems. PMID:18263752

Occupancy in community-level studies

USGS Publications Warehouse

MacKenzie, Darryl I.; Nichols, James; Royle, Andy; Pollock, Kenneth H.; Bailey, Larissa L.; Hines, James

2018-01-01

Another type of multi-species studies, are those focused on community-level metrics such as species richness. In this chapter we detail how some of the single-species occupancy models described in earlier chapters have been applied, or extended, for use in such studies, while accounting for imperfect detection. We highlight how Bayesian methods using MCMC are particularly useful in such settings to easily calculate relevant community-level summaries based on presence/absence data. These modeling approaches can be used to assess richness at a single point in time, or to investigate changes in the species pool over time.

How do Community Pharmacies Recover from E-prescription Errors?

PubMed Central

Odukoya, Olufunmilola K.; Stone, Jamie A.; Chui, Michelle A.

2014-01-01

Background The use of e-prescribing is increasing annually, with over 788 million e-prescriptions received in US pharmacies in 2012. Approximately 9% of e-prescriptions have medication errors. Objective To describe the process used by community pharmacy staff to detect, explain, and correct e-prescription errors. Methods The error recovery conceptual framework was employed for data collection and analysis. 13 pharmacists and 14 technicians from five community pharmacies in Wisconsin participated in the study. A combination of data collection methods were utilized, including direct observations, interviews, and focus groups. The transcription and content analysis of recordings were guided by the three-step error recovery model. Results Most of the e-prescription errors were detected during the entering of information into the pharmacy system. These errors were detected by both pharmacists and technicians using a variety of strategies which included: (1) performing double checks of e-prescription information; (2) printing the e-prescription to paper and confirming the information on the computer screen with information from the paper printout; and (3) using colored pens to highlight important information. Strategies used for explaining errors included: (1) careful review of patient’ medication history; (2) pharmacist consultation with patients; (3) consultation with another pharmacy team member; and (4) use of online resources. In order to correct e-prescription errors, participants made educated guesses of the prescriber’s intent or contacted the prescriber via telephone or fax. When e-prescription errors were encountered in the community pharmacies, the primary goal of participants was to get the order right for patients by verifying the prescriber’s intent. Conclusion Pharmacists and technicians play an important role in preventing e-prescription errors through the detection of errors and the verification of prescribers’ intent. Future studies are needed to examine factors that facilitate or hinder recovery from e-prescription errors. PMID:24373898

Approach to determine the diversity of Legionella species by nested PCR-DGGE in aquatic environments.

PubMed

Huang, Wen-Chien; Tsai, Hsin-Chi; Tao, Chi-Wei; Chen, Jung-Sheng; Shih, Yi-Jia; Kao, Po-Min; Huang, Tung-Yi; Hsu, Bing-Mu

2017-01-01

In this study, we describe a nested PCR-DGGE strategy to detect Legionella communities from river water samples. The nearly full-length 16S rRNA gene was amplified using bacterial primer in the first step. After, the amplicons were employed as DNA templates in the second PCR using Legionella specific primer. The third round of gene amplification was conducted to gain PCR fragments apposite for DGGE analysis. Then the total numbers of amplified genes were observed in DGGE bands of products gained with primers specific for the diversity of Legionella species. The DGGE patterns are thus potential for a high-throughput preliminary determination of aquatic environmental Legionella species before sequencing. Comparative DNA sequence analysis of excised DGGE unique band patterns showed the identity of the Legionella community members, including a reference profile with two pathogenic species of Legionella strains. In addition, only members of Legionella pneumophila and uncultured Legionella sp. were detected. Development of three step nested PCR-DGGE tactic is seen as a useful method for studying the diversity of Legionella community. The method is rapid and provided sequence information for phylogenetic analysis.

Approach to determine the diversity of Legionella species by nested PCR-DGGE in aquatic environments

PubMed Central

Huang, Wen-Chien; Tsai, Hsin-Chi; Tao, Chi-Wei; Chen, Jung-Sheng; Shih, Yi-Jia; Kao, Po-Min; Huang, Tung-Yi; Hsu, Bing-Mu

2017-01-01

In this study, we describe a nested PCR-DGGE strategy to detect Legionella communities from river water samples. The nearly full-length 16S rRNA gene was amplified using bacterial primer in the first step. After, the amplicons were employed as DNA templates in the second PCR using Legionella specific primer. The third round of gene amplification was conducted to gain PCR fragments apposite for DGGE analysis. Then the total numbers of amplified genes were observed in DGGE bands of products gained with primers specific for the diversity of Legionella species. The DGGE patterns are thus potential for a high-throughput preliminary determination of aquatic environmental Legionella species before sequencing. Comparative DNA sequence analysis of excised DGGE unique band patterns showed the identity of the Legionella community members, including a reference profile with two pathogenic species of Legionella strains. In addition, only members of Legionella pneumophila and uncultured Legionella sp. were detected. Development of three step nested PCR-DGGE tactic is seen as a useful method for studying the diversity of Legionella community. The method is rapid and provided sequence information for phylogenetic analysis. PMID:28166249

The ground truth about metadata and community detection in networks

PubMed Central

Peel, Leto; Larremore, Daniel B.; Clauset, Aaron

2017-01-01

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures. PMID:28508065

Stochastic Industrial Source Detection Using Lower Cost Methods

NASA Astrophysics Data System (ADS)

Thoma, E.; George, I. J.; Brantley, H.; Deshmukh, P.; Cansler, J.; Tang, W.

2017-12-01

Hazardous air pollutants (HAPs) can be emitted from a variety of sources in industrial facilities, energy production, and commercial operations. Stochastic industrial sources (SISs) represent a subcategory of emissions from fugitive leaks, variable area sources, malfunctioning processes, and improperly controlled operations. From the shared perspective of industries and communities, cost-effective detection of mitigable SIS emissions can yield benefits such as safer working environments, cost saving through reduced product loss, lower air shed pollutant impacts, and improved transparency and community relations. Methods for SIS detection can be categorized by their spatial regime of operation, ranging from component-level inspection to high-sensitivity kilometer scale surveys. Methods can be temporally intensive (providing snap-shot measures) or sustained in both time-integrated and continuous forms. Each method category has demonstrated utility, however, broad adoption (or routine use) has thus far been limited by cost and implementation viability. Described here are a subset of SIS methods explored by the U.S EPA's next generation emission measurement (NGEM) program that focus on lower cost methods and models. An emerging systems approach that combines multiple forms to help compensate for reduced performance factors of lower cost systems is discussed. A case study of a multi-day HAP emission event observed by a combination of low cost sensors, open-path spectroscopy, and passive samplers is detailed. Early field results of a novel field gas chromatograph coupled with a fast HAP concentration sensor is described. Progress toward near real-time inverse source triangulation assisted by pre-modeled facility profiles using the Los Alamos Quick Urban & Industrial Complex (QUIC) model is discussed.

Application of Reverse Transcriptase-PCR-DGGE as a rapid method for routine determination of Vibrio spp. in foods.

PubMed

Chahorm, Kanchana; Prakitchaiwattana, Cheunjit

2018-01-02

The aim of this research was to evaluate the feasibility of PCR-DGGE and Reverse Transcriptase-PCR-DGGE techniques for rapid detection of Vibrio species in foods. Primers GC567F and 680R were initially evaluated for amplifying DNA and cDNA of ten references Vibrio species by PCR method. The GC-clamp PCR amplicons were separated according to their sequences by the DGGE using 10% (w/v) polyacrylamide gel containing 45-70% urea and formamide denaturants. Two pair of Vibrio species, which could not be differentiated on the gel, was Vibrio fluvialis - Vibrio furnissii and Vibrio parahaemolyticus - Vibrio harveyi. To determine the detection limit, in the community of 10 reference strains containing the same viable population, distinct DNA bands of 3 species; Vibrio cholerae, Vibrio mimicus and Vibrio alginolyticus were consistently observed by PCR-DGGE technique. In fact, 5 species; Vibrio cholerae, Vibrio mimicus, Vibrio alginolyticus, Vibrio parahaemolyticus and Vibrio fluvialis consistently observed by Reverse Transcriptase-PCR-DGGE. In the community containing different viable population increasing from 10 2 to 10 5 CFU/mL, PCR-DGGE analysis only detected the two most prevalent species, while RT-PCR-DGGE detected the five most prevalent species. Therefore, Reverse Transcriptase-PCR-DGGE was also selected for detection of various Vibrio cell conditions, including viable cell (VC), injured cells from frozen cultures (IVC) and injured cells from frozen cultures with pre-enrichment (PIVC). It was found that cDNA band of all cell conditions gave the same migratory patterns, except that multiple cDNA bands of Plesiomonas shigelloides under IVC and PIVC conditions were found. When Reverse Transcriptase-PCR-DGGE was used for detecting Vibrio parahaemolyticus in the pathogen-spiked food samples, Vibrio parahaemolyticus could be detected in the spiked samples containing at least 10 2 CFU/g of this pathogen. The results obtained also corresponded to standard method (USFDA, 2004). In comparison with the detection of the Vibrio profiles in fourteen food samples using standard method, Reverse Transcriptase-PCR-DGGE resulted in 100%, 75% and 50% similarity in 3, 1 and 6 food samples, respectively. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Modularity and the spread of perturbations in complex dynamical systems

NASA Astrophysics Data System (ADS)

Kolchinsky, Artemy; Gates, Alexander J.; Rocha, Luis M.

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

Modularity and the spread of perturbations in complex dynamical systems.

PubMed

Kolchinsky, Artemy; Gates, Alexander J; Rocha, Luis M

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

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.

Considering the ethics of big data research: A case of Twitter and ISIS/ISIL.

PubMed

Buchanan, Elizabeth

2017-01-01

This is a formal commentary, responding to Matthew Curran Benigni, Kenneth Joseph, and Kathleen Carley's contribution, "Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter". This brief review reflects on the ethics of big data research methodologies, and how novel methods complicate long-standing principles of research ethics. Specifically, the concept of the "data subject" as a corollary, or replacement, of "human subject" is considered.

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.

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

Inferences about nested subsets structure when not all species are detected

USGS Publications Warehouse

Cam, E.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.

2000-01-01

Comparisons of species composition among ecological communities of different size have often provided evidence that the species in communities with lower species richness form nested subsets of the species in larger communities. In the vast majority of studies, the question of nested subsets has been addressed using information on presence-absence, where a '0' is interpreted as the absence of a given species from a given location. Most of the methodological discussion in earlier studies investigating nestedness concerns the approach to generation of model-based matrices. However, it is most likely that in many situations investigators cannot detect all the species present in the location sampled. The possibility that zeros in incidence matrices reflect nondetection rather than absence of species has not been considered in studies addressing nested subsets, even though the position of zeros in these matrices forms the basis of earlier inference methods. These sampling artifacts are likely to lead to erroneous conclusions about both variation over space in species richness and the degree of similarity of the various locations. Here we propose an approach to investigation of nestedness, based on statistical inference methods explicitly incorporating species detection probability, that take into account the probabilistic nature of the sampling process. We use presence-absence data collected under Pollock?s robust capture-recapture design, and resort to an estimator of species richness originally developed for closed populations to assess the proportion of species shared by different locations. We develop testable predictions corresponding to the null hypothesis of a nonnested pattern, and an alternative hypothesis of perfect nestedness. We also present an index for assessing the degree of nestedness of a system of ecological communities. We illustrate our approach using avian data from the North American Breeding Bird Survey collected in Florida Keys.

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.

Molecular evaluation of microalgal communities in full-scale waste stabilisation ponds.

PubMed

Eland, Lucy E; Davenport, Russell J; Santos, Andre Bezerra Dos; Mota Filho, Cesar R

2018-02-22

Waste stabilisation ponds (WSPs) are widely used across the world as a passive wastewater treatment for domestic wastewaters, but little is known about their ecology, especially their phototrophic communities. This study uses molecular methods and flow cytometry to assess the cyanobacterial and eukaryotic communities longitudinally throughout two systems, one treating domestic wastewater and the other mixed industrial/domestic wastewaters. More variation was seen between the systems than between different stages in the treatment processes for both eukaryotic and cyanobacterial communities. Chlorella species and Planktophrix cyanobacteria dominated both treatment systems. Arthrospira cyanobacteria were detected only in the industrial/domestic system. The balance between non-photosynthetic and photosynthetic organisms is rarely considered, though both play vital roles in WSP functioning. Flow cytometry showed that the facultative and first maturation pond in the industrial system contained a lower proportion of photosynthetic organisms compared to the domestic system. This is reflected in the species richness data and low dissolved oxygen levels detected. All data indicated that both systems are significantly different from one another and that variation longitudinally throughout the systems is lower. A more systematic study is needed to determine if it is the wastewater source rather than the initial inoculum that drives community composition.

Imperfect asymmetry of life: earth microbial communities prefer D-lactate but can use L-lactate also.

PubMed

Moazeni, Faegheh; Zhang, Gaosen; Sun, Henry J

2010-05-01

Asymmetrical utilization of chiral compounds has been sought on Mars as evidence for biological activity. This method was recently validated in glucose. Earth organisms utilize D-glucose, not L-glucose, a perfect asymmetry. In this study, we tested the method in lactate and found utilization of both enantiomers. Soil-, sediment-, and lake-borne microbial communities prefer D-lactate but can consume L-lactate if given extra time to acclimate. This situation is termed imperfect asymmetry. Future life-detection mission investigators need to be aware of imperfect asymmetry so as not to miss relatively subtle signs of life.

Metatranscriptomic census of active protists in soils.

PubMed

Geisen, Stefan; Tveit, Alexander T; Clark, Ian M; Richter, Andreas; Svenning, Mette M; Bonkowski, Michael; Urich, Tim

2015-10-01

The high numbers and diversity of protists in soil systems have long been presumed, but their true diversity and community composition have remained largely concealed. Traditional cultivation-based methods miss a majority of taxa, whereas molecular barcoding approaches employing PCR introduce significant biases in reported community composition of soil protists. Here, we applied a metatranscriptomic approach to assess the protist community in 12 mineral and organic soil samples from different vegetation types and climatic zones using small subunit ribosomal RNA transcripts as marker. We detected a broad diversity of soil protists spanning across all known eukaryotic supergroups and revealed a strikingly different community composition than shown before. Protist communities differed strongly between sites, with Rhizaria and Amoebozoa dominating in forest and grassland soils, while Alveolata were most abundant in peat soils. The Amoebozoa were comprised of Tubulinea, followed with decreasing abundance by Discosea, Variosea and Mycetozoa. Transcripts of Oomycetes, Apicomplexa and Ichthyosporea suggest soil as reservoir of parasitic protist taxa. Further, Foraminifera and Choanoflagellida were ubiquitously detected, showing that these typically marine and freshwater protists are autochthonous members of the soil microbiota. To the best of our knowledge, this metatranscriptomic study provides the most comprehensive picture of active protist communities in soils to date, which is essential to target the ecological roles of protists in the complex soil system.

Metatranscriptomic census of active protists in soils

PubMed Central

Geisen, Stefan; Tveit, Alexander T; Clark, Ian M; Richter, Andreas; Svenning, Mette M; Bonkowski, Michael; Urich, Tim

2015-01-01

The high numbers and diversity of protists in soil systems have long been presumed, but their true diversity and community composition have remained largely concealed. Traditional cultivation-based methods miss a majority of taxa, whereas molecular barcoding approaches employing PCR introduce significant biases in reported community composition of soil protists. Here, we applied a metatranscriptomic approach to assess the protist community in 12 mineral and organic soil samples from different vegetation types and climatic zones using small subunit ribosomal RNA transcripts as marker. We detected a broad diversity of soil protists spanning across all known eukaryotic supergroups and revealed a strikingly different community composition than shown before. Protist communities differed strongly between sites, with Rhizaria and Amoebozoa dominating in forest and grassland soils, while Alveolata were most abundant in peat soils. The Amoebozoa were comprised of Tubulinea, followed with decreasing abundance by Discosea, Variosea and Mycetozoa. Transcripts of Oomycetes, Apicomplexa and Ichthyosporea suggest soil as reservoir of parasitic protist taxa. Further, Foraminifera and Choanoflagellida were ubiquitously detected, showing that these typically marine and freshwater protists are autochthonous members of the soil microbiota. To the best of our knowledge, this metatranscriptomic study provides the most comprehensive picture of active protist communities in soils to date, which is essential to target the ecological roles of protists in the complex soil system. PMID:25822483

Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species

NASA Astrophysics Data System (ADS)

Hatzenbuhler, Chelsea; Kelly, John R.; Martinson, John; Okum, Sara; Pilgrim, Erik

2017-04-01

High-throughput DNA metabarcoding has gained recognition as a potentially powerful tool for biomonitoring, including early detection of aquatic invasive species (AIS). DNA based techniques are advancing, but our understanding of the limits to detection for metabarcoding complex samples is inadequate. For detecting AIS at an early stage of invasion when the species is rare, accuracy at low detection limits is key. To evaluate the utility of metabarcoding in future fish community monitoring programs, we conducted several experiments to determine the sensitivity and accuracy of routine metabarcoding methods. Experimental mixes used larval fish tissue from multiple “common” species spiked with varying proportions of tissue from an additional “rare” species. Pyrosequencing of genetic marker, COI (cytochrome c oxidase subunit I) and subsequent sequence data analysis provided experimental evidence of low-level detection of the target “rare” species at biomass percentages as low as 0.02% of total sample biomass. Limits to detection varied interspecifically and were susceptible to amplification bias. Moreover, results showed some data processing methods can skew sequence-based biodiversity measurements from corresponding relative biomass abundances and increase false absences. We suggest caution in interpreting presence/absence and relative abundance in larval fish assemblages until metabarcoding methods are optimized for accuracy and precision.

A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks.

PubMed

Lautenschlager, Karin; Hwang, Chiachi; Liu, Wen-Tso; Boon, Nico; Köster, Oliver; Vrouwenvelder, Hans; Egli, Thomas; Hammes, Frederik

2013-06-01

Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52 h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (± 0.6) × 10(4) cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, so far for unknown reasons, recorded a slight but significantly higher TCC (1.3 (± 0.1) × 10(5) cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used provides a powerful and sensitive tool to assess and evaluate biological stability and microbial processes in drinking water distribution systems. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of three methods for the detection of Trichinella spiralis infections in pigs by five European laboratories*

PubMed Central

Kohler, G.; Ruitenberg, E. J.

1974-01-01

Three methods employed in the diagnosis of trichinosis (trichinoscopy, digestion method, and immunofluorescence technique) were compared by laboratories in 5 countries of the European economic community. For this purpose, material from 32 pigs infected with 50, 150, 500, and 1 500 T. spiralis larvae was examined. With none of the three methods was it possible to detect with sufficient reliability a T. spiralis infection in pigs infected with 50 larvae. The digestion method and the immunofluorescence technique yielded more reliable results when the infection dose was 150 larvae or more. With trichinoscopy, reliable results were obtained in pigs infected with 500 and 1 500 larvae. With the digestion method and trichinoscopy, the onset of infections was detectable from 3 weeks post infection, the digestion method being more reliable; the immunofluorescence technique yielded positive results from approximately 4-6 weeks post infection. The immunofluorescence technique is applicable for epidemiological surveys. As a routine diagnostic procedure in the slaughterhouse, trichinoscopy and the digestion method are possible alternatives, the latter being more sensitive. PMID:4616776

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

Comparative analysis on the selection of number of clusters in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Kabashima, Yoshiyuki

2018-02-01

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

On designing of a low leakage patient-centric provider network.

PubMed

Zheng, Yuchen; Lin, Kun; White, Thomas; Pickreign, Jeremy; Yuen-Reed, Gigi

2018-03-27

When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for quality of care and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. By doing so, we learn the features of low service leakage provider networks that can be generalized to different patient population. Data used for this study include de-identified healthcare insurance administrative data acquired from Capital District Physicians' Health Plan (CDPHP) for diabetic patients who resided in four New York state counties (Albany, Rensselaer, Saratoga, and Schenectady) in 2014. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. For each detected community, a profile is built using several new key measures to elucidate stakeholders of our findings. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimize leakage. We identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity.

Increases in HIV Testing and Case Detection from NIMH Project Accept (HPTN 043) among 16–32 Year Olds: A Randomized Community-Based Intervention in Tanzania, Zimbabwe, and Thailand

PubMed Central

Sweat, Michael; Morin, Stephen; Celentano, David; Mulawa, Marta; Singh, Basant; Mbwambo, Jessie; Kawichai, Surinda; Chingono, Alfred; Khumalo-Sakutukwa, Gertrude; Gray, Glenda; Richter, Linda; Kulich, Michal; Sadowski, Andrew; Coates, Thomas

2011-01-01

SUMMARY BACKGROUND HIV counseling and testing is the gateway to treatment and care and provides important preventative and personal benefits to recipients. However, in developing countries the majority of HIV infected persons have not been tested for HIV. Combining community mobilization, mobile community-based HIV testing and counseling, and post-test support may increase HIV testing rates. METHODS We randomly assigned half of 10 rural communities in Tanzania, 8 in Zimbabwe, and 14 in Thailand to receive a multiple component community-based voluntary counseling and testing (CBVCT) intervention together with access to standard clinic-based voluntary counseling and testing (SVCT). The control communities received only SVCT. The intervention was provided for approximately 3 years. The primary study endpoint is HIV incidence and is pending completion of the post-intervention assessment. This is a descriptive interim analysis examining the percentage of the total population aged 16–32 years tested for HIV across study arms, and differences in client characteristics by study arm. FINDINGS A higher percentage of 16–32 year-olds were tested in intervention communities than in control communities (37% vs. 9% in Tanzania; 51% vs. 5% in Zimbabwe; and 69% vs. 23% in Thailand). The mean difference between the percentage of the population tested in CBVCT versus SVCT communities was 40.4% across the 3 country study arm pairs, (95% CI 15.8% – 64.7%, p-value 0.019, df=2). Despite higher prevalence of HIV among those testing at SVCT venues the intervention detected 3.6 times more HIV infected clients in the CBVCT communities than in SVCT communities (952 vs. 264, p< 0.001). Over time the rate of repeat testing grew substantially across all sites to 28% of all those testing for HIV by the end of the intervention period. INTERPRETATION This multiple component, community-level intervention is effective at both increasing HIV testing rates and detecting HIV cases in rural settings in developing countries. PMID:21546309

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.

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

Quinone-based stable isotope probing for assessment of 13C substrate-utilizing bacteria

NASA Astrophysics Data System (ADS)

Kunihiro, Tadao; Katayama, Arata; Demachi, Toyoko; Veuger, Bart; Boschker, Henricus T. S.; van Oevelen, Dick

2015-04-01

In this study, we attempted to establish quinone-stable-isotope probing (SIP) technique to link substrate-utilizing bacterial group to chemotaxonomic group in bacterial community. To identify metabolically active bacterial group in various environments, SIP techniques combined with biomarkers have been widely utilized as an attractive method for environmental study. Quantitative approaches of the SIP technique have unique advantage to assess substrate-incorporation into bacteria. As a most major quantitative approach, SIP technique based on phospholipid-derived fatty acids (PLFA) have been applied to simultaneously assess substrate-incorporation rate into bacteria and microbial community structure. This approach is powerful to estimate the incorporation rate because of the high sensitivity due to the detection by a gas chromatograph-combustion interface-isotope ratio mass spectrometer (GC-c-IRMS). However, its phylogenetic resolution is limited by specificity of a compound-specific marker. We focused on respiratory quinone as a biomarker. Our previous study found a good correlation between concentrations of bacteria-specific PLFAs and quinones over several orders of magnitude in various marine sediments, and the quinone method has a higher resolution (bacterial phylum level) for resolving differences in bacterial community composition more than that of bacterial PLFA. Therefore, respiratory quinones are potentially good biomarkers for quantitative approaches of the SIP technique. The LC-APCI-MS method as molecular-mass based detection method for quinone was developed and provides useful structural information for identifying quinone molecular species in environmental samples. LC-MS/MS on hybrid triple quadrupole/linear ion trap, which enables to simultaneously identify and quantify compounds in a single analysis, can detect high molecular compounds with their isotope ions. Use of LC-MS/MS allows us to develop quinone-SIP based on molecular mass differences due to 13C abundance in the quinone. In this study, we verified carbon stable isotope of quinone compared with bulk carbon stable isotope of bacterial culture. Results indicated a good correlation between carbon stable isotope of quinone compared with bulk carbon stable isotope. However, our measurement conditions for detection of quinone isotope-ions incurred underestimation of 13C abundance in the quinone. The quinone-SIP technique needs further optimization for measurement conditions of LC-MS/MS.

Detection of chemical pollutants by passive LWIR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis

2012-09-01

Toxic industrial chemicals (TICs) represent a major threat to public health and security. Their detection constitutes a real challenge to security and first responder's communities. One promising detection method is based on the passive standoff identification of chemical vapors emanating from the laboratory under surveillance. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test passive Long Wave Infrared (LWIR) hyperspectral imaging (HSI) sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs) and precursors. Sensors such as the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) and the Improved Compact ATmospheric Sounding Interferometer (iCATSI) were developed for this application. This paper describes the sensor developments and presents initial results of standoff detection and identification of TICs and precursors. The standoff sensors are based on the differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak plumes at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios will be presented. These results will serve to establish the potential of the method for standoff detection of TICs precursors and surrogates.

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

PubMed Central

Porter, Teresita M.; Golding, G. Brian

2012-01-01

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys. PMID:22558215

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

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...

Molecular profiling of fungal communities in moisture damaged buildings before and after remediation - a comparison of culture-dependent and culture-independent methods

PubMed Central

2011-01-01

Background Indoor microbial contamination due to excess moisture is an important contributor to human illness in both residential and occupational settings. However, the census of microorganisms in the indoor environment is limited by the use of selective, culture-based detection techniques. By using clone library sequencing of full-length internal transcribed spacer region combined with quantitative polymerase chain reaction (qPCR) for 69 fungal species or assay groups and cultivation, we have been able to generate a more comprehensive description of the total indoor mycoflora. Using this suite of methods, we assessed the impact of moisture damage on the fungal community composition of settled dust and building material samples (n = 8 and 16, correspondingly). Water-damaged buildings (n = 2) were examined pre- and post- remediation, and compared with undamaged reference buildings (n = 2). Results Culture-dependent and independent methods were consistent in the dominant fungal taxa in dust, but sequencing revealed a five to ten times higher diversity at the genus level than culture or qPCR. Previously unknown, verified fungal phylotypes were detected in dust, accounting for 12% of all diversity. Fungal diversity, especially within classes Dothideomycetes and Agaricomycetes tended to be higher in the water damaged buildings. Fungal phylotypes detected in building materials were present in dust samples, but their proportion of total fungi was similar for damaged and reference buildings. The quantitative correlation between clone library phylotype frequencies and qPCR counts was moderate (r = 0.59, p < 0.01). Conclusions We examined a small number of target buildings and found indications of elevated fungal diversity associated with water damage. Some of the fungi in dust were attributable to building growth, but more information on the material-associated communities is needed in order to understand the dynamics of microbial communities between building structures and dust. The sequencing-based method proved indispensable for describing the true fungal diversity in indoor environments. However, making conclusions concerning the effect of building conditions on building mycobiota using this methodology was complicated by the wide natural diversity in the dust samples, the incomplete knowledge of material-associated fungi fungi and the semiquantitative nature of sequencing based methods. PMID:22017920

Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota.

PubMed

Wang, Feng; Kaplan, Jess L; Gold, Benjamin D; Bhasin, Manoj K; Ward, Naomi L; Kellermayer, Richard; Kirschner, Barbara S; Heyman, Melvin B; Dowd, Scot E; Cox, Stephen B; Dogan, Haluk; Steven, Blaire; Ferry, George D; Cohen, Stanley A; Baldassano, Robert N; Moran, Christopher J; Garnett, Elizabeth A; Drake, Lauren; Otu, Hasan H; Mirny, Leonid A; Libermann, Towia A; Winter, Harland S; Korolev, Kirill S

2016-02-02

The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Dynamics of bacterial communities before and after distribution in a full-scale drinking water network.

PubMed

El-Chakhtoura, Joline; Prest, Emmanuelle; Saikaly, Pascal; van Loosdrecht, Mark; Hammes, Frederik; Vrouwenvelder, Hans

2015-05-01

Understanding the biological stability of drinking water distribution systems is imperative in the framework of process control and risk management. The objective of this research was to examine the dynamics of the bacterial community during drinking water distribution at high temporal resolution. Water samples (156 in total) were collected over short time-scales (minutes/hours/days) from the outlet of a treatment plant and a location in its corresponding distribution network. The drinking water is treated by biofiltration and disinfectant residuals are absent during distribution. The community was analyzed by 16S rRNA gene pyrosequencing and flow cytometry as well as conventional, culture-based methods. Despite a random dramatic event (detected with pyrosequencing and flow cytometry but not with plate counts), the bacterial community profile at the two locations did not vary significantly over time. A diverse core microbiome was shared between the two locations (58-65% of the taxa and 86-91% of the sequences) and found to be dependent on the treatment strategy. The bacterial community structure changed during distribution, with greater richness detected in the network and phyla such as Acidobacteria and Gemmatimonadetes becoming abundant. The rare taxa displayed the highest dynamicity, causing the major change during water distribution. This change did not have hygienic implications and is contingent on the sensitivity of the applied methods. The concept of biological stability therefore needs to be revised. Biostability is generally desired in drinking water guidelines but may be difficult to achieve in large-scale complex distribution systems that are inherently dynamic. Copyright © 2015 Elsevier Ltd. All rights reserved.

Detection of Metabolism Function of Microbial Community of Corpses by Biolog-Eco Method.

PubMed

Jiang, X Y; Wang, J F; Zhu, G H; Ma, M Y; Lai, Y; Zhou, H

2016-06-01

To detect the changes of microbial community functional diversity of corpses with different postmortem interval （PMI） and to evaluate forensic application value for estimating PMI. The cultivation of microbial community from the anal swabs of a Sus scrofa and a human corpse placed in field environment from 0 to 240 h after death was performed using the Biolog-Eco Microplate and the variations of the absorbance values were also monitored. Combined with the technology of forensic pathology and flies succession, the metabolic characteristics and changes of microbial community on the decomposed corpse under natural environment were also observed. The diversity of microbial metabolism function was found to be negatively correlated with the number of maggots in the corpses. The freezing processing had the greatest impact on average well color development value at 0 h and the impact almost disappeared after 48 h. The diversity of microbial metabolism of the samples became relatively unstable after 192 h. The principal component analysis showed that 31 carbon sources could be consolidated for 5 principal components （accumulative contribution ratio >90%）.The carbon source tsquare-analysis showed that N -acetyl- D -glucosamine and L -serine were the dominant carbon sources for estimating the PMI （0=240 h） of the Sus scrofa and human corpse. The Biolog-Eco method can be used to reveal the metabolic differences of the carbon resources utilization of the microbial community on the corpses during 0-240 h after death, which could provide a new basis for estimating the PMI. Copyright© by the Editorial Department of Journal of Forensic Medicine

Considering the ethics of big data research: A case of Twitter and ISIS/ISIL

PubMed Central

2017-01-01

This is a formal commentary, responding to Matthew Curran Benigni, Kenneth Joseph, and Kathleen Carley’s contribution, “Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter”. This brief review reflects on the ethics of big data research methodologies, and how novel methods complicate long-standing principles of research ethics. Specifically, the concept of the “data subject” as a corollary, or replacement, of “human subject” is considered. PMID:29194443

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.

