WorldWideScience

Sample records for algorithm identifies community

  1. Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies

    Directory of Open Access Journals (Sweden)

    Kathryn L. Jackson

    2016-11-01

    Full Text Available Abstract Background Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR based CA-MRSA phenotype algorithm utilizing both structured and unstructured data. Methods The algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis. Results Across seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E −8 findings. Conclusions Differences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.

  2. An efficient community detection algorithm using greedy surprise maximization

    International Nuclear Information System (INIS)

    Jiang, Yawen; Jia, Caiyan; Yu, Jian

    2014-01-01

    Community detection is an important and crucial problem in complex network analysis. Although classical modularity function optimization approaches are widely used for identifying communities, the modularity function (Q) suffers from its resolution limit. Recently, the surprise function (S) was experimentally proved to be better than the Q function. However, up until now, there has been no algorithm available to perform searches to directly determine the maximal surprise values. In this paper, considering the superiority of the S function over the Q function, we propose an efficient community detection algorithm called AGSO (algorithm based on greedy surprise optimization) and its improved version FAGSO (fast-AGSO), which are based on greedy surprise optimization and do not suffer from the resolution limit. In addition, (F)AGSO does not need the number of communities K to be specified in advance. Tests on experimental networks show that (F)AGSO is able to detect optimal partitions in both simple and even more complex networks. Moreover, algorithms based on surprise maximization perform better than those algorithms based on modularity maximization, including Blondel–Guillaume–Lambiotte–Lefebvre (BGLL), Clauset–Newman–Moore (CNM) and the other state-of-the-art algorithms such as Infomap, order statistics local optimization method (OSLOM) and label propagation algorithm (LPA). (paper)

  3. Information dynamics algorithm for detecting communities in networks

    Science.gov (United States)

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

    2012-11-01

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

  4. A fast algorithm for identifying friends-of-friends halos

    Science.gov (United States)

    Feng, Y.; Modi, C.

    2017-07-01

    We describe a simple and fast algorithm for identifying friends-of-friends features and prove its correctness. The algorithm avoids unnecessary expensive neighbor queries, uses minimal memory overhead, and rejects slowdown in high over-density regions. We define our algorithm formally based on pair enumeration, a problem that has been heavily studied in fast 2-point correlation codes and our reference implementation employs a dual KD-tree correlation function code. We construct features in a hierarchical tree structure, and use a splay operation to reduce the average cost of identifying the root of a feature from O [ log L ] to O [ 1 ] (L is the size of a feature) without additional memory costs. This reduces the overall time complexity of merging trees from O [ L log L ] to O [ L ] , reducing the number of operations per splay by orders of magnitude. We next introduce a pruning operation that skips merge operations between two fully self-connected KD-tree nodes. This improves the robustness of the algorithm, reducing the number of merge operations in high density peaks from O [δ2 ] to O [ δ ] . We show that for cosmological data set the algorithm eliminates more than half of merge operations for typically used linking lengths b ∼ 0 . 2 (relative to mean separation). Furthermore, our algorithm is extremely simple and easy to implement on top of an existing pair enumeration code, reusing the optimization effort that has been invested in fast correlation function codes.

  5. A Greedy Algorithm for Neighborhood Overlap-Based Community Detection

    Directory of Open Access Journals (Sweden)

    Natarajan Meghanathan

    2016-01-01

    Full Text Available The neighborhood overlap (NOVER of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s after the removal of each edge. The network component(s that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN community detection algorithm.

  6. Identifying Students’ Misconceptions on Basic Algorithmic Concepts Through Flowchart Analysis

    NARCIS (Netherlands)

    Rahimi, E.; Barendsen, E.; Henze, I.; Dagienė, V.; Hellas, A.

    2017-01-01

    In this paper, a flowchart-based approach to identifying secondary school students’ misconceptions (in a broad sense) on basic algorithm concepts is introduced. This approach uses student-generated flowcharts as the units of analysis and examines them against plan composition and construct-based

  7. An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies.

    Directory of Open Access Journals (Sweden)

    Asad Abdi

    Full Text Available Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively.This paper aims to propose an algorithm based on the combination of semantic relations between words and their syntactic composition to identify summarizing strategies employed by students in summary writing. An innovative aspect of our algorithm lies in its ability to identify summarizing strategies at the syntactic and semantic levels. The efficiency of the algorithm is measured in terms of Precision, Recall and F-measure. We then implemented the algorithm for the automated summarization assessment system that can be used to identify the summarizing strategies used by students in summary writing.

  8. Identifying multiple influential spreaders by a heuristic clustering algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)

    2017-03-18

    The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

  9. Identifying multiple influential spreaders by a heuristic clustering algorithm

    International Nuclear Information System (INIS)

    Bao, Zhong-Kui; Liu, Jian-Guo; Zhang, Hai-Feng

    2017-01-01

    The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

  10. Local Community Detection Algorithm Based on Minimal Cluster

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2016-01-01

    Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

  11. Identifying hidden sexual bridging communities in Chicago.

    Science.gov (United States)

    Youm, Yoosik; Mackesy-Amiti, Mary Ellen; Williams, Chyvette T; Ouellet, Lawrence J

    2009-07-01

    Bridge populations can play a central role in the spread of human immunodeficiency virus (HIV) by providing transmission links between higher and lower prevalence populations. While social network methods are well suited to the study of bridge populations, analyses tend to focus on dyads (i.e., risk between drug and/or sex partners) and ignore bridges between distinct subpopulations. This study takes initial steps toward moving the analysis of sexual network linkages beyond individual and risk group levels to a community level in which Chicago's 77 community areas are examined as subpopulations for the purpose of identifying potential bridging communities. Of particular interest are "hidden" bridging communities; that is, areas with above-average levels of sexual ties with other areas but whose below-average AIDS prevalence may hide their potential importance for HIV prevention. Data for this analysis came from the first wave of recruiting at the Chicago Sexual Acquisition and Transmission of HIV Cooperative Agreement Program site. Between August 2005 through October 2006, respondent-driven sampling was used to recruit users of heroin, cocaine, or methamphetamine, men who have sex with men regardless of drug use, the sex partners of these two groups, and sex partners of the sex partners. In this cross-sectional study of the sexual transmission of HIV, participants completed a network-focused computer-assisted self-administered interview, which included questions about the geographic locations of sexual contacts with up to six recent partners. Bridging scores for each area were determined using a matrix representing Chicago's 77 community areas and were assessed using two measures: non-redundant ties and flow betweenness. Bridging measures and acquired immunodeficiency syndrome (AIDS) case prevalence rates were plotted for each community area on charts representing four conditions: below-average bridging and AIDS prevalence, below-average bridging and above

  12. Community detection algorithm evaluation with ground-truth data

    Science.gov (United States)

    Jebabli, Malek; Cherifi, Hocine; Cherifi, Chantal; Hamouda, Atef

    2018-02-01

    Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to compare it versus the one discovered by these algorithms. However, these metrics defined at the node level are fairly insensitive to the variation of the overall community structure. To overcome these limitations, we propose to exploit the topological features of the 'community graphs' (where the nodes are the communities and the links represent their interactions) in order to evaluate the algorithms. To illustrate our methodology, we conduct a comprehensive analysis of overlapping community detection algorithms using a set of real-world networks with known a priori community structure. Results provide a better perception of their relative performance as compared to classical metrics. Moreover, they show that more emphasis should be put on the topology of the community structure. We also investigate the relationship between the topological properties of the community structure and the alternative evaluation measures (quality metrics and clustering metrics). It appears clearly that they present different views of the community structure and that they must be combined in order to evaluate the effectiveness of community detection algorithms.

  13. Identifying opportunities in online-communities

    DEFF Research Database (Denmark)

    Hienerth, C.; Lettl, Christopher

    how this phenomenon - as manifested in user communities - can be used to derive deeper insights into the prominent phases of opportunity identification, evaluation and exploitation. We also outline how user communities create new avenues for empirical research on these early entrepreneurial processes....... Based on our analysis, we develop a set of hypotheses on how processes in user communities affect the outcome of entrepreneurial activities....

  14. An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

  15. Asymmetric intimacy and algorithm for detecting communities in bipartite networks

    Science.gov (United States)

    Wang, Xingyuan; Qin, Xiaomeng

    2016-11-01

    In this paper, an algorithm to choose a good partition in bipartite networks has been proposed. Bipartite networks have more theoretical significance and broader prospect of application. In view of distinctive structure of bipartite networks, in our method, two parameters are defined to show the relationships between the same type nodes and heterogeneous nodes respectively. Moreover, our algorithm employs a new method of finding and expanding the core communities in bipartite networks. Two kinds of nodes are handled separately and merged, and then the sub-communities are obtained. After that, objective communities will be found according to the merging rule. The proposed algorithm has been simulated in real-world networks and artificial networks, and the result verifies the accuracy and reliability of the parameters on intimacy for our algorithm. Eventually, comparisons with similar algorithms depict that the proposed algorithm has better performance.

  16. A Modularity Degree Based Heuristic Community Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Dongming Chen

    2014-01-01

    Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

  17. Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

    Directory of Open Access Journals (Sweden)

    Jin Qi

    2015-01-01

    Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

  18. Algorithms to Identify Statin Intolerance in Medicare Administrative Claim Data.

    Science.gov (United States)

    Colantonio, Lisandro D; Kent, Shia T; Huang, Lei; Chen, Ligong; Monda, Keri L; Serban, Maria-Corina; Manthripragada, Angelika; Kilgore, Meredith L; Rosenson, Robert S; Muntner, Paul

    2016-10-01

    To compare characteristics of patients with possible statin intolerance identified using different claims-based algorithms versus patients with high adherence to statins. We analyzed 134,863 Medicare beneficiaries initiating statins between 2007 and 2011. Statin intolerance and discontinuation, and high adherence to statins, defined by proportion of days covered ≥80 %, were assessed during the 365 days following statin initiation. Definition 1 of statin intolerance included statin down-titration or discontinuation with ezetimibe initiation, having a claim for a rhabdomyolysis or antihyperlipidemic event followed by statin down-titration or discontinuation, or switching between ≥3 types of statins. Definition 2 included beneficiaries who met Definition 1 and those who down-titrated statin intensity. We also analyzed beneficiaries who met Definition 2 of statin intolerance or discontinued statins. The prevalence of statin intolerance was 1.0 % (n = 1320) and 5.2 % (n = 6985) using Definitions 1 and 2, respectively. Overall, 45,266 (33.6 %) beneficiaries had statin intolerance by Definition 2 or discontinued statins and 55,990 (41.5 %) beneficiaries had high adherence to statins. Compared with beneficiaries with high adherence to statins, those with statin intolerance and who had statin intolerance or discontinued statins were more likely to be female versus male, and black, Hispanic or Asian versus white. The multivariable adjusted odds ratio for statin intolerance by Definitions 1 and 2 comparing patients initiating high versus low/moderate intensity statins were 2.82 (95%CI: 2.42-3.29), and 8.58 (8.07-9.12), respectively, and for statin intolerance or statin discontinuation was 2.35 (2.25-2.45). Definitions of statin intolerance presented herein can be applied to analyses using administrative claims data.

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

    Science.gov (United States)

    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.

  20. Coding algorithms for identifying patients with cirrhosis and hepatitis B or C virus using administrative data.

    Science.gov (United States)

    Niu, Bolin; Forde, Kimberly A; Goldberg, David S

    2015-01-01

    Despite the use of administrative data to perform epidemiological and cost-effectiveness research on patients with hepatitis B or C virus (HBV, HCV), there are no data outside of the Veterans Health Administration validating whether International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes can accurately identify cirrhotic patients with HBV or HCV. The validation of such algorithms is necessary for future epidemiological studies. We evaluated the positive predictive value (PPV) of ICD-9-CM codes for identifying chronic HBV or HCV among cirrhotic patients within the University of Pennsylvania Health System, a large network that includes a tertiary care referral center, a community-based hospital, and multiple outpatient practices across southeastern Pennsylvania and southern New Jersey. We reviewed a random sample of 200 cirrhotic patients with ICD-9-CM codes for HCV and 150 cirrhotic patients with ICD-9-CM codes for HBV. The PPV of 1 inpatient or 2 outpatient HCV codes was 88.0% (168/191, 95% CI: 82.5-92.2%), while the PPV of 1 inpatient or 2 outpatient HBV codes was 81.3% (113/139, 95% CI: 73.8-87.4%). Several variations of the primary coding algorithm were evaluated to determine if different combinations of inpatient and/or outpatient ICD-9-CM codes could increase the PPV of the coding algorithm. ICD-9-CM codes can identify chronic HBV or HCV in cirrhotic patients with a high PPV and can be used in future epidemiologic studies to examine disease burden and the proper allocation of resources. Copyright © 2014 John Wiley & Sons, Ltd.

  1. Fast detection of the fuzzy communities based on leader-driven algorithm

    Science.gov (United States)

    Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

    2018-03-01

    In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

  2. Optimal Design of a Hydrogen Community by Genetic Algorithms

    International Nuclear Information System (INIS)

    Rodolfo Dufo Lopez; Jose Luis Bernal Agustin; Luis Correas Uson; Ismael Aso Aguarta

    2006-01-01

    A study was conducted for the implementation of two Hydrogen Communities, following the recommendations of the HY-COM initiative of the European Commission. The proposed communities find their place in the municipality of Sabinanigo (Aragon, Spain). Two cases are analyzed, one off-grid village house near Sabinanigo, and a house situated in the town proper. The study was carried out with the HOGA program, Hybrid Optimization by Genetic Algorithms. A description is provided for the algorithms. The off-grid study deals with a hybrid pv-wind system with hydrogen storage for AC supply to an isolated house. The urban study is related to hydrogen production by means of hybrid renewable sources available locally (photovoltaic, wind and hydro). These complement the existing industrial electrolysis processes, in order to cater for the energy requirements of a small fleet of municipal hydrogen-powered vehicles. HOGA was used to optimize both hybrid systems. Dimensioning and deployment estimations are also provided. (authors)

  3. Optimal Design of a Hydrogen Community by Genetic Algorithms

    International Nuclear Information System (INIS)

    Rodolfo Dufo Lopeza; Jose Luis Bernal Agustin; Luis Correas Uson; Ismael Aso Aguarta

    2006-01-01

    A study was conducted for the implementation of two Hydrogen Communities, following the recommendations of the HY-COM initiative of the European Commission. The proposed communities find their place in the municipality of Sabinanigo (Aragon, Spain). Two cases are analyzed, one off-grid village house near Sabinanigo, and a house situated in the town proper. The study was carried out with the HOGA program, Hybrid Optimization by Genetic Algorithms. A description is provided for the algorithms. The off-grid study deals with a hybrid PV-wind system with hydrogen storage for AC supply to an isolated house. The urban study is related to hydrogen production by means of hybrid renewable sources available locally (photovoltaic, wind and hydro). These complement the existing industrial electrolysis processes, in order to cater for the energy requirements of a small fleet of municipal hydrogen-powered vehicles. HOGA was used to optimize both hybrid systems. Dimensioning and deployment estimations are also provided. (authors)

  4. Identifying vital edges in Chinese air route network via memetic algorithm

    Directory of Open Access Journals (Sweden)

    Wenbo Du

    2017-02-01

    Full Text Available Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.

  5. New algorithms for identifying the flavour of [Formula: see text] mesons using pions and protons.

    Science.gov (United States)

    Aaij, R; Adeva, B; Adinolfi, M; Ajaltouni, Z; Akar, S; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves, A A; Amato, S; Amerio, S; Amhis, Y; An, L; Anderlini, L; Andreassi, G; Andreotti, M; Andrews, J E; Appleby, R B; Archilli, F; d'Argent, P; Arnau Romeu, J; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Baalouch, M; Babuschkin, I; Bachmann, S; Back, J J; Badalov, A; Baesso, C; Baker, S; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Baszczyk, M; Batozskaya, V; Batsukh, B; Battista, V; Bay, A; Beaucourt, L; Beddow, J; Bedeschi, F; Bediaga, I; Bel, L J; Bellee, V; Belloli, N; Belous, K; Belyaev, I; Ben-Haim, E; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bertolin, A; Betti, F; Bettler, M-O; van Beuzekom, M; Bezshyiko, Ia; Bifani, S; Billoir, P; Bird, T; Birnkraut, A; Bitadze, A; Bizzeti, A; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Boettcher, T; Bondar, A; Bondar, N; Bonivento, W; Bordyuzhin, I; Borgheresi, A; Borghi, S; Borisyak, M; Borsato, M; Bossu, F; Boubdir, M; Bowcock, T J V; Bowen, E; Bozzi, C; Braun, S; Britsch, M; Britton, T; Brodzicka, J; Buchanan, E; Burr, C; Bursche, A; Buytaert, J; Cadeddu, S; Calabrese, R; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Campora Perez, D; Campora Perez, D H; Capriotti, L; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carniti, P; Carson, L; Carvalho Akiba, K; Casse, G; Cassina, L; Castillo Garcia, L; Cattaneo, M; Cauet, Ch; Cavallero, G; Cenci, R; Charles, M; Charpentier, Ph; Chatzikonstantinidis, G; Chefdeville, M; Chen, S; Cheung, S F; Chobanova, V; Chrzaszcz, M; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coco, V; Cogan, J; Cogneras, E; Cogoni, V; Cojocariu, L; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombs, G; Coquereau, S; Corti, G; Corvo, M; Costa Sobral, C M; Couturier, B; Cowan, G A; Craik, D C; Crocombe, A; Cruz Torres, M; Cunliffe, S; Currie, R; D'Ambrosio, C; Da Cunha Marinho, F; Dall'Occo, E; Dalseno, J; David, P N Y; Davis, A; De Aguiar Francisco, O; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Serio, M; De Simone, P; Dean, C T; Decamp, D; Deckenhoff, M; Del Buono, L; Demmer, M; Dendek, A; Derkach, D; Deschamps, O; Dettori, F; Dey, B; Di Canto, A; Dijkstra, H; Dordei, F; Dorigo, M; Dosil Suárez, A; Dovbnya, A; Dreimanis, K; Dufour, L; Dujany, G; Dungs, K; Durante, P; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Déléage, N; Easo, S; Ebert, M; Egede, U; Egorychev, V; Eidelman, S; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; Elsasser, Ch; Ely, S; Esen, S; Evans, H M; Evans, T; Falabella, A; Farley, N; Farry, S; Fay, R; Fazzini, D; Ferguson, D; Fernandez Prieto, A; Ferrari, F; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fini, R A; Fiore, M; Fiorini, M; Firlej, M; Fitzpatrick, C; Fiutowski, T; Fleuret, F; Fohl, K; Fontana, M; Fontanelli, F; Forshaw, D C; Forty, R; Franco Lima, V; Frank, M; Frei, C; Fu, J; Furfaro, E; Färber, C; Gallas Torreira, A; Galli, D; Gallorini, S; Gambetta, S; Gandelman, M; Gandini, P; Gao, Y; Garcia Martin, L M; García Pardiñas, J; Garra Tico, J; Garrido, L; Garsed, P J; Gascon, D; Gaspar, C; Gavardi, L; Gazzoni, G; Gerick, D; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gianì, S; Gibson, V; Girard, O G; Giubega, L; Gizdov, K; Gligorov, V V; Golubkov, D; Golutvin, A; Gomes, A; Gorelov, I V; Gotti, C; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graverini, E; Graziani, G; Grecu, A; Griffith, P; Grillo, L; Gruberg Cazon, B R; Grünberg, O; Gushchin, E; Guz, Yu; Gys, T; Göbel, C; Hadavizadeh, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hamilton, B; Han, X; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; Hatch, M; He, J; Head, T; Heister, A; Hennessy, K; Henrard, P; Henry, L; Hernando Morata, J A; van Herwijnen, E; Heß, M; Hicheur, A; Hill, D; Hombach, C; Hopchev, P H; Hulsbergen, W; Humair, T; Hushchyn, M; Hussain, N; Hutchcroft, D; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jalocha, J; Jans, E; Jawahery, A; Jiang, F; John, M; Johnson, D; Jones, C R; Joram, C; Jost, B; Jurik, N; Kandybei, S; Kanso, W; Karacson, M; Kariuki, J M; Karodia, S; Kecke, M; Kelsey, M; Kenyon, I R; Kenzie, M; Ketel, T; Khairullin, E; Khanji, B; Khurewathanakul, C; Kirn, T; Klaver, S; Klimaszewski, K; Koliiev, S; Kolpin, M; Komarov, I; Koopman, R F; Koppenburg, P; Kosmyntseva, A; Kozeiha, M; Kravchuk, L; Kreplin, K; Kreps, M; Krokovny, P; Kruse, F; Krzemien, W; Kucewicz, W; Kucharczyk, M; Kudryavtsev, V; Kuonen, A K; Kurek, K; Kvaratskheliya, T; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J-P; Leflat, A; Lefrançois, J; Lefèvre, R; Lemaitre, F; Lemos Cid, E; Leroy, O; Lesiak, T; Leverington, B; Li, Y; Likhomanenko, T; Lindner, R; Linn, C; Lionetto, F; Liu, B; Liu, X; Loh, D; Longstaff, I; Lopes, J H; Lucchesi, D; Lucio Martinez, M; Luo, H; Lupato, A; Luppi, E; Lupton, O; Lusiani, A; Lyu, X; Machefert, F; Maciuc, F; Maev, O; Maguire, K; Malde, S; Malinin, A; Maltsev, T; Manca, G; Mancinelli, G; Manning, P; Maratas, J; Marchand, J F; Marconi, U; Marin Benito, C; Marino, P; Marks, J; Martellotti, G; Martin, M; Martinelli, M; Martinez Santos, D; Martinez Vidal, F; Martins Tostes, D; Massacrier, L M; Massafferri, A; Matev, R; Mathad, A; Mathe, Z; Matteuzzi, C; Mauri, A; Maurin, B; Mazurov, A; McCann, M; McCarthy, J; McNab, A; McNulty, R; Meadows, B; Meier, F; Meissner, M; Melnychuk, D; Merk, M; Merli, A; Michielin, E; Milanes, D A; Minard, M-N; Mitzel, D S; Mogini, A; Molina Rodriguez, J; Monroy, I A; Monteil, S; Morandin, M; Morawski, P; Mordà, A; Morello, M J; Moron, J; Morris, A B; Mountain, R; Muheim, F; Mulder, M; Mussini, M; Müller, D; Müller, J; Müller, K; Müller, V; Naik, P; Nakada, T; Nandakumar, R; Nandi, A; Nasteva, I; Needham, M; Neri, N; Neubert, S; Neufeld, N; Neuner, M; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Nieswand, S; Niet, R; Nikitin, N; Nikodem, T; Novoselov, A; O'Hanlon, D P; Oblakowska-Mucha, A; Obraztsov, V; Ogilvy, S; Oldeman, R; Onderwater, C J G; Otalora Goicochea, J M; Otto, A; Owen, P; Oyanguren, A; Pais, P R; Palano, A; Palombo, F; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Pappalardo, L L; Parker, W; Parkes, C; Passaleva, G; Pastore, A; Patel, G D; Patel, M; Patrignani, C; Pearce, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perret, P; Pescatore, L; Petridis, K; Petrolini, A; Petrov, A; Petruzzo, M; Picatoste Olloqui, E; Pietrzyk, B; Pikies, M; Pinci, D; Pistone, A; Piucci, A; Playfer, S; Plo Casasus, M; Poikela, T; Polci, F; Poluektov, A; Polyakov, I; Polycarpo, E; Pomery, G J; Popov, A; Popov, D; Popovici, B; Poslavskii, S; Potterat, C; Price, E; Price, J D; Prisciandaro, J; Pritchard, A; Prouve, C; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Quagliani, R; Rachwal, B; Rademacker, J H; Rama, M; Ramos Pernas, M; Rangel, M S; Raniuk, I; Ratnikov, F; Raven, G; Redi, F; Reichert, S; Dos Reis, A C; Remon Alepuz, C; Renaudin, V; Ricciardi, S; Richards, S; Rihl, M; Rinnert, K; Rives Molina, V; Robbe, P; Rodrigues, A B; Rodrigues, E; Rodriguez Lopez, J A; Rodriguez Perez, P; Rogozhnikov, A; Roiser, S; Rollings, A; Romanovskiy, V; Romero Vidal, A; Ronayne, J W; Rotondo, M; Rudolph, M S; Ruf, T; Ruiz Valls, P; Saborido Silva, J J; Sadykhov, E; Sagidova, N; Saitta, B; Salustino Guimaraes, V; Sanchez Mayordomo, C; Sanmartin Sedes, B; Santacesaria, R; Santamarina Rios, C; Santimaria, M; Santovetti, E; Sarti, A; Satriano, C; Satta, A; Saunders, D M; Savrina, D; Schael, S; Schellenberg, M; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmelzer, T; Schmidt, B; Schneider, O; Schopper, A; Schubert, K; Schubiger, M; Schune, M-H; Schwemmer, R; Sciascia, B; Sciubba, A; Semennikov, A; Sergi, A; Serra, N; Serrano, J; Sestini, L; Seyfert, P; Shapkin, M; Shapoval, I; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, V; Shires, A; Siddi, B G; Silva Coutinho, R; Silva de Oliveira, L; Simi, G; Simone, S; Sirendi, M; Skidmore, N; Skwarnicki, T; Smith, E; Smith, I T; Smith, J; Smith, M; Snoek, H; Sokoloff, M D; Soler, F J P; Souza De Paula, B; Spaan, B; Spradlin, P; Sridharan, S; Stagni, F; Stahl, M; Stahl, S; Stefko, P; Stefkova, S; Steinkamp, O; Stemmle, S; Stenyakin, O; Stevenson, S; Stoica, S; Stone, S; Storaci, B; Stracka, S; Straticiuc, M; Straumann, U; Sun, L; Sutcliffe, W; Swientek, K; Syropoulos, V; Szczekowski, M; Szumlak, T; T'Jampens, S; Tayduganov, A; Tekampe, T; Teklishyn, M; Tellarini, G; Teubert, F; Thomas, E; van Tilburg, J; Tilley, M J; Tisserand, V; Tobin, M; Tolk, S; Tomassetti, L; Tonelli, D; Topp-Joergensen, S; Toriello, F; Tournefier, E; Tourneur, S; Trabelsi, K; Traill, M; Tran, M T; Tresch, M; Trisovic, A; Tsaregorodtsev, A; Tsopelas, P; Tully, A; Tuning, N; Ukleja, A; Ustyuzhanin, A; Uwer, U; Vacca, C; Vagnoni, V; Valassi, A; Valat, S; Valenti, G; Vallier, A; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; van Veghel, M; Velthuis, J J; Veltri, M; Veneziano, G; Venkateswaran, A; Vernet, M; Vesterinen, M; Viaud, B; Vieira, D; Vieites Diaz, M; Vilasis-Cardona, X; Volkov, V; Vollhardt, A; Voneki, B; Vorobyev, A; Vorobyev, V; Voß, C; de Vries, J A; Vázquez Sierra, C; Waldi, R; Wallace, C; Wallace, R; Walsh, J; Wang, J; Ward, D R; Wark, H M; Watson, N K; Websdale, D; Weiden, A; Whitehead, M; Wicht, J; Wilkinson, G; Wilkinson, M; Williams, M; Williams, M P; Williams, M; Williams, T; Wilson, F F; Wimberley, J; Wishahi, J; Wislicki, W; Witek, M; Wormser, G; Wotton, S A; Wraight, K; Wyllie, K; Xie, Y; Xu, Z; Yang, Z; Yin, H; Yu, J; Yuan, X; Yushchenko, O; Zarebski, K A; Zavertyaev, M; Zhang, L; Zhang, Y; Zhelezov, A; Zheng, Y; Zhokhov, A; Zhu, X; Zhukov, V; Zucchelli, S

    2017-01-01

    Two new algorithms for use in the analysis of [Formula: see text] collision are developed to identify the flavour of [Formula: see text] mesons at production using pions and protons from the hadronization process. The algorithms are optimized and calibrated on data, using [Formula: see text] decays from [Formula: see text] collision data collected by LHCb at centre-of-mass energies of 7 and 8 TeV . The tagging power of the new pion algorithm is 60% greater than the previously available one; the algorithm using protons to identify the flavour of a [Formula: see text] meson is the first of its kind.