Screening and detection of elder abuse: Research opportunities and lessons learned from emergency geriatric care, intimate partner violence, and child abuse.

PubMed

Beach, Scott R; Carpenter, Christopher R; Rosen, Tony; Sharps, Phyllis; Gelles, Richard

2016-01-01

This article provides an overview of elder abuse screening and detection methods for community-dwelling and institutionalized older adults, including general issues and challenges for the field. Then, discussions of applications in emergency geriatric care, intimate partner violence (IPV), and child abuse are presented to inform research opportunities in elder abuse screening. The article provides descriptions of emerging screening and detection methods and technologies from the emergency geriatric care and IPV fields. We also discuss the variety of potential barriers to effective screening and detection from the viewpoint of the older adult, caregivers, providers, and the health care system, and we highlight the potential harms and unintended negative consequences of increased screening and mandatory reporting. We argue that research should continue on the development of valid screening methods and tools, but that studies of perceived barriers and potential harms of elder abuse screening among key stakeholders should also be conducted.

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.

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

Optimizing occupancy surveys by maximizing detection probability: application to amphibian monitoring in the Mediterranean region.

PubMed

Petitot, Maud; Manceau, Nicolas; Geniez, Philippe; Besnard, Aurélien

2014-09-01

Setting up effective conservation strategies requires the precise determination of the targeted species' distribution area and, if possible, its local abundance. However, detection issues make these objectives complex for most vertebrates. The detection probability is usually <1 and is highly dependent on species phenology and other environmental variables. The aim of this study was to define an optimized survey protocol for the Mediterranean amphibian community, that is, to determine the most favorable periods and the most effective sampling techniques for detecting all species present on a site in a minimum number of field sessions and a minimum amount of prospecting effort. We visited 49 ponds located in the Languedoc region of southern France on four occasions between February and June 2011. Amphibians were detected using three methods: nighttime call count, nighttime visual encounter, and daytime netting. The detection nondetection data obtained was then modeled using site-occupancy models. The detection probability of amphibians sharply differed between species, the survey method used and the date of the survey. These three covariates also interacted. Thus, a minimum of three visits spread over the breeding season, using a combination of all three survey methods, is needed to reach a 95% detection level for all species in the Mediterranean region. Synthesis and applications: detection nondetection surveys combined to site occupancy modeling approach are powerful methods that can be used to estimate the detection probability and to determine the prospecting effort necessary to assert that a species is absent from a site.

Genetic methods for detection of antibiotic resistance: focus on extended-spectrum β-lactamases.

PubMed

Chroma, Magdalena; Kolar, Milan

2010-12-01

In 1928, the first antibiotic, penicillin, was discovered. That was the beginning of a great era in the development and prescription of antibiotics. However, the introduction of these antimicrobial agents into clinical practice was accompanied by the problem of antibiotic resistance. Currently, bacterial resistance to antibiotics poses a major problem in both hospital and community settings throughout the world. This review provides examples of modern genetic methods and their practical application in the field of extended-spectrum β-lactamase detection. Since extended-spectrum β-lactamases are the main mechanism of Gram-negative bacterial resistance to oxyimino-cephalosporins, rapid and accurate detection is requested in common clinical practice. Currently, the detection of extended-spectrum β-lactamases is primarily based on the determination of bacterial phenotypes rather than genotypes. This is because therapeutic decisions are based on assessing the susceptibility rather than presence of resistance genes. One of the main disadvantages of genetic methods is high costs, including those of laboratory equipment. On the other hand, if these modern methods are introduced into diagnostics, they often help in rapid and accurate detection of certain microorganisms or their resistance and pathogenic determinants.

Systematic evaluation of bias in microbial community profiles induced by whole genome amplification.

PubMed

Direito, Susana O L; Zaura, Egija; Little, Miranda; Ehrenfreund, Pascale; Röling, Wilfred F M

2014-03-01

Whole genome amplification methods facilitate the detection and characterization of microbial communities in low biomass environments. We examined the extent to which the actual community structure is reliably revealed and factors contributing to bias. One widely used [multiple displacement amplification (MDA)] and one new primer-free method [primase-based whole genome amplification (pWGA)] were compared using a polymerase chain reaction (PCR)-based method as control. Pyrosequencing of an environmental sample and principal component analysis revealed that MDA impacted community profiles more strongly than pWGA and indicated that this related to species GC content, although an influence of DNA integrity could not be excluded. Subsequently, biases by species GC content, DNA integrity and fragment size were separately analysed using defined mixtures of DNA from various species. We found significantly less amplification of species with the highest GC content for MDA-based templates and, to a lesser extent, for pWGA. DNA fragmentation also interfered severely: species with more fragmented DNA were less amplified with MDA and pWGA. pWGA was unable to amplify low molecular weight DNA (< 1.5 kb), whereas MDA was inefficient. We conclude that pWGA is the most promising method for characterization of microbial communities in low-biomass environments and for currently planned astrobiological missions to Mars. © 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.

Method- and species-specific detection probabilities of fish occupancy in Arctic lakes: Implications for design and management

USGS Publications Warehouse

Haynes, Trevor B.; Rosenberger, Amanda E.; Lindberg, Mark S.; Whitman, Matthew; Schmutz, Joel A.

2013-01-01

Studies examining species occurrence often fail to account for false absences in field sampling. We investigate detection probabilities of five gear types for six fish species in a sample of lakes on the North Slope, Alaska. We used an occupancy modeling approach to provide estimates of detection probabilities for each method. Variation in gear- and species-specific detection probability was considerable. For example, detection probabilities for the fyke net ranged from 0.82 (SE = 0.05) for least cisco (Coregonus sardinella) to 0.04 (SE = 0.01) for slimy sculpin (Cottus cognatus). Detection probabilities were also affected by site-specific variables such as depth of the lake, year, day of sampling, and lake connection to a stream. With the exception of the dip net and shore minnow traps, each gear type provided the highest detection probability of at least one species. Results suggest that a multimethod approach may be most effective when attempting to sample the entire fish community of Arctic lakes. Detection probability estimates will be useful for designing optimal fish sampling and monitoring protocols in Arctic lakes.

Detection, identification, and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites.

PubMed

Hayes, R O; Maxwell, E L; Mitchell, C J; Woodzick, T L

1985-01-01

A method of identifying mosquito larval habitats associated with fresh-water plant communities, wetlands, and other aquatic locations at Lewis and Clark Lake in the states of Nebraska and South Dakota, USA, using remote sensing imagery obtained by multispectral scanners aboard earth-orbiting satellites (Landsat 1 and 2) is described. The advantages and limitations of this method are discussed.

Advances in Candida detection platforms for clinical and point-of-care applications

PubMed Central

Safavieh, Mohammadali; Coarsey, Chad; Esiobu, Nwadiuto; Memic, Adnan; Vyas, Jatin Mahesh; Shafiee, Hadi; Asghar, Waseem

2016-01-01

Invasive candidiasis remains one of the most serious community and healthcare-acquired infections worldwide. Conventional Candida detection methods based on blood and plate culture are time-consuming and require at least 2–4 days to identify various Candida species. Despite considerable advances for candidiasis detection, the development of simple, compact and portable point-of-care diagnostics for rapid and precise testing that automatically performs cell lysis, nucleic acid extraction, purification and detection still remains a challenge. Here, we systematically review most prominent conventional and nonconventional techniques for the detection of various Candida species, including Candida staining, blood culture, serological testing and nucleic acid-based analysis. We also discuss the most advanced lab on a chip devices for candida detection. PMID:27093473

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.

Linking Environmental Exposure with Public Health: Dichlorodiphenyltrichloroethane Extracted from Soils and Water of Recently Exposed Communities of Selected Locations in Zambia

PubMed Central

Sipilanyambe Munyinda, Nosiku; Michelo, Charles; Sichilongo, Kwenga

2015-01-01

Background. In 2000, a Zambian private mining company reintroduced the use of dichlorodiphenyltrichloroethane (DDT) to control malaria in two districts. From 2000 to 2010, DDT had been applied in homes without any studies conducted to ascertain its fate in the environment. We aimed to quantify the presence of DDT and its metabolites in the soil and water around communities where it was recently used. Methods. We collected superficial soil and water samples from drinking sources of three study areas. DDT was extracted by QuEChERS method and solid phase extraction for soils and water, respectively. Analysis was by gas chromatography-mass spectrometry. A revalidated method with limits of detection ranging from 0.034 to 0.04 ppb was used. Results. Median levels of total DDT were found at 100.4 (IQR 90.9–110) and 725.4 ng/L (IQR 540–774.5) for soils and water, respectively. No DDT above detection limits was detected in the reference area. These results are clinically significant given the persistent characteristics of DDT. Conclusion. DDT presence in these media suggests possible limitations in the environmental safeguards during IRS. Such occurrence could have potential effects on humans, especially children; hence, there is a need to further examine possible associations between this exposure and humans. PMID:26579199

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.

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

NASA Technical Reports Server (NTRS)

Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

2016-01-01

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

The Philadelphia Glaucoma Detection and Treatment Project

PubMed Central

Waisbourd, Michael; Pruzan, Noelle L.; Johnson, Deiana; Ugorets, Angela; Crews, John E.; Saaddine, Jinan B.; Henderer, Jeffery D.; Hark, Lisa A.; Katz, L. Jay

2016-01-01

Purpose To evaluate the detection rates of glaucoma-related diagnoses and the initial treatments received in the Philadelphia Glaucoma Detection and Treatment Project, a community-based initiative aimed at improving the detection, treatment, and follow-up care of individuals at risk for glaucoma. Design Retrospective analysis. Participants A total of 1649 individuals at risk for glaucoma who were examined and treated in 43 community centers located in underserved communities of Philadelphia. Methods Individuals were enrolled if they were African American aged ≥50 years, were any other adult aged ≥60 years, or had a family history of glaucoma. After attending an informational glaucoma workshop, participants underwent a targeted glaucoma examination including an ocular, medical, and family history; visual acuity testing, intraocular pressure (IOP) measurement, and corneal pachymetry; slit-lamp and optic nerve examination; automated visual field testing; and fundus color photography. If indicated, treatments included selective laser trabeculoplasty (SLT), laser peripheral iridotomy (LPI), or IOP-lowering medications. Follow-up examinations were scheduled at the community sites after 4 to 6 weeks or 4 to 6 months, depending on the clinical scenario. Main Outcome Measures Detection rates of glaucoma-related diagnoses and types of treatments administered. Results Of the 1649 individuals enrolled, 645 (39.1%) received a glaucoma-related diagnosis; 20.0% (n = 330) were identified as open-angle glaucoma (OAG) suspects, 9.2% (n = 151) were identified as having narrow angles (or as a primary angle closure/suspect), and 10.0% (n = 164) were diagnosed with glaucoma, including 9.0% (n = 148) with OAG and 1.0% (n = 16) with angle-closure glaucoma. Overall, 39.0% (n = 64 of 164) of those diagnosed with glaucoma were unaware of their diagnosis. A total of 196 patients (11.9%) received glaucoma-related treatment, including 84 (5.1%) who underwent LPI, 13 (0.8%) who underwent SLT, and 103 (6.2%) who were prescribed IOP-lowering medication. Conclusions Targeting individuals at risk for glaucoma in underserved communities in Philadelphia yielded a high detection rate (39.1%) of glaucoma-related diagnoses. Providing examinations and offering treatment, including first-line laser procedures, at community-based sites providing services to older adults are effective to improve access to eye care by underserved populations. PMID:27221736

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

Feasibility of detection and intervention for alcohol-related liver disease in the community: the Alcohol and Liver Disease Detection study (ALDDeS)

PubMed Central

Sheron, Nick; Moore, Michael; O’Brien, Wendy; Harris, Scott; Roderick, Paul

2013-01-01

Background In the past 15 years mortality rates from liver disease have doubled in the UK. Brief alcohol advice is cost effective, but clinically meaningful reductions in alcohol consumption only occur in around 1 in 10 individuals. Aim To provide evidence that detecting early liver disease in the community is feasible, practical, and that feedback of liver risk can increase the proportion of subjects reducing alcohol consumption. Design and setting A community feasibility study in nine general practice sites in Hampshire. Method Hazardous and harmful drinkers were identified by WHO AUDIT questionnaire and offered screening for liver fibrosis. Results In total, 4630 individuals responded, of whom 1128 (24%) hazardous or harmful drinkers were offered a liver fibrosis check using the Southampton Traffic Light (STL) test; 393 (38%) attended and test results were returned by post. The STL has a low threshold for liver fibrosis with 45 (11%) red, 157 (40%) amber, and 191 (49%) green results. Follow-up AUDIT data was obtained for 303/393 (77%) and 76/153 (50%) subjects with evidence of liver damage reduced drinking by at least one AUDIT category (harmful to hazardous, or hazardous to low risk) compared with 52/150 (35%, P<0.011) subjects without this evidence; in the subset of harmful drinkers patterns (AUDIT >15), 22/34 (65%) of STL positives, reduced drinking compared with 10/29 (35%, P<0.017) STL negatives. Conclusion Detection of liver disease in the community is feasible, and feedback of liver risk may reduce harmful drinking. PMID:24152485

Electrochemical hydrogen sulfide biosensors.

PubMed

Xu, Tailin; Scafa, Nikki; Xu, Li-Ping; Zhou, Shufeng; Abdullah Al-Ghanem, Khalid; Mahboob, Shahid; Fugetsu, Bunshi; Zhang, Xueji

2016-02-21

The measurement of sulfide, especially hydrogen sulfide, has held the attention of the analytical community due to its unique physiological and pathophysiological roles in biological systems. Electrochemical detection offers a rapid, highly sensitive, affordable, simple, and real-time technique to measure hydrogen sulfide concentration, which has been a well-documented and reliable method. This review details up-to-date research on the electrochemical detection of hydrogen sulfide (ion selective electrodes, polarographic hydrogen sulfide sensors, etc.) in biological samples for potential therapeutic use.

A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System

PubMed Central

Yano, Terdsak; Phornwisetsirikun, Somphorn; Susumpow, Patipat; Visrutaratna, Surasing; Chanachai, Karoon; Phetra, Polawat; Chaisowwong, Warangkhana; Trakarnsirinont, Pairat; Hemwan, Phonpat; Kaewpinta, Boontuan; Singhapreecha, Charuk; Kreausukon, Khwanchai; Charoenpanyanet, Arisara ; Robert, Chongchit Sripun; Robert, Lamar; Rodtian, Pranee; Mahasing, Suteerat; Laiya, Ekkachai; Pattamakaew, Sakulrat; Tankitiyanon, Taweesart; Sansamur, Chalutwan

2018-01-01

Background Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. Objective The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. Methods The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. Results LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. Conclusions By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues. PMID:29563079

Yeast Communities of Chestnut Soils under Vineyards in Dagestan

NASA Astrophysics Data System (ADS)

Abdullabekova, D. A.; Magomedova, E. S.; Magomedov, G. G.; Aliverdieva, D. A.; Kachalkin, A. V.

2017-12-01

The study of yeast communities in chestnut soils (Kastanozems) under vineyards in the Republic of Dagestan made it possible to isolate 20 yeast species. Most of the yeasts under vineyards belonged to ascomycetes, among which species of the Saccharomycetaceae family (in particular, Saccharomyces cerevisiae) comprised a significant part. The obtained results indicate that the soils under vineyards keep the pool of microbial diversity and ensure preservation of many species typical for grapes. The method of enrichment culture on grape juice medium proved to be more efficient than other methods of analysis with respect to the number of isolated species and the rate of their detection. However, implementation of different techniques to study yeasts' diversity can give somewhat different results; a set of methods should be used for an integrated analysis.

Depth-resolved microbial community analyses in the anaerobic co-digester of dewatered sewage sludge with food waste.

PubMed

Xu, Rui; Yang, Zhao-Hui; Zheng, Yue; Zhang, Hai-Bo; Liu, Jian-Bo; Xiong, Wei-Ping; Zhang, Yan-Ru; Ahmad, Kito

2017-11-01

This study evaluated the impacts of FW addition on co-digestion in terms of microbial community. Anaerobic co-digestion (AcoD) reactors were conducted at gradually increased addition of food waste (FW) from 0 to 4kg-VSm -3 d -1 for 220days. Although no markable acidification was found at an OLR of 4kg-VSm -3 d -1 , the unhealthy operation was observed in aspect of an inhibited methane yield (185mLg -1 VS added ), which was restricted by 40% when compared with its peak value. Deterioration of digestion process was timely indicated by the dramatic decrease of archaeal population and microbial biodiversity. Furthermore, the cooperation network showed a considerable number of rare species (<1%) were strongly correlated with methane production, which were frequently overlooked due to the limits of detecting resolution or analysis methods before. Advances in the analysis of sensitive microbial community enable us to detect the early disturbances in AcoD reactors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Research on florfenicol residue in coastal area of dalian (northern china) and analysis of functional diversity of the microbial community in marine sediment.

PubMed

Zong, Humin; Ma, Deyi; Wang, Juying; Hu, Jiangtao

2010-02-01

An analytical method based on high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) has been developed to investigate florfenicol residues. Among 11 stations, florfenicol was detected in six water samples. The concentrations of florfenicol in the six samples were 64.2 microg L(-1), 390.6 microg L(-1), 1.1 x 10(4) microg L(-1), 29.8 microg L(-1), 61.6 microg L(-1), 34.9 microg L(-1), respectively. These results showed that high levels of florfenicol were observed in water samples collected from stations influenced by aquaculture discharges. However, no florfenicol residue was detected in the sediment samples. Furthermore, the functional diversities of microbial community in four marine sediment samples were analyzed by Biolog microplate. For the sediment samples from the stations where antibacterials had been used, the functional diversity of microbial community was much lower than those from the stations where antibacterials were not used.

The dynamic nature of group A streptococcal epidemiology in tropical communities with high rates of rheumatic heart disease

PubMed Central

McDONALD, M. I.; TOWERS, R. J.; ANDREWS, R.; BENGER, N.; FAGAN, P.; CURRIE, B. J.; CARAPETIS, J. R.

2008-01-01

SUMMARY Prospective surveillance was conducted in three remote Aboriginal communities with high rates of rheumatic heart disease in order to investigate the epidemiology of group A β-haemolytic streptococci (GAS). At each household visit, participants were asked about sore throat. Swabs were taken from all throats and any skin sores. GAS isolates were emm sequence and pattern-typed using standard laboratory methods. There were 531 household visits; 43 different emm types and subtypes (emmST) were recovered. Four epidemiological patterns were observed. Multiple emmST were present in the population at any one time and household acquisition rates were high. Household acquisition was most commonly via 5- to 9-year-olds. Following acquisition, there was a 1 in 5 chance of secondary detection in the household. Throat detection of emmST was brief, usually <2 months. The epidemiology of GAS in these remote Aboriginal communities is a highly dynamic process characterized by emmST diversity and turnover. PMID:17540052

History, applications, methodological issues and perspectives for the use of environmental DNA (eDNA) in marine and freshwater environments.

PubMed

Díaz-Ferguson, Edgardo E; Moyer, Gregory R

2014-12-01

Genetic material (short DNA fragments) left behind by species in nonliving components of the environment (e.g. soil, sediment, or water) is defined as environmental DNA (eDNA). This DNA has been previously described as particulate DNA and has been used to detect and describe microbial communities in marine sediments since the mid-1980's and phytoplankton communities in the water column since the early-1990's. More recently, eDNA has been used to monitor invasive or endangered vertebrate and invertebrate species. While there is a steady increase in the applicability of eDNA as a monitoring tool, a variety of eDNA applications are emerging in fields such as forensics, population and community ecology, and taxonomy. This review provides scientist with an understanding of the methods underlying eDNA detection as well as applications, key methodological considerations, and emerging areas of interest for its use in ecology and conservation of freshwater and marine environments.

Large-scale entomologic assessment of Onchocerca volvulus transmission by poolscreen PCR in Mexico.

PubMed

Rodríguez-Pérez, Mario A; Katholi, Charles R; Hassan, Hassan K; Unnasch, Thomas R

2006-06-01

To study the impact of mass Mectizan treatment on Onchocerca volvulus transmission in Mexico, entomological surveys were carried out in the endemic foci of Oaxaca, Southern Chiapas, and Northern Chiapas. Collected flies were screened by polymerase chain reaction (PCR) for O. volvulus parasites. The prevalence of infected and infective flies was estimated using the PoolScreen algorithm and with a novel probability-based method. O. volvulus infective larvae were not detected in flies from 6/13 communities. In 7/13 communities, infective flies were detected, with prevalences ranging from 1.6/10,000 to 29.0/10,000 and seasonal transmission potentials ranging from 0.4 to 3.3. Infected and infective flies were found in a community in Northern Chiapas, suggesting that, according to World Health Organization criteria, autochthonous transmission exists in this focus. These data suggest that O. volvulus transmission in Mexico has been suppressed or brought to a level that may be insufficient to sustain the parasite population.

Measuring cues for stand-off deception detection based on full-body nonverbal features in body-worn cameras

NASA Astrophysics Data System (ADS)

Bouma, Henri; Burghouts, Gertjan; den Hollander, Richard; van der Zee, Sophie; Baan, Jan; ten Hove, Johan-Martijn; van Diepen, Sjaak; van den Haak, Paul; van Rest, Jeroen

2016-10-01

Deception detection is valuable in the security domain to distinguish truth from lies. It is desirable in many security applications, such as suspect and witness interviews and airport passenger screening. Interviewers are constantly trying to assess the credibility of a statement, usually based on intuition without objective technical support. However, psychological research has shown that humans can hardly perform better than random guessing. Deception detection is a multi-disciplinary research area with an interest from different fields, such as psychology and computer science. In the last decade, several developments have helped to improve the accuracy of lie detection (e.g., with a concealed information test, increasing the cognitive load, or measurements with motion capture suits) and relevant cues have been discovered (e.g., eye blinking or fiddling with the fingers). With an increasing presence of mobile phones and bodycams in society, a mobile, stand-off, automatic deception detection methodology based on various cues from the whole body would create new application opportunities. In this paper, we study the feasibility of measuring these visual cues automatically on different parts of the body, laying the groundwork for stand-off deception detection in more flexible and mobile deployable sensors, such as body-worn cameras. We give an extensive overview of recent developments in two communities: in the behavioral-science community the developments that improve deception detection with a special attention to the observed relevant non-verbal cues, and in the computer-vision community the recent methods that are able to measure these cues. The cues are extracted from several body parts: the eyes, the mouth, the head and the fullbody pose. We performed an experiment using several state-of-the-art video-content-analysis (VCA) techniques to assess the quality of robustly measuring these visual cues.

Rapid System to Quantitatively Characterize the Airborne Microbial Community

NASA Technical Reports Server (NTRS)

Macnaughton, Sarah J.

1998-01-01

Bioaerosols have been linked to a wide range of different allergies and respiratory illnesses. Currently, microorganism culture is the most commonly used method for exposure assessment. Such culture techniques, however, generally fail to detect between 90-99% of the actual viable biomass. Consequently, an unbiased technique for detecting airborne microorganisms is essential. In this Phase II proposal, a portable air sampling device his been developed for the collection of airborne microbial biomass from indoor (and outdoor) environments. Methods were evaluated for extracting and identifying lipids that provide information on indoor air microbial biomass, and automation of these procedures was investigated. Also, techniques to automate the extraction of DNA were explored.

Liquid chromatography with absorbance detection and with isotope-dilution mass spectrometry for determination of isoflavones in soy standard reference materials.

PubMed

Phillips, Melissa M; Bedner, Mary; Reitz, Manuela; Burdette, Carolyn Q; Nelson, Michael A; Yen, James H; Sander, Lane C; Rimmer, Catherine A

2017-02-01

Two independent analytical approaches, based on liquid chromatography with absorbance detection and liquid chromatography with mass spectrometric detection, have been developed for determination of isoflavones in soy materials. These two methods yield comparable results for a variety of soy-based foods and dietary supplements. Four Standard Reference Materials (SRMs) have been produced by the National Institute of Standards and Technology to assist the food and dietary supplement community in method validation and have been assigned values for isoflavone content using both methods. These SRMs include SRM 3234 Soy Flour, SRM 3236 Soy Protein Isolate, SRM 3237 Soy Protein Concentrate, and SRM 3238 Soy-Containing Solid Oral Dosage Form. A fifth material, SRM 3235 Soy Milk, was evaluated using the methods and found to be inhomogeneous for isoflavones and unsuitable for value assignment. Graphical Abstract Separation of six isoflavone aglycones and glycosides found in Standard Reference Material (SRM) 3236 Soy Protein Isolate.

Dynamics of distribution and density of phreatophytes and other arid-land plant communities

NASA Technical Reports Server (NTRS)

Turner, R. M. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Six ERTS-1 images of the Tucson area, Arizona were analyzed to detect seasonal flushes of plant growth. Paired MSS-6 and MSS-5 bulk images were analyzed, using a ratioing technique, on the Electronic Satellite Image Analysis Console at Stanford Research Institute. Because of unique phenology, desert areas, covered only briefly by dense growths of ephemeral plants, are readily discerned. Grassland, evergreen forest, and riparian communities are also uniquely defined by their phenologies. Relatively sterile areas with little or no plant growth are easily discerned as are areas with varying degrees of plant productivity. The ratioing procedure detects plant coverage in excess of a threshold lying between 25% and 50%. The method is flexible and other coverage thresholds can be used.

Impact of enzymatic digestion on bacterial community composition in CF airway samples.

PubMed

Williamson, Kayla M; Wagner, Brandie D; Robertson, Charles E; Johnson, Emily J; Zemanick, Edith T; Harris, J Kirk

2017-01-01

Previous studies have demonstrated the importance of DNA extraction methods for molecular detection of Staphylococcus, an important bacterial group in cystic fibrosis (CF). We sought to evaluate the effect of enzymatic digestion (EnzD) prior to DNA extraction on bacterial communities identified in sputum and oropharyngeal swab (OP) samples from patients with CF. DNA from 81 samples (39 sputum and 42 OP) collected from 63 patients with CF was extracted in duplicate with and without EnzD. Bacterial communities were determined by rRNA gene sequencing, and measures of alpha and beta diversity were calculated. Principal Coordinate Analysis (PCoA) was used to assess differences at the community level and Wilcoxon Signed Rank tests were used to compare relative abundance (RA) of individual genera for paired samples with and without EnzD. Shannon Diversity Index (alpha-diversity) decreased in sputum and OP samples with the use of EnzD. Larger shifts in community composition were observed for OP samples (beta-diversity, measured by Morisita-Horn), whereas less change in communities was observed for sputum samples. The use of EnzD with OP swabs resulted in significant increase in RA for the genera Gemella ( p  < 0.01), Streptococcus ( p  < 0.01), and Rothia ( p  < 0.01). Staphylococcus ( p  < 0.01) was the only genus with a significant increase in RA from sputum, whereas the following genera decreased in RA with EnzD: Veillonella ( p  < 0.01), Granulicatella ( p  < 0.01), Prevotella ( p  < 0.01), and Gemella ( p  = 0.02). In OP samples, higher RA of Gram-positive taxa was associated with larger changes in microbial community composition. We show that the application of EnzD to CF airway samples, particularly OP swabs, results in differences in microbial communities detected by sequencing. Use of EnzD can result in large changes in bacterial community composition, and is particularly useful for detection of Staphylococcus in CF OP samples. The enhanced identification of Staphylococcus aureus is a strong indication to utilize EnzD in studies that use OP swabs to monitor CF airway communities.

Interplay Between the Temporal Dynamics of the Vaginal Microbiota and Human Papillomavirus Detection

PubMed Central

Brotman, Rebecca M.; Shardell, Michelle D.; Gajer, Pawel; Tracy, J. Kathleen; Zenilman, Jonathan M.; Ravel, Jacques; Gravitt, Patti E.

2014-01-01

Background. We sought to describe the temporal relationship between vaginal microbiota and human papillomavirus (HPV) detection. Methods. Thirty-two reproductive-age women self-collected midvaginal swabs twice weekly for 16 weeks (937 samples). Vaginal bacterial communities were characterized by pyrosequencing of barcoded 16S rRNA genes and clustered into 6 community state types (CSTs). Each swab was tested for 37 HPV types. The effects of CSTs on the rate of transition between HPV-negative and HPV-positive states were assessed using continuous-time Markov models. Results. Participants had an average of 29 samples, with HPV point prevalence between 58%–77%. CST was associated with changes in HPV status (P < .001). Lactobacillus gasseri–dominated CSTs had the fastest HPV remission rate, and a low Lactobacillus community with high proportions of the genera Atopobium (CST IV-B) had the slowest rate compared to L. crispatus–dominated CSTs (adjusted transition rate ratio [aTRR], 4.43, 95% confidence interval [CI], 1.11–17.7; aTRR, 0.33, 95% CI, .12–1.19, respectively). The rate ratio of incident HPV for low Lactobacillus CST IV-A was 1.86 (95% CI, .52–6.74). Conclusions. Vaginal microbiota dominated by L. gasseri was associated with increased clearance of detectable HPV. Frequent longitudinal sampling is necessary for evaluation of the association between HPV detection and dynamic microbiota. PMID:24943724

Diagnosis of soil-transmitted helminthiasis in an Amazonic community of Peru using multiple diagnostic techniques.

PubMed

Machicado, Jorge D; Marcos, Luis A; Tello, Raul; Canales, Marco; Terashima, Angelica; Gotuzzo, Eduardo

2012-06-01

An observational descriptive study was conducted in a Shipibo-Conibo/Ese'Eja community of the rainforest in Peru to compare the Kato-Katz method and the spontaneous sedimentation in tube technique (SSTT) for the diagnosis of intestinal parasites as well as to report the prevalence of soil-transmitted helminth (STH) infections in this area. A total of 73 stool samples were collected and analysed by several parasitological techniques, including Kato-Katz, SSTT, modified Baermann technique (MBT), agar plate culture, Harada-Mori culture and the direct smear examination. Kato-Katz and SSTT had the same rate of detection for Ascaris lumbricoides (5%), Trichuris trichiura (5%), hookworm (14%) and Hymenolepis nana (26%). The detection rate for Strongyloides stercoralis larvae was 16% by SSTT and 0% by Kato-Katz, but 18% by agar plate culture and 16% by MBT. The SSTT also had the advantage of detecting multiple intestinal protozoa such as Blastocystis hominis (40%), Giardia intestinalis (29%) and Entamoeba histolytica/E. dispar (16%). The most common intestinal parasites found in this community were B. hominis, G. intestinalis, H. nana, S. stercoralis and hookworm. In conclusion, the SSTT is not inferior to Kato-Katz for the diagnosis of common STH infections but is largely superior for detecting intestinal protozoa and S. stercoralis larvae. Copyright © 2012 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

Monitoring of microbial communities in anaerobic digestion sludge for biogas optimisation.

PubMed

Lim, Jun Wei; Ge, Tianshu; Tong, Yen Wah

2018-01-01

This study characterised and compared the microbial communities of anaerobic digestion (AD) sludge using three different methods - (1) Clone library; (2) Pyrosequencing; and (3) Terminal restriction fragment length polymorphism (T-RFLP). Although high-throughput sequencing techniques are becoming increasingly popular and affordable, the reliance of such techniques for frequent monitoring of microbial communities may be a financial burden for some. Furthermore, the depth of microbial analysis revealed by high-throughput sequencing may not be required for monitoring purposes. This study aims to develop a rapid, reliable and economical approach for the monitoring of microbial communities in AD sludge. A combined approach where genetic information of sequences from clone library was used to assign phylogeny to T-RFs determined experimentally was developed in this study. In order to assess the effectiveness of the combined approach, microbial communities determined by the combined approach was compared to that characterised by pyrosequencing. Results showed that both pyrosequencing and clone library methods determined the dominant bacteria phyla to be Proteobacteria, Firmicutes, Bacteroidetes, and Thermotogae. Both methods also found that sludge A and B were predominantly dominated by acetogenic methanogens followed by hydrogenotrophic methanogens. The number of OTUs detected by T-RFLP was significantly lesser than that detected by the clone library. In this study, T-RFLP analysis identified majority of the dominant species of the archaeal consortia. However, many of the more highly diverse bacteria consortia were missed. Nevertheless, the combined approach developed in this study where clone sequences from the clone library were used to assign phylogeny to T-RFs determined experimentally managed to accurately predict the same dominant microbial groups for both sludge A and sludge B, as compared to the pyrosequencing results. Results showed that the combined approach of clone library and T-RFLP accurately predicted the dominant microbial groups and thus is a reliable and more economical way to monitor the evolution of microbial systems in AD sludge. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low prevalence of methicillin-resistant Staphylococcus aureus nasal carriage in urban and rural community settings in Bolivia and Peru.

PubMed

Bartoloni, Alessandro; Pallecchi, Lucia; Fernandez, Connie; Mantella, Antonia; Riccobono, Eleonora; Magnelli, Donata; Mannini, Dario; Strohmeyer, Marianne; Bartalesi, Filippo; Segundo, Higinio; Monasterio, Joaquin; Rodriguez, Hugo; Cabezas, César; Gotuzzo, Eduardo; Rossolini, Gian Maria

2013-05-01

To investigate the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) nasal carriage in rural and urban community settings of Bolivia and Peru. MRSA nasal carriage was investigated in 585 individuals living in rural and urban areas of Bolivia and Peru (one urban area, one small rural village, and two native communities, one of which was highly isolated). MRSA isolates were subjected to molecular analysis for the detection of virulence genes, characterization of the staphylococcal cassette chromosome mec (SCCmec), and genotyping (multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE)). An overall very low prevalence of MRSA nasal carriage was observed (0.5%), with MRSA carriers being detected only in a small rural village of the Bolivian Chaco. The three MRSA isolates showed the characteristics of community-associated MRSA (being susceptible to all non-beta-lactam antibiotics and harboring the SCCmec type IV), were clonally related, and belonged to ST1649. This study provides an insight into the epidemiology of MRSA in community settings of Bolivia and Peru. Reliable, time-saving, and low-cost methods should be implemented to encourage continued surveillance of MRSA dissemination in resource-limited countries. Copyright © 2012 International Society for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

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

AstroCV: Astronomy computer vision library

NASA Astrophysics Data System (ADS)

González, Roberto E.; Muñoz, Roberto P.; Hernández, Cristian A.

2018-04-01

AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.

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

Ultra-Sensitive Detection of Plasmodium falciparum by Amplification of Multi-Copy Subtelomeric Targets

PubMed Central

Hofmann, Natalie; Mwingira, Felista; Shekalaghe, Seif; Robinson, Leanne J.; Mueller, Ivo; Felger, Ingrid

2015-01-01

Background Planning and evaluating malaria control strategies relies on accurate definition of parasite prevalence in the population. A large proportion of asymptomatic parasite infections can only be identified by surveillance with molecular methods, yet these infections also contribute to onward transmission to mosquitoes. The sensitivity of molecular detection by PCR is limited by the abundance of the target sequence in a DNA sample; thus, detection becomes imperfect at low densities. We aimed to increase PCR diagnostic sensitivity by targeting multi-copy genomic sequences for reliable detection of low-density infections, and investigated the impact of these PCR assays on community prevalence data. Methods and Findings Two quantitative PCR (qPCR) assays were developed for ultra-sensitive detection of Plasmodium falciparum, targeting the high-copy telomere-associated repetitive element 2 (TARE-2, ∼250 copies/genome) and the var gene acidic terminal sequence (varATS, 59 copies/genome). Our assays reached a limit of detection of 0.03 to 0.15 parasites/μl blood and were 10× more sensitive than standard 18S rRNA qPCR. In a population cross-sectional study in Tanzania, 295/498 samples tested positive using ultra-sensitive assays. Light microscopy missed 169 infections (57%). 18S rRNA qPCR failed to identify 48 infections (16%), of which 40% carried gametocytes detected by pfs25 quantitative reverse-transcription PCR. To judge the suitability of the TARE-2 and varATS assays for high-throughput screens, their performance was tested on sample pools. Both ultra-sensitive assays correctly detected all pools containing one low-density P. falciparum–positive sample, which went undetected by 18S rRNA qPCR, among nine negatives. TARE-2 and varATS qPCRs improve estimates of prevalence rates, yet other infections might still remain undetected when absent in the limited blood volume sampled. Conclusions Measured malaria prevalence in communities is largely determined by the sensitivity of the diagnostic tool used. Even when applying standard molecular diagnostics, prevalence in our study population was underestimated by 8% compared to the new assays. Our findings highlight the need for highly sensitive tools such as TARE-2 and varATS qPCR in community surveillance and for monitoring interventions to better describe malaria epidemiology and inform malaria elimination efforts. PMID:25734259

[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.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

PubMed Central

Wang, Zhenzhu; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm. PMID:28421125

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

PubMed

Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

A microsampling method for genotyping coral symbionts

NASA Astrophysics Data System (ADS)

Kemp, D. W.; Fitt, W. K.; Schmidt, G. W.

2008-06-01

Genotypic characterization of Symbiodinium symbionts in hard corals has routinely involved coring, or the removal of branches or a piece of the coral colony. These methods can potentially underestimate the complexity of the Symbiodinium community structure and may produce lesions. This study demonstrates that microscale sampling of individual coral polyps provided sufficient DNA for identifying zooxanthellae clades by RFLP analyses, and subclades through the use of PCR amplification of the ITS-2 region of rDNA and denaturing-gradient gel electrophoresis. Using this technique it was possible to detect distinct ITS-2 types of Symbiodinium from two or three adjacent coral polyps. These methods can be used to intensely sample coral-symbiont population/communities while causing minimal damage. The effectiveness and fine scale capabilities of these methods were demonstrated by sampling and identifying phylotypes of Symbiodinium clades A, B, and C that co-reside within a single Montastraea faveolata colony.

Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large, Community Randomized Trial

PubMed Central

Donnell, Deborah; Komárek, Arnošt; Omelka, Marek; Mullis, Caroline E.; Szekeres, Greg; Piwowar-Manning, Estelle; Fiamma, Agnes; Gray, Ronald H.; Lutalo, Tom; Morrison, Charles S.; Salata, Robert A.; Chipato, Tsungai; Celum, Connie; Kahle, Erin M.; Taha, Taha E.; Kumwenda, Newton I.; Karim, Quarraisha Abdool; Naranbhai, Vivek; Lingappa, Jairam R.; Sweat, Michael D.; Coates, Thomas; Eshleman, Susan H.

2013-01-01

Background Accurate methods of HIV incidence determination are critically needed to monitor the epidemic and determine the population level impact of prevention trials. One such trial, Project Accept, a Phase III, community-randomized trial, evaluated the impact of enhanced, community-based voluntary counseling and testing on population-level HIV incidence. The primary endpoint of the trial was based on a single, cross-sectional, post-intervention HIV incidence assessment. Methods and Findings Test performance of HIV incidence determination was evaluated for 403 multi-assay algorithms [MAAs] that included the BED capture immunoassay [BED-CEIA] alone, an avidity assay alone, and combinations of these assays at different cutoff values with and without CD4 and viral load testing on samples from seven African cohorts (5,325 samples from 3,436 individuals with known duration of HIV infection [1 month to >10 years]). The mean window period (average time individuals appear positive for a given algorithm) and performance in estimating an incidence estimate (in terms of bias and variance) of these MAAs were evaluated in three simulated epidemic scenarios (stable, emerging and waning). The power of different test methods to detect a 35% reduction in incidence in the matched communities of Project Accept was also assessed. A MAA was identified that included BED-CEIA, the avidity assay, CD4 cell count, and viral load that had a window period of 259 days, accurately estimated HIV incidence in all three epidemic settings and provided sufficient power to detect an intervention effect in Project Accept. Conclusions In a Southern African setting, HIV incidence estimates and intervention effects can be accurately estimated from cross-sectional surveys using a MAA. The improved accuracy in cross-sectional incidence testing that a MAA provides is a powerful tool for HIV surveillance and program evaluation. PMID:24236054

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

Single gene-based distinction of individual microbial genomes from a mixed population of microbial cells.

PubMed

Tamminen, Manu V; Virta, Marko P J

2015-01-01

Recent progress in environmental microbiology has revealed vast populations of microbes in any given habitat that cannot be detected by conventional culturing strategies. The use of sensitive genetic detection methods such as CARD-FISH and in situ PCR have been limited by the cell wall permeabilization requirement that cannot be performed similarly on all cell types without lysing some and leaving some nonpermeabilized. Furthermore, the detection of low copy targets such as genes present in single copies in the microbial genomes, has remained problematic. We describe an emulsion-based procedure to trap individual microbial cells into picoliter-volume polyacrylamide droplets that provide a rigid support for genetic material and therefore allow complete degradation of cellular material to expose the individual genomes. The polyacrylamide droplets are subsequently converted into picoliter-scale reactors for genome amplification. The amplified genomes are labeled based on the presence of a target gene and differentiated from those that do not contain the gene by flow cytometry. Using the Escherichia coli strains XL1 and MC1061, which differ with respect to the presence (XL1), or absence (MC1061) of a single copy of a tetracycline resistance gene per genome, we demonstrate that XL1 genomes present at 0.1% of MC1061 genomes can be differentiated using this method. Using a spiked sediment microbial sample, we demonstrate that the method is applicable to highly complex environmental microbial communities as a target gene-based screen for individual microbes. The method provides a novel tool for enumerating functional cell populations in complex microbial communities. We envision that the method could be optimized for fluorescence-activated cell sorting to enrich genetic material of interest from complex environmental samples.

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.

Tennessee HIV/AIDS people of color project.

PubMed

Williams, Elizabeth; Kanu, Mohamed; Williams, Charles; Jackman, Robbie M; Alsup, Peggy; Theriot, Rosemary; Wong, Seok

2010-08-01

The 25th anniversary of the acquired immunodeficiency syndrome (AIDS) in the United States occurred in 2006. Despite advances in detection, treatment, and care, AIDS, along with human immunodeficiency virus (HIV), and other sexually transmitted diseases (STDs) remain formidable opponents. Tremendous strides have been made in educating the public about associated risk factors and effective prevention methods. However, this has occurred less in communities of color. The paper describes collaboration among public health practitioners and academics to design and conduct research about HIV/AIDS needs and assets in Tennessee's communities of color.

Relative species richness and community completeness: avian communities and urbanization in the mid-Atlantic states

USGS Publications Warehouse

Cam, E.; Nichols, J.D.; Sauer, J.R.; Hines, J.E.; Flather, C.H.

2000-01-01

The idea that local factors govern local richness has been dominant for years, but recent theoretical and empirical studies have stressed the influence of regional factors on local richness. Fewer species at a site could reflect not only the influence of local factors, but also a smaller regional pool. The possible dependency of local richness on the regional pool should be taken into account when addressing the influence of local factors on local richness. It is possible to account for this potential dependency by comparing relative species richness among sites, rather than species richness per se. We consider estimation of a metric permitting assessment of relative species richness in a typical situation in which not all species are detected during sampling sessions. In this situation, estimates of absolute or relative species richness need to account for variation in species detection probability if they are to be unbiased. We present a method to estimate relative species richness based on capture-recapture models. This approach involves definition of a species list from regional data, and estimation of the number of species in that list that are present at a site-year of interest. We use this approach to address the influence of urbanization on relative richness of avian communities in the Mid-Atlantic region of the United States. There is a negative relationship between relative richness and landscape variables describing the level of urban development. We believe that this metric should prove very useful for conservation and management purposes because it is based on an estimator of species richness that both accounts for potential variation in species detection probability and allows flexibility in the specification of a 'reference community.' This metric can be used to assess ecological integrity, the richness of the community of interest relative to that of the 'original' community, or to assess change since some previous time in a community.

Community Participation in Chagas Disease Vector Surveillance: Systematic Review

PubMed Central

Abad-Franch, Fernando; Vega, M. Celeste; Rolón, Miriam S.; Santos, Walter S.; Rojas de Arias, Antonieta

2011-01-01

Background Vector control has substantially reduced Chagas disease (ChD) incidence. However, transmission by household-reinfesting triatomines persists, suggesting that entomological surveillance should play a crucial role in the long-term interruption of transmission. Yet, infestation foci become smaller and harder to detect as vector control proceeds, and highly sensitive surveillance methods are needed. Community participation (CP) and vector-detection devices (VDDs) are both thought to enhance surveillance, but this remains to be thoroughly assessed. Methodology/Principal Findings We searched Medline, Web of Knowledge, Scopus, LILACS, SciELO, the bibliographies of retrieved studies, and our own records. Data from studies describing vector control and/or surveillance interventions were extracted by two reviewers. Outcomes of primary interest included changes in infestation rates and the detection of infestation/reinfestation foci. Most results likely depended on study- and site-specific conditions, precluding meta-analysis, but we re-analysed data from studies comparing vector control and detection methods whenever possible. Results confirm that professional, insecticide-based vector control is highly effective, but also show that reinfestation by native triatomines is common and widespread across Latin America. Bug notification by householders (the simplest CP-based strategy) significantly boosts vector detection probabilities; in comparison, both active searches and VDDs perform poorly, although they might in some cases complement each other. Conclusions/Significance CP should become a strategic component of ChD surveillance, but only professional insecticide spraying seems consistently effective at eliminating infestation foci. Involvement of stakeholders at all process stages, from planning to evaluation, would probably enhance such CP-based strategies. PMID:21713022

Fungal biodiversity in the periglacial soil of Dosdè Glacier (Valtellina, Northern Italy).

PubMed

Rodolfi, Marinella; Longa, Claudia Maria Oliveira; Pertot, Ilaria; Tosi, Solveig; Savino, Elena; Guglielminetti, Maria; Altobelli, Elisa; Del Frate, Giuseppe; Picco, Anna Maria

2016-03-01

Periglacial areas are one of the least studied habitats on Earth, especially in terms of their fungal communities. In this work, both molecular and culture-dependent methods have been used to analyse the microfungi in soils sampled on the front of the East Dosdè Glacier (Valtellina, Northern Italy). Although this survey revealed a community that was rich in fungal species, a distinct group of psychrophilic microfungi has not been detected. Most of the isolated microfungi were mesophiles, which are well adapted to the sensitive climatic changes that occur in this alpine environment. A discrepancy in the results that were obtained by means of the two diagnostic approaches suggests that the used molecular methods cannot entirely replace traditional culture-dependent methods, and vice versa. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

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.

Evaluation of DNA extraction methods for the analysis of microbial community in biological activated carbon.

PubMed

Zheng, Lu; Gao, Naiyun; Deng, Yang

2012-01-01

It is difficult to isolate DNA from biological activated carbon (BAC) samples used in water treatment plants, owing to the scarcity of microorganisms in BAC samples. The aim of this study was to identify DNA extraction methods suitable for a long-term, comprehensive ecological analysis of BAC microbial communities. To identify a procedure that can produce high molecular weight DNA, maximizes detectable diversity and is relatively free from contaminants, the microwave extraction method, the cetyltrimethylammonium bromide (CTAB) extraction method, a commercial DNA extraction kit, and the ultrasonic extraction method were used for the extraction of DNA from BAC samples. Spectrophotometry, agarose gel electrophoresis and polymerase chain reaction (PCR)-restriction fragment length polymorphisms (RFLP) analysis were conducted to compare the yield and quality of DNA obtained using these methods. The results showed that the CTAB method produce the highest yield and genetic diversity of DNA from BAC samples, but DNA purity was slightly less than that obtained with the DNA extraction-kit method. This study provides a theoretical basis for establishing and selecting DNA extraction methods for BAC samples.

Development and evaluation of a 16S ribosomal DNA array-based approach for describing complex microbial communities in ready-to-eat vegetable salads packed in a modified atmosphere.

PubMed

Rudi, Knut; Flateland, Signe L; Hanssen, Jon Fredrik; Bengtsson, Gunnar; Nissen, Hilde

2002-03-01

There is a clear need for new approaches in the field of microbial community analyses, since the methods used can be severely biased. We have developed a DNA array-based method that targets 16S ribosomal DNA (rDNA), enabling the direct detection and quantification of microorganisms from complex communities without cultivation. The approach is based on the construction of specific probes from the 16S rDNA sequence data retrieved directly from the communities. The specificity of the assay is obtained through a combination of DNA array hybridization and enzymatic labeling of the constructed probes. Cultivation-dependent assays (enrichment and plating) and cultivation-independent assays (direct fluorescence microscopy and scanning electron microscopy) were used as reference methods in the development and evaluation of the method. The description of microbial communities in ready-to-eat vegetable salads in a modified atmosphere was used as the experimental model. Comparisons were made with respect to the effect of storage at different temperatures for up to 12 days and with respect to the geographic origin of the crisphead lettuce (Spanish or Norwegian), the main salad component. The conclusion drawn from the method comparison was that the DNA array-based method gave an accurate description of the microbial communities. Pseudomonas spp. dominated both of the salad batches, containing either Norwegian or Spanish lettuce, before storage and after storage at 4 degrees C. The Pseudomonas population also dominated the batch containing Norwegian lettuce after storage at 10 degrees C. On the contrary, Enterobacteriaceae and lactic acid bacteria dominated the microbial community of the batch containing Spanish lettuce after storage at 10 degrees C. In that batch, the Enterobacteriaceae also were abundant after storage at 4 degrees C as well as before storage. The practical implications of these results are that microbial communities in ready-to-eat vegetable salads can be diverse and that microbial composition is dependent both on the origin of the raw material and on the storage conditions.

Development and Evaluation of a 16S Ribosomal DNA Array-Based Approach for Describing Complex Microbial Communities in Ready-To-Eat Vegetable Salads Packed in a Modified Atmosphere

PubMed Central

Rudi, Knut; Flateland, Signe L.; Hanssen, Jon Fredrik; Bengtsson, Gunnar; Nissen, Hilde

2002-01-01

There is a clear need for new approaches in the field of microbial community analyses, since the methods used can be severely biased. We have developed a DNA array-based method that targets16S ribosomal DNA (rDNA), enabling the direct detection and quantification of microorganisms from complex communities without cultivation. The approach is based on the construction of specific probes from the 16S rDNA sequence data retrieved directly from the communities. The specificity of the assay is obtained through a combination of DNA array hybridization and enzymatic labeling of the constructed probes. Cultivation-dependent assays (enrichment and plating) and cultivation-independent assays (direct fluorescence microscopy and scanning electron microscopy) were used as reference methods in the development and evaluation of the method. The description of microbial communities in ready-to-eat vegetable salads in a modified atmosphere was used as the experimental model. Comparisons were made with respect to the effect of storage at different temperatures for up to 12 days and with respect to the geographic origin of the crisphead lettuce (Spanish or Norwegian), the main salad component. The conclusion drawn from the method comparison was that the DNA array-based method gave an accurate description of the microbial communities. Pseudomonas spp. dominated both of the salad batches, containing either Norwegian or Spanish lettuce, before storage and after storage at 4°C. The Pseudomonas population also dominated the batch containing Norwegian lettuce after storage at 10°C. On the contrary, Enterobacteriaceae and lactic acid bacteria dominated the microbial community of the batch containing Spanish lettuce after storage at 10°C. In that batch, the Enterobacteriaceae also were abundant after storage at 4°C as well as before storage. The practical implications of these results are that microbial communities in ready-to-eat vegetable salads can be diverse and that microbial composition is dependent both on the origin of the raw material and on the storage conditions. PMID:11872462

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.

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.

An infrared spectroscopy method to detect ammonia in gastric juice.

PubMed

Giovannozzi, Andrea M; Pennecchi, Francesca; Muller, Paul; Balma Tivola, Paolo; Roncari, Silvia; Rossi, Andrea M

2015-11-01

Ammonia in gastric juice is considered a potential biomarker for Helicobacter pylori infection and as a factor contributing to gastric mucosal injury. High ammonia concentrations are also found in patients with chronic renal failure, peptic ulcer disease, and chronic gastritis. Rapid and specific methods for ammonia detection are urgently required by the medical community. Here we present a method to detect ammonia directly in gastric juice based on Fourier transform infrared spectroscopy. The ammonia dissolved in biological liquid samples as ammonium ion was released in air as a gas by the shifting of the pH equilibrium of the ammonium/ammonia reaction and was detected in line by a Fourier transform infrared spectroscopy system equipped with a gas cell for the quantification. The method developed provided high sensitivity and selectivity in ammonia detection both in pure standard solutions and in a simulated gastric juice matrix over the range of diagnostic concentrations tested. Preliminary analyses were also performed on real gastric juice samples from patients with gastric mucosal injury and with symptoms of H. pylori infection, and the results were in agreement with the clinicopathology information. The whole analysis, performed in less than 10 min, can be directly applied on the sample without extraction procedures and it ensures high specificity of detection because of the ammonia fingerprint absorption bands in the infrared spectrum. This method could be easily used with endoscopy instrumentation to provide information in real time and would enable the endoscopist to improve and integrate gastroscopic examinations.

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.

Audit of Trichomonas vaginalis test requesting by community referrers after a change from culture to molecular testing, including a cost analysis.

PubMed

Bissessor, Liselle; Wilson, Janet; McAuliffe, Gary; Upton, Arlo

2017-06-16

Trichomonas vaginalis (TV) prevalence varies among different communities and peoples. The availability of robust molecular platforms for the detection of TV has advanced diagnosis; however, molecular tests are more costly than phenotypic methodologies, and testing all urogenital samples is costly. We recently replaced culture methods with the Aptima Trichomonas vaginalis nucleic acid amplification test on specific request and as reflex testing by the laboratory, and have audited this change. Data were collected from August 2015 (microbroth culture and microscopy) and August 2016 (Aptima TV assay) including referrer, testing volumes, results and test cost estimates. In August 2015, 10,299 vaginal swabs, and in August 2016, 2,189 specimens (urogenital swabs and urines), were tested. The positivity rate went from 0.9% to 5.3%, and overall more TV infections were detected in 2016. The number needed to test and cost for one positive TV result respectively was 111 and $902.55 in 2015, and 19 and $368.92 in 2016. Request volumes and positivity rates differed among referrers. The methodology change was associated with higher overall detection of TV, and reductions in the numbers needed to test/cost for one TV diagnosis. Our audit suggests that there is room for improvement with TV test requesting in our community.

Bayesian mixture analysis for metagenomic community profiling.

PubMed

Morfopoulou, Sofia; Plagnol, Vincent

2015-09-15

Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix sofia.morfopoulou.10@ucl.ac.uk Supplementary data are available at Bionformatics online. © The Author 2015. Published by Oxford University Press.

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.

Methodological approaches for studying the microbial ecology of drinking water distribution systems.

PubMed

Douterelo, Isabel; Boxall, Joby B; Deines, Peter; Sekar, Raju; Fish, Katherine E; Biggs, Catherine A

2014-11-15

The study of the microbial ecology of drinking water distribution systems (DWDS) has traditionally been based on culturing organisms from bulk water samples. The development and application of molecular methods has supplied new tools for examining the microbial diversity and activity of environmental samples, yielding new insights into the microbial community and its diversity within these engineered ecosystems. In this review, the currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated. The study of biofilms is considered particularly important as it plays a critical role in the processes and interactions occurring at the pipe wall and bulk water interface. The advantages, limitations and usefulness of methods that can be used to detect and assess microbial abundance, community composition and function are discussed in a DWDS context. This review will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Label propagation algorithm for community detection based on node importance and label influence

NASA Astrophysics Data System (ADS)

Zhang, Xian-Kun; Ren, Jing; Song, Chen; Jia, Jia; Zhang, Qian

2017-09-01

Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

Application of molecular techniques for the assessment of microorganism diversity on cultural heritage objects.

PubMed

Otlewska, Anna; Adamiak, Justyna; Gutarowska, Beata

2014-01-01

As a result of their unpredictable ability to adapt to varying environmental conditions, microorganisms inhabit different types of biological niches on Earth. Owing to the key role of microorganisms in many biogeochemical processes, trends in modern microbiology emphasize the need to know and understand the structure and function of complex microbial communities. This is particularly important if the strategy relates to microbial communities that cause biodeterioration of materials that constitute our cultural heritage. Until recently, the detection and identification of microorganisms inhabiting objects of cultural value was based only on cultivation-dependent methods. In spite of many advantages, these methods provide limited information because they identify only viable organisms capable of growth under standard laboratory conditions. However, in order to carry out proper conservation and renovation, it is necessary to know the complete composition of microbial communities and their activity. This paper presents and characterizes modern techniques such as genetic fingerprinting and clone library construction for the assessment of microbial diversity based on molecular biology. Molecular methods represent a favourable alternative to culture-dependent methods and make it possible to assess the biodiversity of microorganisms inhabiting technical materials and cultural heritage objects.

[Methicillin resistance detection in Staphylococcus aureus: comparison between conventional methods and MRSA-Screen latex agglutination technique].

PubMed

Soloaga, R; Corso, A; Gagetti, P; Faccone, D; Galas, M

2004-01-01

Methicillin-resistant Staphylococcus aureus (MRSA) is a significant pathogen that has emerged over the last four decades, causing both nosocomial and community-acquired infections. Rapid and accurate detection of methicillin resistance in S. aureus is important for the use of appropriate antimicrobial therapy and for the control of nosocomial spread of MRSA strains. We evaluated the efficiency of conventional methods for detection of methicillin resistance such as the disk diffusion, agar dilution, oxacillin agar screen test, and the latex agglutination test MRSA-Screen latex, in 100 isolates of S. aureus, 79 mecA positive and 21 mecA negative. The MRSA-Screen latex (Denka Seiken, Niigata, Japón), is a latex agglutination method that detects the presence of PLP-2a, product of mecA gene in S. aureus. The PCR of the mecA gene was used as the "gold standard" for the evaluation of the different methods tested. The percentages of sensitivity and specificity were as follows: disk difusión 97 and 100%, agar dilution 97 and 95%, oxacillin agar screen test 100 and 100%, and MRSA-Screen latex, 100 and 100 %. All methods presented high sensitivity and specificity, but MRSA-Screen latex had the advantage of giving a reliable result, equivalent to PCR, in only 15 minutes.

Evaluation of a quantitative H2S MPN test for fecal microbes analysis of water using biochemical and molecular identification.

PubMed

McMahan, Lanakila; Grunden, Amy M; Devine, Anthony A; Sobsey, Mark D

2012-04-15

The sensitivity and specificity of the H(2)S test to detect fecal bacteria in water has been variable and uncertain in previous studies, partly due to its presence-absence results. Furthermore, in groundwater samples false-positive results have been reported, with H(2)S-positive samples containing no fecal coliforms or Escherichia coli. False-negative results also have been reported in other studies, with H(2)S-negative samples found to contain E. coli. Using biochemical and molecular methods and a novel quantitative test format, this research identified the types and numbers of microbial community members present in natural water samples, including fecal indicators and pathogens as well as other bacteria. Representative water sources tested in this study included cistern rainwater, a protected lake, and wells in agricultural and forest settings. Samples from quantitative H(2)S tests of water were further cultured for fecal bacteria by spread plating onto the selective media for detection and isolation of Aeromonas spp., E. coli, Clostridium spp., H(2)S-producers, and species of Salmonella and Shigella. Isolates were then tested for H(2)S production, and identified to the genus and species level using biochemical methods. Terminal Restriction Fragment Length Polymorphisms (TRFLP) was the molecular method employed to quantitatively characterize microbial community diversity. Overall, it was shown that water samples testing positive for H(2)S bacteria also had bacteria of likely fecal origin and waters containing fecal pathogens also were positive for H(2)S bacteria. Of the microorganisms isolated from natural water, greater than 70 percent were identified using TRFLP analysis to reveal a relatively stable group of organisms whose community composition differed with water source and over time. These results further document the validity of the H(2)S test for detecting and quantifying fecal contamination of water. Copyright © 2011 Elsevier Ltd. All rights reserved.

Microbial distributions detected by an oligonucleotide microarray across geochemical zones associated with methane in marine sediments from the Ulleung Basin

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

Briggs, Brandon R; Graw, Michael; Brodie, Eoin L

2013-11-01

The biogeochemical processes that occur in marine sediments on continental margins are complex; however, from one perspective they can be considered with respect to three geochemical zones based on the presence and form of methane: sulfate–methane transition (SMTZ), gas hydrate stability zone (GHSZ), and free gas zone (FGZ). These geochemical zones may harbor distinct microbial communities that are important in biogeochemical carbon cycles. The objective of this study was to describe the microbial communities in sediments from the SMTZ, GHSZ, and FGZ using molecular ecology methods (i.e. PhyloChip microarray analysis and terminal restriction fragment length polymorphism (T-RFLP)) and examining themore » results in the context of non-biological parameters in the sediments. Non-metric multidimensional scaling and multi-response permutation procedures were used to determine whether microbial community compositions were significantly different in the three geochemical zones and to correlate samples with abiotic characteristics of the sediments. This analysis indicated that microbial communities from all three zones were distinct from one another and that variables such as sulfate concentration, hydrate saturation of the nearest gas hydrate layer, and depth (or unmeasured variables associated with depth e.g. temperature, pressure) were correlated to differences between the three zones. The archaeal anaerobic methanotrophs typically attributed to performing anaerobic oxidation of methane were not detected in the SMTZ; however, the marine benthic group-B, which is often found in SMTZ, was detected. Within the GHSZ, samples that were typically closer to layers that contained higher hydrate saturation had indicator sequences related to Vibrio-type taxa. These results suggest that the biogeographic patterns of microbial communities in marine sediments are distinct based on geochemical zones defined by methane.« less

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.