  6. Dynamics in microbial communities: Unraveling mechanisms to identify principles

    Energy Technology Data Exchange (ETDEWEB)

    Konopka, Allan; Lindemann, Stephen R.; Fredrickson, Jim K.

    2015-07-01

    Diversity begets higher order properties such as functional stability and robustness in microbial communities, but principles that inform conceptual (and eventually predictive) models of community dynamics are lacking. Recent work has shown that selection as well as dispersal and drift shape communities, but the mechanistic bases for assembly of communities and the forces that maintain their function in the face of environmental perturbation are not well understood. Conceptually, some interactions among community members could generate endogenous dynamics in composition, even in the absence of environmental changes. These endogenous dynamics are further perturbed by exogenous forcing factors to produce a richer network of community interactions, and it is this “system” that is the basis for higher order community properties. Elucidation of principles that follow from this conceptual model requires identifying the mechanisms that (a) optimize diversity within a community and (b) impart community stability. The network of interactions between organisms can be an important element by providing a buffer against disturbance beyond the effect of functional redundancy, as alternative pathways with different combinations of microbes can be recruited to fulfill specific functions.

  7. Comparative evaluation of community detection algorithms: a topological approach

    International Nuclear Information System (INIS)

    Orman, Günce Keziban; Labatut, Vincent; Cherifi, Hocine

    2012-01-01

    Community detection is one of the most active fields in complex network analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing the network structure in such cohesive subgroups to be revealed. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand index, normalized mutual information, etc). However, this type of comparison neglects the topological properties of the communities. In this paper, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic real-world systems, we use artificially generated realistic networks. It turns out there is no equivalence between the two approaches: a high performance does not necessarily correspond to correct topological properties, and vice versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment. (paper)

  8. An improvement of the fast uncovering community algorithm

    International Nuclear Information System (INIS)

    Wang Li; Wang Jiang; Shen Hua-Wei; Cheng Xue-Qi

    2013-01-01

    Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. (Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Stat. Mech. 10 10008) is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree (OV) that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness. (interdisciplinary physics and related areas of science and technology)

  9. New algorithms for identifying the flavour of B0 mesons using pions and protons

    NARCIS (Netherlands)

    Aaij, R.; Adeva, B.; Adinolfi, M.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Alvarez Cartelle, P.; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; Everse, LA; Anderlini, L.; Andreassi, G.; Andreotti, M.; Andrews, J.E.; Appleby, R. B.; Archilli, F.; d’Argent, P.; Arnau Romeu, J.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Babuschkin, I.; Bachmann, S.; Back, J. J.; Badalov, A.; Baesso, C.; Baker, S.C.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Baszczyk, M.; Batozskaya, V.; Batsukh, B.; Battista, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Bel, L. J.; Bellee, V.; Belloli, N.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bertolin, A.; Betti, F.; Bettler, M-O.; Van Beuzekom, Martin; Bezshyiko, Ia; Bifani, S.; Billoir, P.; Bird, T.D.; Birnkraut, A.; Bitadze, A.; Bizzeti, A.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Boettcher, T.; Bondar, A.; Bondar, N.; Bonivento, W.; Bordyuzhin, I.; Borgheresi, A.; Borghi, S.; Borisyak, M.; Borsato, M.; Bossu, F.; Boubdir, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Braun, S.; Britsch, M.; Britton, T.; Brodzicka, J.; Buchanan, E.; Burr, C.; Bursche, A.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Calvo Gomez, M.; Camboni, A.; Campana, P.; Campora Perez, D.; Campora Perez, D. H.; Capriotti, L.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carniti, P.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cassina, L.; Castillo Garcia, L.; Cattaneo, M.; Cauet, Ch; Cavallero, G.; Cenci, R.; Charles, M.; Charpentier, Ph; Chatzikonstantinidis, G.; Chefdeville, M.; Chen, S.; Cheung, S-F.; Chobanova, V.; Chrzaszcz, M.; Cid Vidal, X.; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Cogoni, V.; Cojocariu, L.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombs, G.; Coquereau, S.; Corti, G.; Corvo, M.; Costa Sobral, C. M.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Crocombe, A.; Cruz Torres, M.; Cunliffe, S.; Currie, C.R.; D’Ambrosio, C.; Da Cunha Marinho, F.; Dall’Occo, E.; Dalseno, J.; David, P. N.Y.; Davis, A.; De Aguiar Francisco, O.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J. M.; Paula, L.E.; De Serio, M.; De Simone, P.; Dean, C-T.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Demmer, M.; Dendek, A.; Derkach, D.; Deschamps, O.; Dettori, F.; Dey, B.; Di Canto, A.; Dijkstra, H.; Dordei, F.; Dorigo, M.; Dosil Suárez, A.; Dovbnya, A.; Dreimanis, K.; Dufour, L.; Dujany, G.; Dungs, K.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Déléage, N.; Easo, S.; Ebert, M.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, H. M.; Evans, T. M.; Falabella, A.; Farley, N.; Farry, S.; Fay, R.; Fazzini, D.; Ferguson, D.; Fernandez Prieto, A.; Ferrari, F.; Ferreira Rodrigues, F.; Ferro-Luzzi, M.; Filippov, S.; Fini, R. A.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fleuret, F.; Fohl, K.; Fontana, Mark; Fontanelli, F.; Forshaw, D. C.; Forty, R.; Franco Lima, V.; Frank, M.; Frei, C.; Fu, J.; Furfaro, E.; Färber, C.; Gallas Torreira, A.; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; Garcia Martin, L. M.; García Pardiñas, J.; Garra Tico, J.; Garrido, L.; Garsed, P. J.; Gascon, D.; Carvalho-Gaspar, M.; Gavardi, L.; Gazzoni, G.; Gerick, D.; Gersabeck, E.; Gersabeck, M.; Gershon, T. J.; Ghez, Ph; Gianì, S.; Gibson, V.; Girard, O. G.; Giubega, L.; Gizdov, K.; Gligorov, V. V.; Golubkov, D.; Golutvin, A.; Gomes, A.Q.; Gorelov, I. V.; Gotti, C.; Grabalosa Gándara, M.; Graciani Diaz, R.; Granado Cardoso, L. A.; Graugés, E.; Graverini, E.; Graziani, G.; Grecu, A.; Griffith, P.; Grillo, L.; Gruberg Cazon, B. R.; Grünberg, O.; Gushchin, E.; Guz, Yu; Gys, T.; Göbel, C.; Hadavizadeh, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; Hatch, M.J.; He, J.; Head, T.; Heister, A.J.G.A.M.; Hennessy, K.; Henrard, P.; Henry, L.; Hernando Morata, J. A.; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hombach, C.; Hopchev, P. H.; Hulsbergen, W.; Humair, T.; Hushchyn, M.; Hussain, N.; Hutchcroft, D. E.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jawahery, A.; Jiang, F.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kandybei, S.; Kanso, W.; Karacson, M.; Kariuki, J. M.; Karodia, S.; Kecke, M.; Kelsey, M. H.; Kenyon, I. R.; Kenzie, M.; Ketel, T.; Khairullin, E.; Khanji, B.; Khurewathanakul, C.; Kirn, T.; Klaver, S.M.; Klimaszewski, K.; Koliiev, S.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Kosmyntseva, A.; Kozeiha, M.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krokovny, P.; Kruse, F.; Krzemien, W.; Kucewicz, W.; Kucharczyk, M.; Kudryavtsev, V.; Kuonen, A. K.; Kurek, K.; Kvaratskheliya, T.; Lacarrere, D.; Lafferty, G. D.; Lai, A.; Lambert, D.M.; Lanfranchi, G.; Langenbruch, C.; Latham, T. E.; Lazzeroni, C.; Le Gac, R.; van Leerdam, J.; Lees, J. P.; Leflat, A.; Lefrançois, J.; Lefèvre, R.; Lemaitre, F.; Lemos Cid, E.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Likhomanenko, T.; Lindner, R.; Linn, S.C.; Lionetto, F.; Liu, B.; Liu, X.; Loh, D.; Longstaff, I.; Lopes, J. H.; Lucchesi, D.; Lucio Martinez, M.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Lusiani, A.; Lyu, X.; Machefert, F.; Maciuc, F.; Maev, O.; Maguire, K.; Malde, S.; Malinin, A.; Maltsev, T.; Manca, G.; Mancinelli, G.; Manning, P.; Maratas, J.; Marchand, J. F.; Marconi, U.; Marin Benito, C.; Marino, P.; Marks, J.; Martellotti, G.; Martin, M.; Martinelli-Boneschi, F.; Martinez-Santos, D.; Martinez-Vidal, F.; Martins Tostes, D.; Massacrier, L. M.; Massafferri, A.; Matev, R.; Mathad, A.; Mathe, Z.; Matteuzzi, C.; Mauri, A.; Maurin, B.; Mazurov, A.; McCann, M.; McCarthy, J.; Mcnab, A.; McNulty, R.; Meadows, B. T.; Meier, F.; Meissner, M.; Melnychuk, D.; Merk, M.; Merli, A.; Michielin, E.; Milanes, D. A.; Minard, M. N.; Mitzel, D. S.; Mogini, A.; Molina Rodriguez, J.; Monroy, I. A.; Monteil, S.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Mulder, M.; Mussini, M.; Müller, D.; Müller, J.; Müller, Karl; von Müller, L.; Naik, P.; Nakada, T.; Nandakumar, R.; Nandi, A.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen, T. D.; Nguyen-Mau, C.; Nieswand, S.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; O’Hanlon, D. P.; Oblakowska-Mucha, A.; Obraztsov, V.; Ogilvy, S.; Oldeman, R.; Onderwater, C. J.G.; Otalora Goicochea, J. M.; Otto, E.A.; Owen, R.P.; Oyanguren, A.; Pais, P. R.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Pappalardo, L.L.; Parker, W.S; Parkes, C.; Passaleva, G.; Pastore, A.; Patel, G. D.; Patel, M.; Patrignani, C.; Pearce, D.A.; Pellegrino, A.; Penso, G.; Pepe Altarelli, M.; Perazzini, S.; Perret, P.; Pescatore, L.; Petridis, K.; Petrolini, A.; Petrov, A.; Petruzzo, M.; Picatoste Olloqui, E.; Pietrzyk, B.; Pikies, M.; Pinci, D.; Pistone, A.; Piucci, A.; Playfer, S.; Plo Casasus, M.; Poikela, T.; Polci, F.; Poluektov, A.; Polyakov, I.; Polycarpo, E.; Pomery, G. J.; Popov, A.; Popov, D.; Popovici, B.; Poslavskii, S.; Potterat, C.; Price, M. E.; Price, J.D.; Prisciandaro, J.; Pritchard, C.A.; Prouve, C.; Pugatch, V.; Puig Navarro, A.; Punzi, G.; Qian, Y.W.; Quagliani, R.; Rachwal, B.; Rademacker, J. H.; Rama, M.; Ramos Pernas, M.; Rangel, M. S.; Raniuk, I.; Ratnikov, F.; Raven, G.; Redi, F.; Reichert, S.; dos Reis, A. C.; Remon Alepuz, C.; Renaudin, V.; Ricciardi, S.; Richards, Jennifer S; Rihl, M.; Rinnert, K.; Rives Molina, V.; Robbe, P.; Rodrigues, A. B.; Rodrigues, L.E.T.; Rodriguez Lopez, J. A.; Rodriguez Perez, P.; Rogozhnikov, A.; Roiser, S.; Rollings, A.; Romanovskiy, V.; Romero Vidal, A.; Ronayne, J. W.; Rotondo, M.; Rudolph, M. S.; Ruf, T.; Ruiz Valls, P.; Saborido Silva, J. J.; Sadykhov, E.; Sagidova, N.; Saitta, B.; Salustino Guimaraes, V.; Sanchez Mayordomo, C.; Sanmartin Sedes, B.; Santacesaria, R.; Santamarina Rios, C.; Santimaria, M.; Santovetti, E.; Sarti, A.; Satriano, C.; Satta, A.; Saunders, D. M.; Savrina, D.; Schael, S.; Schellenberg, M.; Schiller, M.; Schindler, R. H.; Schlupp, M.; Schmelling, M.; Schmelzer, T.; Schmidt, B.; Schneider, O.; Schopper, A.; Schubert, K.; Schubiger, M.; Schune, M. H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Semennikov, A.; Sergi, A; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Siddi, B. G.; Silva Coutinho, R.; Silva de Oliveira, L.; Simi, G.; Simone, S.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, E.; Smith, I. T.; Smith, J; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Souza De Paula, B.; Spaan, B.; Spradlin, P.; Sridharan, S.; Stagni, F.; Stahl, M.; Stahl, S.; Stefko, P.; Stefkova, S.; Steinkamp, O.; Stemmle, S.; Stenyakin, O.; Stevenson-Moore, P.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Sun, L.; Sutcliffe, W.; Swientek, K.; Syropoulos, V.; Szczekowski, M.; Szumlak, T.; T’Jampens, S.; Tayduganov, A.; Tekampe, T.; Teklishyn, M.; Tellarini, G.; Teubert, F.; Thomas, E.; van Tilburg, J.; Tilley, M. J.; Tisserand, V.; Tobin, M. N.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Toriello, F.; Tournefier, E.; Tourneur, S.; Trabelsi, K.; Traill, M.; Tran, N.T.M.T.; Tresch, M.; Trisovic, A.; Tsaregorodtsev, A.; Tsopelas, P.; Tully, M.A.; Tuning, N.; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vacca, C.; Vagnoni, V.; Valassi, A.; Valat, S.; Valenti, G.; Vallier, A.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vecchi, S.; van Veghel-Plandsoen, M.M.; Velthuis, M.J.; Veltri, M.; Veneziano, G.; Venkateswaran, A.; Vernet, M.; Vesterinen, M.; Viaud, B.; Vieira, D.; Vieites Diaz, M.; Vilasis-Cardona, X.; Volkov, V.; Vollhardt, A.; Voneki, B.; Vorobyev, A.; Vorobyev, V.; Voß, C.; de Vries, J. A.; Vázquez Sierra, C.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, John; Wang, J.; Ward, D. R.; Wark, H. M.; Watson, N. K.; Websdale, D.; Weiden, A.; Whitehead, M.; Wicht, J.; Wilkinson, G.; Wilkinson, M.; Williams, M.; Williams, M.P.; Williams, M.; Williams, T.; Wilson, James F; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wraight, K.; Wyllie, K.; Xie, Y.; Xu, Z.; Yang, Z.; Yin, H; Yu, J.; Yuan, X.; Yushchenko, O.; Zarebski, K. A.; Zavertyaev, M.; Zhang, L.; Zhang, Y.; Zhelezov, A.; Zheng, Y.; Zhokhov, A.; Zhu, X.; Zhukov, V.; Zucchelli, S.

    2017-01-01

    Two new algorithms for use in the analysis of pp collision are developed to identify the flavour of B0 mesons at production using pions and protons from the hadronization process. The algorithms are optimized and calibrated on data, using B0→D-π+ decays from pp collision data collected by LHCb at

  10. Identifying influential user communities on the social network

    Science.gov (United States)

    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.

  11. IDENTIFYING COMPETENCIES OF VOLUNTEER BOARD MEMBERS OF COMMUNITY SPORTS CLUBS

    OpenAIRE

    A. BALDUCK; A. VAN ROSSEM; M. BUELENS

    2009-01-01

    This study contributes to the emerging empirical studies on roles and responsibilities of boards in nonprofit organizations by identifying competencies of volunteer board members. We identified how two types of constituents—volunteer board members and sports members—perceived competencies of volunteer board members in community sports clubs. We used the repertory grid technique to draw cognitive maps and to reveal the perceived reality of these constituents. Our results suggest that constitue...

  12. Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

    Science.gov (United States)

    Canan, Chelsea; Polinski, Jennifer M; Alexander, G Caleb; Kowal, Mary K; Brennan, Troyen A; Shrank, William H

    2017-11-01

    Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  13. Identifying Priorities for Aging Policies in Two Portuguese Communities.

    Science.gov (United States)

    Bárrios, Maria João; Fernandes, Ana Alexandre; Fonseca, António Manuel

    2018-02-20

    The aging population has an impact on health, social, and economic issues in regard to individuals, communities, and organizations. The challenge for local policies in response to aging is to create sufficient resources to meet the population's needs, wishes, and rights as people age. Active aging constitutes one of the guiding perspectives on policies. Taking into account the local governance perspective, the Model for Aging Local Policies Analysis (MALPA) was created in order to convert the active aging paradigm into a practical approach, as a technique to evaluate and analyze local aging policies. In this research, the MALPA instrument was applied in two Portuguese communities (Coruche and Oeiras). The objective was to identify the intervention priorities of aging policies in both communities, determining whether the instrument can facilitate the development of proposals for the improvement of local aging policies. It was possible to evaluate the communities and programs, identifying the least appropriate policy actions regarding the intervention priorities. The results allowed us to identify 10 priorities about collaborative governance, involvement of the elderly in the policy-making process, lifelong learning, economic hardship, policies for all ages, isolated vulnerable and fragile groups, intergenerational contacts, safety in all policies, labor opportunities, and conditions and transport network improvement.

  14. An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Gao Zhi-Peng

    2016-01-01

    Full Text Available Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMCto find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

  15. Discrete particle swarm optimization for identifying community structures in signed social networks.

    Science.gov (United States)

    Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

    2014-10-01

    Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Community Engagement for Identifying Cancer Education Needs in Puerto Rico.

    Science.gov (United States)

    Jiménez, Julio; Ramos, Axel; Ramos-Rivera, Francisco E; Gwede, Clement; Quinn, Gwendolyn P; Vadaparampil, Susan; Brandon, Thomas; Simmons, Vani; Castro, Eida

    2018-02-01

    Cancer is the leading cause of death in Puerto Rico, suggesting a need for improved strategies, programs, and resources devoted to cancer prevention. Enhanced prevention needs in Puerto Rico were initially identified in pilot studies conducted by the Ponce School of Medicine (PSM) in collaboration with the H. Lee Moffitt Cancer Center (MCC). In the current study, we used community engagement to identify specific needs in cancer prevention and education and strategies to create culturally attuned, effective cancer prevention education programs. A total of 37 participants attended a community forum and were assigned to one of three discussion groups: patients/survivors (n = 14); family/caregivers (n = 11); or healthcare providers (n = 12). Most participants were women (73 %), over 35 years of age, and a majority were married (58 %) and had a university education (81 %). The sessions were recorded and transcribed and analyzed for key themes. Participants wanted improved awareness of cancer prevention in Puerto Rico and believed cancer prevention education should start early, ideally in elementary school. Participants also stressed the importance of creating partnerships with private and government agencies to coordinate educational efforts. Suggested strategies included outreach to communities with limited resources, incorporating the testimony of cancer survivors, and utilizing social media to disseminate cancer prevention information.

  17. Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims.

    Science.gov (United States)

    Chastek, Benjamin J; Oleen-Burkey, Merrikay; Lopez-Bresnahan, Maria V

    2010-01-01

    Relapse is a common measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objective of this study was to test the content validity of an operational algorithm for detecting relapse in claims data. A claims-based relapse detection algorithm was tested by comparing its detection rate over a 1-year period with relapses identified based on medical chart review. According to the algorithm, MS patients in a US healthcare claims database who had either (1) a primary claim for MS during hospitalization or (2) a corticosteroid claim following a MS-related outpatient visit were designated as having a relapse. Patient charts were examined for explicit indication of relapse or care suggestive of relapse. Positive and negative predictive values were calculated. Medical charts were reviewed for 300 MS patients, half of whom had a relapse according to the algorithm. The claims-based criteria correctly classified 67.3% of patients with relapses (positive predictive value) and 70.0% of patients without relapses (negative predictive value; kappa 0.373: p value of the operational algorithm. Limitations of the algorithm include lack of differentiation between relapsing-remitting MS and other types, and that it does not incorporate measures of function and disability. The claims-based algorithm appeared to successfully detect moderate-to-severe MS relapse. This validated definition can be applied to future claims-based MS studies.

  18. Identifying key hospital service quality factors in online health communities.

    Science.gov (United States)

    Jung, Yuchul; Hur, Cinyoung; Jung, Dain; Kim, Minki

    2015-04-07

    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. 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. 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. 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

  19. The development of gamma energy identify algorithm for compact radiation sensors using stepwise refinement technique

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Hyun Jun [Div. of Radiation Regulation, Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of); Kim, Ye Won; Kim, Hyun Duk; Cho, Gyu Seong [Dept. of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Yi, Yun [Dept. of of Electronics and Information Engineering, Korea University, Seoul (Korea, Republic of)

    2017-06-15

    A gamma energy identifying algorithm using spectral decomposition combined with smoothing method was suggested to confirm the existence of the artificial radio isotopes. The algorithm is composed by original pattern recognition method and smoothing method to enhance the performance to identify gamma energy of radiation sensors that have low energy resolution. The gamma energy identifying algorithm for the compact radiation sensor is a three-step of refinement process. Firstly, the magnitude set is calculated by the original spectral decomposition. Secondly, the magnitude of modeling error in the magnitude set is reduced by the smoothing method. Thirdly, the expected gamma energy is finally decided based on the enhanced magnitude set as a result of the spectral decomposition with the smoothing method. The algorithm was optimized for the designed radiation sensor composed of a CsI (Tl) scintillator and a silicon pin diode. The two performance parameters used to estimate the algorithm are the accuracy of expected gamma energy and the number of repeated calculations. The original gamma energy was accurately identified with the single energy of gamma radiation by adapting this modeling error reduction method. Also the average error decreased by half with the multi energies of gamma radiation in comparison to the original spectral decomposition. In addition, the number of repeated calculations also decreased by half even in low fluence conditions under 104 (/0.09 cm{sup 2} of the scintillator surface). Through the development of this algorithm, we have confirmed the possibility of developing a product that can identify artificial radionuclides nearby using inexpensive radiation sensors that are easy to use by the public. Therefore, it can contribute to reduce the anxiety of the public exposure by determining the presence of artificial radionuclides in the vicinity.

  20. Validity of administrative database code algorithms to identify vascular access placement, surgical revisions, and secondary patency.

    Science.gov (United States)

    Al-Jaishi, Ahmed A; Moist, Louise M; Oliver, Matthew J; Nash, Danielle M; Fleet, Jamie L; Garg, Amit X; Lok, Charmaine E

    2018-03-01

    We assessed the validity of physician billing codes and hospital admission using International Classification of Diseases 10th revision codes to identify vascular access placement, secondary patency, and surgical revisions in administrative data. We included adults (≥18 years) with a vascular access placed between 1 April 2004 and 31 March 2013 at the University Health Network, Toronto. Our reference standard was a prospective vascular access database (VASPRO) that contains information on vascular access type and dates of placement, dates for failure, and any revisions. We used VASPRO to assess the validity of different administrative coding algorithms by calculating the sensitivity, specificity, and positive predictive values of vascular access events. The sensitivity (95% confidence interval) of the best performing algorithm to identify arteriovenous access placement was 86% (83%, 89%) and specificity was 92% (89%, 93%). The corresponding numbers to identify catheter insertion were 84% (82%, 86%) and 84% (80%, 87%), respectively. The sensitivity of the best performing coding algorithm to identify arteriovenous access surgical revisions was 81% (67%, 90%) and specificity was 89% (87%, 90%). The algorithm capturing arteriovenous access placement and catheter insertion had a positive predictive value greater than 90% and arteriovenous access surgical revisions had a positive predictive value of 20%. The duration of arteriovenous access secondary patency was on average 578 (553, 603) days in VASPRO and 555 (530, 580) days in administrative databases. Administrative data algorithms have fair to good operating characteristics to identify vascular access placement and arteriovenous access secondary patency. Low positive predictive values for surgical revisions algorithm suggest that administrative data should only be used to rule out the occurrence of an event.

  1. An Evaluation of Algorithms for Identifying Metastatic Breast, Lung, or Colorectal Cancer in Administrative Claims Data.

    Science.gov (United States)

    Whyte, Joanna L; Engel-Nitz, Nicole M; Teitelbaum, April; Gomez Rey, Gabriel; Kallich, Joel D

    2015-07-01

    Administrative health care claims data are used for epidemiologic, health services, and outcomes cancer research and thus play a significant role in policy. Cancer stage, which is often a major driver of cost and clinical outcomes, is not typically included in claims data. Evaluate algorithms used in a dataset of cancer patients to identify patients with metastatic breast (BC), lung (LC), or colorectal (CRC) cancer using claims data. Clinical data on BC, LC, or CRC patients (between January 1, 2007 and March 31, 2010) were linked to a health care claims database. Inclusion required health plan enrollment ≥3 months before initial cancer diagnosis date. Algorithms were used in the claims database to identify patients' disease status, which was compared with physician-reported metastases. Generic and tumor-specific algorithms were evaluated using ICD-9 codes, varying diagnosis time frames, and including/excluding other tumors. Positive and negative predictive values, sensitivity, and specificity were assessed. The linked databases included 14,480 patients; of whom, 32%, 17%, and 14.2% had metastatic BC, LC, and CRC, respectively, at diagnosis and met inclusion criteria. Nontumor-specific algorithms had lower specificity than tumor-specific algorithms. Tumor-specific algorithms' sensitivity and specificity were 53% and 99% for BC, 55% and 85% for LC, and 59% and 98% for CRC, respectively. Algorithms to distinguish metastatic BC, LC, and CRC from locally advanced disease should use tumor-specific primary cancer codes with 2 claims for the specific primary cancer >30-42 days apart to reduce misclassification. These performed best overall in specificity, positive predictive values, and overall accuracy to identify metastatic cancer in a health care claims database.

  2. PedMine – A simulated annealing algorithm to identify maximally unrelated individuals in population isolates

    OpenAIRE

    Douglas, Julie A.; Sandefur, Conner I.

    2008-01-01

    In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a ge...

  3. Evaluation of algorithms to identify incident cancer cases by using French health administrative databases.

    Science.gov (United States)

    Ajrouche, Aya; Estellat, Candice; De Rycke, Yann; Tubach, Florence

    2017-08-01

    Administrative databases are increasingly being used in cancer observational studies. Identifying incident cancer in these databases is crucial. This study aimed to develop algorithms to estimate cancer incidence by using health administrative databases and to examine the accuracy of the algorithms in terms of national cancer incidence rates estimated from registries. We identified a cohort of 463 033 participants on 1 January 2012 in the Echantillon Généraliste des Bénéficiaires (EGB; a representative sample of the French healthcare insurance system). The EGB contains data on long-term chronic disease (LTD) status, reimbursed outpatient treatments and procedures, and hospitalizations (including discharge diagnoses, and costly medical procedures and drugs). After excluding cases of prevalent cancer, we applied 15 algorithms to estimate the cancer incidence rates separately for men and women in 2012 and compared them to the national cancer incidence rates estimated from French registries by indirect age and sex standardization. The most accurate algorithm for men combined information from LTD status, outpatient anticancer drugs, radiotherapy sessions and primary or related discharge diagnosis of cancer, although it underestimated the cancer incidence (standardized incidence ratio (SIR) 0.85 [0.80-0.90]). For women, the best algorithm used the same definition of the algorithm for men but restricted hospital discharge to only primary or related diagnosis with an additional inpatient procedure or drug reimbursement related to cancer and gave comparable estimates to those from registries (SIR 1.00 [0.94-1.06]). The algorithms proposed could be used for cancer incidence monitoring and for future etiological cancer studies involving French healthcare databases. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  4. An Advanced Coupled Genetic Algorithm for Identifying Unknown Moving Loads on Bridge Decks

    Directory of Open Access Journals (Sweden)

    Sang-Youl Lee

    2014-01-01

    Full Text Available This study deals with an inverse method to identify moving loads on bridge decks using the finite element method (FEM and a coupled genetic algorithm (c-GA. We developed the inverse technique using a coupled genetic algorithm that can make global solution searches possible as opposed to classical gradient-based optimization techniques. The technique described in this paper allows us to not only detect the weight of moving vehicles but also find their moving velocities. To demonstrate the feasibility of the method, the algorithm is applied to a bridge deck model with beam elements. In addition, 1D and 3D finite element models are simulated to study the influence of measurement errors and model uncertainty between numerical and real structures. The results demonstrate the excellence of the method from the standpoints of computation efficiency and avoidance of premature convergence.

  5. Identifying Time Measurement Tampering in the Traversal Time and Hop Count Analysis (TTHCA Wormhole Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Jonny Karlsson

    2013-05-01

    Full Text Available Traversal time and hop count analysis (TTHCA is a recent wormhole detection algorithm for mobile ad hoc networks (MANET which provides enhanced detection performance against all wormhole attack variants and network types. TTHCA involves each node measuring the processing time of routing packets during the route discovery process and then delivering the measurements to the source node. In a participation mode (PM wormhole where malicious nodes appear in the routing tables as legitimate nodes, the time measurements can potentially be altered so preventing TTHCA from successfully detecting the wormhole. This paper analyses the prevailing conditions for time tampering attacks to succeed for PM wormholes, before introducing an extension to the TTHCA detection algorithm called ∆T Vector which is designed to identify time tampering, while preserving low false positive rates. Simulation results confirm that the ∆T Vector extension is able to effectively detect time tampering attacks, thereby providing an important security enhancement to the TTHCA algorithm.

  6. An Improved Particle Swarm Optimization Algorithm and Its Application in the Community Division

    Directory of Open Access Journals (Sweden)

    Jiang Hao

    2016-01-01

    Full Text Available With the deepening of the research on complex networks, the method of detecting and classifying social network is springing up. In this essay, the basic particle swarm algorithm is improved based on the GN algorithm. Modularity is taken as a measure of community division [1]. In view of the dynamic network community division, scrolling calculation method is put forward. Experiments show that using the improved particle swarm optimization algorithm can improve the accuracy of the community division and can also get higher value of the modularity in the dynamic community

  7. Global identifiability of linear compartmental models--a computer algebra algorithm.

    Science.gov (United States)

    Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

    1998-01-01

    A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

  8. Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Guo

    2017-01-01

    Full Text Available As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients’ personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

  9. Identifying nuclear power plant transients using the Discrete Binary Artificial Bee Colony (DBABC) algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Iona M.S. de; Schirru, Roberto, E-mail: ioliveira@con.ufrj.br, E-mail: schirru@lmp.ufrj.br [Coordenacoa dos Programas de Pos-Graduacao em Engenharia (UFRJ/PEN/COPPE), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear

    2011-07-01

    The identification of possible transients in a nuclear power plant is a highly relevant problem. This is mainly due to the fact that the operation of a nuclear power plant involves a large number of state variables whose behaviors are extremely dynamic. In risk situations, besides the huge cognitive overload that operators are submitted to, there is also the problem related with the considerable decrease in the effective time for correct decision making. To minimize these problems and help operators to make the corrective actions in due time, this paper presents a new contribution in this area and introduces an experimental transient identification system based exclusively on the abilities of the Discrete Binary Artificial Bee Colony (DBABC) algorithm to find the best centroid positions that correctly identifies a transient in a nuclear power plant. The DBABC is a reworking of the Artificial Bee Colony (ABC) algorithm which presents the advantage of operating in both continuous and discrete search spaces. Through the analysis of experimental results, the effective performance of the proposed DBABC algorithm is shown against some well known best performing algorithms from the literature. (author)

  10. Identifying nuclear power plant transients using the Discrete Binary Artificial Bee Colony (DBABC) algorithm

    International Nuclear Information System (INIS)

    Oliveira, Iona M.S. de; Schirru, Roberto

    2011-01-01

    The identification of possible transients in a nuclear power plant is a highly relevant problem. This is mainly due to the fact that the operation of a nuclear power plant involves a large number of state variables whose behaviors are extremely dynamic. In risk situations, besides the huge cognitive overload that operators are submitted to, there is also the problem related with the considerable decrease in the effective time for correct decision making. To minimize these problems and help operators to make the corrective actions in due time, this paper presents a new contribution in this area and introduces an experimental transient identification system based exclusively on the abilities of the Discrete Binary Artificial Bee Colony (DBABC) algorithm to find the best centroid positions that correctly identifies a transient in a nuclear power plant. The DBABC is a reworking of the Artificial Bee Colony (ABC) algorithm which presents the advantage of operating in both continuous and discrete search spaces. Through the analysis of experimental results, the effective performance of the proposed DBABC algorithm is shown against some well known best performing algorithms from the literature. (author)

  11. An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

    Science.gov (United States)

    Weberg, Micah J.; Morton, Richard J.; McLaughlin, James A.

    2018-01-01

    Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%–2% for displacement amplitudes and 4%–10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%–41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.

  12. A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

    International Nuclear Information System (INIS)

    Nguyen, Sy Dzung; Choi, Seung-Bok

    2012-01-01

    This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input–output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results. (paper)

  13. A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

    Science.gov (United States)

    Dzung Nguyen, Sy; Choi, Seung-Bok

    2012-08-01

    This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

  14. Identifying Septal Support Reconstructions for Saddle Nose Deformity: The Cakmak Algorithm.

    Science.gov (United States)

    Cakmak, Ozcan; Emre, Ismet Emrah; Ozkurt, Fazil Emre

    2015-01-01

    The saddle nose deformity is one of the most challenging problems in nasal surgery with a less predictable and reproducible result than other nasal procedures. The main feature of this deformity is loss of septal support with both functional and aesthetic implications. Most reports on saddle nose have focused on aesthetic improvement and neglected the reestablishment of septal support to improve airway. To explain how the Cakmak algorithm, an algorithm that describes various fixation techniques and grafts in different types of saddle nose deformities, aids in identifying saddle nose reconstructions that restore supportive nasal framework and provide the aesthetic improvements typically associated with procedures to correct saddle nose deformities. This algorithm presents septal support reconstruction of patients with saddle nose deformity based on the experience of the senior author in 206 patients with saddle nose deformity. Preoperative examination, intraoperative assessment, reconstruction techniques, graft materials, and patient evaluation of aesthetic success were documented, and 4 different types of saddle nose deformities were defined. The Cakmak algorithm classifies varying degrees of saddle nose deformity from type 0 to type 4 and helps identify the most appropriate surgical procedure to restore the supportive nasal framework and aesthetic dorsum. Among the 206 patients, 110 women and 96 men, mean (range) age was 39.7 years (15-68 years), and mean (range) of follow-up was 32 months (6-148 months). All but 12 patients had a history of previous nasal surgeries. Application of the Cakmak algorithm resulted in 36 patients categorized with type 0 saddle nose deformities; 79, type 1; 50, type 2; 20, type 3a; 7, type 3b; and 14, type 4. Postoperative photographs showed improvement of deformities, and patient surveys revealed aesthetic improvement in 201 patients and improvement in nasal breathing in 195 patients. Three patients developed postoperative infection

  15. Identifying a borderline personality disorder prodrome: Implications for community screening.

    Science.gov (United States)

    Stepp, Stephanie D; Lazarus, Sophie A

    2017-08-01

    Elucidating early signs and symptoms of borderline personality disorder (BPD) has important implications for screening and identifying youth appropriate for early intervention. The purpose of this study was to identify dimensions of child temperament and psychopathology symptom severity that predict conversion to a positive screen for BPD over a 14-year follow-up period in a large, urban community sample of girls (n = 2 450). Parent and teacher reports of child temperament and psychopathology symptom severity assessed when girls were ages 5-8 years were examined as predictors of new-onset BPD cases when girls were ages 14-22 years. In the final model, parent and teacher ratings of emotionality remained significant predictors of new-onset BPD. Additionally, parent ratings of hyperactivity/impulsivity and depression severity, as well as teacher ratings of inattention severity, were also predictive. Results also revealed that elevations in these dimensions pose a notable increase in risk for conversion to BPD over the follow-up period. Supplementary analyses revealed that with the exception of parent-reported depression severity, these same predictors were associated with increases in BPD symptom severity over the follow-up period. These findings suggest BPD onset in adolescence and early adulthood can be detected from parent and teacher reports of temperament and symptom severity dimensions assessed in childhood. The identification of this prodrome holds promise for advancing early detection of children at risk prior to the development of the full-blown disorder. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.

    Directory of Open Access Journals (Sweden)

    Todd Lingren

    Full Text Available Cohort selection is challenging for large-scale electronic health record (EHR analyses, as International Classification of Diseases 9th edition (ICD-9 diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH (N = 150 and Cincinnati Children's Hospital and Medical Center (CCHMC (N = 152. Two algorithms were created: (1 rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2 predictive classifier. The positive predictive values (PPV achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.The rule-based algorithm produced the best PPV: (a BCH: 0.885 vs. 0.273 (baseline; (b CCHMC: 0.840 vs. 0.645 (baseline; (c combined: 0.864 vs. 0.460 (baseline. A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV. Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients identified psychiatric, developmental, and seizure disorder clusters.In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

  17. [Study of algorithms to identify schizophrenia in the SNIIRAM database conducted by the REDSIAM network].

    Science.gov (United States)

    Quantin, C; Collin, C; Frérot, M; Besson, J; Cottenet, J; Corneloup, M; Soudry-Faure, A; Mariet, A-S; Roussot, A

    2017-10-01

    The aim of the REDSIAM network is to foster communication between users of French medico-administrative databases and to validate and promote analysis methods suitable for the data. Within this network, the working group "Mental and behavioral disorders" took an interest in algorithms to identify adult schizophrenia in the SNIIRAM database and inventoried identification criteria for patients with schizophrenia in these databases. The methodology was based on interviews with nine experts in schizophrenia concerning the procedures they use to identify patients with schizophrenia disorders in databases. The interviews were based on a questionnaire and conducted by telephone. The synthesis of the interviews showed that the SNIIRAM contains various tables which allow coders to identify patients suffering from schizophrenia: chronic disease status, drugs and hospitalizations. Taken separately, these criteria were not sufficient to recognize patients with schizophrenia, an algorithm should be based on all of them. Apparently, only one-third of people living with schizophrenia benefit from the longstanding disease status. Not all patients are hospitalized, and coding for diagnoses at the hospitalization, notably for short stays in medicine, surgery or obstetrics departments, is not exhaustive. As for treatment with antipsychotics, it is not specific enough as such treatments are also prescribed to patients with bipolar disorders, or even other disorders. It seems appropriate to combine these complementary criteria, while keeping in mind out-patient care (every year 80,000 patients are seen exclusively in an outpatient setting), even if these data are difficult to link with other information. Finally, the experts made three propositions for selection algorithms of patients with schizophrenia. Patients with schizophrenia can be relatively accurately identified using SNIIRAM data. Different combinations of the selected criteria must be used depending on the objectives and

  18. Identifying Value Indicators and Social Capital in Community Health Partnerships

    Science.gov (United States)

    Hausman, Alice J.; Becker, Julie; Brawer, Rickie

    2005-01-01

    Increasingly, public health practice is turning to the application of community collaborative models to improve population health status. Despite the growth of these activities, however, evaluations of the national demonstrations have indicated that community health partnerships fail to achieve measurable results and struggle to maintain integrity…

  19. A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

    Directory of Open Access Journals (Sweden)

    Guoqiang Chen

    2013-01-01

    Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.

  20. A simple algorithm for identifying periods of snow accumulation on a radiometer

    Science.gov (United States)

    Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

    2015-09-01

    Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

  1. Community occupational therapists' clinical reasoning: identifying tacit knowledge.

    Science.gov (United States)

    Carrier, Annie; Levasseur, Mélanie; Bédard, Denis; Desrosiers, Johanne

    2010-12-01

      Occupational therapy interventions in the community, a fast expanding practice setting, are central to an important social priority, the ability to live at home. These interventions generally involve only a small number of home visits, which aim at maximising the safety and autonomy of community-dwelling clients. Knowing how community occupational therapists determine their interventions, i.e. their clinical reasoning, can improve intervention efficacy. However, occupational therapists are often uninformed about and neglect the importance of clinical reasoning, which could underoptimise their interventions.   To synthesise current knowledge about community occupational therapists' clinical reasoning.   A scoping study of the literature on community occupational therapists' clinical reasoning was undertaken.   Fifteen textbooks and 25 articles, including six focussing on community occupational therapists' clinical reasoning, were reviewed. Community occupational therapists' clinical reasoning is influenced by internal and external factors. Internal factors include past experiences, expertise and perceived complexity of a problem. One of the external factors, practice context (e.g. organisational or cultural imperatives, physical location of intervention), particularly shapes community occupational therapists' clinical reasoning, which is interactive, complex and multidimensional. However, the exact influence of many factors (personal context, organisational and legal aspects of health care, lack of resources and increased number of referrals) remains unclear.   Further studies are needed to understand better the influence of internal and external factors. The extent to which these factors mould the way community occupational therapists think and act could have a direct influence on the services they provide to their clients. © 2010 The Authors. Australian Occupational Therapy Journal © 2010 Australian Association of Occupational Therapists.

  2. Identifying deterministic signals in simulated gravitational wave data: algorithmic complexity and the surrogate data method

    International Nuclear Information System (INIS)

    Zhao Yi; Small, Michael; Coward, David; Howell, Eric; Zhao Chunnong; Ju Li; Blair, David

    2006-01-01

    We describe the application of complexity estimation and the surrogate data method to identify deterministic dynamics in simulated gravitational wave (GW) data contaminated with white and coloured noises. The surrogate method uses algorithmic complexity as a discriminating statistic to decide if noisy data contain a statistically significant level of deterministic dynamics (the GW signal). The results illustrate that the complexity method is sensitive to a small amplitude simulated GW background (SNR down to 0.08 for white noise and 0.05 for coloured noise) and is also more robust than commonly used linear methods (autocorrelation or Fourier analysis)

  3. Identifying elementary iterated systems through algorithmic inference: The Cantor set example

    Energy Technology Data Exchange (ETDEWEB)

    Apolloni, Bruno [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: apolloni@dsi.unimi.it; Bassis, Simone [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: bassis@dsi.unimi.it

    2006-10-15

    We come back to the old problem of fractal identification within the new framework of algorithmic Inference. The key points are: (i) to identify sufficient statistics to be put in connection with the unknown values of the fractal parameters, and (ii) to manage the timing of the iterated process through spatial statistics. We fill these tasks successfully with the Cantor sets. We are able to compute confidence intervals for both the scaling parameter {theta} and the iteration number n at which we are observing a set. We both check numerically the coverage of these intervals and delineate a general strategy for affording more complex iterated systems.

  4. The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

    Science.gov (United States)

    Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

    2018-04-01

    Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

  5. Highly efficient computer algorithm for identifying layer thickness of atomically thin 2D materials

    Science.gov (United States)

    Lee, Jekwan; Cho, Seungwan; Park, Soohyun; Bae, Hyemin; Noh, Minji; Kim, Beom; In, Chihun; Yang, Seunghoon; Lee, Sooun; Seo, Seung Young; Kim, Jehyun; Lee, Chul-Ho; Shim, Woo-Young; Jo, Moon-Ho; Kim, Dohun; Choi, Hyunyong

    2018-03-01

    The fields of layered material research, such as transition-metal dichalcogenides (TMDs), have demonstrated that the optical, electrical and mechanical properties strongly depend on the layer number N. Thus, efficient and accurate determination of N is the most crucial step before the associated device fabrication. An existing experimental technique using an optical microscope is the most widely used one to identify N. However, a critical drawback of this approach is that it relies on extensive laboratory experiences to estimate N; it requires a very time-consuming image-searching task assisted by human eyes and secondary measurements such as atomic force microscopy and Raman spectroscopy, which are necessary to ensure N. In this work, we introduce a computer algorithm based on the image analysis of a quantized optical contrast. We show that our algorithm can apply to a wide variety of layered materials, including graphene, MoS2, and WS2 regardless of substrates. The algorithm largely consists of two parts. First, it sets up an appropriate boundary between target flakes and substrate. Second, to compute N, it automatically calculates the optical contrast using an adaptive RGB estimation process between each target, which results in a matrix with different integer Ns and returns a matrix map of Ns onto the target flake position. Using a conventional desktop computational power, the time taken to display the final N matrix was 1.8 s on average for the image size of 1280 pixels by 960 pixels and obtained a high accuracy of 90% (six estimation errors among 62 samples) when compared to the other methods. To show the effectiveness of our algorithm, we also apply it to TMD flakes transferred on optically transparent c-axis sapphire substrates and obtain a similar result of the accuracy of 94% (two estimation errors among 34 samples).

  6. Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

    Directory of Open Access Journals (Sweden)

    Yuquan Guo

    2017-01-01

    Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

  7. Algorithms

    Indian Academy of Sciences (India)

    polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.

  8. Positive predictive value of a register-based algorithm using the Danish National Registries to identify suicidal events.

    Science.gov (United States)

    Gasse, Christiane; Danielsen, Andreas Aalkjaer; Pedersen, Marianne Giørtz; Pedersen, Carsten Bøcker; Mors, Ole; Christensen, Jakob

    2018-04-17

    It is not possible to fully assess intention of self-harm and suicidal events using information from administrative databases. We conducted a validation study of intention of suicide attempts/self-harm contacts identified by a commonly applied Danish register-based algorithm (DK-algorithm) based on hospital discharge diagnosis and emergency room contacts. Of all 101 530 people identified with an incident suicide attempt/self-harm contact at Danish hospitals between 1995 and 2012 using the DK-algorithm, we selected a random sample of 475 people. We validated the DK-algorithm against medical records applying the definitions and terminology of the Columbia Classification Algorithm of Suicide Assessment of suicidal events, nonsuicidal events, and indeterminate or potentially suicidal events. We calculated positive predictive values (PPVs) of the DK-algorithm to identify suicidal events overall, by gender, age groups, and calendar time. We retrieved medical records for 357 (75%) people. The PPV of the DK-algorithm to identify suicidal events was 51.5% (95% CI: 46.4-56.7) overall, 42.7% (95% CI: 35.2-50.5) in males, and 58.5% (95% CI: 51.6-65.1) in females. The PPV varied further across age groups and calendar time. After excluding cases identified via the DK-algorithm by unspecific codes of intoxications and injury, the PPV improved slightly (56.8% [95% CI: 50.0-63.4]). The DK-algorithm can reliably identify self-harm with suicidal intention in 52% of the identified cases of suicide attempts/self-harm. The PPVs could be used for quantitative bias analysis and implemented as weights in future studies to estimate the proportion of suicidal events among cases identified via the DK-algorithm. Copyright © 2018 John Wiley & Sons, Ltd.

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

    Science.gov (United States)

    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.

  10. GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

    Directory of Open Access Journals (Sweden)

    John Patrick Mpindi

    Full Text Available BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes', a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29 of these genes, and 17 of these 19 genes (90% showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is

  11. Evaluation of ICD-10 algorithms to identify hypopituitary patients in the Danish National Patient Registry

    DEFF Research Database (Denmark)

    Berglund, Agnethe; Olsen, Morten; Andersen, Marianne

    2017-01-01

    : Patients with International Classification of Diseases (10th edition [ICD-10]) diagnoses of hypopituitarism, or other diagnoses of pituitary disorders assumed to be associated with an increased risk of hypopituitarism, recorded in the DNPR during 2000-2012 were identified. Medical records were reviewed...... to confirm or disprove hypopituitarism. RESULTS: Hypopituitarism was confirmed in 911 patients. In a candidate population of 1,661, this yielded an overall positive predictive value (PPV) of 54.8% (95% confidence interval [CI]: 52.4-57.3). Using algorithms searching for patients recorded at least one, three...... or five times with a diagnosis of hypopituitarism (E23.0x) and/or at least once with a diagnosis of postprocedural hypopituitarism (E89.3x), PPVs gradually increased from 73.3% (95% CI: 70.6-75.8) to 83.3% (95% CI: 80.7-85.7). Completeness for the same algorithms, however, decreased from 90.8% (95% CI: 88...

  12. Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

    Science.gov (United States)

    Wu, Xiaolan; Grubesic, Tony H.

    2010-12-01

    Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

  13. An algorithm for identifying the best current friend in a social network

    Directory of Open Access Journals (Sweden)

    Francisco Javier Moreno

    2015-05-01

    Full Text Available A research field in the area of social networks (SNs is the identification of some types of users and groups. To facilitate this process, a SN is usually represented by a graph. The centrality measures, which identify the most important vertices in a graph according to some criterion, are usual tools to analyze a graph. One of these measures is the PageRank (a measure originally designed to classify web pages. Informally, in the context of a SN, the PageRank of a user i represents the probability that another user of the SN is seeing the page of i after a considerable time of navigation in the SN. In this paper, we define a new type of user in a SN: the best current friend. The idea is to identify, among the friends of a user i, who is the friend k that would generate the highest decrease in the PageRank of i if k stops being his/her friend. This may be useful to identify the users/customers whose friendship/relationship should be a priority to keep. We provide formal definitions, algorithms and some experiments for this subject. Our experiments showed that the best current friend of a user is not necessarily the one who has the highest PageRank in the SN nor the one who has more friends.

  14. Validation of case-finding algorithms derived from administrative data for identifying adults living with human immunodeficiency virus infection.

    Directory of Open Access Journals (Sweden)

    Tony Antoniou

    Full Text Available OBJECTIVE: We sought to validate a case-finding algorithm for human immunodeficiency virus (HIV infection using administrative health databases in Ontario, Canada. METHODS: We constructed 48 case-finding algorithms using combinations of physician billing claims, hospital and emergency room separations and prescription drug claims. We determined the test characteristics of each algorithm over various time frames for identifying HIV infection, using data abstracted from the charts of 2,040 randomly selected patients receiving care at two medical practices in Toronto, Ontario as the reference standard. RESULTS: With the exception of algorithms using only a single physician claim, the specificity of all algorithms exceeded 99%. An algorithm consisting of three physician claims over a three year period had a sensitivity and specificity of 96.2% (95% CI 95.2%-97.9% and 99.6% (95% CI 99.1%-99.8%, respectively. Application of the algorithm to the province of Ontario identified 12,179 HIV-infected patients in care for the period spanning April 1, 2007 to March 31, 2009. CONCLUSIONS: Case-finding algorithms generated from administrative data can accurately identify adults living with HIV. A relatively simple "3 claims in 3 years" definition can be used for assembling a population-based cohort and facilitating future research examining trends in health service use and outcomes among HIV-infected adults in Ontario.

  15. A game theoretic algorithm to detect overlapping community structure in networks

    Science.gov (United States)

    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.

  16. Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission

    Science.gov (United States)

    Xu, Duo; Offner, Stella S. R.