Estimating species richness and accumulation by modeling species occurrence and detectability

USGS Publications Warehouse

Dorazio, R.M.; Royle, J. Andrew; Soderstrom, B.; Glimskarc, A.

2006-01-01

A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for imperfect detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nondetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for imperfect detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.

Influenza Surveillance and Incidence in a Rural Area in China during the 2009/2010 Influenza Pandemic

PubMed Central

Zhang, Ying; Li, Lin; Dong, Xiaochun; Kong, Mei; Gao, Lu; Dong, Xiaojing; Xu, Wenti

2014-01-01

Background Most influenza surveillance is based on data from urban sentinel hospitals; little is known about influenza activity in rural communities. We conducted influenza surveillance in a rural region of China with the aim of detecting influenza activity in the 2009/2010 influenza season. Methods The study was conducted from October 2009 to March 2010. Real-time polymerase chain reaction was used to confirm influenza cases. Over-the-counter (OTC) drug sales were daily collected in drugstores and hospitals/clinics. Space-time scan statistics were used to identify clusters of ILI in community. The incidence rate of ILI/influenza was estimated on the basis of the number of ILI/influenza cases detected by the hospitals/clinics. Results A total of 434 ILI cases (3.88% of all consultations) were reported; 64.71% of these cases were influenza A (H1N1) pdm09. The estimated incidence rate of ILI and influenza were 5.19/100 and 0.40/100, respectively. The numbers of ILI cases and OTC drug purchases in the previous 7 days were strongly correlated (Spearman rank correlation coefficient [r] = 0.620, P = 0.001). Four ILI outbreaks were detected by space-time permutation analysis. Conclusions This rural community surveillance detected influenza A (H1N1) pdm09 activity and outbreaks in the 2009/2010 influenza season and enabled estimation of the incidence rate of influenza. It also provides a scientific data for public health measures. PMID:25542003

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.

Mapping northern Atlantic coastal marshlands, Maryland-Virginia, using ERTS imagery

NASA Technical Reports Server (NTRS)

Anderson, R. R. (Principal Investigator); Carter, V. L.; Mcginness, J. W., Jr.

1973-01-01

The author has identified the following significant results. ERTS-1 data provides repetitive synoptic coverage for DC 00000 of wetland ecology, detection of change, and mapping or inventory of wetland boundaries and plant communities. ERTS-1 positive transparencies of Atlantic Coastal wetlands were enlarged to different scales and mapped using a variety of methods. Results of analysis indicate: (1) mapping of wetland boundaries and vegetative communities from imagery at a scale of 1:1,000,000 is impractical because small details are difficult to illustrate; (2) mapping to a scale of 1:250,000 is practical for defining land-water interface, upper wetland boundary, gross vegetative communities, and spoil disposal/dredge and fill operations; (3) 1:125,000 enlargements provide additional information on transition zones, smaller plant communities, and drainage or mosquito ditching. Overlays may be made directly from prints.

Universal ligation-detection-reaction microarray applied for compost microbes

PubMed Central

Hultman, Jenni; Ritari, Jarmo; Romantschuk, Martin; Paulin, Lars; Auvinen, Petri

2008-01-01

Background Composting is one of the methods utilised in recycling organic communal waste. The composting process is dependent on aerobic microbial activity and proceeds through a succession of different phases each dominated by certain microorganisms. In this study, a ligation-detection-reaction (LDR) based microarray method was adapted for species-level detection of compost microbes characteristic of each stage of the composting process. LDR utilises the specificity of the ligase enzyme to covalently join two adjacently hybridised probes. A zip-oligo is attached to the 3'-end of one probe and fluorescent label to the 5'-end of the other probe. Upon ligation, the probes are combined in the same molecule and can be detected in a specific location on a universal microarray with complementary zip-oligos enabling equivalent hybridisation conditions for all probes. The method was applied to samples from Nordic composting facilities after testing and optimisation with fungal pure cultures and environmental clones. Results Probes targeted for fungi were able to detect 0.1 fmol of target ribosomal PCR product in an artificial reaction mixture containing 100 ng competing fungal ribosomal internal transcribed spacer (ITS) area or herring sperm DNA. The detection level was therefore approximately 0.04% of total DNA. Clone libraries were constructed from eight compost samples. The LDR microarray results were in concordance with the clone library sequencing results. In addition a control probe was used to monitor the per-spot hybridisation efficiency on the array. Conclusion This study demonstrates that the LDR microarray method is capable of sensitive and accurate species-level detection from a complex microbial community. The method can detect key species from compost samples, making it a basis for a tool for compost process monitoring in industrial facilities. PMID:19116002

Dynamics of phytoplankton community composition in the western Gulf of Maine

NASA Astrophysics Data System (ADS)

Moore, Timothy S.

This dissertation is founded on the importance of phytoplankton community composition to marine biogeochemistry and ecosystem processes and motivated by the need to understand their distributions on regional to global scales. The ultimate goal was to predict surface phytoplankton communities using satellite remote sensing by relating marine habitats--defined through a statistical description of environmental properties--to different phytoplankton communities. While phytoplankton community composition is governed by the interplay of abiotic and biotic interactions, the strategy adopted here was to focus on the physical abiotic factors. This allowed for the detection of habitats from ocean satellites based on abiotic factors that were linked to associated phytoplankton communities. The research entailed three studies that addressed different aspects of the main goal using a dataset collected in the western Gulf of Maine over a 3-year period. The first study evaluated a chemotaxonomic method that quantified phytoplankton composition from pigment data. This enabled the characterization of three phytoplankton communities, which were defined by the relative abundance of diatoms and flagellates. The second study examined the cycles of these communities along with environmental variables, and the results revealed that the three phytoplankton communities exhibited an affinity to different hydrographic regimes. The third study focused on the implementation of a classifier that predicted phytoplankton communities from environmental variables. Its ability to differentiate communities dominated by diatoms versus flagellates was shown to be high. However, the increase in data imprecision when using satellite data led to lowered performance and favored an approach that incorporated fuzzy logic. The fuzzy method is well suited to characterize the uncertainties in phytoplankton community prediction, and provides a measure of confidence on predicted communities. The final product of the overall dissertation was a time series of maps generated from satellite observations depicting the likelihood of three phytoplankton communities. This dissertation reached the main goal and, moreover, demonstrated that improvements in the predictive power of the method can be achieved with increased precision and more advanced satellite-derived products. The results of this research can benefit present bio-optical and primary productivity models, and ecosystem-based models of the marine environment.

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.

NQR detection of explosive simulants using RF atomic magnetometers

NASA Astrophysics Data System (ADS)

Monti, Mark C.; Alexson, Dimitri A.; Okamitsu, Jeffrey K.

2016-05-01

Nuclear Quadrupole Resonance (NQR) is a highly selective spectroscopic method that can be used to detect and identify a number of chemicals of interest to the defense, national security, and law enforcement community. In the past, there have been several documented attempts to utilize NQR to detect nitrogen bearing explosives using induction sensors to detect the NQR RF signatures. We present here our work on the NQR detection of explosive simulants using optically pumped RF atomic magnetometers. RF atomic magnetometers can provide an order of magnitude (or more) improvement in sensitivity versus induction sensors and can enable mitigation of RF interference, which has classically has been a problem for conventional NQR using induction sensors. We present the theory of operation of optically pumped RF atomic magnetometers along with the result of laboratory work on the detection of explosive simulant material. An outline of ongoing work will also be presented along with a path for a fieldable detection system.

Clustering analysis of water distribution systems: identifying critical components and community impacts.

PubMed

Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D

2014-01-01

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition

NASA Astrophysics Data System (ADS)

Almeida, Javier; Ruano, Josué; Corredor, Germán.; Romo-Bucheli, David; Navarro-Vargas, José Ricardo; Romero, Eduardo

2017-11-01

Non-invasive fetal electrocardiography (fECG) has attracted the medical community because of the importance of fetal monitoring. However, its implementation in clinical practice is challenging: the fetal signal has a low Signal- to-Noise-Ratio and several signal sources are present in the maternal abdominal electrocardiography (AECG). This paper presents a novel method to detect the fetal signal from a multi-channel maternal AECG. The method begins by applying filters and signal detrending the AECG signals. Afterwards, the maternal QRS complexes are identified and subtracted. The residual signals are used to detect the fetal QRS complex. Intervals of these signals are analyzed by using a wavelet decomposition. The resulting representation feds a previously trained Random Forest (RF) classifier that identifies signal intervals associated to fetal QRS complex. The method was evaluated on a public available dataset: the Physionet2013 challenge. A set of 50 maternal AECG records were used to train the RF classifier. The evaluation was carried out in signals intervals extracted from additional 25 maternal AECG. The proposed method yielded an 83:77% accuracy in the fetal QRS complex classification task.

Effects of social intervention on detection and efficacy of treatment for arterial hypertension. Main results of the Polish Four Cities Programme.

PubMed

Zdrojewski, Tomasz; Głuszek, Jerzy; Posadzy-Małaczyńska, Anna; Drygas, Wojciech; Ornoch-Tabedzka, Małgorzata; Januszko, Wiktor; Tykarski, Andrzej; Dylewicz, Piotr; Kwaśniewska, Magdalena; Krupa-Wojciechowska, Barbara; Wyrzykowski, Bogdan

2004-12-01

Cardiovascular diseases are the main cause of death in the adult Polish population. Beside lipid disorders and cigarette smoking, hypertension represents the most important risk factor leading to cardiovascular complications. Representative studies conducted in Poland in 1994-2002 showed that in 2002 the number of respondents in the survey who stated they knew their own blood pressure values dropped by 3.5 million, compared with 1994. This decrease was predominantly seen in small towns and in the countryside. Preventive programmes should therefore be addressed mainly to the most vulnerable communities. Modern methods of social marketing may play a substantial role in the creation of a healthy lifestyle. The aim of the Polish Four Cities Programme (PP4M), conducted in 2000-2001, was to develop the most effective methods of detection of and improvement in treatment for hypertension among the residents of small towns and rural areas. One of the programme tasks was to compare the effectiveness of a standard medical screening intervention with a similar approach combined with the use of social marketing methods.Methods. The programme was conducted by an interdisciplinary team in three small Polish towns -- Kartuzy, Oborniki Wlkp. and Braniewo, as well as in one of the districts of a large city Łódź -- Olechów. Medical intervention combined with social marketing (community intervention) took place in Oborniki Wlkp. whereas the residents of Kartuzy and Łódź were subjected only to the traditional medical intervention. Braniewo served as a control location -- neither medical nor community intervention was implemented. Community intervention with elements of social marketing consisted of a three-month, intensive education and information campaign, initiated four weeks prior to the start of medical intervention. Epidemiological situation was assessed in all the four cities before and after the completion of the preventive interventions (screening), using representative surveys, with the objective to assess the changes in the awareness of one's own blood pressure values, detection of hypertension and knowledge concerning cardiovascular risk factors.Results. In two survey locations -- Kartuzy and Łódź - awareness of one's own blood pressure values after the medical intervention did not significantly change (61% and 67.6% at baseline versus 62.1% and 71.6% after the intervention, respectively). In contrast, social marketing activities conducted in Oborniki significantly increased this parameter from 61.5% to 79.8% (p<0.01). While medical intervention did not change the proportion of non-diagnosed hypertension in a small town (a non-significant decrease from 49% to 45% in Kartuzy), its effect in a large city was clearly visible (a decrease from 46% to 28% in Łódź). In Oborniki Wlkp. (medical intervention combined with social marketing) the effects were the most noticeable -- a reduction from 50% to 27% was achieved. The efficacy of hypertension treatment at baseline was low (4.7% in Kartuzy, 6.6% in Oborniki, and 6.5% in Łódź), but it then improved significantly (a twofold increase in Kartuzy and Oborniki, and more than twofold increase in Łódź). When the target value of blood pressure was set at 160/95 mmHg, the highest efficacy of hypotensive therapy was observed directly after the completion of medical and community intervention in Oborniki (an almost twofold increase in treatment efficacy). 1. Medical intervention combined with a community intervention and marketing campaign leads to a statistically significant improvement in self-awareness of blood pressure values among residents of small towns. 2. Medical intervention combined with community intervention brings the detection rate of hypertension in small towns up to the level observed in large cities. 3. Medical intervention, especially when combined with community intervention, improves the efficacy of the treatment of hypertension, regardless of the size of agglomeration.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

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

Identifying Key Hospital Service Quality Factors in Online Health Communities

PubMed Central

Jung, Yuchul; Hur, Cinyoung; Jung, Dain

2015-01-01

Background The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. Objective As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. Methods We defined social media–based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea’s two biggest online portals were used to test the effectiveness of detection of social media–based key quality factors for hospitals. Results To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media–based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). Conclusions These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies. PMID:25855612

A network approach for identifying and delimiting biogeographical regions.

PubMed

Vilhena, Daril A; Antonelli, Alexandre

2015-04-24

Biogeographical regions (geographically distinct assemblages of species and communities) constitute a cornerstone for ecology, biogeography, evolution and conservation biology. Species turnover measures are often used to quantify spatial biodiversity patterns, but algorithms based on similarity can be sensitive to common sampling biases in species distribution data. Here we apply a community detection approach from network theory that incorporates complex, higher-order presence-absence patterns. We demonstrate the performance of the method by applying it to all amphibian species in the world (c. 6,100 species), all vascular plant species of the USA (c. 17,600) and a hypothetical data set containing a zone of biotic transition. In comparison with current methods, our approach tackles the challenges posed by transition zones and succeeds in retrieving a larger number of commonly recognized biogeographical regions. This method can be applied to generate objective, data-derived identification and delimitation of the world's biogeographical regions.

Diverse Applications of Environmental DNA Methods in Parasitology.

PubMed

Bass, David; Stentiford, Grant D; Littlewood, D T J; Hartikainen, Hanna

2015-10-01

Nucleic acid extraction and sequencing of genes from organisms within environmental samples encompasses a variety of techniques collectively referred to as environmental DNA or 'eDNA'. The key advantages of eDNA analysis include the detection of cryptic or otherwise elusive organisms, large-scale sampling with fewer biases than specimen-based methods, and generation of data for molecular systematics. These are particularly relevant for parasitology because parasites can be difficult to locate and are morphologically intractable and genetically divergent. However, parasites have rarely been the focus of eDNA studies. Focusing on eukaryote parasites, we review the increasing diversity of the 'eDNA toolbox'. Combining eDNA methods with complementary tools offers much potential to understand parasite communities, disease risk, and parasite roles in broader ecosystem processes such as food web structuring and community assembly. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

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 Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs

PubMed Central

de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

2014-01-01

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. PMID:25036766

Bacterial population in traditional sourdough evaluated by molecular methods.

PubMed

Randazzo, C L; Heilig, H; Restuccia, C; Giudici, P; Caggia, C

2005-01-01

To study the microbial communities in artisanal sourdoughs, manufactured by traditional procedure in different areas of Sicily, and to evaluate the lactic acid bacteria (LAB) population by classical and culture-independent approaches. Forty-five LAB isolates were identified both by phenotypic and molecular methods. The restriction fragment length polymorphism and 16S ribosomal DNA gene sequencing gave evidence of a variety of species with the dominance of Lactobacillus sanfranciscensis and Lactobacillus pentosus, in all sourdoughs tested. Culture-independent method, such as denaturing gradient gel electrophoresis (DGGE) of the V6-V8 regions of the 16S rDNA, was applied for microbial community fingerprint. The DGGE profiles revealed the dominance of L. sanfranciscensis species. In addition, Lactobacillus-specific primers were used to amplify the V1-V3 regions of the 16S rDNA. DGGE profiles flourished the dominance of L. sanfranciscensis and Lactobacillus fermentum in the traditional sourdoughs, and revealed that the closely related species Lactobacillus kimchii and Lactobacillus alimentarius were not discriminated. Lactobacillus-specific PCR-DGGE analysis is a rapid tool for rapid detection of Lactobacillus species in artisanal sourdough. This study reports a characterization of Lactobacillus isolates from artisanal sourdoughs and highlights the value of DGGE approach to detect uncultivable Lactobacillus species.

The impact of malodour on communities: a review of assessment techniques.

PubMed

Hayes, J E; Stevenson, R J; Stuetz, R M

2014-12-01

Malodours remain the biggest source of complaints regarding environmental issues. This factor is likely to increase, as the urban development steadily encroaches into areas that have malodourous emitting industries (such as wastewater and waste management operations and intensive livestock practices), and has the potential to be both time and fiscally expensive. Despite the enormous amount of research involved in odour detection and abatement, as well as the creation of several distinct methodologies, there has yet been no definitive procedure to evaluate odour impact on communities, as well as community response. This paper is a review of the current methods that explore this problem, as well as a précis of this research field's goals and challenges. The first aim of this review is to illustrate the dichotomy between regulatory-established procedures, such as panellist testing, and methods that are centred around producing a more comprehensive explanation of factors that influence an odour's impact on a community or individual. In that regard, we have addressed several predominant paradigms of inquiry for this field: analytical methods, panellist testing, qualitative research, and survey methods, with associated variants. Secondly, the challenges of measuring and monitoring community impact are discussed. While the quantification of odorants is crucial to appreciating impact, individual-based modifiers of perception have an enormous scope for which to shape the effect of those odours. Perceptual differences are also likely the most dominant variables that influence the elicited behaviour of individuals who have experienced malodour exposure. Copyright © 2014 Elsevier B.V. All rights reserved.

Recovery of thermophilic Campylobacter by three sampling methods from classified river sites in Northeast Georgia, USA

USDA-ARS?s Scientific Manuscript database

It is not clear how best to sample streams for the detection of Campylobacter which may be introduced from agricultural or community land use. Fifteen sites in the watershed of the South Fork of the Broad River (SFBR) in Northeastern Georgia, USA, were sampled in three seasons. Seven sites were cl...

IMPACTS OF VEGETATION DYNAMICS ON THE IDENTIFICATION OF LAND COVER CHANGE IN A BIOLOGICALLY COMPLEX COMMUNITY IN NORTH CAROLINA, USA

EPA Science Inventory

A land-cover (LC) change detection experiment was performed in the biologically complex landscape of the Neuse Rive Basin (NRB), NC using Landsat 5 and 7 imagery collected in May of 1993 and 2000. Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Mult...

Evaluating species richness: biased ecological inference results from spatial heterogeneity in species detection probabilities

USGS Publications Warehouse

McNew, Lance B.; Handel, Colleen M.

2015-01-01

Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multi-species occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real dataset of bird observations in northwestern Alaska, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results indicate that neglecting the effects of site covariates on species detection probabilities may lead to significant bias in estimation of species richness, as well as the inferred relationships between community size and environmental covariates.

Loop-mediated isothermal DNA amplification for asymptomatic malaria detection in challenging field settings: Technical performance and pilot implementation in the Peruvian Amazon

PubMed Central

Serra-Casas, Elisa; Manrique, Paulo; Ding, Xavier C.; Carrasco-Escobar, Gabriel; Alava, Freddy; Gave, Anthony; Rodriguez, Hugo; Contreras-Mancilla, Juan; Rosas-Aguirre, Angel; Speybroeck, Niko; González, Iveth J.

2017-01-01

Background Loop-mediated isothermal DNA amplification (LAMP) methodology offers an opportunity for point-of-care (POC) molecular detection of asymptomatic malaria infections. However, there is still little evidence on the feasibility of implementing this technique for population screenings in isolated field settings. Methods Overall, we recruited 1167 individuals from terrestrial (‘road’) and hydric (‘riverine’) communities of the Peruvian Amazon for a cross-sectional survey to detect asymptomatic malaria infections. The technical performance of LAMP was evaluated in a subgroup of 503 samples, using real-time Polymerase Chain Reaction (qPCR) as reference standard. The operational feasibility of introducing LAMP testing in the mobile screening campaigns was assessed based on field-suitability parameters, along with a pilot POC-LAMP assay in a riverine community without laboratory infrastructure. Results LAMP had a sensitivity of 91.8% (87.7–94.9) and specificity of 91.9% (87.8–95.0), and the overall accuracy was significantly better among samples collected during road screenings than riverine communities (p≤0.004). LAMP-based diagnostic strategy was successfully implemented within the field-team logistics and the POC-LAMP pilot in the riverine community allowed for a reduction in the turnaround time for case management, from 12–24 hours to less than 5 hours. Specimens with haemolytic appearance were regularly observed in riverine screenings and could help explaining the hindered performance/interpretation of the LAMP reaction in these communities. Conclusions LAMP-based molecular malaria diagnosis can be deployed outside of reference laboratories, providing similar performance as qPCR. However, scale-up in remote field settings such as riverine communities needs to consider a number of logistical challenges (e.g. environmental conditions, labour-intensiveness in large population screenings) that can influence its optimal implementation. PMID:28982155

Detection of gene communities in multi-networks reveals cancer drivers

NASA Astrophysics Data System (ADS)

Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

2015-12-01

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Rainwater harvesting in American Samoa: current practices and indicative health risks.

PubMed

Kirs, Marek; Moravcik, Philip; Gyawali, Pradip; Hamilton, Kerry; Kisand, Veljo; Gurr, Ian; Shuler, Christopher; Ahmed, Warish

2017-05-01

Roof-harvested rainwater (RHRW) is an important alternative source of water that many island communities can use for drinking and other domestic purposes when groundwater and/or surface water sources are contaminated, limited, or simply not available. The aim of this pilot-scale study was to investigate current RHRW practices in American Samoa (AS) and to evaluate and compare the quality of water from common potable water sources including RHRW stored in tanks, untreated stream water, untreated municipal well water, and treated municipal tap water samples. Samples were analyzed using culture-based methods, quantitative polymerase chain reaction (qPCR), and 16S amplicon sequencing-based methods. Based on indicator bacteria (total coliform and Escherichia coli) concentrations, the quality of RHRW was slightly lower than well and chlorinated tap water but exceeded that of untreated stream water. Although no Giardia or Leptospira spp. were detected in any of the RHRW samples, 86% of the samples were positive for Cryptosporidium spp. All stream water samples tested positive for Cryptosporidium spp. Opportunistic pathogens (Pseudomonas aeruginosa and Mycobacterium intracellulare) were also detected in the RHRW samples (71 and 21% positive samples, respectively). Several potentially pathogenic genera of bacteria were also detected in RHRW by amplicon sequencing. Each RHRW system was characterized by distinct microbial communities, 77% of operational taxonomic units (OTUs) were detected only in a single tank, and no OTU was shared by all the tanks. Risk of water-borne illness increased in the following order: chlorinated tap water/well water < RHRW < stream water. Frequent detection of opportunistic pathogens indicates that RHRW should be treated before use. Stakeholder education on RHRW system design options as well as on importance of regular cleaning and proper management techniques could improve the quality of the RHRW in AS.

Stochastic competitive learning in complex networks.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..

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.

Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations

NASA Astrophysics Data System (ADS)

Zhang, Y. M.; Evans, J. R. G.; Yang, S. F.

2010-11-01

The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.

Damage localization of marine risers using time series of vibration signals

NASA Astrophysics Data System (ADS)

Liu, Hao; Yang, Hezhen; Liu, Fushun

2014-10-01

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

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.

Exploring revictimization risk in a community sample of sexual assault survivors.

PubMed

Chu, Ann T; Deprince, Anne P; Mauss, Iris B

2014-01-01

Previous research points to links between risk detection (the ability to detect danger cues in various situations) and sexual revictimization in college women. Given important differences between college and community samples that may be relevant to revictimization risk (e.g., the complexity of trauma histories), the current study explored the link between risk detection and revictimization in a community sample of women. Community-recruited women (N = 94) reported on their trauma histories in a semistructured interview. In a laboratory session, participants listened to a dating scenario involving a woman and a man that culminated in sexual assault. Participants were instructed to press a button "when the man had gone too far." Unlike in college samples, revictimized community women (n = 47) did not differ in terms of risk detection response times from women with histories of no victimization (n = 10) or single victimization (n = 15). Data from this study point to the importance of examining revictimization in heterogeneous community samples where risk mechanisms may differ from college samples.

[Investigation of bacterial and viral etiology in community acquired central nervous system infections with molecular methods].

PubMed

Kahraman, Hasip; Tünger, Alper; Şenol, Şebnem; Gazi, Hörü; Avcı, Meltem; Örmen, Bahar; Türker, Nesrin; Atalay, Sabri; Köse, Şükran; Ulusoy, Sercan; Işıkgöz Taşbakan, Meltem; Sipahi, Oğuz Reşat; Yamazhan, Tansu; Gülay, Zeynep; Alp Çavuş, Sema; Pullukçu, Hüsnü

2017-07-01

In this multicenter prospective cohort study, it was aimed to evaluate the bacterial and viral etiology in community-acquired central nervous system infections by standart bacteriological culture and multiplex polymerase chain reaction (PCR) methods. Patients hospitalized with central nervous system infections between April 2012 and February 2014 were enrolled in the study. Demographic and clinical information of the patients were collected prospectively. Cerebrospinal fluid (CSF) samples of the patients were examined by standart bacteriological culture methods, bacterial multiplex PCR (Seeplex meningitis-B ACE Detection (Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus influenzae, Listeria monocytogenes, Group B streptococci) and viral multiplex PCR (Seeplex meningitis-V1 ACE Detection kits herpes simplex virus-1 (HSV1), herpes simplex virus-2 (HSV2), varicella zoster virus (VZV), cytomegalovirus (CMV), Epstein Barr virus (EBV) and human herpes virus 6 (HHV6)) (Seeplex meningitis-V2 ACE Detection kit (enteroviruses)). Patients were classified as purulent meningitis, aseptic meningitis and encephalitis according to their clinical, CSF (leukocyte level, predominant cell type, protein and glucose (blood/CSF) levels) and cranial imaging results. Patients who were infected with a pathogen other than the detection of the kit or diagnosed as chronic meningitis and other diseases during the follow up, were excluded from the study. A total of 79 patients (28 female, 51 male, aged 42.1 ± 18.5) fulfilled the study inclusion criteria. A total of 46 patients were classified in purulent meningitis group whereas 33 were in aseptic meningitis/encephalitis group. Pathogens were detected by multiplex PCR in 41 patients. CSF cultures were positive in 10 (21.7%) patients (nine S.pneumoniae, one H.influenzae) and PCR were positive for 27 (58.6%) patients in purulent meningitis group. In this group one type of bacteria were detected in 18 patients (14 S.pneumoniae, two N.meningitidis, one H.influenzae, one L.monocytogenes). Besides, it is noteworthy that multiple pathogens were detected such as bacteria-virus combination in eight patients and two different bacteria in one patient. In the aseptic meningitis/encephalitis group, pathogens were detected in 14 out of 33 patients; single type of viruses in 11 patients (seven enterovirus, two HSV1, one HSV2, one VZV) and two different viruses were determined in three patients. These data suggest that multiplex PCR methods may increase the isolation rate of pathogens in central nervous system infections. Existence of mixed pathogen growth is remarkable in our study. Further studies are needed for the clinical relevance of this result.

Natural Scales in Geographical Patterns

NASA Astrophysics Data System (ADS)

Menezes, Telmo; Roth, Camille

2017-04-01

Human mobility is known to be distributed across several orders of magnitude of physical distances, which makes it generally difficult to endogenously find or define typical and meaningful scales. Relevant analyses, from movements to geographical partitions, seem to be relative to some ad-hoc scale, or no scale at all. Relying on geotagged data collected from photo-sharing social media, we apply community detection to movement networks constrained by increasing percentiles of the distance distribution. Using a simple parameter-free discontinuity detection algorithm, we discover clear phase transitions in the community partition space. The detection of these phases constitutes the first objective method of characterising endogenous, natural scales of human movement. Our study covers nine regions, ranging from cities to countries of various sizes and a transnational area. For all regions, the number of natural scales is remarkably low (2 or 3). Further, our results hint at scale-related behaviours rather than scale-related users. The partitions of the natural scales allow us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion where the introduction of spatial boundaries is pivotal.

Effects of artificial settlement plate materials and methods of deployment on the sessile epibenthic community development in a tropical environment

NASA Astrophysics Data System (ADS)

Field, S. N.; Glassom, D.; Bythell, J.

2007-06-01

The choice of substrata and the methods of deployment in analyses of settlement in benthic communities are often driven by the cost of materials and their local availability, and comparisons are often made between studies using different methodologies. The effects of varying artificial substratum, size of replicates and method of deployment were determined on a shallow reef in Eilat, Israel, while the effect of size of replicates was also investigated on a shallow reef in Sharm El Sheikh, Egypt. When statistical power was high enough, that is, when sufficient numbers of settlers were recorded, significant differences were found between materials used, tile size and methods of deployment. Significant differences were detected in total coral settlement rates and for the two dominant taxonomic groups, acroporids and pocilloporids. Standardisation of tile materials, dimensions, and method of deployment is needed for comparison between coral and other epibiont settlement studies. However, a greater understanding of the effects of these experimental variables on settlement processes may enable retrospective comparisons between studies utilising a range of materials and methods.

Objective comparison of particle tracking methods.

PubMed

Chenouard, Nicolas; Smal, Ihor; de Chaumont, Fabrice; Maška, Martin; Sbalzarini, Ivo F; Gong, Yuanhao; Cardinale, Janick; Carthel, Craig; Coraluppi, Stefano; Winter, Mark; Cohen, Andrew R; Godinez, William J; Rohr, Karl; Kalaidzidis, Yannis; Liang, Liang; Duncan, James; Shen, Hongying; Xu, Yingke; Magnusson, Klas E G; Jaldén, Joakim; Blau, Helen M; Paul-Gilloteaux, Perrine; Roudot, Philippe; Kervrann, Charles; Waharte, François; Tinevez, Jean-Yves; Shorte, Spencer L; Willemse, Joost; Celler, Katherine; van Wezel, Gilles P; Dan, Han-Wei; Tsai, Yuh-Show; Ortiz de Solórzano, Carlos; Olivo-Marin, Jean-Christophe; Meijering, Erik

2014-03-01

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Catalyzed Reporter Deposition-Fluorescence In Situ Hybridization Allows for Enrichment-Independent Detection of Microcolony-Forming Soil Bacteria

PubMed Central

Ferrari, Belinda C.; Tujula, Niina; Stoner, Kate; Kjelleberg, Staffan

2006-01-01

Advances in the growth of hitherto unculturable soil bacteria have emphasized the requirement for rapid bacterial identification methods. Due to the slow-growing strategy of microcolony-forming soil bacteria, successful fluorescence in situ hybridization (FISH) requires an rRNA enrichment step for visualization. In this study, catalyzed reporter deposition (CARD)-FISH was employed as an alternative method to rRNA enhancement and was found to be superior to conventional FISH for the detection of microcolonies that are cultivated by using the soil substrate membrane system. CARD-FISH enabled real-time identification of oligophilic microcolony-forming soil bacteria without the requirement for enrichment on complex media and the associated shifts in community composition. PMID:16391135

Culture dependent and independent analysis of bacterial communities associated with commercial salad leaf vegetables.