    2017-12-01

    Stellar feedback created by radiation and winds from massive stars plays a significant role in both physical and chemical evolution of molecular clouds. This energy and momentum leaves an identifiable signature (“bubbles”) that affects the dynamics and structure of the cloud. Most bubble searches are performed “by eye,” which is usually time-consuming, subjective, and difficult to calibrate. Automatic classifications based on machine learning make it possible to perform systematic, quantifiable, and repeatable searches for bubbles. We employ a previously developed machine learning algorithm, Brut, and quantitatively evaluate its performance in identifying bubbles using synthetic dust observations. We adopt magnetohydrodynamics simulations, which model stellar winds launching within turbulent molecular clouds, as an input to generate synthetic images. We use a publicly available three-dimensional dust continuum Monte Carlo radiative transfer code, HYPERION, to generate synthetic images of bubbles in three Spitzer bands (4.5, 8, and 24 μm). We designate half of our synthetic bubbles as a training set, which we use to train Brut along with citizen-science data from the Milky Way Project (MWP). We then assess Brut’s accuracy using the remaining synthetic observations. We find that Brut’s performance after retraining increases significantly, and it is able to identify yellow bubbles, which are likely associated with B-type stars. Brut continues to perform well on previously identified high-score bubbles, and over 10% of the MWP bubbles are reclassified as high-confidence bubbles, which were previously marginal or ambiguous detections in the MWP data. We also investigate the influence of the size of the training set, dust model, evolutionary stage, and background noise on bubble identification.

  17. Algorithms

    Indian Academy of Sciences (India)

    to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...

  18. An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance.

    Science.gov (United States)

    Widdifield, Jessica; Bombardier, Claire; Bernatsky, Sasha; Paterson, J Michael; Green, Diane; Young, Jacqueline; Ivers, Noah; Butt, Debra A; Jaakkimainen, R Liisa; Thorne, J Carter; Tu, Karen

    2014-06-23

    We have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard. We performed a retrospective chart abstraction study using a random sample of 7500 adult patients under the care of 83 family physicians contributing to the Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Using physician-reported diagnoses as the reference standard, we computed and compared the sensitivity, specificity, and predictive values for over 100 administrative data algorithms for RA case ascertainment. We identified 69 patients with RA for a lifetime RA prevalence of 0.9%. All algorithms had excellent specificity (>97%). However, sensitivity varied (75-90%) among physician billing algorithms. Despite the low prevalence of RA, most algorithms had adequate positive predictive value (PPV; 51-83%). The algorithm of "[1 hospitalization RA diagnosis code] or [3 physician RA diagnosis codes with ≥1 by a specialist over 2 years]" had a sensitivity of 78% (95% CI 69-88), specificity of 100% (95% CI 100-100), PPV of 78% (95% CI 69-88) and NPV of 100% (95% CI 100-100). Administrative data algorithms for detecting RA patients achieved a high degree of accuracy amongst the general population. However, results varied slightly from our previous report, which can be attributed to differences in the reference standards with respect to disease prevalence, spectrum of disease, and type of comparator group.

  19. Algorithm of actions to identify and reduce risks in the production of milk and plant products

    Directory of Open Access Journals (Sweden)

    L. E. Glagoleva

    2016-01-01

    Full Text Available Foods with a new generation of functional and improved consumer properties, corresponds to the modern concepts of nutrition science and consumer needs. functional food production is a major global trend in food science and the subject of innovation. One of the important trends is the use of plant complexes and plant food systems. Using the plant complexes (PC and plant food systems (PFS provides a number of benefits: improved consumer properties of the product, do not need to change the process, it is possible to control directional rheological properties and consistency of the finished products, reduced the number of risk points in the production cycle. This paper describes the development of an algorithm of action to identify and mitigate risks in the production of milk and plant products. Also conducted a risk analysis, identified and assessed the risks in the process of production, installed capacity of available resources to reduce the level of risk. Established and submitted to the critical control points in production processes, as well as the critical limits for each critical control points, and the procedure for corrective action in case of violations of the past. During the study, measured changes in the quantitative and qualitative composition of microflora of semi-finished and Quantity of Mesophilic Aerobic and Facultative Anaerobic Microorganisms (QMAFAnM. To determine QMAFAnM samples were taken: 1 – cheesecakes (control, 2 – cheesecakes with RPS. Microbiological studies analyzed frozen-conjugated semi-finished products was determined within 90 days. It is clear from the data that the cottage cheese with semi-finished products have a lower RPM 11.7%. Analyzing the data, it is possible to conclude that the physico-chemical, organoleptic and microbiological indicators of products was developed to set standards on cheese semi-finished products. multilevel structure that characterizes the quality indicators has been developed and is

  20. GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms.

    Science.gov (United States)

    Moraes, João P A; Pappa, Gisele L; Pires, Douglas E V; Izidoro, Sandro C

    2017-07-03

    Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  1. A new algorithm for identifying the flavour of B0 s mesons at LHCb

    NARCIS (Netherlands)

    Aaij, R.; Abellán Beteta, C.; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Alvarez Cartelle, P.; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; Everse, LA; Anderlini, L.; Andreassi, G.; Andreotti, M.; Andrews, J.E.; Appleby, R. B.; Aquines Gutierrez, O.; Archilli, F.; d'Argent, P.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Baesso, C.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Battista, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Bel, L. J.; Bellee, V.; Belloli, N.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bertolin, A.; Betti, F.; Bettler, M-O.; Van Beuzekom, Martin; Bifani, S.; Billoir, P.; Bird, T.D.; Birnkraut, A.; Bizzeti, A.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borgheresi, A.; Borghi, S.; Borisyak, M.; Borsato, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Braun, S.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N. H.; Buchanan, E.; Burr, C.; Bursche, A.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Calvo Gomez, M.; Campana, P.; Campora Perez, D.; Capriotti, L.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carniti, P.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cassina, L.; Castillo Garcia, L.; Cattaneo, M.; Cauet, Ch; Cavallero, G.; Cenci, R.; Charles, M.; Charpentier, Ph; Chatzikonstantinidis, G.; Chefdeville, M.; Chen, S.; Cheung, S-F.; Chiapolini, N.; Chrzaszcz, M.; Cid Vidal, X.; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Cogoni, V.; Cojocariu, L.; Collazuol, G.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Crocombe, A.; Cruz Torres, M.; Cunliffe, S.; Currie, C.R.; D'Ambrosio, C.; Dall'Occo, E.; Dalseno, J.; David, P. N.Y.; Davis, A.; De Aguiar Francisco, O.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J. M.; Paula, L.E.; De Simone, P.; Dean, C-T.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Déléage, N.; Demmer, M.; Derkach, D.; Deschamps, O.; Dettori, F.; Dey, B.; Di Canto, A.; Di Ruscio, F.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Dosil Suárez, A.; Dovbnya, A.; Dreimanis, K.; Dufour, L.; Dujany, G.; Dungs, K.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, H. M.; Evans, T. M.; Falabella, A.; Färber, C.; Farley, N.; Farry, S.; Fay, R.; Fazzini, D.; Ferguson, D.; Fernandez Albor, V.; Ferrari, F.; Ferreira Rodrigues, F.; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fleuret, F.; Fohl, K.; Fol, P.; Fontana, Mark; Fontanelli, F.; Forshaw, D. C.; Forty, R.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Gallas Torreira, A.; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; García Pardiñas, J.; Garra Tico, J.; Garrido, L.; Gascon, D.; Carvalho-Gaspar, M.; Gavardi, L.; Gazzoni, G.; Gerick, D.; Gersabeck, E.; Gersabeck, M.; Gershon, T. J.; Ghez, Ph; Gian, S.; Gibson, V.; Girard, O. G.; Giubega, L.; Gligorov, V. V.; Göbel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.Q.; Gotti, C.; Grabalosa Gándara, M.; Graciani Diaz, R.; Granado Cardoso, L. A.; Graugés, E.; Graverini, E.; Graziani, G.; Grecu, A.; Griffith, P.; Grillo, L.; Grönberg, O.; Gui, B.; Gushchin, E.; Guz, Yu; Gys, T.; Hadavizadeh, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; He, J.; Head, T.; Heijne, V.; Heister, A.J.G.A.M.; Hennessy, K.; Henrard, P.; Henry, L.; Hernando Morata, J. A.; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hulsbergen, W.; Humair, T.; Hushchyn, M.; Hussain, N.; Hutchcroft, D. E.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jawahery, A.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T. M.; Karodia, S.; Kecke, M.; Kelsey, M. H.; Kenyon, I. R.; Kenzie, M.; Ketel, T.; Khairullin, E.; Khanji, B.; Khurewathanakul, C.; Kirn, T.; Klaver, S.M.; Klimaszewski, K.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Kozeiha, M.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Krzemien, W.; Kucewicz, W.; Kucharczyk, M.; Kudryavtsev, V.; Kuonen, A. K.; Kurek, K.; Kvaratskheliya, T.; Lacarrere, D.; Lafferty, G. D.; Lai, A.; Lambert, D.M.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T. E.; Lazzeroni, C.; Le Gac, R.; Van Leerdam, J.; Lees, J. P.; Lefèvre, R.; Leflat, A.; Lefrançois, J.; Lemos Cid, E.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Likhomanenko, T.; Liles, M.; Lindner, R.; Linn, S.C.; Lionetto, F.; Liu, B.; Liu, X.; Loh, D.; Longstaff, I.; Lopes, J. H.; Lucchesi, D.; Lucio Martinez, M.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Lusardi, N.; Lusiani, A.; Machefert, F.; Maciuc, F.; Maev, O.; Maguire, K.; Malde, S.; Malinin, A.; Manca, G.; Mancinelli, G.; Manning, P.; Mapelli, A.; Maratas, J.; Marchand, J. F.; Marconi, U.; Marin Benito, C.; Marino, P.; Marks, J.; Martellotti, G.; Martin, M.; Martinelli-Boneschi, F.; Martinez-Santos, D.; Martinez-Vidal, F.; Martins Tostes, D.; Massacrier, L. M.; Massafferri, A.; Matev, R.; Mathad, A.; Mathe, Z.; Matteuzzi, C.; Mauri, A.; Maurin, B.; Mazurov, A.; McCann, M.; McCarthy, J.; Mcnab, A.; McNulty, R.; Meadows, B. T.; Meier, F.; Meissner, M.; Melnychuk, D.; Merk, M.; Merli, A.; Michielin, E.; Milanes, D. A.; Minard, M. N.; Mitzel, D. S.; Molina Rodriguez, J.; Monroy, I. A.; Monteil, S.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Möller, D.; Möller, J.; Möller, K.; Möller, V.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nandi, A.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen-Mau, C.; Niess, V.; Nieswand, S.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; O'Hanlon, D. P.; Oblakowska-Mucha, A.; Obraztsov, V.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, C. J.G.; Osorio Rodrigues, B.; Otalora Goicochea, J. M.; Otto, E.A.; Owen, R.P.; Oyanguren, A.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Pappalardo, L.L.; Pappenheimer, C.; Parker, W.S; Parkes, C.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrignani, C.; Pearce, D.A.; Pellegrino, A.; Penso, G.; Pepe Altarelli, M.; Perazzini, S.; Perret, P.; Pescatore, L.; Petridis, K.; Petrolini, A.; Petruzzo, M.; Picatoste Olloqui, E.; Pietrzyk, B.; Pikies, M.; Pinci, D.; Pistone, A.; Piucci, A.; Playfer, S.; Plo Casasus, M.; Poikela, T.; Polci, F.; Poluektov, A.; Polyakov, I.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Price, M. E.; Price, J.D.; Prisciandaro, J.; Pritchard, C.A.; Prouve, C.; Pugatch, V.; Puig Navarro, A.; Punzi, G.; Qian, Y.W.; Quagliani, R.; Rachwal, B.; Rademacker, J. H.; Rama, M.; Ramos Pernas, M.; Rangel, M. S.; Raniuk, I.; Raven, G.; Redi, F.; Reichert, S.; dos Reis, A. C.; Renaudin, V.; Ricciardi, S.; Richards, Jennifer S; Rihl, M.; Rinnert, K.; Rives Molina, V.; Robbe, P.; Rodrigues, A. B.; Rodrigues, L.E.T.; Rodriguez Lopez, J. A.; Rodriguez Perez, P.; Rogozhnikov, A.; Roiser, S.; Romanovsky, V.; Romero Vidal, A.; Ronayne, J. W.; Rotondo, M.; Ruf, T.; Ruiz Valls, P.; Saborido Silva, J. J.; Sagidova, N.; Saitta, B.; Salustino Guimaraes, V.; Sanchez Mayordomo, C.; Sanmartin Sedes, B.; Santacesaria, R.; Santamarina Rios, C.; Santimaria, M.; Santovetti, E.; Sarti, A.; Satriano, C.; Satta, A.; Saunders, D. M.; Savrina, D.; Schael, S.; Schiller, M.; Schindler, R. H.; Schlupp, M.; Schmelling, M.; Schmelzer, T.; Schmidt, B.; Schneider, O.; Schopper, A.; Schubiger, M.; Schune, M. H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Semennikov, A.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Siddi, B. G.; Silva Coutinho, R.; Silva de Oliveira, L.; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, E.; Smith, I. T.; Smith, J; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Soomro, F.; de Souza, D.K.; Souza De Paula, B.; Spaan, B.; Spradlin, P.; Sridharan, S.; Stagni, F.; Stahl, M.; Stahl, S.; Stefkova, S.; Steinkamp, O.; Stenyakin, O.; Stevenson-Moore, P.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szumlak, T.; T'Jampens, S.; Tayduganov, A.; Tekampe, T.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; Van Tilburg, J.; Tisserand, V.; Tobin, M. N.; Todd, Jim; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Tournefier, E.; Tourneur, S.; Trabelsi, K.; Traill, M.; Tran, N.T.M.T.; Tresch, M.; Trisovic, A.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vacca, C.; Vagnoni, V.; Valenti, G.; Vallier, A.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vázquez Sierra, C.; Vecchi, S.; van Veghel-Plandsoen, M.M.; Velthuis, M.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Vieites Diaz, M.; Vilasis-Cardona, X.; Volkov, V.; Vollhardt, A.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voß, C.; De Vries, J. A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, John; Wang, J.; Ward, D. R.; Watson, N. K.; Websdale, D.; Weiden, A.; Whitehead, M.; Wicht, J.; Wilkinson, G.; Wilkinson, M.; Williams, M.; Williams, M.P.; Williams, M.; Williams, T.; Wilson, James F; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wraight, K.; Wright, S.J.; Wyllie, K.; Xie, Y.; Xu, Z.; Yang, Z.; Yu, J.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, L.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zhukov, V.; Zucchelli, S.

    2016-01-01

    A new algorithm for the determination of the initial flavour of B0 s mesons is presented. The algorithm is based on two neural networks and exploits the b hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the B0 s

  2. MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors.

    Directory of Open Access Journals (Sweden)

    Katerina Gkirtzou

    Full Text Available BACKGROUND: MicroRNAs (miRNAs are small, single stranded RNAs with a key role in post-transcriptional regulation of thousands of genes across numerous species. While several computational methods are currently available for identifying miRNA genes, accurate prediction of the mature miRNA remains a challenge. Existing approaches fall short in predicting the location of mature miRNAs but also in finding the functional strand(s of miRNA precursors. METHODOLOGY/PRINCIPAL FINDINGS: Here, we present a computational tool that incorporates a Naive Bayes classifier to identify mature miRNA candidates based on sequence and secondary structure information of their miRNA precursors. We take into account both positive (true mature miRNAs and negative (same-size non-mature miRNA sequences examples to optimize sensitivity as well as specificity. Our method can accurately predict the start position of experimentally verified mature miRNAs for both human and mouse, achieving a significantly larger (often double performance accuracy compared with two existing methods. Moreover, the method exhibits a very high generalization performance on miRNAs from two other organisms. More importantly, our method provides direct evidence about the features of miRNA precursors which may determine the location of the mature miRNA. We find that the triplet of positions 7, 8 and 9 from the mature miRNA end towards the closest hairpin have the largest discriminatory power, are relatively conserved in terms of sequence composition (mostly contain a Uracil and are located within or in very close proximity to the hairpin loop, suggesting the existence of a possible recognition site for Dicer and associated proteins. CONCLUSIONS: This work describes a novel algorithm for identifying the start position of mature miRNA(s produced by miRNA precursors. Our tool has significantly better (often double performance than two existing approaches and provides new insights about the potential use

  3. An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

    Directory of Open Access Journals (Sweden)

    Zhixiao Wang

    2014-01-01

    Full Text Available Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

  4. Algorithms

    Indian Academy of Sciences (India)

    ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...

  5. Use of genetic algorithm to identify thermophysical properties of deposited fouling in heat exchanger tubes

    International Nuclear Information System (INIS)

    Adili, Ali; Ben Salah, Mohieddine; Kerkeni, Chekib; Ben Nasrallah, Sassi

    2009-01-01

    At high temperature, the circulation of fluid in heat exchangers provides a tendency for fouling accumulation to take place on the internal surface of tubes. This paper shows an experimental process of thermophysical properties estimation of the fouling deposited on internal surface of a heat exchanger tube using genetic algorithms (GAs). The genetic algorithm is used to minimize an objective function containing calculated and measured temperatures. The experimental bench using a photothermal method with a finite width pulse heat excitation is used and the estimated parameters are obtained with high accuracy

  6. Positive predictive value of a register-based algorithm using the Danish National Registries to identify suicidal events

    DEFF Research Database (Denmark)

    Gasse, Christiane; Danielsen, Andreas Aalkjaer; Pedersen, Marianne Giørtz

    2018-01-01

    events overall, by gender, age groups, and calendar time. RESULTS: We retrieved medical records for 357 (75%) people. The PPV of the DK-algorithm to identify suicidal events was 51.5% (95% CI: 46.4-56.7) overall, 42.7% (95% CI: 35.2-50.5) in males, and 58.5% (95% CI: 51.6-65.1) in females. The PPV varied...... further across age groups and calendar time. After excluding cases identified via the DK-algorithm by unspecific codes of intoxications and injury, the PPV improved slightly (56.8% [95% CI: 50.0-63.4]). CONCLUSIONS: The DK-algorithm can reliably identify self-harm with suicidal intention in 52......PURPOSE: It is not possible to fully assess intention of self-harm and suicidal events using information from administrative databases. We conducted a validation study of intention of suicide attempts/self-harm contacts identified by a commonly applied Danish register-based algorithm (DK...

  7. Tobacco control recommendations identified by LGBT Atlantans in a community-based participatory research project.

    Science.gov (United States)

    Bryant, Lawrence; Damarin, Amanda K; Marshall, Zack

    2014-01-01

    Lesbian, gay, bisexual, and transgender (LGBT) people are increasingly aware that disproportionately high smoking rates severely impact the health of their communities. Motivated to make a change, a group of LGBT community members, policymakers, and researchers from Atlanta carried out a community-based participatory research (CBPR) project. This formative research study sought to identify recommendations for culturally relevant smoking prevention and cessation interventions that could improve the health of Atlanta's LGBT communities. Data presented here come from four focus groups with 36 participants and a community meeting with 30 participants. Among study participants, the most favored interventions were providing LGBT-specific cessation programs, raising awareness about LGBT smoking rates, and getting community venues to go smoke-free. Participants also suggested providing reduced-cost cessation products for low-income individuals, using LGBT "role models" to promote cessation, and ensuring that interventions reach all parts of the community. Findings reinforce insights from community-based research with other marginalized groups. Similarities include the importance of tailoring cessation programs for specific communities, the need to acknowledge differences within communities, and the significance of community spaces in shaping discussions of cessation. Further, this study highlights the need for heightened awareness. The Atlanta LGBT community is largely unaware that high smoking rates affect its health, and is unlikely to take collective action to address this problem until it is understood.

  8. Using the Chandra Source-Finding Algorithm to Automatically Identify Solar X-ray Bright Points

    Science.gov (United States)

    Adams, Mitzi L.; Tennant, A.; Cirtain, J. M.

    2009-01-01

    This poster details a technique of bright point identification that is used to find sources in Chandra X-ray data. The algorithm, part of a program called LEXTRCT, searches for regions of a given size that are above a minimum signal to noise ratio. The algorithm allows selected pixels to be excluded from the source-finding, thus allowing exclusion of saturated pixels (from flares and/or active regions). For Chandra data the noise is determined by photon counting statistics, whereas solar telescopes typically integrate a flux. Thus the calculated signal-to-noise ratio is incorrect, but we find we can scale the number to get reasonable results. For example, Nakakubo and Hara (1998) find 297 bright points in a September 11, 1996 Yohkoh image; with judicious selection of signal-to-noise ratio, our algorithm finds 300 sources. To further assess the efficacy of the algorithm, we analyze a SOHO/EIT image (195 Angstroms) and compare results with those published in the literature (McIntosh and Gurman, 2005). Finally, we analyze three sets of data from Hinode, representing different parts of the decline to minimum of the solar cycle.

  9. A new algorithm for identifying the flavour of B 0s mesons at LHCb

    International Nuclear Information System (INIS)

    Aaij, R.; Alessio, F.; Beteta, C. Abellán; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alexander, M.; Ali, S.; Alkhazov, G.; Cartelle, P. Alvarez; Jr, A.A. Alves; Amato, S.; Amerio, S.; Amhis, Y.; An, L.; Anderlini, L.; Andreassi, G.

    2016-01-01

    A new algorithm for the determination of the initial flavour of B 0 s mesons is presented. The algorithm is based on two neural networks and exploits the b hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the B 0 s meson. The second network combines the kaon charges to assign the B 0 s flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb −1 collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the B 0 s – B-bar 0 s flavour oscillations in B 0 s  →  D − s π + decays, and by analysing flavour-specific B * s2 (5840) 0  →  B + K − decays. The tagging power measured in B 0 s  →  D − s π + decays is found to be (1.80 ± 0.19 (stat) ± 0.18 (syst))%, which is an improvement of about 50% compared to a similar algorithm previously used in the LHCb experiment.

  10. Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.

    Directory of Open Access Journals (Sweden)

    Feng YongE

    Full Text Available Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.

  11. ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus.

    Science.gov (United States)

    Afzal, Zubair; Pons, Ewoud; Kang, Ning; Sturkenboom, Miriam C J M; Schuemie, Martijn J; Kors, Jan A

    2014-11-29

    In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists' letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners' entries and a regular expression based temporality module. The ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively. The ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development.

  12. An interactive algorithm for identifying multiattribute measurable value functions based on finite-order independence of structural difference

    International Nuclear Information System (INIS)

    Tamura, Hiroyuki; Hikita, Shiro

    1985-01-01

    In this paper, we develop an interactive algorithm for identifying multiattribute measurable value functions based on the concept of finite-order independence of structural difference. This concept includes Dyer and Sarin's weak difference independence as special cases. The algorithm developed is composed of four major parts: 1) formulation of the problem 2) assessment of normalized conditional value functions and structural difference functions 3) assessment of corner values 4) assessment of the order of independence of structural difference and selection of the model. A hypothetical numerical example of a trade-off analysis for siting a nuclear power plant is included. (author)

  13. Algorithms

    Indian Academy of Sciences (India)

    algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).

  14. Algorithms

    Indian Academy of Sciences (India)

    algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...

  15. IMPLANT-ASSOCIATED PATHOLOGY: AN ALGORITHM FOR IDENTIFYING PARTICLES IN HISTOPATHOLOGIC SYNOVIALIS/SLIM DIAGNOSTICS

    Directory of Open Access Journals (Sweden)

    V. Krenn

    2014-01-01

    Full Text Available In histopathologic SLIM diagnostic (synovial-like interface membrane, SLIM apart from diagnosing periprosthetic infection particle identification has an important role to play. The differences in particle pathogenesis and variability of materials in endoprosthetics explain the particle heterogeneity that hampers the diagnostic identification of particles. For this reason, a histopathological particle algorithm has been developed. With minimal methodical complexity this histopathological particle algorithm offers a guide to prosthesis material-particle identification. Light microscopic-morphological as well as enzyme-histochemical characteristics and polarization-optical proporties have set and particles are defined by size (microparticles, macroparticles and supra- macroparticles and definitely characterized in accordance with a dichotomous principle. Based on these criteria, identification and validation of the particles was carried out in 120 joint endoprosthesis pathological cases. A histopathological particle score (HPS is proposed that summarizes the most important information for the orthopedist, material scientist and histopathologist concerning particle identification in the SLIM.

  16. A new algorithm for identifying the flavour of $B_s^0$ mesons at LHCb

    CERN Document Server

    Aaij, Roel; Adeva, Bernardo; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Akar, Simon; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio Augusto; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Andreassi, Guido; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; d'Argent, Philippe; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baesso, Clarissa; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Battista, Vincenzo; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Bel, Lennaert; Bellee, Violaine; Belloli, Nicoletta; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bertolin, Alessandro; Betti, Federico; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bifani, Simone; Billoir, Pierre; Bird, Thomas; Birnkraut, Alex; Bizzeti, Andrea; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borgheresi, Alessio; Borghi, Silvia; Borisyak, Maxim; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Braun, Svende; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Buchanan, Emma; Burr, Christopher; Bursche, Albert; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Campana, Pierluigi; Campora Perez, Daniel; Capriotti, Lorenzo; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carniti, Paolo; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cavallero, Giovanni; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chatzikonstantinidis, Georgios; Chefdeville, Maximilien; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Cogoni, Violetta; Cojocariu, Lucian; Collazuol, Gianmaria; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Crocombe, Andrew; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dall'Occo, Elena; Dalseno, Jeremy; David, Pieter; Davis, Adam; De Aguiar Francisco, Oscar; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Simone, Patrizia; Dean, Cameron Thomas; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Demmer, Moritz; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Dey, Biplab; Di Canto, Angelo; Di Ruscio, Francesco; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dovbnya, Anatoliy; Dreimanis, Karlis; Dufour, Laurent; Dujany, Giulio; Dungs, Kevin; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Evans, Hannah Mary; Evans, Timothy; Falabella, Antonio; Färber, Christian; Farley, Nathanael; Farry, Stephen; Fay, Robert; Fazzini, Davide; Ferguson, Dianne; Fernandez Albor, Victor; Ferrari, Fabio; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fleuret, Frederic; Fohl, Klaus; Fol, Philip; Fontana, Marianna; Fontanelli, Flavio; Forshaw, Dean Charles; Forty, Roger; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; García Pardiñas, Julián; Garra Tico, Jordi; Garrido, Lluis; Gascon, David; Gaspar, Clara; Gavardi, Laura; Gazzoni, Giulio; Gerick, David; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianì, Sebastiana; Gibson, Valerie; Girard, Olivier Göran; Giubega, Lavinia-Helena; Gligorov, V.V.; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graverini, Elena; Graziani, Giacomo; Grecu, Alexandru; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadavizadeh, Thomas; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; He, Jibo; Head, Timothy; Heijne, Veerle; Heister, Arno; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Humair, Thibaud; Hushchyn, Mikhail; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jawahery, Abolhassan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Karodia, Sarah; Kecke, Matthieu; Kelsey, Matthew; Kenyon, Ian; Kenzie, Matthew; Ketel, Tjeerd; Khairullin, Egor; Khanji, Basem; Khurewathanakul, Chitsanu; Kirn, Thomas; Klaver, Suzanne; Klimaszewski, Konrad; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Kozeiha, Mohamad; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Krzemien, Wojciech; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kuonen, Axel Kevin; Kurek, Krzysztof; Kvaratskheliya, Tengiz; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Lemos Cid, Edgar; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Likhomanenko, Tatiana; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Liu, Xuesong; Loh, David; Longstaff, Iain; Lopes, Jose; Lucchesi, Donatella; Lucio Martinez, Miriam; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Lusardi, Nicola; Lusiani, Alberto; Machefert, Frederic; Maciuc, Florin; Maev, Oleg; Maguire, Kevin; Malde, Sneha; Malinin, Alexander; Manca, Giulia; Mancinelli, Giampiero; Manning, Peter Michael; Mapelli, Alessandro; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Marks, Jörg; Martellotti, Giuseppe; Martin, Morgan; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massacrier, Laure Marie; Massafferri, André; Matev, Rosen; Mathad, Abhijit; Mathe, Zoltan; Matteuzzi, Clara; Mauri, Andrea; Maurin, Brice; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; Meadows, Brian; Meier, Frank; Meissner, Marco; Melnychuk, Dmytro; Merk, Marcel; Merli, Andrea; Michielin, Emanuele; Milanes, Diego Alejandro; Minard, Marie-Noelle; Mitzel, Dominik Stefan; Molina Rodriguez, Josue; Monroy, Ignacio Alberto; Monteil, Stephane; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Dominik; Müller, Janine; Müller, Katharina; Müller, Vanessa; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nandi, Anita; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen-Mau, Chung; Niess, Valentin; Nieswand, Simon; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Osorio Rodrigues, Bruno; Otalora Goicochea, Juan Martin; Otto, Adam; Owen, Patrick; Oyanguren, Maria Aranzazu; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Pappenheimer, Cheryl; Parker, William; Parkes, Christopher; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perret, Pascal; Pescatore, Luca; Petridis, Konstantinos; Petrolini, Alessandro; Petruzzo, Marco; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pikies, Malgorzata; Pinci, Davide; Pistone, Alessandro; Piucci, Alessio; Playfer, Stephen; Plo Casasus, Maximo; Poikela, Tuomas; Polci, Francesco; Poluektov, Anton; Polyakov, Ivan; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Price, Eugenia; Price, Joseph David; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Quagliani, Renato; Rachwal, Bartolomiej; Rademacker, Jonas; Rama, Matteo; Ramos Pernas, Miguel; Rangel, Murilo; Raniuk, Iurii; Raven, Gerhard; Redi, Federico; Reichert, Stefanie; dos Reis, Alberto; Renaudin, Victor; Ricciardi, Stefania; Richards, Sophie; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Lopez, Jairo Alexis; Rodriguez Perez, Pablo; Rogozhnikov, Alexey; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Ronayne, John William; Rotondo, Marcello; Ruf, Thomas; Ruiz Valls, Pablo; Saborido Silva, Juan Jose; Sagidova, Naylya; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santimaria, Marco; Santovetti, Emanuele; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saunders, Daniel Martin; Savrina, Darya; Schael, Stefan; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmelzer, Timon; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schubiger, Maxime; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Semennikov, Alexander; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Siddi, Benedetto Gianluca; Silva Coutinho, Rafael; Silva de Oliveira, Luiz Gustavo; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Eluned; Smith, Iwan Thomas; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Spradlin, Patrick; Sridharan, Srikanth; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Stefkova, Slavomira; Steinkamp, Olaf; Stenyakin, Oleg; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szumlak, Tomasz; T'Jampens, Stephane; Tayduganov, Andrey; Tekampe, Tobias; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Todd, Jacob; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Tournefier, Edwige; Tourneur, Stephane; Trabelsi, Karim; Traill, Murdo; Tran, Minh Tâm; Tresch, Marco; Trisovic, Ana; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vacca, Claudia; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; van Veghel, Maarten; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Volkov, Vladimir; Vollhardt, Achim; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Weiden, Andreas; Whitehead, Mark; Wicht, Jean; Wilkinson, Guy; Wilkinson, Michael; Williams, Mark Richard James; Williams, Matthew; Williams, Mike; Williams, Timothy; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wraight, Kenneth; Wright, Simon; Wyllie, Kenneth; Xie, Yuehong; Xu, Zhirui; Yang, Zhenwei; Yu, Jiesheng; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Liming; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zhukov, Valery; Zucchelli, Stefano

    2016-05-17

    A new algorithm for the determination of the initial flavour of $B_s^0$ mesons is presented. The algorithm is based on two neural networks and exploits the $b$ hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the $B_s^0$ meson. The second network combines the kaon charges to assign the $B_s^0$ flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb$^{-1}$ collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the $B_s^0$-$\\bar{B}_s^0$ flavour oscillations in $B_s^0 \\to D_s^- \\pi^+$ decays, and by analysing flavour-specific $B_{s 2}^{*}(5840)^0 \\to B^+ K^-$ decays. The tagging power measured in $B_s^0 \\to D_s^- \\pi^+$ decays is found to be $(1.80 \\pm 0.19({\\rm stat}) \\pm 0.18({\\rm syst}))$\\%, which is an improvement of about 50\\% compare...

  17. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches.

    Science.gov (United States)

    Jin, Zhenong; Zhuang, Qianlai; Tan, Zeli; Dukes, Jeffrey S; Zheng, Bangyou; Melillo, Jerry M

    2016-09-01

    Stresses from heat and drought are expected to increasingly suppress crop yields, but the degree to which current models can represent these effects is uncertain. Here we evaluate the algorithms that determine impacts of heat and drought stress on maize in 16 major maize models by incorporating these algorithms into a standard model, the Agricultural Production Systems sIMulator (APSIM), and running an ensemble of simulations. Although both daily mean temperature and daylight temperature are common choice of forcing heat stress algorithms, current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for daily mean temperature. Different drought algorithms (i.e., a function of soil water content, of soil water supply to demand ratio, and of actual to potential transpiration ratio) simulated considerably different patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. Using the selected combination of algorithms, our simulations show that maize yield reduction was more sensitive to drought stress than to heat stress for the US Midwest since the 1980s, and this pattern will continue under future scenarios; the influence of excessive heat will become increasingly prominent by the late 21st century. Our review of algorithms in 16 crop models suggests that the impacts of heat and drought stress on plant yield can be best described by crop models that: (i) incorporate event-based descriptions of heat and drought stress, (ii) consider the effects of nighttime warming, and (iii) coordinate the interactions among multiple stresses. Our study identifies the proficiency with which different model formulations capture the impacts of heat and drought stress on maize biomass and yield production. The framework presented here can be applied to other modeled processes and used to improve yield predictions of other crops with a wide variety of crop models. © 2016 John

  18. Derivation and validation of the automated search algorithms to identify cognitive impairment and dementia in electronic health records.

    Science.gov (United States)

    Amra, Sakusic; O'Horo, John C; Singh, Tarun D; Wilson, Gregory A; Kashyap, Rahul; Petersen, Ronald; Roberts, Rosebud O; Fryer, John D; Rabinstein, Alejandro A; Gajic, Ognjen

    2017-02-01

    Long-term cognitive impairment is a common and important problem in survivors of critical illness. We developed electronic search algorithms to identify cognitive impairment and dementia from the electronic medical records (EMRs) that provide opportunity for big data analysis. Eligible patients met 2 criteria. First, they had a formal cognitive evaluation by The Mayo Clinic Study of Aging. Second, they were hospitalized in intensive care unit at our institution between 2006 and 2014. The "criterion standard" for diagnosis was formal cognitive evaluation supplemented by input from an expert neurologist. Using all available EMR data, we developed and improved our algorithms in the derivation cohort and validated them in the independent validation cohort. Of 993 participants who underwent formal cognitive testing and were hospitalized in intensive care unit, we selected 151 participants at random to form the derivation and validation cohorts. The automated electronic search algorithm for cognitive impairment was 94.3% sensitive and 93.0% specific. The search algorithms for dementia achieved respective sensitivity and specificity of 97% and 99%. EMR search algorithms significantly outperformed International Classification of Diseases codes. Automated EMR data extractions for cognitive impairment and dementia are reliable and accurate and can serve as acceptable and efficient alternatives to time-consuming manual data review. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Algorithms

    Indian Academy of Sciences (India)

    will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...

  20. [Development and validation of an algorithm to identify cancer recurrences from hospital data bases].

    Science.gov (United States)

    Manzanares-Laya, S; Burón, A; Murta-Nascimento, C; Servitja, S; Castells, X; Macià, F

    2014-01-01

    Hospital cancer registries and hospital databases are valuable and efficient sources of information for research into cancer recurrences. The aim of this study was to develop and validate algorithms for the detection of breast cancer recurrence. A retrospective observational study was conducted on breast cancer cases from the cancer registry of a third level university hospital diagnosed between 2003 and 2009. Different probable cancer recurrence algorithms were obtained by linking the hospital databases and the construction of several operational definitions, with their corresponding sensitivity, specificity, positive predictive value and negative predictive value. A total of 1,523 patients were diagnosed of breast cancer between 2003 and 2009. A request for bone gammagraphy after 6 months from the first oncological treatment showed the highest sensitivity (53.8%) and negative predictive value (93.8%), and a pathology test after 6 months after the diagnosis showed the highest specificity (93.8%) and negative predictive value (92.6%). The combination of different definitions increased the specificity and the positive predictive value, but decreased the sensitivity. Several diagnostic algorithms were obtained, and the different definitions could be useful depending on the interest and resources of the researcher. A higher positive predictive value could be interesting for a quick estimation of the number of cases, and a higher negative predictive value for a more exact estimation if more resources are available. It is a versatile and adaptable tool for other types of tumors, as well as for the needs of the researcher. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

  1. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Science.gov (United States)

    Chin, Wei-Chien-Benny; Wen, Tzai-Hung

    2015-01-01

    A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  2. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Directory of Open Access Journals (Sweden)

    Wei-Chien-Benny Chin

    Full Text Available A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR and Geographical PageRank (GPR-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  3. Improving the recommender algorithms with the detected communities in bipartite networks

    Science.gov (United States)

    Zhang, Peng; Wang, Duo; Xiao, Jinghua

    2017-04-01

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

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

    Science.gov (United States)

    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.

  5. The CARPEDIEM Algorithm: A Rule-Based System for Identifying Heart Failure Phenotype with a Precision Public Health Approach

    Directory of Open Access Journals (Sweden)

    Michela Franchini

    2018-01-01

    Full Text Available Modern medicine remains dependent on the accurate evaluation of a patient’s health state, recognizing that disease is a process that evolves over time and interacts with many factors unique to that patient. The CARPEDIEM project represents a concrete attempt to address these issues by developing reproducible algorithms to support the accuracy in detection of complex diseases. This study aims to establish and validate the CARPEDIEM approach and algorithm for identifying those patients presenting with or at risk of heart failure (HF by studying 153,393 subjects in Italy, based on patient information flow databases and is not reliant on the electronic health record to accomplish its goals. The resulting algorithm has been validated in a two-stage process, comparing predicted results with (1 HF diagnosis as identified by general practitioners (GPs among the reference cohort and (2 HF diagnosis as identified by cardiologists within a randomly sampled subpopulation of 389 patients. The sources of data used to detect HF cases are numerous and were standardized for this study. The accuracy and the predictive values of the algorithm with respect to the GPs and the clinical standards are highly consistent with those from previous studies. In particular, the algorithm is more efficient in detecting the more severe cases of HF according to the GPs’ validation (specificity increases according to the number of comorbidities and external validation (NYHA: II–IV; HF severity index: 2, 3. Positive and negative predictive values reveal that the CARPEDIEM algorithm is most consistent with clinical evaluation performed in the specialist setting, while it presents a greater ability to rule out false-negative HF cases within the GP practice, probably as a consequence of the different HF prevalence in the two different care settings. Further development includes analyzing the clinical features of false-positive and -negative predictions, to explore the natural

  6. Hillslope characterization: Identifying key controls on local-scale plant communities' distribution using remote sensing and subsurface data fusion.

    Science.gov (United States)

    Falco, N.; Wainwright, H. M.; Dafflon, B.; Leger, E.; Peterson, J.; Steltzer, H.; Wilmer, C.; Williams, K. H.; Hubbard, S. S.

    2017-12-01

    Mountainous watershed systems are characterized by extreme heterogeneity in hydrological and pedological properties that influence biotic activities, plant communities and their dynamics. To gain predictive understanding of how ecosystem and watershed system evolve under climate change, it is critical to capture such heterogeneity and to quantify the effect of key environmental variables such as topography, and soil properties. In this study, we exploit advanced geophysical and remote sensing techniques - coupled with machine learning - to better characterize and quantify the interactions between plant communities' distribution and subsurface properties. First, we have developed a remote sensing data fusion framework based on the random forest (RF) classification algorithm to estimate the spatial distribution of plant communities. The framework allows the integration of both plant spectral and structural information, which are derived from multispectral satellite images and airborne LiDAR data. We then use the RF method to evaluate the estimated plant community map, exploiting the subsurface properties (such as bedrock depth, soil moisture and other properties) and geomorphological parameters (such as slope, curvature) as predictors. Datasets include high-resolution geophysical data (electrical resistivity tomography) and LiDAR digital elevation maps. We demonstrate our approach on a mountain hillslope and meadow within the East River watershed in Colorado, which is considered to be a representative headwater catchment in the Upper Colorado Basin. The obtained results show the existence of co-evolution between above and below-ground processes; in particular, dominant shrub communities in wet and flat areas. We show that successful integration of remote sensing data with geophysical measurements allows identifying and quantifying the key environmental controls on plant communities' distribution, and provides insights into their potential changes in the future

  7. Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

    International Nuclear Information System (INIS)

    Zio, E.; Golea, L.R.; Rocco S, C.M.

    2012-01-01

    In this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.

  8. A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Haitao Guo

    2017-01-01

    Full Text Available The discovery of cis-regulatory modules (CRMs is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.

  9. A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

    Science.gov (United States)

    2017-01-01

    The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. PMID:28497059

  10. Identifying Perceived Neighborhood Stressors Across Diverse Communities in New York City.

    Science.gov (United States)

    Shmool, Jessie L C; Yonas, Michael A; Newman, Ogonnaya Dotson; Kubzansky, Laura D; Joseph, Evelyn; Parks, Ana; Callaway, Charles; Chubb, Lauren G; Shepard, Peggy; Clougherty, Jane E

    2015-09-01

    There is growing interest in the role of psychosocial stress in health disparities. Identifying which social stressors are most important to community residents is critical for accurately incorporating stressor exposures into health research. Using a community-academic partnered approach, we designed a multi-community study across the five boroughs of New York City to characterize resident perceptions of key neighborhood stressors. We conducted 14 community focus groups; two to three in each borough, with one adolescent group and one Spanish-speaking group per borough. We then used systematic content analysis and participant ranking data to describe prominent neighborhood stressors and identify dominant themes. Three inter-related themes regarding the social and structural sources of stressful experiences were most commonly identified across neighborhoods: (1) physical disorder and perceived neglect, (2) harassment by police and perceived safety and (3) gentrification and racial discrimination. Our findings suggest that multiple sources of distress, including social, political, physical and economic factors, should be considered when investigating health effects of community stressor exposures and psychological distress. Community expertise is essential for comprehensively characterizing the range of neighborhood stressors that may be implicated in psychosocial exposure pathways.

  11. Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems

    Science.gov (United States)

    Meng, Luming; Sheong, Fu Kit; Zeng, Xiangze; Zhu, Lizhe; Huang, Xuhui

    2017-07-01

    Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.

  12. Identifying barriers to mental health system improvements: an examination of community participation in assertive community treatment programs

    Directory of Open Access Journals (Sweden)

    Wakefield Patricia A

    2011-11-01

    Full Text Available Abstract Background Integrating the best available evidence into program standards is essential if system-wide improvements in the delivery of community-based mental health services are to be achieved. Since the beginning of the Assertive Community Treatment (ACT program movement, program standards have included a role for the community. In particular, ACT program standards have sought to ensure that members of the local community are involved in governance and that former clients participate in service delivery as "Peer Support Specialists". This paper reports on the extent to which ACT program standards related to community participation have been implemented and identifies barriers to full compliance. Methods Qualitative and quantitative data were collected through a telephone survey of ACT Program Coordinators in Ontario, Canada, using a census sample of the existing 66 ACT programs. A thematic approach to content analysis was used to analyze respondents' qualitative comments. Quantitative data were analyzed using SPSS 16.0 and included means, frequencies, independent t-tests and Pearson Correlations. Results An 85% response rate was achieved. Of the 33 program standards, the two that received the lowest perceived compliance ratings were the two standards directly concerning community participation. Specifically, the standard to have a functioning Community Advisory Body and the standard requiring the inclusion of a Peer Support Specialist. The three major themes that emerged from the survey data with respect to the barriers to fully implementing the Community Advisory Body were: external issues; standard related issues; and, organizational/structural related issues. The three major themes concerning barriers to implementing the Peer Support Specialist role were: human resource related issues; organizational/structural related issues; and, standard related issues. Conclusions The reasons for low compliance of ACT programs with community

  13. A Clinical Algorithm to Identify HIV Patients at High Risk for Incident Active Tuberculosis: A Prospective 5-Year Cohort Study.

    Directory of Open Access Journals (Sweden)

    Susan Shin-Jung Lee

    Full Text Available Predicting the risk of tuberculosis (TB in people living with HIV (PLHIV using a single test is currently not possible. We aimed to develop and validate a clinical algorithm, using baseline CD4 cell counts, HIV viral load (pVL, and interferon-gamma release assay (IGRA, to identify PLHIV who are at high risk for incident active TB in low-to-moderate TB burden settings where highly active antiretroviral therapy (HAART is routinely provided.A prospective, 5-year, cohort study of adult PLHIV was conducted from 2006 to 2012 in two hospitals in Taiwan. HAART was initiated based on contemporary guidelines (CD4 count < = 350/μL. Cox regression was used to identify the predictors of active TB and to construct the algorithm. The validation cohorts included 1455 HIV-infected individuals from previous published studies. Area under the receiver operating characteristic (ROC curve was calculated.Seventeen of 772 participants developed active TB during a median follow-up period of 5.21 years. Baseline CD4 < 350/μL or pVL ≥ 100,000/mL was a predictor of active TB (adjusted HR 4.87, 95% CI 1.49-15.90, P = 0.009. A positive baseline IGRA predicted TB in patients with baseline CD4 ≥ 350/μL and pVL < 100,000/mL (adjusted HR 6.09, 95% CI 1.52-24.40, P = 0.01. Compared with an IGRA-alone strategy, the algorithm improved the sensitivity from 37.5% to 76.5%, the negative predictive value from 98.5% to 99.2%. Compared with an untargeted strategy, the algorithm spared 468 (60.6% from unnecessary TB preventive treatment. Area under the ROC curve was 0.692 (95% CI: 0.587-0.798 for the study cohort and 0.792 (95% CI: 0.776-0.808 and 0.766 in the 2 validation cohorts.A validated algorithm incorporating the baseline CD4 cell count, HIV viral load, and IGRA status can be used to guide targeted TB preventive treatment in PLHIV in low-to-moderate TB burden settings where HAART is routinely provided to all PLHIV. The implementation of this algorithm will avoid unnecessary

  14. Development and validation of an algorithm for identifying urinary retention in a cohort of patients with epilepsy in a large US administrative claims database.

    Science.gov (United States)

    Quinlan, Scott C; Cheng, Wendy Y; Ishihara, Lianna; Irizarry, Michael C; Holick, Crystal N; Duh, Mei Sheng

    2016-04-01

    The aim of this study was to develop and validate an insurance claims-based algorithm for identifying urinary retention (UR) in epilepsy patients receiving antiepileptic drugs to facilitate safety monitoring. Data from the HealthCore Integrated Research Database(SM) in 2008-2011 (retrospective) and 2012-2013 (prospective) were used to identify epilepsy patients with UR. During the retrospective phase, three algorithms identified potential UR: (i) UR diagnosis code with a catheterization procedure code; (ii) UR diagnosis code alone; or (iii) diagnosis with UR-related symptoms. Medical records for 50 randomly selected patients satisfying ≥1 algorithm were reviewed by urologists to ascertain UR status. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated for the three component algorithms and the overall algorithm (defined as satisfying ≥1 component algorithms). Algorithms were refined using urologist review notes. In the prospective phase, the UR algorithm was refined using medical records for an additional 150 cases. In the retrospective phase, the PPV of the overall algorithm was 72.0% (95%CI: 57.5-83.8%). Algorithm 3 performed poorly and was dropped. Algorithm 1 was unchanged; urinary incontinence and cystitis were added as exclusionary diagnoses to Algorithm 2. The PPV for the modified overall algorithm was 89.2% (74.6-97.0%). In the prospective phase, the PPV for the modified overall algorithm was 76.0% (68.4-82.6%). Upon adding overactive bladder, nocturia and urinary frequency as exclusionary diagnoses, the PPV for the final overall algorithm was 81.9% (73.7-88.4%). The current UR algorithm yielded a PPV > 80% and could be used for more accurate identification of UR among epilepsy patients in a large claims database. Copyright © 2016 John Wiley & Sons, Ltd.

  15. Automated cross-identifying radio to infrared surveys using the LRPY algorithm: a case study

    Science.gov (United States)

    Weston, S. D.; Seymour, N.; Gulyaev, S.; Norris, R. P.; Banfield, J.; Vaccari, M.; Hopkins, A. M.; Franzen, T. M. O.

    2018-02-01

    Cross-identifying complex radio sources with optical or infra red (IR) counterparts in surveys such as the Australia Telescope Large Area Survey (ATLAS) has traditionally been performed manually. However, with new surveys from the Australian Square Kilometre Array Pathfinder detecting many tens of millions of radio sources, such an approach is no longer feasible. This paper presents new software (LRPY - Likelihood Ratio in PYTHON) to automate the process of cross-identifying radio sources with catalogues at other wavelengths. LRPY implements the likelihood ratio (LR) technique with a modification to account for two galaxies contributing to a sole measured radio component. We demonstrate LRPY by applying it to ATLAS DR3 and a Spitzer-based multiwavelength fusion catalogue, identifying 3848 matched sources via our LR-based selection criteria. A subset of 1987 sources have flux density values for all IRAC bands which allow us to use criteria to distinguish between active galactic nuclei (AGNs) and star-forming galaxies (SFG). We find that 936 radio sources ( ≈ 47 per cent) meet both of the Lacy and Stern AGN selection criteria. Of the matched sources, 295 have spectroscopic redshifts and we examine the radio to IR flux ratio versus redshift, proposing an AGN selection criterion below the Elvis radio-loud AGN limit for this dataset. Taking the union of all three AGNs selection criteria we identify 956 as AGNs ( ≈ 48 per cent). From this dataset, we find a decreasing fraction of AGNs with lower radio flux densities consistent with other results in the literature.

  16. Phylogenetic & Physiological Profiling of Microbial Communities of Contaminated Soils/Sediments: Identifying Microbial consortia...

    Energy Technology Data Exchange (ETDEWEB)

    Terence L. Marsh

    2004-05-26

    The goals of this study were: (1) survey the microbial community in soil samples from a site contaminated with heavy metals using new rapid molecular techniques that are culture-independent; (2) identify phylogenetic signatures of microbial populations that correlate with metal ion contamination; and (3) cultivate these diagnostic strains using traditional as well as novel cultivation techniques in order to identify organisms that may be of value in site evaluation/management or bioremediation.

  17. Identifying Effective Methods of Instruction for Adult Emergent Readers through Community-Based Research

    Science.gov (United States)

    Blackmer, Rachel; Hayes-Harb, Rachel

    2016-01-01

    We present a community-based research project aimed at identifying effective methods and materials for teaching English literacy skills to adult English as a second language emergent readers. We conducted a quasi-experimental study whereby we evaluated the efficacy of two approaches, one based on current practices at the English Skills Learning…

  18. Identifying Pedophiles "Eligible" for Community Notification under Megan's Law: A Multivariate Model for Actuarially Anchored Decisions.

    Science.gov (United States)

    Pallone, Nathaniel J.; Hennessy, James J.; Voelbel, Gerald T.

    1998-01-01

    A scientifically sound methodology for identifying offenders about whose presence the community should be notified is demonstrated. A stepwise multiple regression was calculated among incarcerated pedophiles (N=52) including both psychological and legal data; a precision-weighted equation produced 90.4% "true positives." This methodology can be…

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

    Science.gov (United States)

    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.

  20. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (αtexture features.

  1. Algorithms to identify colonic ischemia, complications of constipation and irritable bowel syndrome in medical claims data: development and validation.

    Science.gov (United States)

    Sands, Bruce E; Duh, Mei-Sheng; Cali, Clorinda; Ajene, Anuli; Bohn, Rhonda L; Miller, David; Cole, J Alexander; Cook, Suzanne F; Walker, Alexander M

    2006-01-01

    A challenge in the use of insurance claims databases for epidemiologic research is accurate identification and verification of medical conditions. This report describes the development and validation of claims-based algorithms to identify colonic ischemia, hospitalized complications of constipation, and irritable bowel syndrome (IBS). From the research claims databases of a large healthcare company, we selected at random 120 potential cases of IBS and 59 potential cases each of colonic ischemia and hospitalized complications of constipation. We sought the written medical records and were able to abstract 107, 57, and 51 records, respectively. We established a 'true' case status for each subject by applying standard clinical criteria to the available chart data. Comparing the insurance claims histories to the assigned case status, we iteratively developed, tested, and refined claims-based algorithms that would capture the diagnoses obtained from the medical records. We set goals of high specificity for colonic ischemia and hospitalized complications of constipation, and high sensitivity for IBS. The resulting algorithms substantially improved on the accuracy achievable from a naïve acceptance of the diagnostic codes attached to insurance claims. The specificities for colonic ischemia and serious complications of constipation were 87.2 and 92.7%, respectively, and the sensitivity for IBS was 98.9%. U.S. commercial insurance claims data appear to be usable for the study of colonic ischemia, IBS, and serious complications of constipation. (c) 2005 John Wiley & Sons, Ltd.