PubMed

Jackson, Colin R; Randolph, Kevin C; Osborn, Shelly L; Tyler, Heather L

2013-12-01

Plants harbor a diverse bacterial community, both as epiphytes on the plant surface and as endophytes within plant tissue. While some plant-associated bacteria act as plant pathogens or promote plant growth, others may be human pathogens. The aim of the current study was to determine the bacterial community composition of organic and conventionally grown leafy salad vegetables at the point of consumption using both culture-dependent and culture-independent methods. Total culturable bacteria on salad vegetables ranged from 8.0 × 10(3) to 5.5 × 10(8) CFU g(-1). The number of culturable endophytic bacteria from surface sterilized plants was significantly lower, ranging from 2.2 × 10(3) to 5.8 × 10(5) CFU g(-1). Cultured isolates belonged to six major bacterial phyla, and included representatives of Pseudomonas, Pantoea, Chryseobacterium, and Flavobacterium. Eleven different phyla and subphyla were identified by culture-independent pyrosequencing, with Gammaproteobacteria, Betaproteobacteria, and Bacteroidetes being the most dominant lineages. Other bacterial lineages identified (e.g. Firmicutes, Alphaproteobacteria, Acidobacteria, and Actinobacteria) typically represented less than 1% of sequences obtained. At the genus level, sequences classified as Pseudomonas were identified in all samples and this was often the most prevalent genus. Ralstonia sequences made up a greater portion of the community in surface sterilized than non-surface sterilized samples, indicating that it was largely endophytic, while Acinetobacter sequences appeared to be primarily associated with the leaf surface. Analysis of molecular variance indicated there were no significant differences in bacterial community composition between organic versus conventionally grown, or surface-sterilized versus non-sterilized leaf vegetables. While culture-independent pyrosequencing identified significantly more bacterial taxa, the dominant taxa from pyrosequence data were also detected by traditional culture-dependent methods. The use of pyrosequencing allowed for the identification of low abundance bacteria in leaf salad vegetables not detected by culture-dependent methods. The presence of a range of bacterial populations as endophytes presents an interesting phenomenon as these microorganisms cannot be removed by washing and are thus ingested during salad consumption.

Culture dependent and independent analysis of bacterial communities associated with commercial salad leaf vegetables

PubMed Central

2013-01-01

Background Plants harbor a diverse bacterial community, both as epiphytes on the plant surface and as endophytes within plant tissue. While some plant-associated bacteria act as plant pathogens or promote plant growth, others may be human pathogens. The aim of the current study was to determine the bacterial community composition of organic and conventionally grown leafy salad vegetables at the point of consumption using both culture-dependent and culture-independent methods. Results Total culturable bacteria on salad vegetables ranged from 8.0 × 103 to 5.5 × 108 CFU g-1. The number of culturable endophytic bacteria from surface sterilized plants was significantly lower, ranging from 2.2 × 103 to 5.8 × 105 CFU g-1. Cultured isolates belonged to six major bacterial phyla, and included representatives of Pseudomonas, Pantoea, Chryseobacterium, and Flavobacterium. Eleven different phyla and subphyla were identified by culture-independent pyrosequencing, with Gammaproteobacteria, Betaproteobacteria, and Bacteroidetes being the most dominant lineages. Other bacterial lineages identified (e.g. Firmicutes, Alphaproteobacteria, Acidobacteria, and Actinobacteria) typically represented less than 1% of sequences obtained. At the genus level, sequences classified as Pseudomonas were identified in all samples and this was often the most prevalent genus. Ralstonia sequences made up a greater portion of the community in surface sterilized than non-surface sterilized samples, indicating that it was largely endophytic, while Acinetobacter sequences appeared to be primarily associated with the leaf surface. Analysis of molecular variance indicated there were no significant differences in bacterial community composition between organic versus conventionally grown, or surface-sterilized versus non-sterilized leaf vegetables. While culture-independent pyrosequencing identified significantly more bacterial taxa, the dominant taxa from pyrosequence data were also detected by traditional culture-dependent methods. Conclusions The use of pyrosequencing allowed for the identification of low abundance bacteria in leaf salad vegetables not detected by culture-dependent methods. The presence of a range of bacterial populations as endophytes presents an interesting phenomenon as these microorganisms cannot be removed by washing and are thus ingested during salad consumption. PMID:24289725

The impact of hotspot-targeted interventions on malaria transmission: study protocol for a cluster-randomized controlled trial

PubMed Central

2013-01-01

Background Malaria transmission is highly heterogeneous in most settings, resulting in the formation of recognizable malaria hotspots. Targeting these hotspots might represent a highly efficacious way of controlling or eliminating malaria if the hotspots fuel malaria transmission to the wider community. Methods/design Hotspots of malaria will be determined based on spatial patterns in age-adjusted prevalence and density of antibodies against malaria antigens apical membrane antigen-1 and merozoite surface protein-1. The community effect of interventions targeted at these hotspots will be determined. The intervention will comprise larviciding, focal screening and treatment of the human population, distribution of long-lasting insecticide-treated nets and indoor residual spraying. The impact of the intervention will be determined inside and up to 500 m outside the targeted hotspots by PCR-based parasite prevalence in cross-sectional surveys, malaria morbidity by passive case detection in selected facilities and entomological monitoring of larval and adult Anopheles populations. Discussion This study aims to provide direct evidence for a community effect of hotspot-targeted interventions. The trial is powered to detect large effects on malaria transmission in the context of ongoing malaria interventions. Follow-up studies will be needed to determine the effect of individual components of the interventions and the cost-effectiveness of a hotspot-targeted approach, where savings made by reducing the number of compounds that need to receive interventions should outweigh the costs of hotspot-detection. Trial registration NCT01575613. The protocol was registered online on 20 March 2012; the first community was randomized on 26 March 2012. PMID:23374910

Automated methods for multiplexed pathogen detection.

PubMed

Straub, Timothy M; Dockendorff, Brian P; Quiñonez-Díaz, Maria D; Valdez, Catherine O; Shutthanandan, Janani I; Tarasevich, Barbara J; Grate, Jay W; Bruckner-Lea, Cynthia J

2005-09-01

Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cycler where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides "live vs. dead" capabilities. However, sensitivity of the method will need to be improved for RNA analysis to replace PCR.

Automated Methods for Multiplexed Pathogen Detection

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

Straub, Tim M.; Dockendorff, Brian P.; Quinonez-Diaz, Maria D.

2005-09-01

Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cyclermore » where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides ''live vs. dead'' capabilities. However, sensitivity of the method will need to be improved for RNA analysis to replace PCR.« less

[Health diagnosis and risk perception: key elements of a proposed intervention for indigenous communities in Mexico].

PubMed

Terán-Hernández, Mónica; Díaz-Barriga, Fernando; Cubillas-Tejeda, Ana Cristina

2016-02-01

Objective To carry out a diagnosis of children's environmental health and an analysis of risk perception in indigenous communities of the Huasteca Sur region of San Luis Potosí, Mexico, in order to design an intervention strategy in line with their needs. Methods The study used mixed methods research, carried out in two phases. It was conducted in three indigenous communities of Tancanhuitz municipality from January 2005 to June 2006. In the adult population, risk perception was analyzed through focus groups, in-depth interviews, and questionnaires. In the child population, analysis of children's drawings was used to study perception. An assessment of health risks was carried out through biological monitoring and environmental monitoring of water and soil. Results The three communities face critical problems that reveal their vulnerability. When the results were triangulated and integrated, it was found that the principal problems relate to exposure to pathogenic microorganisms in water and soil, exposure to indoor wood smoke, exposure to smoke from the burning of refuse, use of insecticides, exposure to lead from the use of glazed ceramics, and alcoholism. Conclusions To ensure that the intervention strategy is adapted to the target population, it is essential to incorporate risk perception analysis and to promote the participation of community members. The proposed intervention strategy to address the detected problems is based on the principles of risk communication, community participation, and interinstitutional linkage.

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

Evaluation of a Commercial Multiplex PCR for Rapid Detection of Multi Drug Resistant Gram Negative Infections

PubMed Central

Chavada, Ruchir; Maley, Michael

2015-01-01

Introduction: Community and healthcare associated infections caused by multi-drug resistant gram negative organisms (MDR GN) represent a worldwide threat. Nucleic Acid Detection tests are becoming more common for their detection; however they can be expensive requiring specialised equipment and local expertise. This study was done to evaluate the utility of a commercial multiplex tandem (MT) PCR for detection of MDR GN. Methods: The study was done on stored laboratory MDR GN isolates from sterile and non-sterile specimens (n=126, out of stored 567 organisms). Laboratory validation of the MT PCR was done to evaluate sensitivity, specificity and agreement with the current phenotypic methods used in the laboratory. Amplicon sequencing was also done on selected isolates for assessing performance characteristics. Workflow and cost implications of the MT PCR were evaluated. Results: The sensitivity and specificity of the MT PCR were calculated to be 95% and 96.7% respectively. Agreement with the phenotypic methods was 80%. Major lack of agreement was seen in detection of AmpC beta lactamase in enterobacteriaceae and carbapenemase in non-fermenters. Agreement of the MT PCR with another multiplex PCR was found to be 87%. Amplicon sequencing confirmed the genotype detected by MT PCR in 94.2 % of cases tested. Time to result was faster for the MT PCR but cost per test was higher. Conclusion: This study shows that with carefully chosen targets for detection of resistance genes in MDR GN, rapid and efficient identification is possible. MT PCR was sensitive and specific and likely more accurate than phenotypic methods. PMID:26464612

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.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

PubMed

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

PubMed Central

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

Evaluation of seven aquatic sampling methods for amphibians and other aquatic fauna

USGS Publications Warehouse

Gunzburger, M.S.

2007-01-01

To design effective and efficient research and monitoring programs researchers must have a thorough understanding of the capabilities and limitations of their sampling methods. Few direct comparative studies exist for aquatic sampling methods for amphibians. The objective of this study was to simultaneously employ seven aquatic sampling methods in 10 wetlands to compare amphibian species richness and number of individuals detected with each method. Four sampling methods allowed counts of individuals (metal dipnet, D-frame dipnet, box trap, crayfish trap), whereas the other three methods allowed detection of species (visual encounter, aural, and froglogger). Amphibian species richness was greatest with froglogger, box trap, and aural samples. For anuran species, the sampling methods by which each life stage was detected was related to relative length of larval and breeding periods and tadpole size. Detection probability of amphibians varied across sampling methods. Box trap sampling resulted in the most precise amphibian count, but the precision of all four count-based methods was low (coefficient of variation > 145 for all methods). The efficacy of the four count sampling methods at sampling fish and aquatic invertebrates was also analyzed because these predatory taxa are known to be important predictors of amphibian habitat distribution. Species richness and counts were similar for fish with the four methods, whereas invertebrate species richness and counts were greatest in box traps. An effective wetland amphibian monitoring program in the southeastern United States should include multiple sampling methods to obtain the most accurate assessment of species community composition at each site. The combined use of frogloggers, crayfish traps, and dipnets may be the most efficient and effective amphibian monitoring protocol. ?? 2007 Brill Academic Publishers.

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

The Human Variome Project (HVP) 2009 Forum "Towards Establishing Standards".

PubMed

Howard, Heather J; Horaitis, Ourania; Cotton, Richard G H; Vihinen, Mauno; Dalgleish, Raymond; Robinson, Peter; Brookes, Anthony J; Axton, Myles; Hoffmann, Robert; Tuffery-Giraud, Sylvie

2010-03-01

The May 2009 Human Variome Project (HVP) Forum "Towards Establishing Standards" was a round table discussion attended by delegates from groups representing international efforts aimed at standardizing several aspects of the HVP: mutation nomenclature, description and annotation, clinical ontology, means to better characterize unclassified variants (UVs), and methods to capture mutations from diagnostic laboratories for broader distribution to the medical genetics research community. Methods for researchers to receive credit for their effort at mutation detection were also discussed. (c) 2010 Wiley-Liss, Inc.

Response-Guided Community Detection: Application to Climate Index Discovery

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

Bello, Gonzalo; Angus, Michael; Pedemane, Navya

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability.more » Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.« less

Overlapping communities detection based on spectral analysis of line graphs

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Hu, Rongjing; Huang, Guoming; Wei, Jiaxuan

2018-05-01

Community in networks are often overlapping where one vertex belongs to several clusters. Meanwhile, many networks show hierarchical structure such that community is recursively grouped into hierarchical organization. In order to obtain overlapping communities from a global hierarchy of vertices, a new algorithm (named SAoLG) is proposed to build the hierarchical organization along with detecting the overlap of community structure. SAoLG applies the spectral analysis into line graphs to unify the overlap and hierarchical structure of the communities. In order to avoid the limitation of absolute distance such as Euclidean distance, SAoLG employs Angular distance to compute the similarity between vertices. Furthermore, we make a micro-improvement partition density to evaluate the quality of community structure and use it to obtain the more reasonable and sensible community numbers. The proposed SAoLG algorithm achieves a balance between overlap and hierarchy by applying spectral analysis to edge community detection. The experimental results on one standard network and six real-world networks show that the SAoLG algorithm achieves higher modularity and reasonable community number values than those generated by Ahn's algorithm, the classical CPM and GN ones.

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model.

PubMed

Yang, Liang; Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection.

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model

PubMed Central

Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection. PMID:28678864

Clinical signs and bacterial communities of deciduous necrotic root canals detected by PCR-DGGE analysis: research association.

PubMed

de Paula, Viviane Andrade Cancio; de Carvalho Ferreira, Dennis; Cavalcante, Fernanda Sampaio; do Carmo, Flávia Lima; Rosado, Alexandre Soares; Primo, Laura Guimarães; dos Santos, Kátia Regina Netto

2014-08-01

This study sought to investigate the possible association between clinical and radiographic data of the patients with the bacterial community profiles involved in cases of necrosis in primary root canals. Microbial community profiles for 25 samples from necrotic deciduous root canals were analyzed using the polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting approach. These results were related to the clinical and radiographic data of these patients. The analysis showed a large diversity of microbial communities in necrotic deciduous root canals. The statistical results pointed out that posterior and anterior teeth were associated with <20 bands and >20 bands in PCR-DGGE method, respectively. A relationship was verified between ages >4 years old and posterior teeth and, ages ≤4 years old and anterior teeth. The data showed a polymicrobial community and pointed out the association of age with necrosis in anterior and posterior teeth. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of heavy metals on soil microbial community

NASA Astrophysics Data System (ADS)

Chu, Dian

2018-02-01

Soil is one of the most important environmental natural resources for human beings living, which is of great significance to the quality of ecological environment and human health. The study of the function of arable soil microbes exposed to heavy metal pollution for a long time has a very important significance for the usage of farmland soil. In this paper, the effects of heavy metals on soil microbial community were reviewed. The main contents were as follows: the effects of soil microbes on soil ecosystems; the effects of heavy metals on soil microbial activity, soil enzyme activities and the composition of soil microbial community. In addition, a brief description of main methods of heavy metal detection for soil pollution is given, and the means of researching soil microbial community composition are introduced as well. Finally, it is concluded that the study of soil microbial community can well reflect the degree of soil heavy metal pollution and the impact of heavy metal pollution on soil ecology.

Nasal PCR assay for the detection of Mycobacterium leprae pra gene to study subclinical infection in a community.

PubMed

Arunagiri, Kamalanathan; Sangeetha, Gopalakrishnan; Sugashini, Padmavathy Krishnan; Balaraman, Sekar; Showkath Ali, M K

2017-03-01

Leprosy is a chronic infectious disease caused by Mycobacterium leprae. Identification of Mycobacterium leprae is difficult in part due to the inability of the leprosy bacillus to grow in vitro. A number of diagnostic methods for leprosy diagnosis have been proposed. Both serological tests and molecular probes have shown certain potential for detection and identification of Mycobacterium leprae in patients. In this study, we have investigated whether Mycobacterium leprae DNA from the nasal secretion of healthy household contacts and the non contacts could be detected through PCR amplification as a method to study the sub clinical infection in a community. A total of 200 samples, 100 each from contacts and non contacts representing all age groups and sex were included in this study. The M. leprae specific primer (proline-rich region) of pra gene was selected and PCR was performed using extracted DNA from the sample. A total of 13 samples were found to be positive for nasal PCR for pra gene among the male and female contacts out of which 7% were males and 6% were females. Even though several diagnostic tools are available to detect the cases of leprosy, they lack the specificity and sensitivity. PCR technology has demonstrated the improved diagnostic accuracy for epidemiological studies and requires minimal time. Although nasal PCR studies have been reported from many countries it is not usually recommended due to the high percentage of negative results in the contact. Copyright © 2017 Elsevier Ltd. All rights reserved.

Validation approach for a fast and simple targeted screening method for 75 antibiotics in meat and aquaculture products using LC-MS/MS.

PubMed

Dubreil, Estelle; Gautier, Sophie; Fourmond, Marie-Pierre; Bessiral, Mélaine; Gaugain, Murielle; Verdon, Eric; Pessel, Dominique

2017-04-01

An approach is described to validate a fast and simple targeted screening method for antibiotic analysis in meat and aquaculture products by LC-MS/MS. The strategy of validation was applied for a panel of 75 antibiotics belonging to different families, i.e., penicillins, cephalosporins, sulfonamides, macrolides, quinolones and phenicols. The samples were extracted once with acetonitrile, concentrated by evaporation and injected into the LC-MS/MS system. The approach chosen for the validation was based on the Community Reference Laboratory (CRL) guidelines for the validation of screening qualitative methods. The aim of the validation was to prove sufficient sensitivity of the method to detect all the targeted antibiotics at the level of interest, generally the maximum residue limit (MRL). A robustness study was also performed to test the influence of different factors. The validation showed that the method is valid to detect and identify 73 antibiotics of the 75 antibiotics studied in meat and aquaculture products at the validation levels.

Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study.

PubMed

Baker, Arthur W; Haridy, Salah; Salem, Joseph; Ilieş, Iulian; Ergai, Awatef O; Samareh, Aven; Andrianas, Nicholas; Benneyan, James C; Sexton, Daniel J; Anderson, Deverick J

2017-11-24

Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data. We performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations. Conventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7 months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data. Our findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Establishment of a Canine Rabies Burden in Haiti through the Implementation of a Novel Surveillance Program

PubMed Central

Wallace, Ryan M; Reses, Hannah; Franka, Richard; Dilius, Pierre; Fenelon, Natael; Orciari, Lillian; Etheart, Melissa; Destine, Apollon; Crowdis, Kelly; Blanton, Jesse D; Francisco, Calvin; Ludder, Fleurinord; Del Rio Vilas, Victor; Haim, Joseph; Millien, Max

2015-01-01

The Republic of Haiti is one of only several countries in the Western Hemisphere in which canine rabies is still endemic. Estimation methods have predicted that 130 human deaths occur per year, yet existing surveillance mechanisms have detected few of these rabies cases. Likewise, canine rabies surveillance capacity has had only limited capacity, detecting only two rabid dogs per year, on average. In 2013, Haiti initiated a community-based animal rabies surveillance program comprised of two components: active community bite investigation and passive animal rabies investigation. From January 2013 –December 2014, 778 rabies suspect animals were reported for investigation. Rabies was laboratory-confirmed in 70 animals (9%) and an additional 36 cases were identified based on clinical diagnosis (5%), representing an 18-fold increase in reporting of rabid animals compared to the three years before the program was implemented. Dogs were the most frequent rabid animal (90%). Testing and observation ruled out rabies in 61% of animals investigated. A total of 639 bite victims were reported to the program and an additional 364 bite victims who had not sought medical care were identified during the course of investigations. Only 31% of people with likely rabies exposures had initiated rabies post-exposure prophylaxis prior to the investigation. Rabies is a neglected disease in-part due to a lack of surveillance and understanding about the burden. The surveillance methods employed by this program established a much higher burden of canine rabies in Haiti than previously recognized. The active, community-based bite investigations identified numerous additional rabies exposures and bite victims were referred for appropriate medical care, averting potential human rabies deaths. The use of community-based rabies surveillance programs such as HARSP should be considered in canine rabies endemic countries. PMID:26600437

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.

Metagenomic detection of phage-encoded platelet-binding factors in the human oral cavity

PubMed Central

Willner, Dana; Furlan, Mike; Schmieder, Robert; Grasis, Juris A.; Pride, David T.; Relman, David A.; Angly, Florent E.; McDole, Tracey; Mariella, Ray P.; Rohwer, Forest; Haynes, Matthew

2011-01-01

The human oropharynx is a reservoir for many potential pathogens, including streptococcal species that cause endocarditis. Although oropharyngeal microbes have been well described, viral communities are essentially uncharacterized. We conducted a metagenomic study to determine the composition of oropharyngeal DNA viral communities (both phage and eukaryotic viruses) in healthy individuals and to evaluate oropharyngeal swabs as a rapid method for viral detection. Viral DNA was extracted from 19 pooled oropharyngeal swabs and sequenced. Viral communities consisted almost exclusively of phage, and complete genomes of several phage were recovered, including Escherichia coli phage T3, Propionibacterium acnes phage PA6, and Streptococcus mitis phage SM1. Phage relative abundances changed dramatically depending on whether samples were chloroform treated or filtered to remove microbial contamination. pblA and pblB genes of phage SM1 were detected in the metagenomes. pblA and pblB mediate the attachment of S. mitis to platelets and play a significant role in S. mitis virulence in the endocardium, but have never previously been detected in the oral cavity. These genes were also identified in salivary metagenomes from three individuals at three time points and in individual saliva samples by PCR. Additionally, we demonstrate that phage SM1 can be induced by commonly ingested substances. Our results indicate that the oral cavity is a reservoir for pblA and pblB genes and for phage SM1 itself. Further studies will determine the association between pblA and pblB genes in the oral cavity and the risk of endocarditis. PMID:20547834

Early detection of potentially invasive invertebrate species in Mytilus galloprovincialis Lamarck, 1819 dominated communities in harbours

NASA Astrophysics Data System (ADS)

Preda, Cristina; Memedemin, Daniyar; Skolka, Marius; Cogălniceanu, Dan

2012-12-01

Constanţa harbour is a major port on the western coast of the semi-enclosed Black Sea. Its brackish waters and low species richness make it vulnerable to invasions. The intensive maritime traffic through Constanţa harbour facilitates the arrival of alien species. We investigated the species composition of the mussel beds on vertical artificial concrete substrate inside the harbour. We selected this habitat for study because it is frequently affected by fluctuating levels of temperature, salinity and dissolved oxygen, and by accidental pollution episodes. The shallow communities inhabiting it are thus unstable and often restructured, prone to accept alien species. Monthly samples were collected from three locations from the upper layer of hard artificial substrata (maximum depth 2 m) during two consecutive years. Ten alien macro-invertebrate species were inventoried, representing 13.5% of the total number of species. Two of these alien species were sampled starting the end of summer 2010, following a period of high temperatures that triggered hypoxia, causing mass mortalities of benthic organisms. Based on the species accumulation curve, we estimated that we have detected all benthic alien species on artificial substrate from Constanţa harbour, but additional effort is required to detect all the native species. Our results suggest that monitoring of benthic communities at small depths in harbours is a simple and useful tool in early detection of potentially invasive alien species. The selected habitat is easily accessible, the method is low-cost, and the samples represent reliable indicators of alien species establishment.

[Prokaryotic community of subglacial bottom sediments of Antarctic Lake Untersee: detection by cultural and direct microscopic techniques].

PubMed

Muliukin, A L; Demkina, E V; Manucharova, N A; Akimov, V N; Andersen, D; McKay, C; Gal'chenko, V F

2014-01-01

The heterotrophic mesophilic component was studied in microbial communities of the samples of frozen regolith collected from the glacier near Lake Untersee collected in 2011 during the joint Russian-American expedition to central Dronning Maud Land (Eastern Antarctica). Cultural techniques revealed high bacterial numbers in the samples. For enumeration of viable cells, the most probable numbers (MPN) method proved more efficient than plating on agar media. Fluorescent in situ hybridization with the relevant oligonucleotide probes revealed members of the groups Eubacteria (Actinobacteria, Firmicutes) and Archaea. Application of the methods of cell resuscitation, such as the use of diluted media and prevention of oxidative stress, did not result in a significant increase in the numbers of viable cells retrieved form subglacial sediment samples. Our previous investigations demonstrated the necessity for special procedures for efficient reactivation of the cells from microbial communities of preserved fossil soil and permafrost samples collected in the Arctic zone. The differences in response to the special resuscitation procedures may reflect the differences in the physiological and morphological state of bacterial cells in microbial communities subject to continuous or periodic low temperatures and dehydration.

Outcomes of a Breast Health Project for Hmong Women and Men in California

PubMed Central

Tanjasiri, Sora Park; Valdez, Annalyn; Yu, Hongjian; Foo, Mary Anne

2009-01-01

Objectives. We used a community-based research approach to test a culturally based breast cancer screening program among low-income Hmong women in central and southern California. Methods. We designed a culturally informed educational program with measures at baseline and 1-year follow-up in 2 intervention cities and 1 comparison city. Measures included changes in breast cancer screening, knowledge, and attitudes. Results. Compared with women in the comparison community, women in the intervention community significantly improved their attitudes toward, and increased their knowledge and receipt of, breast cancer screenings. Odds of women in the intervention group having had a mammogram, having had a clinical breast examination, and having performed breast self-examination was 6.75, 12.16, and 20.06, respectively, compared with women in the comparison group. Conclusions. Culturally informed education materials and intervention design were effective methods in conveying the importance of maintaining and monitoring proper breast health. The strength of community collaboration in survey development and intervention design highlighted the challenges of early detection and screening programs among newer immigrants, who face significant language and cultural barriers to care, and identified promising practices to overcome these health literacy challenges. PMID:19443830

Topic segmentation via community detection in complex networks

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Topic segmentation via community detection in complex networks.

PubMed

de Arruda, Henrique F; Costa, Luciano da F; Amancio, Diego R

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

A primary assessment of the endophytic bacterial community in a xerophilous moss (Grimmia montana) using molecular method and cultivated isolates

PubMed Central

Liu, Xiao Lei; Liu, Su Lin; Liu, Min; Kong, Bi He; Liu, Lei; Li, Yan Hong

2014-01-01

Investigating the endophytic bacterial community in special moss species is fundamental to understanding the microbial-plant interactions and discovering the bacteria with stresses tolerance. Thus, the community structure of endophytic bacteria in the xerophilous moss Grimmia montana were estimated using a 16S rDNA library and traditional cultivation methods. In total, 212 sequences derived from the 16S rDNA library were used to assess the bacterial diversity. Sequence alignment showed that the endophytes were assigned to 54 genera in 4 phyla (Proteobacteria, Firmicutes, Actinobacteria and Cytophaga/Flexibacter/Bacteroids). Of them, the dominant phyla were Proteobacteria (45.9%) and Firmicutes (27.6%), the most abundant genera included Acinetobacter, Aeromonas, Enterobacter, Leclercia, Microvirga, Pseudomonas, Rhizobium, Planococcus, Paenisporosarcina and Planomicrobium. In addition, a total of 14 species belonging to 8 genera in 3 phyla (Proteobacteria, Firmicutes, Actinobacteria) were isolated, Curtobacterium, Massilia, Pseudomonas and Sphingomonas were the dominant genera. Although some of the genera isolated were inconsistent with those detected by molecular method, both of two methods proved that many different endophytic bacteria coexist in G. montana. According to the potential functional analyses of these bacteria, some species are known to have possible beneficial effects on hosts, but whether this is the case in G. montana needs to be confirmed. PMID:24948927

Biodegradation of Enteromorpha prolifera by mangrove degrading micro-community with physical-chemical pretreatment.

PubMed

Zhao, Chao; Ruan, Lingwei

2011-11-01

The bacteria involved in the biodegradation of Enteromorpha prolifera (EP) are largely unknown, especially in offshore mangrove environments. In order to obtain the bacterial EP-degrading communities, sediments from a typical mangrove forest were sampled on the roots of mangrove in Dongzhai Port (Haikou, China). The sediments were enriched with crude EP powders as the sole carbon source. The bacterial composition of the resulting mangrove-degrading micro-community (MDMC), named D2-1, was analysed. With methods of plate cultivation and polymerase chain reaction-denaturing gradient gel electrophoresis and 16S rRNA library analysis, 18 bacteria belonging to nine genera were detected from this community. Among these detected bacteria, five major bands closely related to Bacillus, Marinobacter, Paenibacillus, Photobacterium, and Zhouia were determined. A novel two-step pretreatment for EP was proposed to lower the severity requirement of biodegraded pretreatment time. It consisted of a mild physical or chemical step (ultrasonic or H(2)O(2)) and a subsequent biological treatment with community D2-1. The combined treatment led to significant increases in the EP degradation. After combined treatment, the net yields of total soluble sugars and reducing sugars increased. The combined pretreatment of H(2)O(2) (2%, 48 h) and MDMC (7 days) was more effective than the treatment of MDMC only for 15 days. It could remarkably shorten the residence time and reduce the losses of carbohydrates. © Springer-Verlag 2011

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.

Improving diversity in cultures of bacteria from an extreme environment.

PubMed

Vester, Jan Kjølhede; Glaring, Mikkel Andreas; Stougaard, Peter

2013-08-01

The ikaite columns in the Ikka Fjord in Greenland represent one of the few permanently cold and alkaline environments on Earth, and the interior of the columns is home to a bacterial community adapted to these extreme conditions. The community is characterized by low cell numbers imbedded in a calcium carbonate matrix, making extraction of bacterial cells and DNA a challenge and limiting molecular and genomic studies of this environment. To utilize this genetic resource, cultivation at high pH and low temperature was studied as a method for obtaining biomass and DNA from the fraction of this community that would not otherwise be amenable to genetic analyses. The diversity and community dynamics in mixed cultures of bacteria from ikaite columns was investigated using denaturing gradient gel electrophoresis and pyrosequencing of 16S rDNA. Both medium composition and incubation time influenced the diversity of the culture and many hitherto uncharacterized genera could be brought into culture by extended incubation time. Extended incubation time also gave rise to a more diverse community with a significant number of rare species not detected in the initial community.