  2. The non-contact biometric identified bio signal measurement sensor and algorithms.

    Science.gov (United States)

    Kim, Chan-Il; Lee, Jong-Ha

    2018-04-25

    In these days, wearable devices have been developed for effectively measuring biological data. However, these devices have tissue allege and noise problem. To solve these problems, biometric measurement based on a non-contact method, such as face image sequencing is developed. This makes it possible to measure biometric data without any operation and side effects. However, it is impossible for a remote center to identify the person whose data are measured by the novel methods. In this paper, we propose the novel non-contact heart rate and blood pressure imaging system, Deep Health Eye. This system has authentication process at the same time as measuring bio signals, through non-contact method. In the future, this system can be convenient home bio signal monitoring system by combined with smart mirror.

  3. Using Community-Based Participatory Research to Identify Environmental Justice Issues in an Inner-City Community and Inform Urban Planning.

    Science.gov (United States)

    Mansyur, Carol Leler; Jeng, Hueiwang Anna; Holloman, Erica; DeBrew, Linwood

    2016-01-01

    The Southeast CARE Coalition has been using community-based participatory research to examine environmental degradation in the Southeast Community, Newport News, Virginia. A survey was developed to collect assessment data. Up to 66% of respondents were concerned about environmental problems in their community. Those with health conditions were significantly more likely to identify specific environmental problems. The top 5 environmental concerns included coal dust, air quality, crime, water quality, and trash. The community-based participatory research process is building community capacity and participation, providing community input into strategic planning, and empowering community members to take control of environmental justice issues in their community.

  4. Coupling mode-destination accessibility with seismic risk assessment to identify at-risk communities

    International Nuclear Information System (INIS)

    Miller, Mahalia; Baker, Jack W.

    2016-01-01

    In this paper, we develop a framework for coupling mode-destination accessibility with quantitative seismic risk assessment to identify communities at high risk for travel disruptions after an earthquake. Mode-destination accessibility measures the ability of people to reach destinations they desire. We use a probabilistic seismic risk assessment procedure, including a stochastic set of earthquake events, ground-motion intensity maps, damage maps, and realizations of traffic and accessibility impacts. For a case study of the San Francisco Bay Area, we couple our seismic risk framework with a practical activity-based traffic model. As a result, we quantify accessibility risk probabilistically by community and household type. We find that accessibility varies more strongly as a function of travelers' geographic location than as a function of their income class, and we identify particularly at-risk communities. We also observe that communities more conducive to local trips by foot or bike are predicted to be less impacted by losses in accessibility. This work shows the potential to link quantitative risk assessment methodologies with high-resolution travel models used by transportation planners. Quantitative risk metrics of this type should have great utility for planners working to reduce risk to a region's infrastructure systems. - Highlights: • We couple mode-destination accessibility with probabilistic seismic risk assessment. • Results identify communities at high risk for post-earthquake travel disruptions. • Accessibility varies more as a function of home location than by income. • Our model predicts reduced accessibility risk for more walking-friendly communities.

  5. Derivation and validation of the Personal Support Algorithm: an evidence-based framework to inform allocation of personal support services in home and community care.

    Science.gov (United States)

    Sinn, Chi-Ling Joanna; Jones, Aaron; McMullan, Janet Legge; Ackerman, Nancy; Curtin-Telegdi, Nancy; Eckel, Leslie; Hirdes, John P

    2017-11-25

    Personal support services enable many individuals to stay in their homes, but there are no standard ways to classify need for functional support in home and community care settings. The goal of this project was to develop an evidence-based clinical tool to inform service planning while allowing for flexibility in care coordinator judgment in response to patient and family circumstances. The sample included 128,169 Ontario home care patients assessed in 2013 and 25,800 Ontario community support clients assessed between 2014 and 2016. Independent variables were drawn from the Resident Assessment Instrument-Home Care and interRAI Community Health Assessment that are standardised, comprehensive, and fully compatible clinical assessments. Clinical expertise and regression analyses identified candidate variables that were entered into decision tree models. The primary dependent variable was the weekly hours of personal support calculated based on the record of billed services. The Personal Support Algorithm classified need for personal support into six groups with a 32-fold difference in average billed hours of personal support services between the highest and lowest group. The algorithm explained 30.8% of the variability in billed personal support services. Care coordinators and managers reported that the guidelines based on the algorithm classification were consistent with their clinical judgment and current practice. The Personal Support Algorithm provides a structured yet flexible decision-support framework that may facilitate a more transparent and equitable approach to the allocation of personal support services.

  6. Derivation and validation of the Personal Support Algorithm: an evidence-based framework to inform allocation of personal support services in home and community care

    Directory of Open Access Journals (Sweden)

    Chi-Ling Joanna Sinn

    2017-11-01

    Full Text Available Abstract Background Personal support services enable many individuals to stay in their homes, but there are no standard ways to classify need for functional support in home and community care settings. The goal of this project was to develop an evidence-based clinical tool to inform service planning while allowing for flexibility in care coordinator judgment in response to patient and family circumstances. Methods The sample included 128,169 Ontario home care patients assessed in 2013 and 25,800 Ontario community support clients assessed between 2014 and 2016. Independent variables were drawn from the Resident Assessment Instrument-Home Care and interRAI Community Health Assessment that are standardised, comprehensive, and fully compatible clinical assessments. Clinical expertise and regression analyses identified candidate variables that were entered into decision tree models. The primary dependent variable was the weekly hours of personal support calculated based on the record of billed services. Results The Personal Support Algorithm classified need for personal support into six groups with a 32-fold difference in average billed hours of personal support services between the highest and lowest group. The algorithm explained 30.8% of the variability in billed personal support services. Care coordinators and managers reported that the guidelines based on the algorithm classification were consistent with their clinical judgment and current practice. Conclusions The Personal Support Algorithm provides a structured yet flexible decision-support framework that may facilitate a more transparent and equitable approach to the allocation of personal support services.

  7. Use of GIS to identify optimal settings for cancer prevention and control in African American communities

    Science.gov (United States)

    Alcaraz, Kassandra I.; Kreuter, Matthew W.; Bryan, Rebecca P.

    2009-01-01

    Objective Rarely have Geographic Information Systems (GIS) been used to inform community-based outreach and intervention planning. This study sought to identify community settings most likely to reach individuals from geographically localized areas. Method An observational study conducted in an urban city in Missouri during 2003–2007 placed computerized breast cancer education kiosks in seven types of community settings: beauty salons, churches, health fairs, neighborhood health centers, Laundromats, public libraries and social service agencies. We used GIS to measure distance between kiosk users’ (n=7,297) home ZIP codes and the location where they used the kiosk. Mean distances were compared across settings. Results Mean distance between individuals’ home ZIP codes and the location where they used the kiosk varied significantly (pLaundromats (2.3 miles) and public libraries (2.8 miles) and greatest among kiosk users at health fairs (7.6 miles). Conclusion Some community settings are more likely than others to reach highly localized populations. A better understanding of how and where to reach specific populations can complement the progress already being made in identifying populations at increased disease risk. PMID:19422844

  8. Identifying context-specific competencies required by community Australian Football sports trainers.

    Science.gov (United States)

    Donaldson, Alex; Finch, Caroline F

    2012-08-01

    First-aid is a recommended injury prevention and risk management strategy in community sport; however, little is known about the sport-specific competencies required by first-aid providers. To achieve expert consensus on the competencies required by community Australian Football (community-AF) sports trainers. A three-round online Delphi process. Community-AF. 16 Australian sports first-aid and community-AF experts. Rating of competencies as either 'essential', 'expected', 'ideal' or 'not required'. Results After Round 3, 47 of the 77 (61%) competencies were endorsed as 'essential' or 'expected' for a sports trainer to effectively perform the activities required to the standards expected at a community-AF club by ≥75% of experts. These competencies covered: the role of the sports trainer; the responsibilities of the sports trainer; emergency management; injury and illness assessment and immediate management; taping; and injury prevention and risk management. Four competencies (5%) were endorsed as 'ideal' or 'not required' by ≥85% of experts and were excluded from further consideration. The 26 competencies where consensus was not reached were retained as second-tier, optional competencies. Sports trainers are important members of on-field first-aid teams, providing support to both injured players and other sports medicine professionals. The competencies identified in this study provide the basis of a proposed two-tiered community-AF-specific sports trainer education structure that can be implemented by the peak sports body. This includes six mandatory modules, relating to the 'required' competencies, and a further six optional modules covering competencies on which consensus was not reached.

  9. Identifying Social-ecological Linkages to Develop a Community Fire Plan in Mexico

    Directory of Open Access Journals (Sweden)

    Rachel A.S Sheridan

    2015-01-01

    Full Text Available Community forestry in rural Mexico presents a unique opportunity to study the linkages and feedback within coupled social-ecological systems due to the fact that agrarian or indigenous communities control approximately half of the national territory of Mexico. We used social and ecological diagnostic tools to develop a fire management strategy for a communal forest containing an endemic piñón pine species, Pinus cembroides subs. orizabensis, in the state of Tlaxcala, Mexico. The ecological diagnostic was done through fuel inventory, forest structure sampling, and fire behaviour modelling. The social assessment was conducted through household interviews, community workshops, and direct participant observation. The ecological fire hazard was quantified and coupled with the social assessment to develop a fire management plan. Vertical fuel continuity and flashy surface fuels created a high fire hazard. Modelled fire behaviour showed a rapid rate of spread and high flame lengths under multiple scenarios. Relative impunity for starting forest fires, poor community and inter-agency organisation, and lack of project continuity across organisational sectors appear to be the most significant social limiting factors for wildfire management. Combining both social and ecological diagnostic tools provides a comprehensive understanding of the actual risks to forests, and identifies realistic community-supported options for conservation on cooperatively managed lands.

  10. Identifying the Entrepreneurship Characteristics of the Oil Palm Community Plantation Farmers in the Riau Area

    OpenAIRE

    Brilliant Asmit; Deddy P. Koesrindartoto

    2015-01-01

    Oil palm is an essential and strategic commodity in the Riau area because of its considerable role in supporting the peoples’ economy, especially for plantation farmers. Oil palm plantation activities have brought economic impacts to society there, both for the people who are directly involved with the plantations and for their surrounding communities. This regional advantage is a facility for farmers to be able to develop their farms as plantations. The aims of this research are to identify ...

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

    Science.gov (United States)

    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.

  12. Source Water Protection Planning for Ontario First Nations Communities: Case Studies Identifying Challenges and Outcomes

    Directory of Open Access Journals (Sweden)

    Leslie Collins

    2017-07-01

    Full Text Available After the Walkerton tragedy in 2000, where drinking water contamination left seven people dead and many suffering from chronic illness, the Province of Ontario, Canada implemented policies to develop Source Water Protection (SWP plans. Under the Clean Water Act (2006, thirty-six regional Conservation Authorities were mandated to develop watershed-based SWP plans under 19 Source Protection Regions. Most First Nations in Ontario are outside of these Source Protection Regions and reserve lands are under Federal jurisdiction. This paper explores how First Nations in Ontario are attempting to address SWP to improve drinking water quality in their communities even though these communities are not part of the Ontario SWP framework. The case studies highlight the gap between the regulatory requirements of the Federal and Provincial governments and the challenges for First Nations in Ontario from lack of funding to implement solutions to address the threats identified in SWP planning. This analysis of different approaches taken by Ontario First Nations shows that the Ontario framework for SWP planning is not an option for the majority of First Nations communities, and does not adequately address threats originating on reserve lands. First Nations attempting to address on-reserve threats to drinking water are using a variety of resources and approaches to develop community SWP plans. However, a common theme of all the cases surveyed is a lack of funding to support implementing solutions for the threats identified by the SWP planning process. Federal government initiatives to address the chronic problem of boil water advisories within Indigenous communities do not recognize SWP planning as a cost-effective tool for improving drinking water quality.

  13. Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm.

    Science.gov (United States)

    Grolmusz, Vince I

    2015-04-01

    Diabetes is a growing concern for the developed nations worldwide. New genomic, metagenomic and gene-technologic approaches may yield considerable results in the next several years in its early diagnosis, or in advances in therapy and management. In this work, we highlight some human proteins that may serve as new targets in the early diagnosis and therapy. With the help of a very successful mathematical tool for network analysis that formed the basis of the early successes of Google(TM), Inc., we analyse the human protein-protein interaction network gained from the IntAct database with a mathematical algorithm. The novelty of our approach is that the new protein targets suggested do not have many interacting partners (so, they are not hubs or super-hubs), so their inhibition or promotion probably will not have serious side effects. We have identified numerous possible protein targets for diabetes therapy and/or management; some of these have been well known for a long time (these validate our method), some of them appeared in the literature in the last 12 months (these show the cutting edge of the algorithm), and the remainder are still unknown to be connected with diabetes, witnessing completely new hits of the method.

  14. Neural network based automated algorithm to identify joint locations on hand/wrist radiographs for arthritis assessment

    International Nuclear Information System (INIS)

    Duryea, J.; Zaim, S.; Wolfe, F.

    2002-01-01

    Arthritis is a significant and costly healthcare problem that requires objective and quantifiable methods to evaluate its progression. Here we describe software that can automatically determine the locations of seven joints in the proximal hand and wrist that demonstrate arthritic changes. These are the five carpometacarpal (CMC1, CMC2, CMC3, CMC4, CMC5), radiocarpal (RC), and the scaphocapitate (SC) joints. The algorithm was based on an artificial neural network (ANN) that was trained using independent sets of digitized hand radiographs and manually identified joint locations. The algorithm used landmarks determined automatically by software developed in our previous work as starting points. Other than requiring user input of the location of nonanatomical structures and the orientation of the hand on the film, the procedure was fully automated. The software was tested on two datasets: 50 digitized hand radiographs from patients participating in a large clinical study, and 60 from subjects participating in arthritis research studies and who had mild to moderate rheumatoid arthritis (RA). It was evaluated by a comparison to joint locations determined by a trained radiologist using manual tracing. The success rate for determining the CMC, RC, and SC joints was 87%-99%, for normal hands and 81%-99% for RA hands. This is a first step in performing an automated computer-aided assessment of wrist joints for arthritis progression. The software provides landmarks that will be used by subsequent image processing routines to analyze each joint individually for structural changes such as erosions and joint space narrowing

  15. BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc

    Science.gov (United States)

    Gagné, Jonathan; Mamajek, Eric E.; Malo, Lison; Riedel, Adric; Rodriguez, David; Lafrenière, David; Faherty, Jacqueline K.; Roy-Loubier, Olivier; Pueyo, Laurent; Robin, Annie C.; Doyon, René

    2018-03-01

    BANYAN Σ is a new Bayesian algorithm to identify members of young stellar associations within 150 pc of the Sun. It includes 27 young associations with ages in the range ∼1–800 Myr, modeled with multivariate Gaussians in six-dimensional (6D) XYZUVW space. It is the first such multi-association classification tool to include the nearest sub-groups of the Sco-Cen OB star-forming region, the IC 2602, IC 2391, Pleiades and Platais 8 clusters, and the ρ Ophiuchi, Corona Australis, and Taurus star formation regions. A model of field stars is built from a mixture of multivariate Gaussians based on the Besançon Galactic model. The algorithm can derive membership probabilities for objects with only sky coordinates and proper motion, but can also include parallax and radial velocity measurements, as well as spectrophotometric distance constraints from sequences in color–magnitude or spectral type–magnitude diagrams. BANYAN Σ benefits from an analytical solution to the Bayesian marginalization integrals over unknown radial velocities and distances that makes it more accurate and significantly faster than its predecessor BANYAN II. A contamination versus hit rate analysis is presented and demonstrates that BANYAN Σ achieves a better classification performance than other moving group tools available in the literature, especially in terms of cross-contamination between young associations. An updated list of bona fide members in the 27 young associations, augmented by the Gaia-DR1 release, as well as all parameters for the 6D multivariate Gaussian models for each association and the Galactic field neighborhood within 300 pc are presented. This new tool will make it possible to analyze large data sets such as the upcoming Gaia-DR2 to identify new young stars. IDL and Python versions of BANYAN Σ are made available with this publication, and a more limited online web tool is available at http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php.

  16. Identifying the community structure of the food-trade international multi-network

    Science.gov (United States)

    Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

    2018-05-01

    Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

  17. Identifying Effective and Sustainable Measures for Community-Based Environmental Monitoring

    Science.gov (United States)

    McKay, Ariana J.; Johnson, Chris J.

    2017-09-01

    Resource development projects typically result in monitoring programs that fail to fully consider the values and participation of surrounding communities. Also, monitoring protocols for single environmental values can be insufficient for addressing the cumulative impacts of resource development. Community-based environmental monitoring (CBEM) has emerged as a way to meaningfully include local citizens in the decision-making process and assessment of the development of natural resources. Our research explored how to develop effective and sustainable CBEM. Interviews were conducted with staff from 15 CBEM programs established across Canada to identify criteria of what constitutes effective CBEM. Results demonstrate that CBEM offers an effective, locally adapted, and culturally applicable approach to facilitate community participation in natural resource management and to track environmental change. Benefits of CBEM include: locally relevant monitoring protocols, inclusion of cumulative impacts, better informed decision-making, and increased awareness and collaboration amongst community, governments, and proponents. Challenges associated with CBEM are cost, capacity, longevity, distribution of results, and establishing credibility. This research validates the use of CBEM for improving resource management.

  18. A Netnographic Study of Entrepreneurial Traits: Evaluating classic typologies using the crowdsourcing algorithm of an online community

    Directory of Open Access Journals (Sweden)

    Marcos Cerqueira Lima

    2014-09-01

    Full Text Available This paper evaluates how the advices of experienced entrepreneurs to young start-up creators in an online community reflect entrepreneurship traits commonly found in conceptual typologies. The overall goal is to contrast and evaluate existing models based on evidence from an online community. This should facilitate future studies to improve current typologies by ranking entrepreneurial traits according to perceived relevance. In order to achieve these objectives, we have conducted a “netnographic study” (i.e., the qualitative analysis of web-based content of 96 answers to the question “What is the best advice for a young, first-time startup CEO?” on Quora.com. Relying on Quora’s ranking algorithm (based on crowdsourcing of votes and community prestige, we focused on the top 50% of answers (which we shall call the “above Quora 50” category considered the most relevant by its 2000+ followers and 120,000+ viewers. We used Nvivo as a Qualitative Data Analysis Software to code all the entries into the literature categories. These codes were then later retrieved using matrix queries to compare the incidence of traits and the perceived relevance of answers. We found that among the 50% highest ranking answers on Quora, the following traits are perceived as the most important for young entrepreneurs to develop: management style, attitude in interpersonal relations, vision, self-concept, leadership style, marketing, market and customer knowledge, innovation, technical knowledge and skills, attitude to growth, ability to adapt, purpose and relations system. These results could lead to improving existing typologies and creating new models capable of better identifying people with the highest potential to succeed in new venture creation.

  19. An Empirical Comparison of Algorithms to Find Communities in Directed Graphs and Their Application in Web Data Analytics

    DEFF Research Database (Denmark)

    Agreste, Santa; De Meo, Pasquale; Fiumara, Giacomo

    2017-01-01

    Detecting communities in graphs is a fundamental tool to understand the structure of Web-based systems and predict their evolution. Many community detection algorithms are designed to process undirected graphs (i.e., graphs with bidirectional edges) but many graphs on the Web-e.g., microblogging ...... the best trade-off between accuracy and computational performance and, therefore, it has to be considered as a promising tool for Web Data Analytics purposes....

  20. Identifying medication-related needs of HIV patients: foundation for community pharmacist-based services

    Directory of Open Access Journals (Sweden)

    Yardlee Kauffman

    2014-01-01

    Full Text Available Background: Patients living with HIV/AIDS have complex medication regimens. Pharmacists within community pharmacy settings can have a role managing patients living with HIV/AIDS. Patients' perspectives surrounding implementation about community pharmacist-based services is needed as limited information is available. Objective: To identify medication-related needs of HIV-infected patients who receive prescriptions from a community pharmacy. To determine patient perspectives and knowledge of community pharmacist-based services. Methods: A qualitative research study involving in-depth, semi-structured interviews with patients was conducted. Inclusion criteria included: HIV positive men and women at least 18 years of age who receive care at a HIV clinic, currently take medication(s and use a community pharmacy for all prescription fills. Patients were recruited from one urban and one rural health center. Patients answered questions about their perceptions and knowledge about the role and value of pharmacy services and completed a demographic survey. The recordings of the interviews were transcribed verbatim and were analyzed using principles of Grounded Theory. Results: Twenty-nine interviews were conducted: 15 participants from the urban site and 14 from the rural site. Five main themes emerged including: patients experience ongoing and varying medication-related needs; patients desire a pharmacist who is caring, knowledgeable and integrated with health care providers; patients expect ready access to drug therapy; patients value an individualized patient encounter, and patients need to be informed that a pharmacist-service exists. Conclusion: Patients with HIV value individualized and personal encounters with pharmacists at time intervals that are convenient for the patient. Patients felt that a one-on-one encounter with a pharmacist would be most valuable when initiating or modifying medication therapy. These patient perspectives can be useful for

  1. Towards a community effort to identify ethical principles for research in hydrology

    Science.gov (United States)

    Montanari, Alberto

    2010-05-01

    The hydrological community in Europe is growing rapidly in both size and, more importantly, scientific relevance and integrity. The Hydrological Sciences (HS) Division of EGU actively is promoting the above development by identifying research targets, stimulating the involvement of young scientists and managing a scientific open access journal based on a public peer review process. The management of the Division itself and the organisation of the General Assembly are carried out transparently, with the aim to seek an improved involvement of top and young scientists, with a bottom up approach. I believe the HS community is animated by a strong enthusiasm which, however, is not adequately supported by economical funding. In my opinion this is a major problem which HS should consider and discuss. The relevance of the societal and environmental problems dealt with by hydrologists, in a professional way and with exceptional scientific skills, is without doubt and therefore the limited amount of funding is not justified in practice. In my opinion, in order to refine the structure of the HS community, and promote its visibility, we should formally identify HS ethical principles for research in environmental science. The principles should highlight the role of hydrology as well as the ethical and scientific solidity of the HS community. Establishing ethical principles is even more important in view of the transparent approach HS is adopting for reviewing and publishing contributions and in view of the increasing need to transparently prove how public funding for research is administered. Establishing ethical principles for hydrology is not a trivial task. Hydrology is characterised by a relevant uncertainty in data, models and parameters. Hydrology is also relying on a large variety of approaches, ranging from statistical to physically based. The purpose of this poster is to present a collection of ethical principles for scientific research presented by the literature and

  2. Desired attributes of new graduate nurses as identified by the rural community.

    Science.gov (United States)

    Sivamalai, S

    2008-01-01

    Preparing nurse graduates for practice is challenging because of the diversity of skills expected of them. Increasingly consumers are more informed and expect quality care. To identify the attributes a rural community expect in new graduate nurses in order for them to provide quality care. A questionnaire was designed to assess the importance attached to a set of attributes of graduate nurses expected by a rural community. The community included a range of professionals working with government and hospitals, community volunteers and retired people. After pilot testing, the questionnaire was distributed using a cluster sampling technique. A total of 656 completed questionnaires were returned, giving a response rate of 69%. The respondents were asked to rate the importance of each item for the community on a five-point Likert scale (5 = extremely important, 4 = very important, 3 = moderately important, 2 = possibly importantly, and 1 = not important at all). Exploratory factor analysis was performed on the 38 items using SPSS (SPSS inc; Chicago, IL, USA). Principal Components Analysis was applied to identify the number of factors followed by Oblimin rotation. The sample of 656 respondents consisted of 68% females and 30% males (2% did not identify their gender). The majority of the respondents (75.6%) were born in Australia, while 3.2% were born in the UK Kingdom. Principal Components Analysis identified five factors with eigenvalues above one, explaining 47.4% of the total variance. Items that loaded greater than + or - 0.3, (approximately 10% of the common factor variance) was associated with the factor in question. Component 1 was labelled Sympathetic/ Patients' welfare with the item 'Nurses should be sensitive to the emotional needs of patients' showing the highest loading. Component 2 was called Contextual knowledge/ Interpersonal skills. It contained items indicating that nurses should have good personal skills and possess a broad contextual knowledge of issues

  3. Costs per Diagnosis of Acute HIV Infection in Community-based Screening Strategies: A Comparative Analysis of Four Screening Algorithms

    Science.gov (United States)

    Hoenigl, Martin; Graff-Zivin, Joshua; Little, Susan J.

    2016-01-01

    Background. In nonhealthcare settings, widespread screening for acute human immunodeficiency virus (HIV) infection (AHI) is limited by cost and decision algorithms to better prioritize use of resources. Comparative cost analyses for available strategies are lacking. Methods. To determine cost-effectiveness of community-based testing strategies, we evaluated annual costs of 3 algorithms that detect AHI based on HIV nucleic acid amplification testing (EarlyTest algorithm) or on HIV p24 antigen (Ag) detection via Architect (Architect algorithm) or Determine (Determine algorithm) as well as 1 algorithm that relies on HIV antibody testing alone (Antibody algorithm). The cost model used data on men who have sex with men (MSM) undergoing community-based AHI screening in San Diego, California. Incremental cost-effectiveness ratios (ICERs) per diagnosis of AHI were calculated for programs with HIV prevalence rates between 0.1% and 2.9%. Results. Among MSM in San Diego, EarlyTest was cost-savings (ie, ICERs per AHI diagnosis less than $13.000) when compared with the 3 other algorithms. Cost analyses relative to regional HIV prevalence showed that EarlyTest was cost-effective (ie, ICERs less than $69.547) for similar populations of MSM with an HIV prevalence rate >0.4%; Architect was the second best alternative for HIV prevalence rates >0.6%. Conclusions. Identification of AHI by the dual EarlyTest screening algorithm is likely to be cost-effective not only among at-risk MSM in San Diego but also among similar populations of MSM with HIV prevalence rates >0.4%. PMID:26508512

  4. Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm.

    Science.gov (United States)

    Zhang, Yunhua; Dai, Li; Liu, Ying; Zhang, YuHang; Wang, ShaoPeng

    2017-01-01

    Fruit is essential for plant reproduction and is responsible for protection and dispersal of seeds. The development and maturation of fruit is tightly regulated by numerous genetic factors that respond to environmental and internal stimulation. In this study, we attempted to identify novel fruit-related genes in a model organism, Arabidopsis thaliana, using a computational method. Based on validated fruit-related genes, the random walk with restart (RWR) algorithm was applied on a protein-protein interaction (PPI) network using these genes as seeds. The identified genes with high probabilities were filtered by the permutation test and linkage tests. In the permutation test, the genes that were selected due to the structure of the PPI network were discarded. In the linkage tests, the importance of each candidate gene was measured from two aspects: (1) its functional associations with validated genes and (2) its similarity with validated genes on gene ontology (GO) terms and KEGG pathways. Finally, 255 inferred genes were obtained, subsequent extensive analysis of important genes revealed that they mainly contribute to ubiquitination (UBQ9, UBQ8, UBQ11, UBQ10), serine hydroxymethyl transfer (SHM7, SHM5, SHM6) or glycol-metabolism (HXKL2_ARATH, CSY5, GAPCP1), suggesting essential roles during the development and maturation of fruit in Arabidopsis thaliana.

  5. Identifying Health Needs in Peru Through Use of a Community Survey.

    Science.gov (United States)

    Renn, McCartney; Steffen, Lori

    2016-11-01

    Students and faculty from a Midwestern college conducted a neighborhood community needs assessment in an impoverished area of a Peruvian city to identify health needs of residents. Students interviewed residents in their homes, asking about the need for medical, dental, and ophthalmic care and screening for chronic conditions such as diabetes, heart disease, and tuberculosis. The survey provided necessary information to medical mission workers and allowed students to directly observe family living conditions while assessing psychosocial needs of the families interviewed. The challenges of this survey included differing expectations, language barriers, recruiting neighborhood volunteers, safety risks to students, and mistrust by neighborhood residents.

  6. Automated Means of Identifying Landslide Deposits using LiDAR Data using the Contour Connection Method Algorithm

    Science.gov (United States)

    Olsen, M. J.; Leshchinsky, B. A.; Tanyu, B. F.

    2014-12-01

    Landslides are a global natural hazard, resulting in severe economic, environmental and social impacts every year. Often, landslides occur in areas of repeated slope instability, but despite these trends, significant residential developments and critical infrastructure are built in the shadow of past landslide deposits and marginally stable slopes. These hazards, despite their sometimes enormous scale and regional propensity, however, are difficult to detect on the ground, often due to vegetative cover. However, new developments in remote sensing technology, specifically Light Detection and Ranging mapping (LiDAR) are providing a new means of viewing our landscape. Airborne LiDAR, combined with a level of post-processing, enable the creation of spatial data representative of the earth beneath the vegetation, highlighting the scars of unstable slopes of the past. This tool presents a revolutionary technique to mapping landslide deposits and their associated regions of risk; yet, their inventorying is often done manually, an approach that can be tedious, time-consuming and subjective. However, the associated LiDAR bare earth data present the opportunity to use this remote sensing technology and typical landslide geometry to create an automated algorithm that can detect and inventory deposits on a landscape scale. This algorithm, called the Contour Connection Method (CCM), functions by first detecting steep gradients, often associated with the headscarp of a failed hillslope, and initiating a search, highlighting deposits downslope of the failure. Based on input of search gradients, CCM can assist in highlighting regions identified as landslides consistently on a landscape scale, capable of mapping more than 14,000 hectares rapidly (help better define these regions of risk.

  7. Soil Fungal Community Associated with Peat in Sarawak Identified Using 18S rDNA Marker

    International Nuclear Information System (INIS)

    Siti Ramlah Ahmad Ali; Sakinah Safari; Mohd Shawal Thakib; Shamsilawani Ahamed Bakeri; Nur Aziemah Ab Ghani

    2016-01-01

    Fungi are principal decomposing microorganisms in acidic environment of peat lands. A useful tool for molecular screening of soil fungal communities using the 18S ribosomal DNA primer has been proven capable of identifying a broad range of fungi species within Ascomycota, Basidiomycota, Zygomycota and Chytridiomycota. Currently, very little information is available on fungal communities in deep peat of Sarawak, Malaysia. In this study, we have isolated the fungi from soil samples taken in deep peat forests and oil palm cultivated areas. The fungal identity was undertaken using 18S ribosomal DNA primer which is EF4-F/ fung5-R. The microscopic structures were conducted to confirm the identity of the isolates. Based on this study, the fungal division most commonly found in deep peat is the Ascomycota. Aspergillus fumigatus was the most common species and more dominant in oil palm cultivated areas and logged-over forest than in primary forest. In the primary forest, the dominant species was the A. flavus, while Hypocrea atroviridis was commonly associated with oil palm cultivated areas and logged-over forest. Other species of fungi isolated in peat primary forests were Penicillium chrysogenum, Trichoderma sp., Phanerochaete sp., Mortierella chlamydospora, A. niger, A. alliaceus, etc. The in-depth difference in the fungal communities for the different sites will be further investigated using the next generation sequencing technology. (author)

  8. Assessment of a Novel Approach to Identify Trichiasis Cases Using Community Treatment Assistants in Tanzania.

    Science.gov (United States)

    Greene, Gregory S; West, Sheila K; Mkocha, Harran; Munoz, Beatriz; Merbs, Shannath L

    2015-12-01

    Simple surgical intervention advocated by the World Health Organization can alleviate trachomatous trichiasis (TT) and prevent subsequent blindness. A large backlog of TT cases remain unidentified and untreated. To increase identification and referral of TT cases, a novel approach using standard screening questions, a card, and simple training for Community Treatment Assistants (CTAs) to use during Mass Drug Administration (MDA) was developed and evaluated in Kongwa District, a trachoma-endemic area of central Tanzania. A community randomized trial was conducted in 36 communities during MDA. CTAs in intervention villages received an additional half-day of training and a TT screening card in addition to the training received by CTAs in villages assigned to usual care. All MDA participants 15 years and older were screened for TT, and senior TT graders confirmed case status by evaluating all screened-positive cases. A random sample of those screened negative for TT and those who did not present at MDA were also evaluated by the master graders. Intervention CTAs identified 5.6 times as many cases (n = 50) as those assigned to usual care (n = 9, p card significantly increased the ability of CTAs to recognize and refer TT cases during MDA; however, further efforts are needed to improve case detection and reduce the number of false positive cases.

  9. Mapping radioactivity in groundwater to identify elevated exposure in remote and rural communities

    Energy Technology Data Exchange (ETDEWEB)

    Kleinschmidt, Ross, E-mail: ross_kleinschmidt@health.qld.gov.a [Queensland University of Technology, Faculty of Science and Technology, Discipline of Physics, 2 George Street, Brisbane, Queensland 4000 (Australia); Health Physics Unit, Queensland Health Forensic and Scientific Services, 39 Kessels Road, Coopers Plains, Queensland 4108 (Australia); Black, Jeffrey [Health Physics Unit, Queensland Health Forensic and Scientific Services, 39 Kessels Road, Coopers Plains, Queensland 4108 (Australia); Akber, Riaz [Queensland University of Technology, Faculty of Science and Technology, Discipline of Physics, 2 George Street, Brisbane, Queensland 4000 (Australia)

    2011-03-15

    A survey of radioactivity in groundwater (110 sites) was conducted as a precursor to providing a baseline of radiation exposure in rural and remote communities in Queensland, Australia, that may be impacted upon by exposure pathways associated with the supply, treatment, use and wastewater treatment of the resource. Radionuclides in groundwater, including {sup 238}U, {sup 226}Ra, {sup 222}Rn, {sup 228}Ra, {sup 224}Ra and {sup 40}K were measured and found to contain activity concentration levels of up to 0.71 BqL{sup -1}, 0.96 BqL{sup -1}, 108 BqL{sup -1}, 2.8 BqL{sup -1}, 0.11 BqL{sup -1} and 0.19 BqL{sup -1} respectively. Activity concentration results were classified by aquifer lithology, showing correlation between increased radium isotope concentration and basic volcanic host rock. The groundwater survey and mapping results were further assessed using an investigation assessment tool to identify seven remote or rural communities that may require additional radiation dose assessment beyond that attributed to ingestion of potable water. - Research highlights: {yields} We studied the concentration of naturally occurring radioactivity in groundwater in Queensland, Australia. {yields} Groundwater radioactivity concentrations were classified by aquifer type, location and magnitude. {yields} Radioactivity concentration in groundwater was used to develop a tool to determine the potential for elevated radiation exposure to rural and remote communities, based on a case study of a reference site. {yields} Of 110 groundwater bores tested, seven were assessed as requiring further community dose assessment.

  10. Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study.

    Directory of Open Access Journals (Sweden)

    Taro Takeshima

    Full Text Available (1 To develop a clinical prediction rule to identify patients with bacteremia, using only information that is readily available in the emergency room (ER of community hospitals, and (2 to test the validity of that rule with a separate, independent set of data.Multicenter retrospective cohort study.To derive the clinical prediction rule we used data from 3 community hospitals in Japan (derivation. We tested the rule using data from one other community hospital (validation, which was not among the three "derivation" hospitals.Adults (age ≥ 16 years old who had undergone blood-culture testing while in the ER between April 2011 and March 2012. For the derivation data, n = 1515 (randomly sampled from 7026 patients, and for the validation data n = 467 (from 823 patients.We analyzed 28 candidate predictors of bacteremia, including demographic data, signs and symptoms, comorbid conditions, and basic laboratory data. Chi-square tests and multiple logistic regression were used to derive an integer risk score (the "ID-BactER" score. Sensitivity, specificity, likelihood ratios, and the area under the receiver operating characteristic curve (i.e., the AUC were computed.There were 241 cases of bacteremia in the derivation data. Eleven candidate predictors were used in the ID-BactER score: age, chills, vomiting, mental status, temperature, systolic blood pressure, abdominal sign, white blood-cell count, platelets, blood urea nitrogen, and C-reactive protein. The AUCs was 0.80 (derivation and 0.74 (validation. For ID-BactER scores ≥ 2, the sensitivities for derivation and validation data were 98% and 97%, and specificities were 20% and 14%, respectively.The ID-BactER score can be computed from information that is readily available in the ERs of community hospitals. Future studies should focus on developing a score with a higher specificity while maintaining the desired sensitivity.

  11. Identifying mechanisms that structure ecological communities by snapping model parameters to empirically observed tradeoffs.

    Science.gov (United States)

    Thomas Clark, Adam; Lehman, Clarence; Tilman, David

    2018-04-01

    Theory predicts that interspecific tradeoffs are primary determinants of coexistence and community composition. Using information from empirically observed tradeoffs to augment the parametrisation of mechanism-based models should therefore improve model predictions, provided that tradeoffs and mechanisms are chosen correctly. We developed and tested such a model for 35 grassland plant species using monoculture measurements of three species characteristics related to nitrogen uptake and retention, which previous experiments indicate as important at our site. Matching classical theoretical expectations, these characteristics defined a distinct tradeoff surface, and models parameterised with these characteristics closely matched observations from experimental multi-species mixtures. Importantly, predictions improved significantly when we incorporated information from tradeoffs by 'snapping' characteristics to the nearest location on the tradeoff surface, suggesting that the tradeoffs and mechanisms we identify are important determinants of local community structure. This 'snapping' method could therefore constitute a broadly applicable test for identifying influential tradeoffs and mechanisms. © 2018 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  12. Identifying gender-preferred communication styles within online cancer communities: a retrospective, longitudinal analysis.

    Science.gov (United States)

    Durant, Kathleen T; McCray, Alexa T; Safran, Charles

    2012-01-01

    The goal of this research is to determine if different gender-preferred social styles can be observed within the user interactions at an online cancer community. To achieve this goal, we identify and measure variables that pertain to each gender-specific social style. We perform social network and statistical analysis on the communication flow of 8,388 members at six different cancer forums over eight years. Kruskal-Wallis tests were conducted to measure the difference between the number of intimate (and highly intimate) dyads, relationship length, and number of communications. We determine that two patients are more likely to form an intimate bond on a gender-specific cancer forum (ovarian P = communicates with more members than a female patient (Ovarian forum P = 0.0406, Breast forum P = 0.0013). A relationship between two patients is longer on the gender-specific cancer forums than a connection between two members not identified as patients (ovarian forum P = 0.00406, breast forum P = 0.00013, prostate forum P = .0.0003). The high level of interconnectedness among the prostate patients supports the hypothesis that men prefer to socialize in large, interconnected, less-intimate groups. A female patient is more likely to form a highly intimate connection with another female patient; this finding is consistent with the hypothesis that woman prefer fewer, more intimate connections. The relationships of same-gender cancer patients last longer than other relationships; this finding demonstrates homophily within these online communities. Our findings regarding online communication preferences are in agreement with research findings from person-to-person communication preference studies. These findings should be considered when designing online communities as well as designing and evaluating psychosocial and educational interventions for cancer patients.

  13. A community effort to assess and improve drug sensitivity prediction algorithms.

    Science.gov (United States)

    Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

    2014-12-01

    Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

  14. Identifying the Entrepreneurship Characteristics of the Oil Palm Community Plantation Farmers in the Riau Area

    Directory of Open Access Journals (Sweden)

    Brilliant Asmit

    2015-12-01

    Full Text Available Oil palm is an essential and strategic commodity in the Riau area because of its considerable role in supporting the peoples’ economy, especially for plantation farmers. Oil palm plantation activities have brought economic impacts to society there, both for the people who are directly involved with the plantations and for their surrounding communities. This regional advantage is a facility for farmers to be able to develop their farms as plantations. The aims of this research are to identify the entrepreneurship characteristics of the oil palm farmers, and also to identify the entrepreneurship characteristics that differentiate the farmers, as seen from their business’ achievements. The research used a grounded theory approach to identify the characteristics of oil palm farmers systematically. The sampling method used for the research was theoretical sampling, which is data gathering driven by the concepts derived from the theory of previous entrepreneurship characteristics studies. The research object is the oil palm farmers in Riau, Indonesia. The results of the analysis identified the entrepreneurship characteristics of the oil palm farmers, they are growth oriented, risk-taking, innovative, with a sense of personal control, self confident, and cooperative. But, among the characteristics, only the characteristic of their cooperation did not differentiate the oil palm farmers in the achievement of their business activities.

  15. Thresher: an improved algorithm for peak height thresholding of microbial community profiles.

    Science.gov (United States)

    Starke, Verena; Steele, Andrew

    2014-11-15

    This article presents Thresher, an improved technique for finding peak height thresholds for automated rRNA intergenic spacer analysis (ARISA) profiles. We argue that thresholds must be sample dependent, taking community richness into account. In most previous fragment analyses, a common threshold is applied to all samples simultaneously, ignoring richness variations among samples and thereby compromising cross-sample comparison. Our technique solves this problem, and at the same time provides a robust method for outlier rejection, selecting for removal any replicate pairs that are not valid replicates. Thresholds are calculated individually for each replicate in a pair, and separately for each sample. The thresholds are selected to be the ones that minimize the dissimilarity between the replicates after thresholding. If a choice of threshold results in the two replicates in a pair failing a quantitative test of similarity, either that threshold or that sample must be rejected. We compare thresholded ARISA results with sequencing results, and demonstrate that the Thresher algorithm outperforms conventional thresholding techniques. The software is implemented in R, and the code is available at http://verenastarke.wordpress.com or by contacting the author. vstarke@ciw.edu or http://verenastarke.wordpress.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Administrative Algorithms to identify Avascular necrosis of bone among patients undergoing upper or lower extremity magnetic resonance imaging: a validation study.

    Science.gov (United States)

    Barbhaiya, Medha; Dong, Yan; Sparks, Jeffrey A; Losina, Elena; Costenbader, Karen H; Katz, Jeffrey N

    2017-06-19

    Studies of the epidemiology and outcomes of avascular necrosis (AVN) require accurate case-finding methods. The aim of this study was to evaluate performance characteristics of a claims-based algorithm designed to identify AVN cases in administrative data. Using a centralized patient registry from a US academic medical center, we identified all adults aged ≥18 years who underwent magnetic resonance imaging (MRI) of an upper/lower extremity joint during the 1.5 year study period. A radiologist report confirming AVN on MRI served as the gold standard. We examined the sensitivity, specificity, positive predictive value (PPV) and positive likelihood ratio (LR + ) of four algorithms (A-D) using International Classification of Diseases, 9th edition (ICD-9) codes for AVN. The algorithms ranged from least stringent (Algorithm A, requiring ≥1 ICD-9 code for AVN [733.4X]) to most stringent (Algorithm D, requiring ≥3 ICD-9 codes, each at least 30 days apart). Among 8200 patients who underwent MRI, 83 (1.0% [95% CI 0.78-1.22]) had AVN by gold standard. Algorithm A yielded the highest sensitivity (81.9%, 95% CI 72.0-89.5), with PPV of 66.0% (95% CI 56.0-75.1). The PPV of algorithm D increased to 82.2% (95% CI 67.9-92.0), although sensitivity decreased to 44.6% (95% CI 33.7-55.9). All four algorithms had specificities >99%. An algorithm that uses a single billing code to screen for AVN among those who had MRI has the highest sensitivity and is best suited for studies in which further medical record review confirming AVN is feasible. Algorithms using multiple billing codes are recommended for use in administrative databases when further AVN validation is not feasible.

  17. A database of phylogenetically atypical genes in archaeal and bacterial genomes, identified using the DarkHorse algorithm

    Directory of Open Access Journals (Sweden)

    Allen Eric E

    2008-10-01

    Full Text Available Abstract Background The process of horizontal gene transfer (HGT is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not. Description The DarkHorse algorithm has been applied to 955 microbial genome sequences, and the results organized into a web-searchable relational database, called the DarkHorse HGT Candidate Resource http://darkhorse.ucsd.edu. Users can select individual genomes or groups of genomes to screen by LPI score, search for protein functions by descriptive annotation or amino acid sequence similarity, or select proteins with unusual G+C composition in their underlying coding sequences. The search engine reports LPI scores for match partners as well as query sequences, providing the opportunity to explore whether potential HGT donor sequences are phylogenetically typical or atypical within their own genomes. This information can be used to predict whether or not sufficient information is available to build a well-supported phylogenetic tree using the potential donor sequence. Conclusion The DarkHorse HGT Candidate database provides a powerful, flexible set of tools for identifying phylogenetically atypical proteins, allowing researchers to explore both individual HGT events in single genomes, and

  18. Assessment of a Novel Approach to Identify Trichiasis Cases Using Community Treatment Assistants in Tanzania.

    Directory of Open Access Journals (Sweden)

    Gregory S Greene

    2015-12-01

    Full Text Available Simple surgical intervention advocated by the World Health Organization can alleviate trachomatous trichiasis (TT and prevent subsequent blindness. A large backlog of TT cases remain unidentified and untreated. To increase identification and referral of TT cases, a novel approach using standard screening questions, a card, and simple training for Community Treatment Assistants (CTAs to use during Mass Drug Administration (MDA was developed and evaluated in Kongwa District, a trachoma-endemic area of central Tanzania.A community randomized trial was conducted in 36 communities during MDA. CTAs in intervention villages received an additional half-day of training and a TT screening card in addition to the training received by CTAs in villages assigned to usual care. All MDA participants 15 years and older were screened for TT, and senior TT graders confirmed case status by evaluating all screened-positive cases. A random sample of those screened negative for TT and those who did not present at MDA were also evaluated by the master graders. Intervention CTAs identified 5.6 times as many cases (n = 50 as those assigned to usual care (n = 9, p < 0.05. While specificity was above 90% for both groups, the sensitivity for the novel screening tool was 31.2% compared to 5.6% for the usual care group (p < 0.05.CTAs appear to be viable resources for the identification of TT cases. Additional training and use of a TT screening card significantly increased the ability of CTAs to recognize and refer TT cases during MDA; however, further efforts are needed to improve case detection and reduce the number of false positive cases.

  19. Factor Analysis of Therapist-Identified Treatment Targets in Community-Based Children's Mental Health.

    Science.gov (United States)

    Love, Allison R; Okado, Izumi; Orimoto, Trina E; Mueller, Charles W

    2018-01-01

    The present study used exploratory and confirmatory factor analyses to identify underlying latent factors affecting variation in community therapists' endorsement of treatment targets. As part of a statewide practice management program, therapist completed monthly reports of treatment targets (up to 10 per month) for a sample of youth (n = 790) receiving intensive in-home therapy. Nearly 75 % of youth were diagnosed with multiple co-occurring disorders. Five factors emerged: Disinhibition, Societal Rules Evasion, Social Engagement Deficits, Emotional Distress, and Management of Biodevelopmental Outcomes. Using logistic regression, primary diagnosis predicted therapist selection of Disinhibition and Emotional Distress targets. Client age predicted endorsement of Societal Rules Evasion targets. Practice-to-research implications are discussed.

  20. How well do discharge diagnoses identify hospitalised patients with community-acquired infections? - a validation study

    DEFF Research Database (Denmark)

    Henriksen, Daniel Pilsgaard; Nielsen, Stig Lønberg; Laursen, Christian Borbjerg

    2014-01-01

    -10 diagnoses was 79.9% (95%CI: 78.1-81.3%), specificity 83.9% (95%CI: 82.6-85.1%), positive likelihood ratio 4.95 (95%CI: 4.58-5.36) and negative likelihood ratio 0.24 (95%CI: 0.22-0.26). The two most common sites of infection, the lower respiratory tract and urinary tract, had positive likelihood......BACKGROUND: Credible measures of disease incidence, trends and mortality can be obtained through surveillance using manual chart review, but this is both time-consuming and expensive. ICD-10 discharge diagnoses are used as surrogate markers of infection, but knowledge on the validity of infections...... in general is sparse. The aim of the study was to determine how well ICD-10 discharge diagnoses identify patients with community-acquired infections in a medical emergency department (ED), overall and related to sites of infection and patient characteristics. METHODS: We manually reviewed 5977 patients...

  1. Community Health Workers in the United States: Challenges in Identifying, Surveying, and Supporting the Workforce.

    Science.gov (United States)

    Sabo, Samantha; Allen, Caitlin G; Sutkowi, Katherine; Wennerstrom, Ashley

    2017-12-01

    Community health workers (CHWs) are members of a growing profession in the United States. Studying this dynamic labor force is challenging, in part because its members have more than 100 different job titles. The demand for timely, accurate information about CHWs is increasing as the profession gains recognition for its ability to improve health outcomes and reduce costs. Although numerous surveys of CHWs have been conducted, the field lacks well-delineated methods for gaining access to this hard-to-identify workforce. We outline methods for surveying CHWs and promising approaches to engage the workforce and other stakeholders in conducting local, state, and national studies. We also highlight successful strategies to overcome challenges in CHW surveys and future directions for surveying the field.

  2. Identifying gender-preferred communication styles within online cancer communities: a retrospective, longitudinal analysis.

    Directory of Open Access Journals (Sweden)

    Kathleen T Durant

    Full Text Available BACKGROUND: The goal of this research is to determine if different gender-preferred social styles can be observed within the user interactions at an online cancer community. To achieve this goal, we identify and measure variables that pertain to each gender-specific social style. METHODS AND FINDINGS: We perform social network and statistical analysis on the communication flow of 8,388 members at six different cancer forums over eight years. Kruskal-Wallis tests were conducted to measure the difference between the number of intimate (and highly intimate dyads, relationship length, and number of communications. We determine that two patients are more likely to form an intimate bond on a gender-specific cancer forum (ovarian P = <0.0001, breast P = 0.0089, prostate P = 0.0021. Two female patients are more likely to form a highly intimate bond on a female-specific cancer forum (Ovarian P<0.0001, Breast P<0.01. Typically a male patient communicates with more members than a female patient (Ovarian forum P = 0.0406, Breast forum P = 0.0013. A relationship between two patients is longer on the gender-specific cancer forums than a connection between two members not identified as patients (ovarian forum P = 0.00406, breast forum P = 0.00013, prostate forum P = .0.0003. CONCLUSION: The high level of interconnectedness among the prostate patients supports the hypothesis that men prefer to socialize in large, interconnected, less-intimate groups. A female patient is more likely to form a highly intimate connection with another female patient; this finding is consistent with the hypothesis that woman prefer fewer, more intimate connections. The relationships of same-gender cancer patients last longer than other relationships; this finding demonstrates homophily within these online communities. Our findings regarding online communication preferences are in agreement with research findings from person-to-person communication

  3. Community-Engaged Research to Identify House Parent Perspectives on Support and Risk within the House and Ball Scene

    Science.gov (United States)

    Kubicek, Katrina; Beyer, William H.; McNeeley, Miles; Weiss, George; Omni, Legendary Father Taz Ultra; Kipke, Michele D.

    2012-01-01

    This paper describes a community-engaged study with the Los Angeles House and Ball scene, in which the perspectives of the leaders of these communities are captured to better understand how the House and Ball communities may protect and/or increase its members’ risks for HIV infection. Data were collected through in-depth interviews with House parents (N=26). This study identified key features of both support (e.g., family and support; acceptance; validation and recognition) and risk (e.g., members’ struggle to maintain status in the Ballroom scene; sex work; substance use; danger of becoming too involved in the Ball community; perception and stigma of Ballroom scene within the larger gay community) within these communities. Findings are discussed in relation to framing how to leverage the supportive aspects of the House and Ball communities to design relevant HIV prevention interventions. PMID:22206442

  4. A matter of timing: identifying significant multi-dose radiotherapy improvements by numerical simulation and genetic algorithm search.

    Directory of Open Access Journals (Sweden)

    Simon D Angus

    Full Text Available Multi-dose radiotherapy protocols (fraction dose and timing currently used in the clinic are the product of human selection based on habit, received wisdom, physician experience and intra-day patient timetabling. However, due to combinatorial considerations, the potential treatment protocol space for a given total dose or treatment length is enormous, even for relatively coarse search; well beyond the capacity of traditional in-vitro methods. In constrast, high fidelity numerical simulation of tumor development is well suited to the challenge. Building on our previous single-dose numerical simulation model of EMT6/Ro spheroids, a multi-dose irradiation response module is added and calibrated to the effective dose arising from 18 independent multi-dose treatment programs available in the experimental literature. With the developed model a constrained, non-linear, search for better performing cadidate protocols is conducted within the vicinity of two benchmarks by genetic algorithm (GA techniques. After evaluating less than 0.01% of the potential benchmark protocol space, candidate protocols were identified by the GA which conferred an average of 9.4% (max benefit 16.5% and 7.1% (13.3% improvement (reduction on tumour cell count compared to the two benchmarks, respectively. Noticing that a convergent phenomenon of the top performing protocols was their temporal synchronicity, a further series of numerical experiments was conducted with periodic time-gap protocols (10 h to 23 h, leading to the discovery that the performance of the GA search candidates could be replicated by 17-18 h periodic candidates. Further dynamic irradiation-response cell-phase analysis revealed that such periodicity cohered with latent EMT6/Ro cell-phase temporal patterning. Taken together, this study provides powerful evidence towards the hypothesis that even simple inter-fraction timing variations for a given fractional dose program may present a facile, and highly cost

  5. A matter of timing: identifying significant multi-dose radiotherapy improvements by numerical simulation and genetic algorithm search.