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.

Shifting the burden or expanding access to care? Assessing malaria trends following scale-up of community health worker malaria case management and reactive case detection.

PubMed

Larsen, David A; Winters, Anna; Cheelo, Sanford; Hamainza, Busiku; Kamuliwo, Mulakwa; Miller, John M; Bridges, Daniel J

2017-11-02

Malaria is a significant burden to health systems and is responsible for a large proportion of outpatient cases at health facilities in endemic regions. The scale-up of community management of malaria and reactive case detection likely affect both malaria cases and outpatient attendance at health facilities. Using health management information data from 2012 to 2013 this article examines health trends before and after the training of volunteer community health workers to test and treat malaria cases in Southern Province, Zambia. An estimated 50% increase in monthly reported malaria infections was found when community health workers were involved with malaria testing and treating in the community (incidence rate ratio 1.52, p < 0.001). Furthermore, an estimated 6% decrease in outpatient attendance at the health facility was found when community health workers were involved with malaria testing and treating in the community. These results suggest a large public health benefit to both community case management of malaria and reactive case detection. First, the capacity of the malaria surveillance system to identify malaria infections was increased by nearly one-third. Second, the outpatient attendance at health facilities was modestly decreased. Expanding the capacity of the malaria surveillance programme through systems such as community case management and reactive case detection is an important step toward malaria elimination.

Development of an Itemwise Efficiency Scoring Method: Concurrent, Convergent, Discriminant, and Neuroimaging-Based Predictive Validity Assessed in a Large Community Sample

PubMed Central

Moore, Tyler M.; Reise, Steven P.; Roalf, David R.; Satterthwaite, Theodore D.; Davatzikos, Christos; Bilker, Warren B.; Port, Allison M.; Jackson, Chad T.; Ruparel, Kosha; Savitt, Adam P.; Baron, Robert B.; Gur, Raquel E.; Gur, Ruben C.

2016-01-01

Traditional “paper-and-pencil” testing is imprecise in measuring speed and hence limited in assessing performance efficiency, but computerized testing permits precision in measuring itemwise response time. We present a method of scoring performance efficiency (combining information from accuracy and speed) at the item level. Using a community sample of 9,498 youths age 8-21, we calculated item-level efficiency scores on four neurocognitive tests, and compared the concurrent, convergent, discriminant, and predictive validity of these scores to simple averaging of standardized speed and accuracy-summed scores. Concurrent validity was measured by the scores' abilities to distinguish men from women and their correlations with age; convergent and discriminant validity were measured by correlations with other scores inside and outside of their neurocognitive domains; predictive validity was measured by correlations with brain volume in regions associated with the specific neurocognitive abilities. Results provide support for the ability of itemwise efficiency scoring to detect signals as strong as those detected by standard efficiency scoring methods. We find no evidence of superior validity of the itemwise scores over traditional scores, but point out several advantages of the former. The itemwise efficiency scoring method shows promise as an alternative to standard efficiency scoring methods, with overall moderate support from tests of four different types of validity. This method allows the use of existing item analysis methods and provides the convenient ability to adjust the overall emphasis of accuracy versus speed in the efficiency score, thus adjusting the scoring to the real-world demands the test is aiming to fulfill. PMID:26866796

Comparison of Standard Culture-Based Method to Culture-Independent Method for Evaluation of Hygiene Effects on the Hand Microbiome

PubMed Central

Leff, J.; Henley, J.; Tittl, J.; De Nardo, E.; Butler, M.; Griggs, R.; Fierer, N.

2017-01-01

ABSTRACT Hands play a critical role in the transmission of microbiota on one’s own body, between individuals, and on environmental surfaces. Effectively measuring the composition of the hand microbiome is important to hand hygiene science, which has implications for human health. Hand hygiene products are evaluated using standard culture-based methods, but standard test methods for culture-independent microbiome characterization are lacking. We sampled the hands of 50 participants using swab-based and glove-based methods prior to and following four hand hygiene treatments (using a nonantimicrobial hand wash, alcohol-based hand sanitizer [ABHS], a 70% ethanol solution, or tap water). We compared results among culture plate counts, 16S rRNA gene sequencing of DNA extracted directly from hands, and sequencing of DNA extracted from culture plates. Glove-based sampling yielded higher numbers of unique operational taxonomic units (OTUs) but had less diversity in bacterial community composition than swab-based sampling. We detected treatment-induced changes in diversity only by using swab-based samples (P < 0.001); we were unable to detect changes with glove-based samples. Bacterial cell counts significantly decreased with use of the ABHS (P < 0.05) and ethanol control (P < 0.05). Skin hydration at baseline correlated with bacterial abundances, bacterial community composition, pH, and redness across subjects. The importance of the method choice was substantial. These findings are important to ensure improvement of hand hygiene industry methods and for future hand microbiome studies. On the basis of our results and previously published studies, we propose recommendations for best practices in hand microbiome research. PMID:28351915

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.

Human touch vs. axillary digital thermometry for detection of neonatal hypothermia at community level.

PubMed

Agarwal, Siddharth; Sethi, Vani; Pandey, Ravindra Mohan; Kondal, Dimple

2008-06-01

We examined the diagnostic accuracy of human touch (HT) method in assessing hypothermia against axillary digital thermometry (ADT) by a trained non-medical field investigator (who supervised activities of community health volunteers) in seven villages of Agra district, Uttar Pradesh, India. Body temperature of 148 newborns born between March and August 2005 was measured at four points in time for each enrolled newborn (within 48 h and on days 7, 30 and 60) by the field investigator under the axilla using a digital thermometer and by HT method using standard methodology. Total observations were 533. Hypothermia assessed by HT was in agreement with that assessed by ADT (<36.5 degrees C) in 498 observations. Hypothermia assessed by HT showed a high diagnostic accuracy when compared against ADT (kappa 0.65-0.81; sensitivity 74%; specificity 96.7%; positive predictive value 22; negative predictive value 0.26). HT is a simple, quick, inexpensive and programmatically important method. However, being a subjective assessment, its reliability depends on the investigator being adequately trained and competent in making consistently accurate assessments. There is also a need to assess whether with training and supervision even the less literate mothers, traditional birth attendants and community health volunteers can accurately assess mild and moderate hypothermia before promoting HT for early identification of neonatal risk in community-based programs.

Line Transect Surveys Underdetect Terrestrial Mammals: Implications for the Sustainability of Subsistence Hunting

PubMed Central

Levi, Taal; Oliveira, Luiz F. B.; Luzar, Jeffrey B.; Overman, Han; Read, Jane M.

2016-01-01

Conservation of Neotropical game species must take into account the livelihood and food security needs of local human populations. Hunting management decisions should therefore rely on abundance and distribution data that are as representative as possible of true population sizes and dynamics. We simultaneously applied a commonly used encounter-based method and an infrequently used sign-based method to estimate hunted vertebrate abundance in a 48,000-km2 indigenous landscape in southern Guyana. Diurnal direct encounter data collected during three years along 216, four-kilometer -long transects consistently under-detected many diurnal and nocturnal mammal species readily detected through sign. Of 32 species analyzed, 31 were detected by both methods; however, encounters did not detect one and under-detected another 12 of the most heavily hunted species relative to sign, while sign under-detected 12 never or rarely collected species relative to encounters. The six most important game animals in the region, all ungulates, were not encountered at 11–40% of village and control sites or on 29–72% of transects where they were detected by sign. Using the sign methodology, we find that tapirs, one of the terrestrial vertebrates considered most sensitive to overexploitation, are present at many sites where they were never visually detected during distance sampling. We find that this is true for many other species as well. These high rates of under-detection suggest that behavioral changes in hunted populations may affect apparent occurrence and abundance of these populations. Accumulation curves (detection of species on transects) were much steeper for sign for 12 of 16 hunted species than for encounters, but that pattern was reversed for 12 of 16 species unhunted in our area. We conclude that collection of sign data is an efficient and effective method of monitoring hunted vertebrate populations that complements encounter and camera-trapping methods in areas impacted by hunting. Sign surveys may be the most viable method for large-scale, management-oriented studies in remote areas, particularly those focused on community-based wildlife management. PMID:27074025

Accelerated active case detection of visceral leishmaniasis patients in endemic villages of Bangladesh.

PubMed

Khatun, Jahanara; Huda, M Mamun; Hossain, Md Shakhawat; Presber, Wolfgang; Ghosh, Debashis; Kroeger, Axel; Matlashewski, Greg; Mondal, Dinesh

2014-01-01

The visceral leishmaniasis (VL) elimination program in Bangladesh is in its attack phase. The primary goal of this phase is to decrease the burden of VL as much as possible. Active case detection (ACD) by the fever camp method and an approach using past VL cases in the last 6-12 months have been found useful for detection of VL patients in the community. We aimed to explore the yield of Accelerated Active Case Detection (AACD) of non-self reporting VL as well as the factors that are associated with non-self reporting to hospitals in endemic communities of Bangladesh. Our study was conducted in the Trishal sub-district of Mymensingh, a highly VL endemic region of Bangladesh. We used a two-stage sampling strategy from 12 VL endemic unions of Trishal. Two villages from each union were selected at random. We looked for VL patients who had self-reported to the hospital and were under treatment from these villages. Then we conducted AACD for VL cases in those villages using house-to-house visit. Suspected VL cases were referred to the Trishal hospital where diagnosis and treatment of VL was done following National Guidelines for VL case management. We collected socio-demographic information from patients or a patient guardian using a structured questionnaire. The total number of VL cases was 51. Nineteen of 51 (37.3%) were identified by AACD. Poverty, female gender and poor knowledge about VL were independent factors associated with non self-reporting to the hospital. Our primary finding is that AACD is a useful method for early detection of VL cases that would otherwise go unreported to the hospital in later stage due to poverty, poor knowledge about VL and gender inequity. We recommend that the National VL Program should consider AACD to strengthen its early VL case detection strategy.

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

Determining Prevalence of Acute Bilirubin Encephalopathy in Developing Countries

ClinicalTrials.gov

2015-11-11

Demonstrate BIND II Score of >=5, is Valid for Detecting Moderate to Severe ABE in Neonates <14 Days Old.; Demonstrate Community-BIND Instrument, a Modified BIND II, is a Valid and Reliable Tool for Detecting ABE.; Demonstrate That Community-BIND Can be Used for Acquiring Population-based Prevalence of ABE in the Community.

Community Perceptions of Specific Skin Features of Possible Melanoma

ERIC Educational Resources Information Center

Baade, Peter D.; Balanda, Kevin P.; Stanton, Warren R.; Lowe, John B.; Del Mar, Chris B.

2004-01-01

Background: Melanoma can be curable if detected early. One component of detecting melanoma is an awareness of the important features of the disease. It is currently not clear which features the community view as indicative of melanoma. Objective: To investigate which features of the skin members of an urban community believe may indicate skin…

Molecular methods for pathogen and microbial community detection and characterization: current and potential application in diagnostic microbiology.

PubMed

Sibley, Christopher D; Peirano, Gisele; Church, Deirdre L

2012-04-01

Clinical microbiology laboratories worldwide have historically relied on phenotypic methods (i.e., culture and biochemical tests) for detection, identification and characterization of virulence traits (e.g., antibiotic resistance genes, toxins) of human pathogens. However, limitations to implementation of molecular methods for human infectious diseases testing are being rapidly overcome allowing for the clinical evaluation and implementation of diverse technologies with expanding diagnostic capabilities. The advantages and limitation of molecular techniques including real-time polymerase chain reaction, partial or whole genome sequencing, molecular typing, microarrays, broad-range PCR and multiplexing will be discussed. Finally, terminal restriction fragment length polymorphism (T-RFLP) and deep sequencing are introduced as technologies at the clinical interface with the potential to dramatically enhance our ability to diagnose infectious diseases and better define the epidemiology and microbial ecology of a wide range of complex infections. Copyright © 2012 Elsevier B.V. All rights reserved.

Near surface IP investigations: Four case studies

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

Hearst, R.B.; Morris, W.A.; Clark, M.A.

1995-12-31

The use of the Induced Polarisation (IP) method of geophysical surveying for near surface site investigations is gaining acceptance within the geophysical community. In this study the IP method is evaluated as a tool for the delineation of ground water resources; contamination plume detection in a lateritic horizon; and acid mine drainage leak detection from decommissioned mine tailings. A time domain IP system was selected for this study primarily for the flexibility in the selection and setting of receiver time windows and diagnostic characteristics attributed to submitting the data to Cole-Cole analysis. Analysis of the acquired data in conjunction withmore » available borehole and geological information illustrates the effectiveness and usefulness of the survey method for solving near surface problems. In all of the locations tested, it was found that with a properly designed IP survey it was possible to resolve the target and/or related structures.« less

Sandia Simple Particle Tracking (Sandia SPT) v. 1.0

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

Anthony, Stephen M.

2015-06-15

Sandia SPT is designed as software to accompany a book chapter being published a methods chapter which provides an introduction on how to label and track individual proteins. The Sandia Simple Particle Tracking code uses techniques common to the image processing community, where its value is that it facilitates implementing the methods described in the book chapter by providing the necessary open-source code. The code performs single particle spot detection (or segmentation and localization) followed by tracking (or connecting the detected particles into trajectories). The book chapter, which along with the headers in each file, constitutes the documentation for themore » code is: Anthony, S.M.; Carroll-Portillo, A.; Timlon, J.A., Dynamics and Interactions of Individual Proteins in the Membrane of Living Cells. In Anup K. Singh (Ed.) Single Cell Protein Analysis Methods in Molecular Biology. Springer« less

Diagnostic Accuracy and Cost-Effectiveness of Alternative Methods for Detection of Soil-Transmitted Helminths in a Post-Treatment Setting in Western Kenya

PubMed Central

Kepha, Stella; Kihara, Jimmy H.; Njenga, Sammy M.; Pullan, Rachel L.; Brooker, Simon J.

2014-01-01

Objectives This study evaluates the diagnostic accuracy and cost-effectiveness of the Kato-Katz and Mini-FLOTAC methods for detection of soil-transmitted helminths (STH) in a post-treatment setting in western Kenya. A cost analysis also explores the cost implications of collecting samples during school surveys when compared to household surveys. Methods Stool samples were collected from children (n = 652) attending 18 schools in Bungoma County and diagnosed by the Kato-Katz and Mini-FLOTAC coprological methods. Sensitivity and additional diagnostic performance measures were analyzed using Bayesian latent class modeling. Financial and economic costs were calculated for all survey and diagnostic activities, and cost per child tested, cost per case detected and cost per STH infection correctly classified were estimated. A sensitivity analysis was conducted to assess the impact of various survey parameters on cost estimates. Results Both diagnostic methods exhibited comparable sensitivity for detection of any STH species over single and consecutive day sampling: 52.0% for single day Kato-Katz; 49.1% for single-day Mini-FLOTAC; 76.9% for consecutive day Kato-Katz; and 74.1% for consecutive day Mini-FLOTAC. Diagnostic performance did not differ significantly between methods for the different STH species. Use of Kato-Katz with school-based sampling was the lowest cost scenario for cost per child tested ($10.14) and cost per case correctly classified ($12.84). Cost per case detected was lowest for Kato-Katz used in community-based sampling ($128.24). Sensitivity analysis revealed the cost of case detection for any STH decreased non-linearly as prevalence rates increased and was influenced by the number of samples collected. Conclusions The Kato-Katz method was comparable in diagnostic sensitivity to the Mini-FLOTAC method, but afforded greater cost-effectiveness. Future work is required to evaluate the cost-effectiveness of STH surveillance in different settings. PMID:24810593

Total coliform and E. coli in public water systems using undisinfected ground water in the United States.

PubMed

Messner, Michael J; Berger, Philip; Javier, Julie

2017-06-01

Public water systems (PWSs) in the United States generate total coliform (TC) and Escherichia coli (EC) monitoring data, as required by the Total Coliform Rule (TCR). We analyzed data generated in 2011 by approximately 38,000 small (serving fewer than 4101 individuals) undisinfected public water systems (PWSs). We used statistical modeling to characterize a distribution of TC detection probabilities for each of nine groupings of PWSs based on system type (community, non-transient non-community, and transient non-community) and population served (less than 101, 101-1000 and 1001-4100 people). We found that among PWS types sampled in 2011, on average, undisinfected transient PWSs test positive for TC 4.3% of the time as compared with 3% for undisinfected non-transient PWSs and 2.5% for undisinfected community PWSs. Within each type of PWS, the smaller systems have higher median TC detection than the larger systems. All TC-positive samples were assayed for EC. Among TC-positive samples from small undisinfected PWSs, EC is detected in about 5% of samples, regardless of PWS type or size. We evaluated the upper tail of the TC detection probability distributions and found that significant percentages of some system types have high TC detection probabilities. For example, assuming the systems providing data are nationally-representative, then 5.0% of the ∼50,000 small undisinfected transient PWSs in the U.S. have TC detection probabilities of 20% or more. Communities with such high TC detection probabilities may have elevated risk of acute gastrointestinal (AGI) illness - perhaps as great or greater than the attributable risk to drinking water (6-22%) calculated for 14 Wisconsin community PWSs with much lower TC detection probabilities (about 2.3%, Borchardt et al., 2012). Published by Elsevier GmbH.

Challenges from Tuberculosis Diagnosis to Care in Community-Based Active Case Finding among the Urban Poor in Cambodia: A Mixed-Methods Study

PubMed Central

Malhotra, Shelly; Koeut, Pichenda; Thai, Sopheak; Khun, Kim Eam; Colebunders, Robert; Lynen, Lut

2015-01-01

Background While community-based active case finding (ACF) for tuberculosis (TB) holds promise for increasing early case detection among hard-to-reach populations, limited data exist on the acceptability of active screening. We aimed to identify barriers and explore facilitators on the pathway from diagnosis to care among TB patients and health providers. Methods Mixed-methods study. We administered a survey questionnaire to, and performed in-depth interviews with, TB patients identified through ACF from poor urban settlements in Phnom Penh, Cambodia. Additionally, we conducted focus group discussions and in-depth interviews with community and public health providers involved in ACF, respectively. Results Acceptance of home TB screening was strong among key stakeholders due to perceived reductions in access barriers and in direct and indirect patient costs. Privacy and stigma were not an issue. To build trust and facilitate communication, the participation of community representatives alongside health workers was preferred. Most health providers saw ACF as complementary to existing TB services; however, additional workload as a result of ACF was perceived as straining operating capacity at public sector sites. Proximity to a health facility and disease severity were the strongest determinants of prompt care-seeking. The main reasons reported for delays in treatment-seeking were non-acceptance of diagnosis, high indirect costs related to lost income/productivity and transportation expenses, and anticipated side-effects from TB drugs. Conclusions TB patients and health providers considered home-based ACF complementary to facility-based TB screening. Strong engagement with community representatives was believed critical in gaining access to high risk communities. The main barriers to prompt treatment uptake in ACF were refusal of diagnosis, high indirect costs, and anticipated treatment side-effects. A patient-centred approach and community involvement were essential in mitigating barriers to care in marginalised communities. PMID:26222545

Infectious helminth ova in wastewater and sludge: A review on public health issues and current quantification practices.

PubMed

Gyawali, P

2018-02-01

Raw and partially treated wastewater has been widely used to maintain the global water demand. Presence of viable helminth ova and larvae in the wastewater raised significant public health concern especially when used for agriculture and aquaculture. Depending on the prevalence of helminth infections in communities, up to 1.0 × 10 3 ova/larvae can be presented per litre of wastewater and 4 gm (dry weight) of sludge. Multi-barrier approaches including pathogen reduction, risk assessment, and exposure reduction have been suggested by health regulators to minimise the potential health risk. However, with a lack of a sensitive and specific method for the quantitative detection of viable helminth ova from wastewater, an accurate health risk assessment is difficult to achieve. As a result, helminth infections are difficult to control from the communities despite two decades of global effort (mass drug administration). Molecular methods can be more sensitive and specific than currently adapted culture-based and vital stain methods. The molecular methods, however, required more and thorough investigation for its ability with accurate quantification of viable helminth ova/larvae from wastewater and sludge samples. Understanding different cell stages and corresponding gene copy numbers is pivotal for accurate quantification of helminth ova/larvae in wastewater samples. Identifying specific genetic markers including protein, lipid, and metabolites using multiomics approach could be utilized for cheap, rapid, sensitive, specific and point of care detection tools for helminth ova and larva in the wastewater.

Taxonomy and clustering in collaborative systems: The case of the on-line encyclopedia Wikipedia

NASA Astrophysics Data System (ADS)

Capocci, A.; Rao, F.; Caldarelli, G.

2008-01-01

In this paper we investigate the nature and structure of the relation between imposed classifications and real clustering in a particular case of a scale-free network given by the on-line encyclopedia Wikipedia. We find a statistical similarity in the distributions of community sizes both by using the top-down approach of the categories division present in the archive and in the bottom-up procedure of community detection given by an algorithm based on the spectral properties of the graph. Regardless of the statistically similar behaviour, the two methods provide a rather different division of the articles, thereby signaling that the nature and presence of power laws is a general feature for these systems and cannot be used as a benchmark to evaluate the suitability of a clustering method.

DEVELOPMENT OF MOLECULAR METHODS TO DETECT ...

EPA Pesticide Factsheets

A large number of human enteric viruses are known to cause gastrointestinal illness and waterborne outbreaks. Many of these are emerging viruses that do not grow or grow poorly in cell culture and so molecular detectoin methods based on the polymerase chain reaction (PCR) are being developed. Current studies focus on detecting two virus groups, the caliciviruses and the hepatitis E virus strains, both of which have been found to cause significant outbraks via contaminated drinking water. Once developed, these methods will be used to collect occurrence data for risk assessment studies. Develop sensitive techniques to detect and identify emerging human waterborne pathogenic viruses and viruses on the CCL.Determine effectiveness of viral indicators to measure microbial quality in water matrices.Support activities: (a) culture and distribution of mammalian cells for Agency and scientific community research needs, (b) provide operator expertise for research requiring confocal and electron microscopy, (c) glassware cleaning, sterilization and biological waste disposal for the Cincinnati EPA facility, (d) operation of infectious pathogenic suite, (e) maintenance of walk-in constant temperature rooms and (f) provide Giardia cysts.

The Evolution of Advanced Molecular Diagnostics for the Detection and Characterization of Mycoplasma pneumoniae.

PubMed

Diaz, Maureen H; Winchell, Jonas M

2016-01-01

Over the past decade there have been significant advancements in the methods used for detecting and characterizing Mycoplasma pneumoniae, a common cause of respiratory illness and community-acquired pneumonia worldwide. The repertoire of available molecular diagnostics has greatly expanded from nucleic acid amplification techniques (NAATs) that encompass a variety of chemistries used for detection, to more sophisticated characterizing methods such as multi-locus variable-number tandem-repeat analysis (MLVA), Multi-locus sequence typing (MLST), matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS), single nucleotide polymorphism typing, and numerous macrolide susceptibility profiling methods, among others. These many molecular-based approaches have been developed and employed to continually increase the level of discrimination and characterization in order to better understand the epidemiology and biology of M. pneumoniae. This review will summarize recent molecular techniques and procedures and lend perspective to how each has enhanced the current understanding of this organism and will emphasize how Next Generation Sequencing may serve as a resource for researchers to gain a more comprehensive understanding of the genomic complexities of this insidious pathogen.

Detection and monitoring of anaerobic rumen fungi using an ARISA method.

PubMed

Denman, S E; Nicholson, M J; Brookman, J L; Theodorou, M K; McSweeney, C S

2008-12-01

To develop an automated ribosomal intergenic spacer region analysis (ARISA) method for the detection of anaerobic rumen fungi and also to demonstrate utility of the technique to monitor colonization and persistence of fungi, and diet-induced changes in community structure. The method could discriminate between three genera of anaerobic rumen fungal isolates, representing Orpinomyces, Piromyces and Neocallimastix species. Changes in anaerobic fungal composition were observed between animals fed a high-fibre diet compared with a grain-based diet. ARISA analysis of rumen samples from animals on grain showed a decrease in fungal diversity with a dominance of Orpinomyces and Piromyces spp. Clustering analysis of ARISA profile patterns grouped animals based on diet. A single strain of Orpinomyces was dosed into a cow and was detectable within the rumen fungal population for several weeks afterwards. The ARISA technique was capable of discriminating between pure cultures at the genus level. Diet composition has a significant influence on the diversity of anaerobic fungi in the rumen and the method can be used to monitor introduced strains. Through the use of ARISA analysis, a better understanding of the effect of diets on rumen anaerobic fungi populations is provided.

Microbial deterioration of vacuum-packaged chilled beef cuts and techniques for microbiota detection and characterization: a review.

PubMed

Hernández-Macedo, Maria Lucila; Barancelli, Giovana Verginia; Contreras-Castillo, Carmen Josefina

2011-01-01

Gas production from microbial deterioration in vacuum-packs of chilled meat leads to pack distension, which is commonly referred as blown pack. This phenomenon is attributed to some psychrophilic and psychrotrophic Clostridium species, as well as Enterobacteria. The ability of these microorganisms to grow at refrigeration temperatures makes the control by the meat industry a challenge. This type of deterioration has been reported in many countries including some plants in the Midwestern and Southeastern regions of Brazil. In addition to causing economic losses, spoilage negatively impacts the commercial product brand, thereby impairing the meat industry. In the case of strict anaerobes species they are difficult to grow and isolate using culture methods in conventional microbiology laboratories. Furthermore, conventional culture methods are sometimes not capable of distinguishing species or genera. DNA-based molecular methods are alternative strategies for detecting viable and non-cultivable microorganisms and strict anaerobic microorganisms that are difficult to cultivate. Here, we review the microorganisms and mechanisms involved in the deterioration of vacuum-packaged chilled meat and address the use of molecular methods for detecting specific strict anaerobic microorganisms and microbial communities in meat samples.

Analysis of Low-Biomass Microbial Communities in the Deep Biosphere.

PubMed

Morono, Y; Inagaki, F

2016-01-01

Over the past few decades, the subseafloor biosphere has been explored by scientific ocean drilling to depths of about 2.5km below the seafloor. Although organic-rich anaerobic sedimentary habitats in the ocean margins harbor large numbers of microbial cells, microbial populations in ultraoligotrophic aerobic sedimentary habitats in the open ocean gyres are several orders of magnitude less abundant. Despite advances in cultivation-independent molecular ecological techniques, exploring the low-biomass environment remains technologically challenging, especially in the deep subseafloor biosphere. Reviewing the historical background of deep-biosphere analytical methods, the importance of obtaining clean samples and tracing contamination, as well as methods for detecting microbial life, technological aspects of molecular microbiology, and detecting subseafloor metabolic activity will be discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

"Communities" in community engagement: lessons learned from autism research in South Korea and South Africa.

PubMed

Grinker, Roy Richard; Chambers, Nola; Njongwe, Nono; Lagman, Adrienne E; Guthrie, Whitney; Stronach, Sheri; Richard, Bonnie O; Kauchali, Shuaib; Killian, Beverley; Chhagan, Meera; Yucel, Fikri; Kudumu, Mwenda; Barker-Cummings, Christie; Grether, Judith; Wetherby, Amy M

2012-06-01

Little research has been conducted on behavioral characteristics of children with autism spectrum disorder (ASD) from diverse cultures within the US, or from countries outside of the US or Europe, with little reliable information yet reported from developing countries. We describe the process used to engage diverse communities in ASD research in two community-based research projects-an epidemiologic investigation of 7- to 12-year olds in South Korea and the Early Autism Project, an ASD detection program for 18- to 36-month-old Zulu-speaking children in South Africa. Despite the differences in wealth between these communities, ASD is underdiagnosed in both settings, and generally not reported in clinical or educational records. Moreover, in both countries, there is low availability of services. In both cases, local knowledge helped researchers to address both ethnographic as well as practical problems. Researchers identified the ways in which these communities generate and negotiate the cultural meanings of developmental disorders. Researchers incorporated that knowledge, as they engaged communities in a research protocol, adapted and translated screening and diagnostic tools, and developed methods for screening, evaluating, and diagnosing children with ASD. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

Objective comparison of particle tracking methods

PubMed Central

Chenouard, Nicolas; Smal, Ihor; de Chaumont, Fabrice; Maška, Martin; Sbalzarini, Ivo F.; Gong, Yuanhao; Cardinale, Janick; Carthel, Craig; Coraluppi, Stefano; Winter, Mark; Cohen, Andrew R.; Godinez, William J.; Rohr, Karl; Kalaidzidis, Yannis; Liang, Liang; Duncan, James; Shen, Hongying; Xu, Yingke; Magnusson, Klas E. G.; Jaldén, Joakim; Blau, Helen M.; Paul-Gilloteaux, Perrine; Roudot, Philippe; Kervrann, Charles; Waharte, François; Tinevez, Jean-Yves; Shorte, Spencer L.; Willemse, Joost; Celler, Katherine; van Wezel, Gilles P.; Dan, Han-Wei; Tsai, Yuh-Show; de Solórzano, Carlos Ortiz; Olivo-Marin, Jean-Christophe; Meijering, Erik

2014-01-01

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Since manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized, for the first time, an open competition, in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to important practical conclusions for users and developers. PMID:24441936

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

Identification of carriers among individuals recruited in the typhoid registry in Malaysia using stool culture, polymerase chain reaction, and dot enzyme immunoassay as detection tools.

PubMed

Chua, Ang Lim; Aziah, Ismail; Balaram, Prabha; Bhuvanendran, Saatheeyavaane; Anthony, Amy Amilda; Mohmad, Siti Norazura; Nasir, Norhafiza M; Hassan, Haslizai; Naim, Rochman; Meran, Lila P; Hussin, Hani M; Ismail, Asma

2015-03-01

Chronic carriers of Salmonella Typhi act as reservoirs for the organism and become the agents of typhoid outbreaks in a community. In this study, chronic carriers in Kelantan, Malaysia were first identified using the culture and polymerase chain reaction method. Then, a novel serological tool, designated Typhidot-C, was evaluated in retrospect using the detected individuals as control positives. Chronic carriage positive by the culture and polymerase chain reaction method was recorded at 3.6% (4 out of 110) among individuals who previously had acute typhoid fever and a 9.4% (10 out of 106) carriage rate was observed among food handlers screened during outbreaks. The Typhidot-C assay was able to detect all these positive carriers showing its potential as a viable carrier screening tool and can be used for efficient detection of typhoid carriers in an endemic area. These findings were used to establish the first carrier registry for S Typhi carriers in Malaysia. © 2012 APJPH.

Seasonal dynamics of extraradical mycelium and mycorrhizas in a black truffle (Tuber melanosporum) plantation.

PubMed

Queralt, Mikel; Parladé, Javier; Pera, Joan; DE Miguel, Ana María

2017-08-01

Seasonal dynamics of black truffle (Tuber melanosporum) extraradical mycelium as well as the associated mycorrhizal community have been evaluated in a 16-year-old plantation with productive and non-productive trees. Mycelium biomass was seasonally quantified by real-time PCR over two consecutive years and the correlation with environmental variables explored. Extraradical mycelium biomass varied seasonally and between the two consecutive years, being correlated with the precipitation that occurred 1 month before sampling. In addition, productive trees had more mycelium in the brûlé area than non-productive trees did. The ectomycorrhizal community composition inside the burnt areas was seasonally evaluated during a year. Ten mycorrhizal morphotypes were detected; T. melanosporum was the most abundant in productive and non-productive trees. Black truffle mycorrhizas were more abundant (mycorrhizal tips per unit of soil volume) in productive trees, and no seasonal variation was observed. The occurrence of black truffle mycorrhizas was significantly and positively correlated with the biomass of extraradical mycelium. The mycorrhizal community within the brûlé areas was significantly different between productive and non-productive trees, and no variation was detected between seasons. The assessment of the fungal vegetative structures in a mature plantation is of paramount importance to develop trufficulture methods based on the knowledge of the biological cycle of the fungus and its relationships with the associated ectomycorrhizal communities.

Methane turnover and methanotrophic communities in arctic aquatic ecosystems of the Lena Delta, Northeast Siberia.

PubMed

Osudar, Roman; Liebner, Susanne; Alawi, Mashal; Yang, Sizhong; Bussmann, Ingeborg; Wagner, Dirk

2016-08-01

Large amounts of organic carbon are stored in Arctic permafrost environments, and microbial activity can potentially mineralize this carbon into methane, a potent greenhouse gas. In this study, we assessed the methane budget, the bacterial methane oxidation (MOX) and the underlying environmental controls of arctic lake systems, which represent substantial sources of methane. Five lake systems located on Samoylov Island (Lena Delta, Siberia) and the connected river sites were analyzed using radiotracers to estimate the MOX rates, and molecular biology methods to characterize the abundance and the community composition of methane-oxidizing bacteria (MOB). In contrast to the river, the lake systems had high variation in the methane concentrations, the abundance and composition of the MOB communities, and consequently, the MOX rates. The highest methane concentrations and the highest MOX rates were detected in the lake outlets and in a lake complex in a flood plain area. Though, in all aquatic systems, we detected both, Type I and II MOB, in lake systems, we observed a higher diversity including MOB, typical of the soil environments. The inoculation of soil MOB into the aquatic systems, resulting from permafrost thawing, might be an additional factor controlling the MOB community composition and potentially methanotrophic capacity. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Employment of Near Full-Length Ribosome Gene TA-Cloning and Primer-Blast to Detect Multiple Species in a Natural Complex Microbial Community Using Species-Specific Primers Designed with Their Genome Sequences.

PubMed

Zhang, Huimin; He, Hongkui; Yu, Xiujuan; Xu, Zhaohui; Zhang, Zhizhou

2016-11-01

It remains an unsolved problem to quantify a natural microbial community by rapidly and conveniently measuring multiple species with functional significance. Most widely used high throughput next-generation sequencing methods can only generate information mainly for genus-level taxonomic identification and quantification, and detection of multiple species in a complex microbial community is still heavily dependent on approaches based on near full-length ribosome RNA gene or genome sequence information. In this study, we used near full-length rRNA gene library sequencing plus Primer-Blast to design species-specific primers based on whole microbial genome sequences. The primers were intended to be specific at the species level within relevant microbial communities, i.e., a defined genomics background. The primers were tested with samples collected from the Daqu (also called fermentation starters) and pit mud of a traditional Chinese liquor production plant. Sixteen pairs of primers were found to be suitable for identification of individual species. Among them, seven pairs were chosen to measure the abundance of microbial species through quantitative PCR. The combination of near full-length ribosome RNA gene library sequencing and Primer-Blast may represent a broadly useful protocol to quantify multiple species in complex microbial population samples with species-specific primers.

Effects of Agronomic Treatments on Structure and Function of Ammonia-Oxidizing Communities

PubMed Central

Phillips, Carol J.; Harris, Dave; Dollhopf, Sherry L.; Gross, Katherine L.; Prosser, James I.; Paul, Eldor A.

2000-01-01

The aim of this study was to determine the effects of different agricultural treatments and plant communities on the diversity of ammonia oxidizer populations in soil. Denaturing gradient gel electrophoresis (DGGE), coupled with specific oligonucleotide probing, was used to analyze 16S rRNA genes of ammonia oxidizers belonging to the β subgroup of the division Proteobacteria by use of DNA extracted from cultivated, successional, and native deciduous forest soils. Community profiles of the different soil types were compared with nitrification rates and most-probable-number (MPN) counts. Despite significant variation in measured nitrification rates among communities, there were no differences in the DGGE banding profiles of DNAs extracted from these soils. DGGE profiles of DNA extracted from samples of MPN incubations, cultivated at a range of ammonia concentrations, showed the presence of bands not amplified from directly extracted DNA. Nitrosomonas-like bands were seen in the MPN DNA but were not detected in the DNA extracted directly from soils. These bands were detected in some samples taken from MPN incubations carried out with medium containing 1,000 μg of NH4+-N ml−1, to the exclusion of bands detected in the native DNA. Cell concentrations of ammonia oxidizers determined by MPN counts were between 10- and 100-fold lower than those determined by competitive PCR (cPCR). Although no differences were seen in ammonia oxidizer MPN counts from the different soil treatments, cPCR revealed higher numbers in fertilized soils. The use of a combination of traditional and molecular methods to investigate the activities and compositions of ammonia oxidizers in soil demonstrates differences in fine-scale compositions among treatments that may be associated with changes in population size and function. PMID:11097922

Use of large-scale acoustic monitoring to assess anthropogenic pressures on Orthoptera communities.

PubMed

Penone, Caterina; Le Viol, Isabelle; Pellissier, Vincent; Julien, Jean-François; Bas, Yves; Kerbiriou, Christian

2013-10-01

Biodiversity monitoring at large spatial and temporal scales is greatly needed in the context of global changes. Although insects are a species-rich group and are important for ecosystem functioning, they have been largely neglected in conservation studies and policies, mainly due to technical and methodological constraints. Sound detection, a nondestructive method, is easily applied within a citizen-science framework and could be an interesting solution for insect monitoring. However, it has not yet been tested at a large scale. We assessed the value of a citizen-science program in which Orthoptera species (Tettigoniidae) were monitored acoustically along roads. We used Bayesian model-averaging analyses to test whether we could detect widely known patterns of anthropogenic effects on insects, such as the negative effects of urbanization or intensive agriculture on Orthoptera populations and communities. We also examined site-abundance correlations between years and estimated the biases in species detection to evaluate and improve the protocol. Urbanization and intensive agricultural landscapes negatively affected Orthoptera species richness, diversity, and abundance. This finding is consistent with results of previous studies of Orthoptera, vertebrates, carabids, and butterflies. The average mass of communities decreased as urbanization increased. The dispersal ability of communities increased as the percentage of agricultural land and, to a lesser extent, urban area increased. Despite changes in abundances over time, we found significant correlations between yearly abundances. We identified biases linked to the protocol (e.g., car speed or temperature) that can be accounted for ease in analyses. We argue that acoustic monitoring of Orthoptera along roads offers several advantages for assessing Orthoptera biodiversity at large spatial and temporal extents, particularly in a citizen science framework. © 2013 Society for Conservation Biology.

Simple, Rapid, Sensitive, and Versatile SWNT-Paper Sensor for Environmental Toxin Detection Competitive with ELISA

PubMed Central

Wang, Libing; Chen, Wei; Xu, Dinghua; Shim, Bong Sup; Zhu, Yingyue; Sun, Fengxia; Liu, Liqiang; Peng, Chifang; Jin, Zhengyu; Xu, Chuanlai; Kotov, Nicholas A.

2009-01-01

Safety of water was for a long time and still is one of the most pressing needs for many countries and different communities. Despite the fact that there are potentially many methods to evaluate water safety, finding a simple, rapid, versatile, and inexpensive method for detection of toxins in everyday items is still a great challenge. In this study, we extend the concept of composites obtained impregnation of porous fibrous materials, such as fabrics and papers, by single walled carbon-nanotubes (SWNTs) toward very simple but high-performance biosensors. They utilize the strong dependence of electrical conductivity through nanotubes percolation network on the width of nanotubes-nanotube tunneling gap and can potentially satisfy all the requirements outlined above for the routine toxin monitoring. An antibody to the microcystin-LR (MC-LR), one of the common culprits in mass poisonings, was dispersed together with SWNTs. This dispersion was used to dip-coat the paper rendering it conductive. The change in conductivity of the paper was used to sense the MC-LR in the water rapidly and accurately. The method has the linear detection range up to 10 nmol/L and non-linear detection up to 40 nmol/L. The limit of detection was found to be 0.6 nmol/L (0.6 ng/mL), which satisfies the strictest World Health Organization standard for MC-LR content in drinking water (1 ng/mL), and is comparable to the detection limit of traditional ELISA method of MC-LR detection, while drastically reducing the time of analysis by more than an order of magnitude, which is one of the major hurdles in practical applications. Similar technology of sensor preparation can also be used for a variety of other rapid environmental sensors. PMID:19928776

Sequence-based methods for detecting and evaluating the human gut mycobiome.

PubMed

Suhr, M J; Banjara, N; Hallen-Adams, H E

2016-03-01

We surveyed the fungal microbiota in 16 faecal samples from healthy humans with a vegetarian diet. Fungi were identified using molecular cloning, 454 pyrosequencing and a Luminex analyte-specific reagent (ASR) assay, all targeting the ITS region of the rRNA genes. Fungi were detected in each faecal sample and at least 46 distinct fungal operational taxonomic units (OTUs) were detected, from two phyla - Ascomycota and Basidiomycota. Fusarium was the most abundant genus, followed by Malassezia, Penicillium, Aspergillus and Candida. Commonly detected fungi such as Aspergillus and Penicillium, as well as known dietary fungi Agaricus bisporus and Ophiocordyceps sinensis, are presumed to be transient, allochthonous members due to their abundance in the environment or dietary associations. No single method identified the full diversity of fungi in all samples; pyrosequencing detected more distinct OTUs than the other methods, but failed to detect OTUs in some samples that were detected by cloning and/or ASR assays. ASRs were limited by the commercially available assays, but the potential to design new, optimized assays, coupled with speed and cost, makes the ASR method worthy of further study. Fungi play a role in human gut ecology and health. The field lags immensely behind bacterial gut microbiota research, and studies continue to identify new fungi in faecal samples from healthy humans. However, many of these 'new' species are incapable of growth in the human GI tract, let alone making a meaningful contribution to the gut microbial community. Fungi actually inhabiting and impacting the gut likely constitute a small set of species, and an optimized, targeted, probe-based assay may prove to be the most sensible way of quantifying their abundances. © 2015 The Society for Applied Microbiology.

PCR method for the rapid detection and discrimination of Legionella spp. based on the amplification of pcs, pmtA, and 16S rRNA genes.

PubMed

Janczarek, Monika; Palusińska-Szysz, Marta

2016-05-01

Legionella bacteria are organisms of public health interest due to their ability to cause pneumonia (Legionnaires' disease) in susceptible humans and their ubiquitous presence in water supply systems. Rapid diagnosis of Legionnaires' disease allows the use of therapy specific for the disease. L. pneumophila serogroup 1 is the most common cause of infection acquired in community and hospital environments. The non-L. pneumophila infections are likely under-detected because of a lack of effective diagnosis. In this work, simplex and duplex PCR assays with the use of new molecular markers pcs and pmtA involved in phosphatidylcholine synthesis were specified for rapid and cost-efficient identification and distinguishing Legionella species. The sets of primers developed were found to be sensitive and specific for reliable detection of Legionella belonging to the eight most clinically relevant species. Among these, four primer sets I, II, VI, and VII used for duplex-PCRs proved to have the highest identification power and reliability in the detection of the bacteria. Application of this PCR-based method should improve detection of Legionella spp. in both clinical and environmental settings and facilitate molecular typing of these organisms.

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.

Research and Development of Non-Spectroscopic MEMS-Based Sensor Arrays for Targeted Gas Detection

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

Loui, A; McCall, S K

2011-10-24

The ability to monitor the integrity of gas volumes is of interest to the stockpile surveillance community. Specifically, the leak detection of noble gases, at relevant concentration ranges and distinguished from other chemical species that may be simultaneously present, is particularly challenging. Aside from the laboratory-based method of gas chromatography-mass spectrometry (GC-MS), where samples may be collected by solid-phase microextraction (SPME) or cryofocusing, the other major approaches for gas-phase detection employ lasers typically operating in the mid-infrared wavelength region. While mass spectrometry can readily detect noble gases - the helium leak detector is an obvious example - laser-based methods suchmore » as infrared (IR) or Raman spectroscopy are completely insensitive to them as their monatomic nature precludes a non-zero dipole moment or changes in polarizability upon excitation. Therefore, noble gases can only be detected by one of two methods: (1) atomic emission spectroscopies which require the generation of plasmas through laser-induced breakdown, electrical arcing, or similar means; (2) non-spectroscopic methods which measure one or more physical properties (e.g., mass, thermal conductivity, density). In this report, we present our progress during Fiscal Year 2011 (FY11) in the research and development of a non-spectroscopic method for noble gas detection. During Fiscal Year 2010 (FY10), we demonstrated via proof-of-concept experiments that the combination of thermal conductivity detection (TCD) and coating-free damped resonance detection (CFDRD) using micro-electromechanical systems (MEMS) could provide selective sensing of these inert species. Since the MEMS-based TCD technology was directly adapted from a brassboard prototype commissioned by a previous chemical sensing project, FY11 efforts focused on advancing the state of the newer CFDRD method. This work, guided by observations previously reported in the open literature, has not only resulted in a substantially measureable increase in selectivity but has also revealed a potential method for mitigating or eliminating thermal drift that does not require a secondary reference sensor. The design of an apparatus to test this drift compensation scheme will be described. We will conclude this report with a discussion of planned efforts in Fiscal Year 2012 (FY12).« less

Use of Length Heterogeneity PCR and Fatty Acid Methyl Ester Profiles To Characterize Microbial Communities in Soil†

PubMed Central

Ritchie, Nancy J.; Schutter, Mary E.; Dick, Richard P.; Myrold, David D.

2000-01-01

In length heterogeneity PCR (LH-PCR) a fluorescently labeled primer is used to determine the relative amounts of amplified sequences originating from different microorganisms. Labeled fragments are separated by gel electrophoresis and detected by laser-induced fluorescence with an automated gene sequencer. We used LH-PCR to evaluate the composition of the soil microbial community. Four soils, which differed in terms of soil type and/or crop management practice, were studied. Previous data for microbial biomass, nitrogen and carbon contents, and nitrogen mineralization rates suggested that the microbial characteristics of these soils were different. One site received two different treatments: no-till and conventional till perennial ryegrass. The other sites were no-till continuous grass plots at separate locations with different soil types. Community composition was characterized by assessing the natural length heterogeneity in eubacterial sequences amplified from the 5′ domain of the 16S rRNA gene and by determining fatty acid methyl ester (FAME) profiles. We found that LH-PCR results were reproducible. Both methods distinguished the three sites. The most abundant bacterial community members, based on cloned LH-PCR products, were members of the β subclass of the class Proteobacteria, the Cytophaga-Flexibacter-Bacteriodes group, and the high-G+C-content gram-positive bacterial group. Strong correlations were found between LH-PCR results and FAME results. We found that the LH-PCR method is an efficient, reliable, and highly reproducible method that should be a useful tool in future assessments of microbial community composition. PMID:10742258

Unsupervised Structure Detection in Biomedical Data.

PubMed

Vogt, Julia E

2015-01-01

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Search for Hepatitis A Viruses by New Methods

DTIC Science & Technology

1981-09-01

VII. Detection of HAV and Rotavirus In a Community Water Supply Following an Outbreak of Gastroenteritls and Infectious Hepatitis . . . 1* VUL...22). An "aliquot of each concentrate was further concentrated by ultracentrifugation to assay for HAV antigen and rotavirus . Samples were assayed for... rotavirus using an Indirect "immunofluorescence test (23) and for HAV antigen using a radloirmunoassay (24, 25). Ř. Table 9 shows the concentrations

Human Papilloma Virus in Head and Neck Squamous Cell Cancer

PubMed Central

Asvadi Kermani, I; Seifi, SH; Dolatkhah, R; Sakhinia, E; Dastgiri, S; Ebrahimi, A; Lotfy, A; Esmaeili, HA; G, Mohammadi; M, Naderpour; SH, Hajalipour; Haggi A, Asghari; M, Nadri

2012-01-01

Background Epidemiologic and molecular evidences have established a strong link between high risk types of Human Papilloma Virus and a subgroup of Head and Neck Squamous Cell Carcinomas (HNSCC). We evaluated the frequency of HPV positivity in HNSCC and its relationship to demographic and some risk factor variables in an open case- control study. Methods Fourteen recently diagnosed patients with squamous cell cancer of oropharynx, hypopharynx and larynx aged 18-50 years were examined from 2008-2010 in Tabriz, Iran. HPV DNA was extracted from paraffin-embedded blocks of each patient's sample for PCR evaluation. Saliva samples of 94 control cancer-free subjects were collected for DNA analysis. Multivariable logistic regression method was used to calculate odds ratio for case-control comparisons. Results High risk HPV was detected in 6(42.8%) patients, and 6(5.3%) control subjects which was statistically significant (p<0.0001). HPV-18 was the most frequent type both in the cases and controls. HPV-16 DNA was detected in two patients of the case group, but it was not detected in any of the controls. The relation between demographic and risk factor variables was not statistically significant. Conclusion HPV infection has a significant impact on HNSCC. Despite HPV-16 stronger impact, HPV-18 is more likely to cause malignant degeneration in such cancers amongst some communities. It is vital to introduce and conduct immunization schedules in health care systems to protect communities to some extent. PMID:25780535

Diversity of Basidiomycetes in Michigan Agricultural Soils▿

PubMed Central

Lynch, Michael D. J.; Thorn, R. Greg

2006-01-01

We analyzed the communities of soil basidiomycetes in agroecosystems that differ in tillage history at the Kellogg Biological Station Long-Term Ecological Research site near Battle Creek, Michigan. The approach combined soil DNA extraction through a bead-beating method modified to increase recovery of fungal DNA, PCR amplification with basidiomycete-specific primers, cloning and restriction fragment length polymorphism screening of mixed PCR products, and sequencing of unique clones. Much greater diversity was detected than was anticipated in this habitat on the basis of culture-based methods or surveys of fruiting bodies. With “species” defined as organisms yielding PCR products with ≥99% identity in the 5′ 650 bases of the nuclear large-subunit ribosomal DNA, 241 “species” were detected among 409 unique basidiomycete sequences recovered. Almost all major clades of basidiomycetes from basidiomycetous yeasts and other heterobasidiomycetes through polypores and euagarics (gilled mushrooms and relatives) were represented, with a majority from the latter clade. Only 24 of 241 “species” had 99% or greater sequence similarity to named reference sequences in GenBank, and several clades with multiple “species” could not be identified at the genus level by phylogenetic comparisons with named sequences. The total estimated “species” richness for this 11.2-ha site was 367 “species” of basidiomycetes. Since >99% of the study area has not been sampled, the accuracy of our diversity estimate is uncertain. Replication in time and space is required to detect additional diversity and the underlying community structure. PMID:16950900

Indicator saturation: a novel approach to detect multiple breaks in geodetic time series.

NASA Astrophysics Data System (ADS)

Jackson, L. P.; Pretis, F.; Williams, S. D. P.

2016-12-01

Geodetic time series can record long term trends, quasi-periodic signals at a variety of time scales from days to decades, and sudden breaks due to natural or anthropogenic causes. The causes of breaks range from instrument replacement to earthquakes to unknown (i.e. no attributable cause). Furthermore, breaks can be permanent or short-lived and range at least two orders of magnitude in size (mm to 100's mm). To account for this range of possible signal-characteristics requires a flexible time series method that can distinguish between true and false breaks, outliers and time-varying trends. One such method, Indicator Saturation (IS) comes from the field of econometrics where analysing stochastic signals in these terms is a common problem. The IS approach differs from alternative break detection methods by considering every point in the time series as a break until it is demonstrated statistically that it is not. A linear model is constructed with a break function at every point in time, and all but statistically significant breaks are removed through a general-to-specific model selection algorithm for more variables than observations. The IS method is flexible because it allows multiple breaks of different forms (e.g. impulses, shifts in the mean, and changing trends) to be detected, while simultaneously modelling any underlying variation driven by additional covariates. We apply the IS method to identify breaks in a suite of synthetic GPS time series used for the Detection of Offsets in GPS Experiments (DOGEX). We optimise the method to maximise the ratio of true-positive to false-positive detections, which improves estimates of errors in the long term rates of land motion currently required by the GPS community.

Diagnostic accuracy and cost-effectiveness of alternative methods for detection of soil-transmitted helminths in a post-treatment setting in western Kenya.

PubMed

Assefa, Liya M; Crellen, Thomas; Kepha, Stella; Kihara, Jimmy H; Njenga, Sammy M; Pullan, Rachel L; Brooker, Simon J

2014-05-01

This study evaluates the diagnostic accuracy and cost-effectiveness of the Kato-Katz and Mini-FLOTAC methods for detection of soil-transmitted helminths (STH) in a post-treatment setting in western Kenya. A cost analysis also explores the cost implications of collecting samples during school surveys when compared to household surveys. Stool samples were collected from children (n = 652) attending 18 schools in Bungoma County and diagnosed by the Kato-Katz and Mini-FLOTAC coprological methods. Sensitivity and additional diagnostic performance measures were analyzed using Bayesian latent class modeling. Financial and economic costs were calculated for all survey and diagnostic activities, and cost per child tested, cost per case detected and cost per STH infection correctly classified were estimated. A sensitivity analysis was conducted to assess the impact of various survey parameters on cost estimates. Both diagnostic methods exhibited comparable sensitivity for detection of any STH species over single and consecutive day sampling: 52.0% for single day Kato-Katz; 49.1% for single-day Mini-FLOTAC; 76.9% for consecutive day Kato-Katz; and 74.1% for consecutive day Mini-FLOTAC. Diagnostic performance did not differ significantly between methods for the different STH species. Use of Kato-Katz with school-based sampling was the lowest cost scenario for cost per child tested ($10.14) and cost per case correctly classified ($12.84). Cost per case detected was lowest for Kato-Katz used in community-based sampling ($128.24). Sensitivity analysis revealed the cost of case detection for any STH decreased non-linearly as prevalence rates increased and was influenced by the number of samples collected. The Kato-Katz method was comparable in diagnostic sensitivity to the Mini-FLOTAC method, but afforded greater cost-effectiveness. Future work is required to evaluate the cost-effectiveness of STH surveillance in different settings.

Detecting the changes in rural communities in Taiwan by applying multiphase segmentation on FORMOSA-2 satellite imagery

NASA Astrophysics Data System (ADS)

Huang, Yishuo

2015-09-01

Agricultural activities mainly occur in rural areas; recently, ecological conservation and biological diversity are being emphasized in rural communities to promote sustainable development for rural communities, especially for rural communities in Taiwan. Therefore, since 2005, many rural communities in Taiwan have compiled their own development strategies in order to create their own unique characteristics to attract people to visit and stay in rural communities. By implementing these strategies, young people can stay in their own rural communities and the rural communities are rejuvenated. However, some rural communities introduce artificial construction into the community such that the ecological and biological environments are significantly degraded. The strategies need to be efficiently monitored because up to 67 rural communities have proposed rejuvenation projects. In 2015, up to 440 rural communities were estimated to be involved in rural community rejuvenations. How to monitor the changes occurring in those rural communities participating in rural community rejuvenation such that ecological conservation and ecological diversity can be satisfied is an important issue in rural community management. Remote sensing provides an efficient and rapid method to achieve this issue. Segmentation plays a fundamental role in human perception. In this respect, segmentation can be used as the process of transforming the collection of pixels of an image into a group of regions or objects with meaning. This paper proposed an algorithm based on the multiphase approach to segment the normalized difference vegetation index, NDVI, of the rural communities into several sub-regions, and to have the NDVI distribution in each sub-region be homogeneous. Those regions whose values of NDVI are close will be merged into the same class. In doing so, a complex NDVI map can be simplified into two groups: the high and low values of NDVI. The class with low NDVI values corresponds to those regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005-2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.

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.

Effect of multi-stage inoculation on the bacterial and fungal community structure during organic municipal solid wastes composting.

PubMed

Xi, Beidou; He, Xiaosong; Dang, Qiuling; Yang, Tianxue; Li, Mingxiao; Wang, Xiaowei; Li, Dan; Tang, Jun

2015-11-01

In this study, PCR-DGGE method was applied to investigate the impact of multi-stage inoculation treatment on the community composition of bacterial and fungal during municipal solid wastes (MSW) composting process. The results showed that the high temperature period was extended by the multi-stage inoculation treatment, 1day longer than initial-stage inoculation treatment, and 5days longer than non-inoculation treatment. The temperature of the secondary fermentation increased to 51°C with multi-stage inoculation treatment. The multi-stage inoculation method improved the community diversity of bacteria and fungi that the diversity indexes reached the maximum on the 17days and 20days respectively, avoided the competition between inoculations and indigenous microbes, and enhanced the growth of dominant microorganisms. The DNA sequence indicated that various kinds of uncultured microorganisms with determined ratios were detected, which were dominant microbes during the whole fermentation process. These findings call for further researches of compost microbial cultivation technology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Burnout, associated comorbidities and coping strategies in French community pharmacies—BOP study: A nationwide cross-sectional study

PubMed Central

Balayssac, David; Pereira, Bruno; Virot, Julie; Collin, Aurore; Alapini, David; Cuny, Damien; Gagnaire, Jean-Marc; Authier, Nicolas; Vennat, Brigitte

2017-01-01

Background Work-related stress and burnout syndromes are unfortunately common comorbidities found in health professionals. However, burnout syndrome has only been partly and episodically assessed for community pharmacists whereas these professionals are exposed to patients’ demands and difficulties every day. Prevalence of burnout, associated comorbidities and coping strategies were assessed in pharmacy teams (pharmacists and pharmacy technicians) in French community pharmacies. Methods This online survey was performed by emails sent to all French community pharmacies over 3 months. The survey assessed the prevalence of burnout (Maslach Burnout Inventory—MBI—questionnaire), anxiety, depression and strategies for coping with work-related stress. Results Of the 1,339 questionnaires received, 1,322 were completed and useable for the analysis. Burnout syndrome was detected in 56.2% of respondents and 10.5% of them presented severe burnout syndrome. Severe burnout syndrome was significantly associated with men, large urban areas and the number of hours worked. Depression and anxiety were found in 15.7% and 42.4% of respondents, respectively. These co-morbidities were significantly associated with severe burnout syndrome. Higher MBI scores were significantly associated with medical consultations and medicinal drug use. Conversely, respondents suffering from burnout syndrome declared they resorted less to non-medical strategies to manage their work-related stress (leisure, psychotherapy, holidays and time off). Conclusion This study demonstrated that community pharmacists and pharmacy technicians presented high prevalence of burnout syndrome, such as many healthcare professionals. Unfortunately, burnout syndrome was associated with several comorbidities (anxiety, depression and alcohol abuse) and the consumption of health resources. The psychological suffering of these healthcare professionals underlines the necessity to deploy a strategy to detect and manage burnout in community pharmacy. PMID:28800612

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.

Fungal Endophytes of Alpinia officinarum Rhizomes: Insights on Diversity and Variation across Growth Years, Growth Sites, and the Inner Active Chemical Concentration

PubMed Central

Shubin, Li; Juan, Huang; RenChao, Zhou; ShiRu, Xu; YuanXiao, Jin

2014-01-01

In the present study, the terminal-restriction fragment length polymorphism (T-RFLP) technique, combined with the use of a clone library, was applied to assess the baseline diversity of fungal endophyte communities associated with rhizomes of Alpinia officinarum Hance, a medicinal plant with a long history of use. A total of 46 distinct T-RFLP fragment peaks were detected using HhaI or MspI mono-digestion-targeted, amplified fungal rDNA ITS sequences from A. officinarum rhizomes. Cloning and sequencing of representative sequences resulted in the detection of members of 10 fungal genera: Pestalotiopsis, Sebacina, Penicillium, Marasmius, Fusarium, Exserohilum, Mycoleptodiscus, Colletotrichum, Meyerozyma, and Scopulariopsis. The T-RFLP profiles revealed an influence of growth year of the host plant on fungal endophyte communities in rhizomes of this plant species; whereas, the geographic location where A. officinarum was grown contributed to only limited variation in the fungal endophyte communities of the host tissue. Furthermore, non-metric multidimensional scaling (NMDS) analysis across all of the rhizome samples showed that the fungal endophyte community assemblages in the rhizome samples could be grouped according to the presence of two types of active indicator chemicals: total volatile oils and galangin. Our present results, for the first time, address a diverse fungal endophyte community is able to internally colonize the rhizome tissue of A. officinarum. The diversity of the fungal endophytes found in the A. officinarum rhizome appeared to be closely correlated with the accumulation of active chemicals in the host plant tissue. The present study also provides the first systematic overview of the fungal endophyte communities in plant rhizome tissue using a culture-independent method. PMID:25536070

Fungal endophytes of Alpinia officinarum rhizomes: insights on diversity and variation across growth years, growth sites, and the inner active chemical concentration.

PubMed

Shubin, Li; Juan, Huang; RenChao, Zhou; ShiRu, Xu; YuanXiao, Jin

2014-01-01

In the present study, the terminal-restriction fragment length polymorphism (T-RFLP) technique, combined with the use of a clone library, was applied to assess the baseline diversity of fungal endophyte communities associated with rhizomes of Alpinia officinarum Hance, a medicinal plant with a long history of use. A total of 46 distinct T-RFLP fragment peaks were detected using HhaI or MspI mono-digestion-targeted, amplified fungal rDNA ITS sequences from A. officinarum rhizomes. Cloning and sequencing of representative sequences resulted in the detection of members of 10 fungal genera: Pestalotiopsis, Sebacina, Penicillium, Marasmius, Fusarium, Exserohilum, Mycoleptodiscus, Colletotrichum, Meyerozyma, and Scopulariopsis. The T-RFLP profiles revealed an influence of growth year of the host plant on fungal endophyte communities in rhizomes of this plant species; whereas, the geographic location where A. officinarum was grown contributed to only limited variation in the fungal endophyte communities of the host tissue. Furthermore, non-metric multidimensional scaling (NMDS) analysis across all of the rhizome samples showed that the fungal endophyte community assemblages in the rhizome samples could be grouped according to the presence of two types of active indicator chemicals: total volatile oils and galangin. Our present results, for the first time, address a diverse fungal endophyte community is able to internally colonize the rhizome tissue of A. officinarum. The diversity of the fungal endophytes found in the A. officinarum rhizome appeared to be closely correlated with the accumulation of active chemicals in the host plant tissue. The present study also provides the first systematic overview of the fungal endophyte communities in plant rhizome tissue using a culture-independent method.

Modeling the effects of environmental disturbance on wildlife communities: avian responses to prescribed fire.

PubMed

Russell, Robin E; Royle, J Andrew; Saab, Victoria A; Lehmkuhl, John F; Block, William M; Sauer, John R

2009-07-01

Prescribed fire is a management tool used to reduce fuel loads on public lands in forested areas in the western United States. Identifying the impacts of prescribed fire on bird communities in ponderosa pine (Pinus ponderosa) forests is necessary for providing land management agencies with information regarding the effects of fuel reduction on sensitive, threatened, and migratory bird species. Recent developments in occupancy modeling have established a framework for quantifying the impacts of management practices on wildlife community dynamics. We describe a Bayesian hierarchical model of multi-species occupancy accounting for detection probability, and we demonstrate the model's usefulness for identifying effects of habitat disturbances on wildlife communities. Advantages to using the model include the ability to estimate the effects of environmental impacts on rare or elusive species, the intuitive nature of the modeling, the incorporation of detection probability, the estimation of parameter uncertainty, the flexibility of the model to suit a variety of experimental designs, and the composite estimate of the response that applies to the collection of observed species as opposed to merely a small subset of common species. Our modeling of the impacts of prescribed fire on avian communities in a ponderosa pine forest in Washington indicate that prescribed fire treatments result in increased occupancy rates for several bark-insectivore, cavity-nesting species including a management species of interest, Black-backed Woodpeckers (Picoides arcticus). Three aerial insectivore species, and the ground insectivore, American Robin (Turdus migratorius), also responded positively to prescribed fire, whereas three foliage insectivores and two seed specialists, Clark's Nutcracker (Nucifraga columbiana) and the Pine Siskin (Carduelis pinus), declined following treatments. Land management agencies interested in determining the effects of habitat manipulations on wildlife communities can use these methods to provide guidance for future management activities.

Rapid Detection of Methicillin-Resistant Staphylococcus aureus Isolates by Turanose Fermentation Method

PubMed Central

Raeisi, Javad; Saifi, Mahnaz; Pourshafie, Mohammad Reza; Asadi Karam, Mohammad Reza; Mohajerani, Hamid Reza

2015-01-01

Background: Methicillin-Resistant Staphylococcus aureus (MRSA) is a major pathogen in the hospital and community settings. Rapid methods to diagnose S. aureus infections are sought by many researchers worldwide. The current study aimed to utilize a phenotypic method of turanose fermentation to identify methicillin-susceptible and resistant S. aureus. Objectives: The current study aimed to assay the turanose metabolism at different dilutions as a rapid phenotypic method to identify MRSA isolates. Materials and Methods: A total of 150 Staphylococcus isolates were collected from Tehran health centers. Staphylococcus aureus isolates were identified based on cultural characteristics, biochemical reactions and positive tube coagulase test. Methicillin resistance was determined by the disk diffusion method. The Polymerase Chain Reaction amplification was used to detect the mecA gene in MRSA isolates. All the methicillin-resistant and susceptible isolates were evaluated for turanose metabolism with 1%, 0.7% and 0.5% dilutions using the microplate method. Results: Out of the 150 staphylococcal isolates, 80 were identified as S. aureus. Among which 40 (50%) of the isolates were MRSA. The mecA gene was present in all S. aureus isolates resistant to methicillin. A considerable difference was also observed between susceptible and resistant isolates of S. aureus at a 0.7% dilution of turanose. Conclusions: Since it is highly important to rapidly detect MRSA isolates, especially in nosocomial infections, phenotypic methods may certainly be useful for this purpose. Resistance to methicillin in S. aureus shows a substantially increased ability in turanose metabolism. It is concluded that fermentation of turanose at 0.7% dilution could be a rapid detection method for primary screening of MRSA isolates. PMID:26495105

Analysis of Actinobacteria from mould-colonized water damaged building material.

PubMed

Schäfer, Jenny; Jäckel, Udo; Kämpfer, Peter

2010-08-01

Mould-colonized water damaged building materials are frequently co-colonized by actinomycetes. Here, we report the results of the analyses of Actinobacteria on different wall materials from water damaged buildings obtained by both cultivation-dependent and cultivation-independent methods. Actinobacteria were detected in all but one of the investigated materials by both methods. The detected concentrations of Actinobacteria ranged between 1.8 x 10(4) and 7.6 x 10(7) CFUg(-1) of investigated material. A total of 265 isolates from 17 materials could be assigned to 31 different genera of the class Actinobacteria on the basis of 16S rRNA gene sequence analyses. On the basis of the cultivation-independent approach, 16S rRNA gene inserts of 800 clones (50%) were assigned to 47 different genera. Representatives of the genera Streptomyces, Amycolatopsis, Nocardiopsis, Saccharopolyspora, Promicromonospora, and Pseudonocardia were found most frequently. The results derived from both methods indicated a high abundance and variety of Actinobacteria in water damaged buildings. Four bioaerosol samples were investigated by the cultivation-based approach in order to compare the communities of Actinobacteria in building material and associated air samples. A comparison of the detected genera of bioaerosol samples with those directly obtained from material samples resulted in a congruent finding of 9 of the overall 35 detected genera (25%), whereas four genera were only detected in bioaerosol samples. Copyright 2010 Elsevier GmbH. All rights reserved.

Use of a Hierarchical Oligonucleotide Primer Extension Approach for Multiplexed Relative Abundance Analysis of Methanogens in Anaerobic Digestion Systems

PubMed Central

Chuang, Hui-Ping; Hsu, Mao-Hsuan; Chen, Wei-Yu

2013-01-01

In this study, we established a rapid multiplex method to detect the relative abundances of amplified 16S rRNA genes from known cultivatable methanogens at hierarchical specificities in anaerobic digestion systems treating industrial wastewater and sewage sludge. The method was based on the hierarchical oligonucleotide primer extension (HOPE) technique and combined with a set of 27 primers designed to target the total archaeal populations and methanogens from 22 genera within 4 taxonomic orders. After optimization for their specificities and detection sensitivity under the conditions of multiple single-nucleotide primer extension reactions, the HOPE approach was applied to analyze the methanogens in 19 consortium samples from 7 anaerobic treatment systems (i.e., 513 reactions). Among the samples, the methanogen populations detected with order-level primers accounted for >77.2% of the PCR-amplified 16S rRNA genes detected using an Archaea-specific primer. The archaeal communities typically consisted of 2 to 7 known methanogen genera within the Methanobacteriales, Methanomicrobiales, and Methanosarcinales and displayed population dynamic and spatial distributions in anaerobic reactor operations. Principal component analysis of the HOPE data further showed that the methanogen communities could be clustered into 3 distinctive groups, in accordance with the distribution of the Methanosaeta, Methanolinea, and Methanomethylovorans, respectively. This finding suggested that in addition to acetotrophic and hydrogenotrophic methanogens, the methylotrophic methanogens might play a key role in the anaerobic treatment of industrial wastewater. Overall, the results demonstrated that the HOPE approach is a specific, rapid, and multiplexing platform to determine the relative abundances of targeted methanogens in PCR-amplified 16S rRNA gene products. PMID:24077716

Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method.

PubMed

Prest, E I; Hammes, F; Kötzsch, S; van Loosdrecht, M C M; Vrouwenvelder, J S

2013-12-01

Flow cytometry (FCM) is a rapid, cultivation-independent tool to assess and evaluate bacteriological quality and biological stability of water. Here we demonstrate that a stringent, reproducible staining protocol combined with fixed FCM operational and gating settings is essential for reliable quantification of bacteria and detection of changes in aquatic bacterial communities. Triplicate measurements of diverse water samples with this protocol typically showed relative standard deviation values and 95% confidence interval values below 2.5% on all the main FCM parameters. We propose a straightforward and instrument-independent method for the characterization of water samples based on the combination of bacterial cell concentration and fluorescence distribution. Analysis of the fluorescence distribution (or so-called fluorescence fingerprint) was accomplished firstly through a direct comparison of the raw FCM data and subsequently simplified by quantifying the percentage of large and brightly fluorescent high nucleic acid (HNA) content bacteria in each sample. Our approach enables fast differentiation of dissimilar bacterial communities (less than 15 min from sampling to final result), and allows accurate detection of even small changes in aquatic environments (detection above 3% change). Demonstrative studies on (a) indigenous bacterial growth in water, (b) contamination of drinking water with wastewater, (c) household drinking water stagnation and (d) mixing of two drinking water types, univocally showed that this FCM approach enables detection and quantification of relevant bacterial water quality changes with high sensitivity. This approach has the potential to be used as a new tool for application in the drinking water field, e.g. for rapid screening of the microbial water quality and stability during water treatment and distribution in networks and premise plumbing. Copyright © 2013 Elsevier Ltd. All rights reserved.

Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review.

PubMed

Kassianos, A P; Emery, J D; Murchie, P; Walter, F M

2015-06-01

Smartphone health applications ('apps') are widely available but experts remain cautious about their utility and safety. We reviewed currently available apps for the detection of melanoma (July 2014), aimed at general community, patient and generalist clinician users. A proforma was used to extract and assess each app that met the inclusion criteria, and we undertook content analysis to evaluate their content and the evidence applied in their development. Thirty-nine apps were identified with the majority available only for Apple users. Over half (n = 22) provided information or education about melanoma, ultraviolet radiation exposure prevention advice, and skin self-examination strategies, mainly using the ABCDE (A, Asymmetry; B, Border; C, Colour; D, Diameter; E, Evolving) method. Half (n = 19) helped users take and store images of their skin lesions either for review by a dermatologist or for self-monitoring to identify change, an important predictor of melanoma; a similar number (n = 18) used reminders to help users monitor their skin lesions. A few (n = 9) offered expert review of images. Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign, and one app calculated users' future risk of melanoma. None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods. Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change. Despite their potential usefulness, and while clinicians may choose to use apps that provide information to educate their patients, apps for melanoma detection require further validation of their utility and safety. © 2015 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.

Community trees: Identifying codiversification in the Páramo dipteran community.

PubMed

Carstens, Bryan C; Gruenstaeudl, Michael; Reid, Noah M

2016-05-01

Groups of codistributed species that responded in a concerted manner to environmental events are expected to share patterns of evolutionary diversification. However, the identification of such groups has largely been based on qualitative, post hoc analyses. We develop here two methods (posterior predictive simulation [PPS], Kuhner-Felsenstein [K-F] analysis of variance [ANOVA]) for the analysis of codistributed species that, given a group of species with a shared pattern of diversification, allow empiricists to identify those taxa that do not codiversify (i.e., "outlier" species). The identification of outlier species makes it possible to jointly estimate the evolutionary history of co-diversifying taxa. To evaluate the approaches presented here, we collected data from Páramo dipterans, identified outlier species, and estimated a "community tree" from species that are identified as having codiversified. Our results demonstrate that dipteran communities from different Páramo habitats in the same mountain range are more closely related than communities in other ranges. We also conduct simulation testing to evaluate this approach. Results suggest that our approach provides a useful addition to comparative phylogeographic methods, while identifying aspects of the analysis that require careful interpretation. In particular, both the PPS and K-F ANOVA perform acceptably when there are one or two outlier species, but less so as the number of outliers increases. This is likely a function of the corresponding degradation of the signal of community divergence; without a strong signal from a codiversifying community, there is no dominant pattern from which to detect an outlier species. For this reason, both the magnitude of K-F distance distribution and outside knowledge about the phylogeographic history of each putative member of the community should be considered when interpreting the results. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Insights into the bacterial community and its temporal succession during the fermentation of wine grapes

PubMed Central

Piao, Hailan; Hawley, Erik; Kopf, Scott; DeScenzo, Richard; Sealock, Steven; Henick-Kling, Thomas; Hess, Matthias

2015-01-01

Grapes harbor complex microbial communities. It is well known that yeasts, typically Saccharomyces cerevisiae, and bacteria, commonly the lactic acid fermenting Oenococcus oeni, work sequentially during primary and secondary wine fermentation. In addition to these main players, several microbes, often with undesirable effects on wine quality, have been found in grapes and during wine fermentation. However, still little is known about the dynamics of the microbial community during the fermentation process. In previous studies culture dependent methods were applied to detect and identify microbial organisms associated with grapes and grape products, which resulted in a picture that neglected the non-culturable fraction of the microbes. To obtain a more complete picture of how microbial communities change during grape fermentation and how different fermentation techniques might affect the microbial community composition, we employed next-generation sequencing (NGS)—a culture-independent method. A better understanding of the microbial dynamics and their effect on the final product is of great importance to help winemakers produce wine styles of consistent and high quality. In this study, we focused on the bacterial community dynamics during wine vinification by amplifying and sequencing the hypervariable V1–V3 region of the 16S rRNA gene—a phylogenetic marker gene that is ubiquitous within prokaryotes. Bacterial communities and their temporal succession was observed for communities associated with organically and conventionally produced wines. In addition, we analyzed the chemical characteristics of the grape musts during the organic and conventional fermentation process. These analyses revealed distinct bacterial population with specific temporal changes as well as different chemical profiles for the organically and conventionally produced wines. In summary these results suggest a possible correlation between the temporal succession of the bacterial population and the chemical wine profiles. PMID:26347718

Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species

PubMed Central

Renner, Kurra; Cole, Ellen; Seabloom, Eric W.; Borer, Elizabeth T.; Malmstrom, Carolyn M.

2016-01-01

Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. PMID:26773088

Environmental DNA detection of rare and invasive fish species in two Great Lakes tributaries.

PubMed

Balasingham, Katherine D; Walter, Ryan P; Mandrak, Nicholas E; Heath, Daniel D

2018-01-01

The extraction and characterization of DNA from aquatic environmental samples offers an alternative, noninvasive approach for the detection of rare species. Environmental DNA, coupled with PCR and next-generation sequencing ("metabarcoding"), has proven to be very sensitive for the detection of rare aquatic species. Our study used a custom-designed group-specific primer set and next-generation sequencing for the detection of three species at risk (Eastern Sand Darter, Ammocrypta pellucida; Northern Madtom, Noturus stigmosus; and Silver Shiner, Notropis photogenis), one invasive species (Round Goby, Neogobius melanostomus) and an additional 78 native species from two large Great Lakes tributary rivers in southern Ontario, Canada: the Grand River and the Sydenham River. Of 82 fish species detected in both rivers using capture-based and eDNA methods, our eDNA method detected 86.2% and 72.0% of the fish species in the Grand River and the Sydenham River, respectively, which included our four target species. Our analyses also identified significant positive and negative species co-occurrence patterns between our target species and other identified species. Our results demonstrate that eDNA metabarcoding that targets the fish community as well as individual species of interest provides a better understanding of factors affecting the target species spatial distribution in an ecosystem than possible with only target species data. Additionally, eDNA is easily implemented as an initial survey tool, or alongside capture-based methods, for improved mapping of species distribution patterns. © 2017 John Wiley & Sons Ltd.

Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species.

PubMed

Lacroix, Christelle; Renner, Kurra; Cole, Ellen; Seabloom, Eric W; Borer, Elizabeth T; Malmstrom, Carolyn M

2016-01-15

Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Modelling community dynamics based on species-level abundance models from detection/nondetection data

USGS Publications Warehouse

Yamaura, Yuichi; Royle, J. Andrew; Kuboi, Kouji; Tada, Tsuneo; Ikeno, Susumu; Makino, Shun'ichi

2011-01-01

1. In large-scale field surveys, a binary recording of each species' detection or nondetection has been increasingly adopted for its simplicity and low cost. Because of the importance of abundance in many studies, it is desirable to obtain inferences about abundance at species-, functional group-, and community-levels from such binary data. 2. We developed a novel hierarchical multi-species abundance model based on species-level detection/nondetection data. The model accounts for the existence of undetected species, and variability in abundance and detectability among species. Species-level detection/nondetection is linked to species- level abundance via a detection model that accommodates the expectation that probability of detection (at least one individuals is detected) increases with local abundance of the species. We applied this model to a 9-year dataset composed of the detection/nondetection of forest birds, at a single post-fire site (from 7 to 15 years after fire) in a montane area of central Japan. The model allocated undetected species into one of the predefined functional groups by assuming a prior distribution on individual group membership. 3. The results suggest that 15–20 species were missed in each year, and that species richness of communities and functional groups did not change with post-fire forest succession. Overall abundance of birds and abundance of functional groups tended to increase over time, although only in the winter, while decreases in detectabilities were observed in several species. 4. Synthesis and applications. Understanding and prediction of large-scale biodiversity dynamics partly hinge on how we can use data effectively. Our hierarchical model for detection/nondetection data estimates abundance in space/time at species-, functional group-, and community-levels while accounting for undetected individuals and species. It also permits comparison of multiple communities by many types of abundance-based diversity and similarity measures under imperfect detection.

The assessment of hepatitis B seroprevalence in persons with intravenous drug use history in the Isfahan province: Community-based study.

PubMed

Khodadoostan, Mahsa; Ataei, Behrooz; Shavakhi, Ahmad; Tavakoli, Tahmine; Nokhodian, Zary; Yaran, Majid

2014-01-01

Hepatitis B with its complications has become one of the universal problems. Injection drug use is one of the most important risk factors in the transmission of hepatitis B. Therefore, we assessed hepatitis B virus prevalence among cases with a history of intravenous drug use (IVDU) as the first announcement-based study in this regard. The announcement-based detection of hepatitis B seroprevalence in volunteers with a history of intravenous drug use was conducted in the Isfahan province. A comprehensive community announcement was made in all the public places and to all physicians, in all the regions. One thousand five hundred and eighty-eight volunteers were invited to the Isfahan reference laboratories and serum samples were tested for HBs-Ag, HBc Ab, and HBs-Ab, using the enzyme-linked immunosorbent assay (ELISA) method. In this study, 1588 individuals volunteered, who were estimated to be 50% of all the expected intravenous drug users in the community. HBs Ag was detected in 4.2% of them. HBc Ab and HBs Ab were detected in order in 11.4 and 17.3%, respectively. We estimated that the seroprevalence of hepatitis B positivity in intravenous drug users was moderate to high. Therefore, it was suggested that this group be encouraged to prevent acquiring infection by vaccination, education, counseling for risk reduction, and treatment of substance abuse, and finally hepatitis B virus (HBV) screening.

Accounting for Limited Detection Efficiency and Localization Precision in Cluster Analysis in Single Molecule Localization Microscopy

PubMed Central

Shivanandan, Arun; Unnikrishnan, Jayakrishnan; Radenovic, Aleksandra

2015-01-01

Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques’ inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10 – 30nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley’s L(r) – r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization. PMID:25794150

The importance of scaling for detecting community patterns: success and failure in assemblages of introduced species

USGS Publications Warehouse

Allen, Craig R.; Angeler, David G.; Moulton, Michael P.; Holling, Crawford S.

2015-01-01

Community saturation can help to explain why biological invasions fail. However, previous research has documented inconsistent relationships between failed invasions (i.e., an invasive species colonizes but goes extinct) and the number of species present in the invaded community. We use data from bird communities of the Hawaiian island of Oahu, which supports a community of 38 successfully established introduced birds and where 37 species were introduced but went extinct (failed invasions). We develop a modified approach to evaluate the effects of community saturation on invasion failure. Our method accounts (1) for the number of species present (NSP) when the species goes extinct rather than during its introduction; and (2) scaling patterns in bird body mass distributions that accounts for the hierarchical organization of ecosystems and the fact that interaction strength amongst species varies with scale. We found that when using NSP at the time of extinction, NSP was higher for failed introductions as compared to successful introductions, supporting the idea that increasing species richness and putative community saturation mediate invasion resistance. Accounting for scale-specific patterns in body size distributions further improved the relationship between NSP and introduction failure. Results show that a better understanding of invasion outcomes can be obtained when scale-specific community structure is accounted for in the analysis.

Spatio-Temporal Detection of the Thiomonas Population and the Thiomonas Arsenite Oxidase Involved in Natural Arsenite Attenuation Processes in the Carnoulès Acid Mine Drainage

PubMed Central

Hovasse, Agnès; Bruneel, Odile; Casiot, Corinne; Desoeuvre, Angélique; Farasin, Julien; Hery, Marina; Van Dorsselaer, Alain; Carapito, Christine; Arsène-Ploetze, Florence

2016-01-01

The acid mine drainage (AMD) impacted creek of the Carnoulès mine (Southern France) is characterized by acid waters with a high heavy metal content. The microbial community inhabiting this AMD was extensively studied using isolation, metagenomic and metaproteomic methods, and the results showed that a natural arsenic (and iron) attenuation process involving the arsenite oxidase activity of several Thiomonas strains occurs at this site. A sensitive quantitative Selected Reaction Monitoring (SRM)-based proteomic approach was developed for detecting and quantifying the two subunits of the arsenite oxidase and RpoA of two different Thiomonas groups. Using this approach combined with FISH and pyrosequencing-based 16S rRNA gene sequence analysis, it was established here for the first time that these Thiomonas strains are ubiquitously present in minor proportions in this AMD and that they express the key enzymes involved in natural remediation processes at various locations and time points. In addition to these findings, this study also confirms that targeted proteomics applied at the community level can be used to detect weakly abundant proteins in situ. PMID:26870729

Wireless Falling Detection System Based on Community.

PubMed

Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

2015-06-01

The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

Comparison of whole-cell fatty acid (MIDI) or phospholipid fatty acid (PLFA) extractants as biomarkers to profile soil microbial communities.

PubMed

Fernandes, Marcelo F; Saxena, Jyotisna; Dick, Richard P

2013-07-01

The whole-cell lipid extraction to profile microbial communities on soils using fatty acid (FA) biomarkers is commonly done with the two extractants associated with the phospholipid fatty acid (PLFA) or Microbial IDentification Inc. (MIDI) methods. These extractants have very different chemistry and lipid separation procedures, but often shown a similar ability to discriminate soils from various management and vegetation systems. However, the mechanism and the chemistry of the exact suite of FAs extracted by these two methods are poorly understood. Therefore, the objective was to qualitatively and quantitatively compare the MIDI and PLFA microbial profiling methods for detecting microbial community shifts due to soil type or management. Twenty-nine soil samples were collected from a wide range of soil types across Oregon and extracted FAs by each method were analyzed by gas chromatography (GC) and GC-mass spectrometry. Unlike PLFA profiles, which were highly related to microbial FAs, the overall MIDI-FA profiles were highly related to the plant-derived FAs. Plant-associated compounds were quantitatively related to particulate organic matter (POM) and qualitatively related to the standing vegetation at sampling. These FAs were negatively correlated to respiration rate normalized to POM (RespPOM), which increased in systems under more intensive management. A strong negative correlation was found between MIDI-FA to PLFA ratios and total organic carbon (TOC). When the reagents used in MIDI procedure were tested for the limited recovery of MIDI-FAs from soil with high organic matter, the recovery of MIDI-FA microbial signatures sharply decreased with increasing ratios of soil to extractant. Hence, the MIDI method should be used with great caution for interpreting changes in FA profiles due to shifts in microbial communities.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

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.

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

Response of fish assemblages to decreasing acid deposition in Adirondack Mountain lakes

USGS Publications Warehouse

Baldigo, Barry P.; Roy, Karen; Driscoll, Charles T.

2016-01-01

The CAA and other federal regulations have clearly reduced emissions of NOx and SOx, acidic deposition, and the acidity and toxicity of waters in the ALTM lakes, but these changes have not triggered widespread recovery of brook trout populations or fish communities. The lack of detectable biological recovery appears to result from relatively recent chemical recovery and an insufficient period for species populations to take advantage of improved water quality. Recovery of extirpated species’ populations may simply require more time for individuals to migrate to and repopulate formerly occupied lakes. Supplemental stocking of selected species may be required in some lakes with no remnant (or nearby) populations or with physical barriers between the recovered lake and source populations. The lack of detectable biological recovery could also be related to our inability to calculate measures of uncertainty or error and, thus, examine temporal changes or differences in populations and community metrics in more depth (e.g., within individual lakes) using existing datasets. Indeed, recovery of brook trout populations and partial recovery of fish communities are documented in several lakes of the region, both with and without human intervention. Multiple fish surveys (annually or within the same year) or the use of mark and recapture methods within individual lakes would help alleviate the issue (provide measures of error for key fishery metrics) within the context of a more focused sampling strategy. Efforts to evaluate and detect recovery in fish assemblages from streams may be more effective than in lakes because various life stages, species’ populations, and entire assemblages are easier to quantify, with known levels of error, in streams than in lakes. Such long-term monitoring efforts could increase our ability to detect and quantify biological recovery in recovering (neutralizing) surface waters throughout the Adirondack Region.

Antiretroviral drug use in a cross-sectional population survey in Africa: NIMH Project Accept (HPTN 043)

PubMed Central

Fogel, Jessica M.; Clarke, William; Kulich, Michal; Piwowar-Manning, Estelle; Breaud, Autumn; Olson, Matthew T.; Marzinke, Mark A.; Laeyendecker, Oliver; Fiamma, Agnès; Donnell, Deborah; Mbwambo, Jessie K. K.; Richter, Linda; Gray, Glenda; Sweat, Michael; Coates, Thomas J.; Eshleman, Susan H.

2016-01-01

Background Antiretroviral (ARV) drug treatment benefits the treated individual and can prevent HIV transmission. We assessed ARV drug use in a community-randomized trial that evaluated the impact of behavioral interventions on HIV incidence. Methods Samples were collected in a cross-sectional survey after a 3-year intervention period. ARV drug testing was performed using samples from HIV-infected adults at four study sites (Zimbabwe; Tanzania; KwaZulu-Natal and Soweto, South Africa; survey period 2009–2011), using an assay that detects 20 ARV drugs (6 nucleoside/nucleotide reverse transcriptase inhibitors [NRTIs]; 3 non-nucleoside reverse transcriptase inhibitors [NNRTIs]; 9 protease inhibitors; maraviroc; raltegravir). Results ARV drugs were detected in 2,011 (27.4%) of 7,347 samples; 88.1% had 1 NNRTI +/− 1–2 NRTIs. ARV drug detection was associated with sex (women>men), pregnancy, older age (>24 years), and study site (p<0.0001 for all four variables). ARV drugs were also more frequently detected in adults who were widowed (p=0.006) or unemployed (p=0.02). ARV drug use was more frequent in intervention versus control communities early in the survey (p=0.01), with a significant increase in control (p=0.004) but not in intervention communities during the survey period. In KwaZulu-Natal, a 1% increase in ARV drug use was associated with a 0.14% absolute decrease in HIV incidence (p=0.018). Conclusions This study used an objective, biomedical approach to assess ARV drug use on a population level. This analysis identified factors associated with ARV drug use and provided information on ARV drug use over time. ARV drug use was associated with lower HIV incidence at one study site. PMID:27828875

Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management.

PubMed

Malmstrom, Carolyn M; Butterfield, H Scott; Planck, Laura; Long, Christopher W; Eviner, Valerie T

2017-01-01

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics.

Using Environmental DNA to Census Marine Fishes in a Large Mesocosm

PubMed Central

Kelly, Ryan P.; Port, Jesse A.; Yamahara, Kevan M.; Crowder, Larry B.

2014-01-01

The ocean is a soup of its resident species' genetic material, cast off in the forms of metabolic waste, shed skin cells, or damaged tissue. Sampling this environmental DNA (eDNA) is a potentially powerful means of assessing whole biological communities, a significant advance over the manual methods of environmental sampling that have historically dominated marine ecology and related fields. Here, we estimate the vertebrate fauna in a 4.5-million-liter mesocosm aquarium tank at the Monterey Bay Aquarium of known species composition by sequencing the eDNA from its constituent seawater. We find that it is generally possible to detect mitochondrial DNA of bony fishes sufficient to identify organisms to taxonomic family- or genus-level using a 106 bp fragment of the 12S ribosomal gene. Within bony fishes, we observe a low false-negative detection rate, although we did not detect the cartilaginous fishes or sea turtles present with this fragment. We find that the rank abundance of recovered eDNA sequences correlates with the abundance of corresponding species' biomass in the mesocosm, but the data in hand do not allow us to develop a quantitative relationship between biomass and eDNA abundance. Finally, we find a low false-positive rate for detection of exogenous eDNA, and we were able to diagnose non-native species' tissue in the food used to maintain the mesocosm, underscoring the sensitivity of eDNA as a technique for community-level ecological surveys. We conclude that eDNA has substantial potential to become a core tool for environmental monitoring, but that a variety of challenges remain before reliable quantitative assessments of ecological communities in the field become possible. PMID:24454960

Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management

PubMed Central

Butterfield, H. Scott; Planck, Laura; Long, Christopher W.; Eviner, Valerie T.

2017-01-01

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics. PMID:29016604

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

PubMed Central

White, James Robert; Nagarajan, Niranjan; Pop, Mihai

2009-01-01

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/. PMID:19360128

Detection of the Early Warning Signs of Cancer by Community Pharmacists: An Evaluation of Training on Professional Behavior

ERIC Educational Resources Information Center

Benfield, William R.; And Others

1977-01-01

In a study of 702 pharmacists in 211 communities, an effort was made to determine the effect of a unit of education on the community pharmacist's ability and/or tendency to detect the early warning signs of cancer when manifested by patrons. The success of such a program is shown. (LBH)

The impact of caregivers on the effectiveness of an early community mental health detection and intervention programme in Hong Kong.

PubMed

Pan, Jia-Yan; Ng, Yat-Nam Petrus; Young, Kim-Wan Daniel

2016-12-01

The prevalence rate of mental illness in Chinese communities is high, but Chinese clients tend to underutilize mental health services. Caregivers may play an important role in mental health early detection and intervention, but few studies have investigated their roles in community mental health services. This study compared the effectiveness of an early detection and intervention programme, the Community Mental Health Intervention Project, for two groups in the context of Hong Kong - clients with and without caregivers. A comparison group pre-post-test design was adopted. A total of 170 service users joined this study, including 100 with caregivers and 70 without caregivers. Both groups showed a significant decrease in psychiatric symptoms and increase in community living skills; the group without caregivers indicated a greater reduction in psychiatric symptoms. Different social work intervention components had different predictive effects on these changes. The Community Mental Health Intervention Project is an effective early detection and intervention programme in working with Hong Kong Chinese people who are suspected of having mental health problems, especially for those without caregivers. © 2014 Wiley Publishing Asia Pty Ltd.