    Science.gov (United States)

    Angus, Simon D; Piotrowska, Monika Joanna

    2014-01-01

    Multi-dose radiotherapy protocols (fraction dose and timing) currently used in the clinic are the product of human selection based on habit, received wisdom, physician experience and intra-day patient timetabling. However, due to combinatorial considerations, the potential treatment protocol space for a given total dose or treatment length is enormous, even for relatively coarse search; well beyond the capacity of traditional in-vitro methods. In constrast, high fidelity numerical simulation of tumor development is well suited to the challenge. Building on our previous single-dose numerical simulation model of EMT6/Ro spheroids, a multi-dose irradiation response module is added and calibrated to the effective dose arising from 18 independent multi-dose treatment programs available in the experimental literature. With the developed model a constrained, non-linear, search for better performing cadidate protocols is conducted within the vicinity of two benchmarks by genetic algorithm (GA) techniques. After evaluating less than 0.01% of the potential benchmark protocol space, candidate protocols were identified by the GA which conferred an average of 9.4% (max benefit 16.5%) and 7.1% (13.3%) improvement (reduction) on tumour cell count compared to the two benchmarks, respectively. Noticing that a convergent phenomenon of the top performing protocols was their temporal synchronicity, a further series of numerical experiments was conducted with periodic time-gap protocols (10 h to 23 h), leading to the discovery that the performance of the GA search candidates could be replicated by 17-18 h periodic candidates. Further dynamic irradiation-response cell-phase analysis revealed that such periodicity cohered with latent EMT6/Ro cell-phase temporal patterning. Taken together, this study provides powerful evidence towards the hypothesis that even simple inter-fraction timing variations for a given fractional dose program may present a facile, and highly cost-effecitive means

  6. Use of a quality improvement tool, the prioritization matrix, to identify and prioritize triage software algorithm enhancement.

    Science.gov (United States)

    North, Frederick; Varkey, Prathiba; Caraballo, Pedro; Vsetecka, Darlene; Bartel, Greg

    2007-10-11

    Complex decision support software can require significant effort in maintenance and enhancement. A quality improvement tool, the prioritization matrix, was successfully used to guide software enhancement of algorithms in a symptom assessment call center.

  7. Identifying professionals' needs in integrating electronic pain monitoring in community palliative care services: An interview study.

    Science.gov (United States)

    Taylor, Sally; Allsop, Matthew J; Bekker, Hilary L; Bennett, Michael I; Bewick, Bridgette M

    2017-07-01

    Poor pain assessment is a barrier to effective pain control. There is growing interest internationally in the development and implementation of remote monitoring technologies to enhance assessment in cancer and chronic disease contexts. Findings describe the development and testing of pain monitoring systems, but research identifying the needs of health professionals to implement routine monitoring systems within clinical practice is limited. To inform the development and implementation strategy of an electronic pain monitoring system, PainCheck, by understanding palliative care professionals' needs when integrating PainCheck into routine clinical practice. Qualitative study using face-to-face interviews. Data were analysed using framework analysis Setting/participants: Purposive sample of health professionals managing the palliative care of patients living in the community Results: A total of 15 interviews with health professionals took place. Three meta-themes emerged from the data: (1) uncertainties about integration of PainCheck and changes to current practice, (2) appraisal of current practice and (3) pain management is everybody's responsibility Conclusion: Even the most sceptical of health professionals could see the potential benefits of implementing an electronic patient-reported pain monitoring system. Health professionals have reservations about how PainCheck would work in practice. For optimal use, PainCheck needs embedding within existing electronic health records. Electronic pain monitoring systems have the potential to enable professionals to support patients' pain management more effectively but only when barriers to implementation are appropriately identified and addressed.

  8. Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016

    Science.gov (United States)

    Smith, Taylor; Bookhagen, Bodo; Rheinwalt, Aljoscha

    2017-10-01

    High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade-1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes

  9. Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

    Directory of Open Access Journals (Sweden)

    T. Smith

    2017-10-01

    Full Text Available High Mountain Asia (HMA – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years; our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1 The end of the snowmelt season is trending almost universally earlier in HMA (negative trends. Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total. The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive

  10. Who is Next? Identifying Communities with the Potential for Increased Implementation of Sustainability Policies and Programs

    Science.gov (United States)

    Understanding the system of connections between societal contexts and policy outcomes in municipal governments provides important insights into how community sustainability happens, and why it happens differently in various communities. A growing body of research in recent years ...

  11. Identifying Overlapping Language Communities: The Case of Chiriquí and Panamanian Signed Languages

    Science.gov (United States)

    Parks, Elizabeth S.

    2016-01-01

    In this paper, I use a holographic metaphor to explain the identification of overlapping sign language communities in Panama. By visualizing Panama's complex signing communities as emitting community "hotspots" through social drama on multiple stages, I employ ethnographic methods to explore overlapping contours of Panama's sign language…

  12. Identifying and Assessing Community-Based Social Behavior of Adolescents and Young Adults with EBD.

    Science.gov (United States)

    Bullis, Michael; And Others

    1994-01-01

    A battery of three measures for assessing the community-based social behavior of adolescents and young adults with emotional and behavioral disorders is described. The measures, in male and female forms, are "Test of Community-Based Social Skill Knowledge,""Scale of Community-Based Social Skill Performance," and "Behaviors That Are Undesirable for…

  13. Identifying and intervening on barriers to healthcare access among members of a small Korean community in the southern USA.

    Science.gov (United States)

    Rhodes, Scott D; Song, Eunyoung; Nam, Sang; Choi, Sarah J; Choi, Seungyong

    2015-04-01

    We used community-based participatory research (CBPR) to explore barriers to healthcare access and utilization and identify potentially effective intervention strategies to increase access among members of the Korean community in North Carolina (NC). Our CBPR partnership conducted 8 focus groups with 63 adult Korean immigrants in northwest NC and 15 individual in-depth interviews and conducted an empowerment-based community forum. We identified 20 themes that we organized into four domains, including practical barriers to health care, negative perceptions about care, contingencies for care, and provider misconceptions about local needs. Forum attendees identified four strategies to improve Korean community health. Despite the implementation of the Patient Protection and Affordable Care Act (ACA), many Korean community members will continue to remain uninsured, and among those who obtain insurance, many barriers will remain. It is imperative to ensure the health of this highly neglected and vulnerable community. Potential strategies include the development of (1) low-literacy materials to educate members of the Korean community about how to access healthcare services, (2) lay health advisor programs to support navigation of service access and utilization, (3) church-based programming, and (4) provider education to reduce misconceptions about Korean community needs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  14. Implementation of Winnowing Algorithm Based K-Gram to Identify Plagiarism on File Text-Based Document

    Directory of Open Access Journals (Sweden)

    Nurdiansyah Yanuar

    2018-01-01

    Full Text Available Plagiarism occurs when the students have tasks and pursued by the deadline. Plagiarism is considered as the fastest way to accomplish the tasks. This reason makes the author tried to build a plagiarism detection system with Winnowing algorithm as document similarity search algorithm. The documents that being tested are Indonesian journals with extension .doc, .docx, and/or .txt. Similarity calculation process through two stages, the first is the process of making a document fingerprint using Winnowing algorithm and the second is using Jaccard coefficient similarity. In order to develop this system, the author used iterative waterfall model approach. The main objective of this project is to determine the level of plagiarism. It is expected to prevent plagiarism either intentionally or unintentionally before our journal published by displaying the percentage of similarity in the journals that we make.

  15. A scalable community detection algorithm for large graphs using stochastic block models

    KAUST Repository

    Peng, Chengbin

    2017-11-24

    Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of

  16. A scalable community detection algorithm for large graphs using stochastic block models

    KAUST Repository

    Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.

    2017-01-01

    Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of

  17. MIIB: A Metric to Identify Top Influential Bloggers in a Community.

    Science.gov (United States)

    Khan, Hikmat Ullah; Daud, Ali; Malik, Tahir Afzal

    2015-01-01

    Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.

  18. MIIB: A Metric to Identify Top Influential Bloggers in a Community.

    Directory of Open Access Journals (Sweden)

    Hikmat Ullah Khan

    Full Text Available Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers, based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.

  19. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

    Science.gov (United States)

    Fisher, Stacey; Hsu, Amy; Mojaverian, Nassim; Taljaard, Monica; Huyer, Gregory; Manuel, Douglas G; Tanuseputro, Peter

    2017-10-24

    The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modelling techniques and will be used to project dementia prevalence. The derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers. Research ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses. ClinicalTrials.gov NCT03155815, pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No

  20. An experiment framework to identify community functional components driving ecosystem processes and services delivery.

    NARCIS (Netherlands)

    Dias, A.; Berg, M.P.; de Bello, F.; van Oosten, A.R.; Bila, K.; Moretti, M.

    2013-01-01

    There is a growing consensus that the distribution of species trait values in a community can greatly determine ecosystem processes and services delivery. Two distinct components of community trait composition are hypothesized to chiefly affect ecosystem processes: (i) the average trait value of the

  1. Beyond the Gates: Identifying and Managing Offenders with Attention Deficit Hyperactivity Disorder in Community Probation Services

    Directory of Open Access Journals (Sweden)

    Young Susan

    2014-03-01

    Full Text Available Research has indicated that, compared with the general population, the prevalence of offenders with ADHD in prison is high. The situation for offenders managed in the community by the Probation Service is unknown. This study aimed to bridge the gap in our knowledge by (1 surveying the awareness of probation staff about ADHD and (2 screening the rate of offenders with ADHD managed within the service. In the first study, a brief survey was circulated to offender managers working in 7 Probation Trusts in England and Wales asking them to estimate the prevalence of offenders with ADHD on their caseload, the presenting problems of these offenders and challenges to their management, and the training received on the treatment and management of offenders with ADHD. The survey had a return rate of 11%. Probation staff perceived that 7.6% of their caseload had ADHD and identified this group to have difficulties associated with neuropsychological dysfunction, lifestyle problems and compliance problems. They perceived that these problems hindered meaningful engagement with the service and rehabilitation. Challenges to their management were perceived to be due to both internal processes (motivation and engagement and external processes (inadequate or inappropriate interventions. Few respondents had received training in the management of offenders with ADHD and most wanted more support. In the second study, a sub-sample of 88 offenders in one Probation Trust completed questionnaires to screen for DSM-IV ADHD in childhood and current symptoms. The screen found an estimated prevalence of 45.45% and 20.51% for childhood and adulthood ADHD respectively and these were strongly associated with functional impairment. Thus probation staff considerably underestimated the likely rate, suggesting there are high rates of under-detection and/or misdiagnosis among offenders with ADHD in their service. The results indicate that screening provisions are needed in probation

  2. Can community members identify tropical tree species for REDD+ carbon and biodiversity measurements?

    DEFF Research Database (Denmark)

    Zhao, Mingxu; Brofeldt, Søren; Li, Qiaohong

    2016-01-01

    to take advantage of the same data for detecting changes in the tree diversity, using the richness and abundance of canopy trees as a proxy for biodiversity. If local community members are already assessing the above-ground biomass in a representative network of forest vegetation plots, it may require...... minimal further effort to collect data on the diversity of trees. We compare community members and trained scientists' data on tree diversity in permanent vegetation plots in montane forest in Yunnan, China. We show that local community members here can collect tree diversity data of comparable quality...... to trained botanists, at one third the cost. Without access to herbaria, identification guides or the Internet, community members could provide the ethno-taxonomical names for 95% of 1071 trees in 60 vegetation plots. Moreover, we show that the community-led survey spent 89% of the expenses at village level...

  3. Can Community Members Identify Tropical Tree Species for REDD+ Carbon and Biodiversity Measurements?

    Science.gov (United States)

    Zhao, Mingxu; Brofeldt, Søren; Li, Qiaohong; Xu, Jianchu; Danielsen, Finn; Læssøe, Simon Bjarke Lægaard; Poulsen, Michael Køie; Gottlieb, Anna; Maxwell, James Franklin; Theilade, Ida

    2016-01-01

    Biodiversity conservation is a required co-benefit of REDD+. Biodiversity monitoring is therefore needed, yet in most areas it will be constrained by limitations in the available human professional and financial resources. REDD+ programs that use forest plots for biomass monitoring may be able to take advantage of the same data for detecting changes in the tree diversity, using the richness and abundance of canopy trees as a proxy for biodiversity. If local community members are already assessing the above-ground biomass in a representative network of forest vegetation plots, it may require minimal further effort to collect data on the diversity of trees. We compare community members and trained scientists' data on tree diversity in permanent vegetation plots in montane forest in Yunnan, China. We show that local community members here can collect tree diversity data of comparable quality to trained botanists, at one third the cost. Without access to herbaria, identification guides or the Internet, community members could provide the ethno-taxonomical names for 95% of 1071 trees in 60 vegetation plots. Moreover, we show that the community-led survey spent 89% of the expenses at village level as opposed to 23% of funds in the monitoring by botanists. In participatory REDD+ programs in areas where community members demonstrate great knowledge of forest trees, community-based collection of tree diversity data can be a cost-effective approach for obtaining tree diversity information.

  4. Can Community Members Identify Tropical Tree Species for REDD+ Carbon and Biodiversity Measurements?

    Directory of Open Access Journals (Sweden)

    Mingxu Zhao

    Full Text Available Biodiversity conservation is a required co-benefit of REDD+. Biodiversity monitoring is therefore needed, yet in most areas it will be constrained by limitations in the available human professional and financial resources. REDD+ programs that use forest plots for biomass monitoring may be able to take advantage of the same data for detecting changes in the tree diversity, using the richness and abundance of canopy trees as a proxy for biodiversity. If local community members are already assessing the above-ground biomass in a representative network of forest vegetation plots, it may require minimal further effort to collect data on the diversity of trees. We compare community members and trained scientists' data on tree diversity in permanent vegetation plots in montane forest in Yunnan, China. We show that local community members here can collect tree diversity data of comparable quality to trained botanists, at one third the cost. Without access to herbaria, identification guides or the Internet, community members could provide the ethno-taxonomical names for 95% of 1071 trees in 60 vegetation plots. Moreover, we show that the community-led survey spent 89% of the expenses at village level as opposed to 23% of funds in the monitoring by botanists. In participatory REDD+ programs in areas where community members demonstrate great knowledge of forest trees, community-based collection of tree diversity data can be a cost-effective approach for obtaining tree diversity information.

  5. Detecting highly overlapping community structure by greedy clique expansion

    OpenAIRE

    Lee, Conrad; Reid, Fergal; McDaid, Aaron; Hurley, Neil

    2010-01-01

    In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies d...

  6. Identifying individual- and population-level characteristics that influence rates of risky alcohol consumption in regional communities.

    Science.gov (United States)

    Breen, Courtney; Shakeshaft, Anthony; Sanson-Fisher, Rob; D'Este, Catherine; Mattick, Richard P; Gilmour, Stuart

    2014-02-01

    To examine the extent to which individual- and community- level characteristics account for differences in risky alcohol consumption. A cross-sectional postal survey of 2,977 randomly selected individuals from 20 regional communities in NSW, Australia. Individuals drinking at harmful levels on the AUDIT and for risk of harm in the short term and long-term were identified. Multi-level modelling of the correlates of risky alcohol consumption at the individual and community level was conducted. There were differences between communities in alcohol consumption patterns. Being male, unmarried and reporting worse health were significant individual-level correlates for drinking at levels for risk of harm in the long term. The number of GPs (+) and police (-) were significant community characteristics. Being younger (≤25), unmarried, Australian born and with a larger income was associated with drinking at levels for risk of harm in the short term and harmful drinking on the AUDIT. The number of hotels and clubs was positively associated with drinking at levels for risk of harm in the short term. Rates of risky drinking vary significantly between communities and both individual and community characteristics are significantly associated with risky alcohol consumption. A combination of individual- and population-level interventions, tailored to the risk profile of individual communities, is most likely to be optimally effective. © 2014 The Authors. ANZJPH © 2014 Public Health Association of Australia.

  7. Application and validation of case-finding algorithms for identifying individuals with human immunodeficiency virus from administrative data in British Columbia, Canada.

    Directory of Open Access Journals (Sweden)

    Bohdan Nosyk

    Full Text Available To define a population-level cohort of individuals infected with the human immunodeficiency virus (HIV in the province of British Columbia from available registries and administrative datasets using a validated case-finding algorithm.Individuals were identified for possible cohort inclusion from the BC Centre for Excellence in HIV/AIDS (CfE drug treatment program (antiretroviral therapy and laboratory testing datasets (plasma viral load (pVL and CD4 diagnostic test results, the BC Centre for Disease Control (CDC provincial HIV surveillance database (positive HIV tests, as well as databases held by the BC Ministry of Health (MoH; the Discharge Abstract Database (hospitalizations, the Medical Services Plan (physician billing and PharmaNet databases (additional HIV-related medications. A validated case-finding algorithm was applied to distinguish true HIV cases from those likely to have been misclassified. The sensitivity of the algorithms was assessed as the proportion of confirmed cases (those with records in the CfE, CDC and MoH databases positively identified by each algorithm. A priori hypotheses were generated and tested to verify excluded cases.A total of 25,673 individuals were identified as having at least one HIV-related health record. Among 9,454 unconfirmed cases, the selected case-finding algorithm identified 849 individuals believed to be HIV-positive. The sensitivity of this algorithm among confirmed cases was 88%. Those excluded from the cohort were more likely to be female (44.4% vs. 22.5%; p<0.01, had a lower mortality rate (2.18 per 100 person years (100PY vs. 3.14/100PY; p<0.01, and had lower median rates of health service utilization (days of medications dispensed: 9745/100PY vs. 10266/100PY; p<0.01; days of inpatient care: 29/100PY vs. 98/100PY; p<0.01; physician billings: 602/100PY vs. 2,056/100PY; p<0.01.The application of validated case-finding algorithms and subsequent hypothesis testing provided a strong framework for

  8. COMPETITIVE METAGENOMIC DNA HYBRIDIZATION IDENTIFIES HOST-SPECIFIC GENETIC MARKERS IN HUMAN FECAL MICROBIAL COMMUNITIES

    Science.gov (United States)

    Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for mo...

  9. Tacit knowledge of public health nurses in identifying community health problems and need for new services: a case study.

    Science.gov (United States)

    Yoshioka-Maeda, Kyoko; Murashima, Sachiyo; Asahara, Kiyomi

    2006-09-01

    The purpose of this study was to explore the tacit knowledge of public health nurses in identifying community health problems and developing relevant new projects. Previous research only roughly showed those skills for creating new community health services, such as lobbying. Nine Japanese public health nurses who had created new projects in their municipalities were selected by theoretical sampling and interviewed in 2002-2003. Yin's Case Study Method, especially the multiple-case study design, was used. All 9 public health nurses used similar approaches in identifying community health problems and the need for creating new services, even though their experiences differed and the kinds of projects varied. They identified the difficulties of clients, recognized clients who had the same problems, elucidated the limitations of existing services, and forecasted outcomes from the neglect of the clients' problems. Then they succeeded in creating a new project by examining individual health problems in the context of their community's characteristics, societal factors, and using existing policies to support their clients. This is the first study to explore the skills of public health nurses and their intention to use such skills in creating new projects as well as the exact process. They could identify community health problems that will be the basis for developing new services to provide care for individual clients. This is different from the traditional community assessment approach that requires the collection of a huge amount of information to clarify community health problems. The tacit knowledge of public health nurses will help to create needs-oriented new services more smoothly.

  10. Identifying ozone-sensitive communities of (semi-)natural vegetation suitable for mapping exceedance of critical levels

    International Nuclear Information System (INIS)

    Mills, G.; Hayes, F.; Jones, M.L.M.; Cinderby, S.

    2007-01-01

    Using published data on the responses of individual species to ozone, 54 EUNIS (European Nature Information System) level 4 communities with six or more ozone-sensitive species (%OS) and c. 20% or more species tested for ozone sensitivity, were identified as potentially ozone-sensitive. The largest number of these communities (23) was associated with Grasslands, with Heathland, scrub and tundra, and Mires, bogs and fens having the next highest representation at 11 and 8 level 4 communities each respectively. Within the grasslands classification, E4 (Alpine and sub-alpine grasslands), E5 (Woodland fringes and clearings) and E1 (Dry grasslands) were the most sensitive with 68.1, 51.6 and 48.6%OS respectively. It is feasible to map the land-cover for these and other communities at level 2, but it may not be currently possible to map the land-cover for all communities identified to be ozone-sensitive at levels 3 and 4. - Grassland communities such as alpine and sub-alpine grasslands have the highest potential sensitivity ozone, based on the responses of their component species

  11. Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease

    Directory of Open Access Journals (Sweden)

    Vinusha Kalatharan

    2016-12-01

    Full Text Available Background: International Classification of Diseases, 10th Revision codes (ICD-10 for autosomal dominant polycystic kidney disease (ADPKD is used within several administrative health care databases. It is unknown whether these codes identify patients who meet strict clinical criteria for ADPKD. Objective: The objective of this study is (1 to determine whether different ICD-10 coding algorithms identify adult patients who meet strict clinical criteria for ADPKD as assessed through medical chart review and (2 to assess the number of patients identified with different ADPKD coding algorithms in Ontario. Design: Validation study of health care database codes, and prevalence. Setting: Ontario, Canada. Patients: For the chart review, 201 adult patients with hospital encounters between April 1, 2002, and March 31, 2014, assigned either ICD-10 codes Q61.2 or Q61.3. Measurements: This study measured positive predictive value of the ICD-10 coding algorithms and the number of Ontarians identified with different coding algorithms. Methods: We manually reviewed a random sample of medical charts in London, Ontario, Canada, and determined whether or not ADPKD was present according to strict clinical criteria. Results: The presence of either ICD-10 code Q61.2 or Q61.3 in a hospital encounter had a positive predictive value of 85% (95% confidence interval [CI], 79%-89% and identified 2981 Ontarians (0.02% of the Ontario adult population. The presence of ICD-10 code Q61.2 in a hospital encounter had a positive predictive value of 97% (95% CI, 86%-100% and identified 394 adults in Ontario (0.003% of the Ontario adult population. Limitations: (1 We could not calculate other measures of validity; (2 the coding algorithms do not identify patients without hospital encounters; and (3 coding practices may differ between hospitals. Conclusions: Most patients with ICD-10 code Q61.2 or Q61.3 assigned during their hospital encounters have ADPKD according to the clinical

  12. A study to identify winning strategies for the business community during the next pandemic.

    Science.gov (United States)

    Spriggs, Martin

    2013-01-01

    This study examines the relationship between the healthcare system and the corporate sector to answer the following research question: how does the healthcare system best prepare small to medium-sized businesses for the next pandemic influenza? Data were collected and collated through a literature review, electronic survey and semi-structured follow-up telephone interviews. The participants were businesses with membership in the Alberta Chambers of Commerce, a provincial lobby group in Alberta, Canada. The findings indicate strategies that were effective in minimising impact to the business community during the H1N1 pandemic and suggest areas for the business community to improve in preparation for the next pandemic influenza. Recommendations focus on establishing new links for communication between the business community and the healthcare sector and improving strategies to increase the resilience of small to medium-sized businesses for the next pandemic influenza.

  13. Involving forest communities in identifying and constructing ecosystems services: millennium assessment and place specificity

    Science.gov (United States)

    Stanley T. Asah; Dale J. Blahna; Clare M. Ryan

    2012-01-01

    The ecosystem services (ES) approach entails integrating people into public forest management and managing to meet their needs and wants. Managers must find ways to understand what these needs are and how they are met. In this study, we used small group discussions, in a case study of the Deschutes National Forest, to involve community members and forest staff in...

  14. Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry

    International Nuclear Information System (INIS)

    Schryver, Jack C.; Brandt, Craig C.; Pfiffner, Susan M.; Palumbo, A V.; Peacock, Aaron D.; White, David C.; McKinley, James P.; Long, Philip E.

    2006-01-01

    The relationship between groundwater geochemistry and microbial community structure can be complex and difficult to assess. We applied nonlinear and generalized linear data analysis methods to relate microbial biomarkers (phospholipids fatty acids, PLFA) to groundwater geochemical characteristics at the Shiprock uranium mill tailings disposal site that is primarily contaminated by uranium, sulfate, and nitrate. First, predictive models were constructed using feedforward artificial neural networks (NN) to predict PLFA classes from geochemistry. To reduce the danger of overfitting, parsimonious NN architectures were selected based on pruning of hidden nodes and elimination of redundant predictor (geochemical) variables. The resulting NN models greatly outperformed the generalized linear models. Sensitivity analysis indicated that tritium, which was indicative of riverine influences, and uranium were important in predicting the distributions of the PLFA classes. In contrast, nitrate concentration and inorganic carbon were least important, and total ionic strength was of intermediate importance. Second, nonlinear principal components (NPC) were extracted from the PLFA data using a variant of the feedforward NN. The NPC grouped the samples according to similar geochemistry. PLFA indicators of Gram-negative bacteria and eukaryotes were associated with the groups of wells with lower levels of contamination. The more contaminated samples contained microbial communities that were predominated by terminally branched saturates and branched monounsaturates that are indicative of metal reducers, actinomycetes, and Gram-positive bacteria. These results indicate that the microbial community at the site is coupled to the geochemistry and knowledge of the geochemistry allows prediction of the community composition

  15. Environmental drivers of viral community composition in Antarctic soils identified by viromics.

    Science.gov (United States)

    Adriaenssens, Evelien M; Kramer, Rolf; Van Goethem, Marc W; Makhalanyane, Thulani P; Hogg, Ian; Cowan, Don A

    2017-07-19

    The Antarctic continent is considered the coldest and driest place on earth with simple ecosystems, devoid of higher plants. Soils in the ice-free regions of Antarctica are known to harbor a wide range of microorganisms from primary producers to grazers, yet their ecology and particularly the role of viruses is poorly understood. In this study, we examined the virus community structures of 14 soil samples from the Mackay Glacier region. Viral communities were extracted from soil and the dsDNA was extracted, amplified using single-primer amplification, and sequenced using the Ion Torrent Proton platform. Metadata on soil physico-chemistry was collected from all sites. Both read and contig datasets were analyzed with reference-independent and reference-dependent methods to assess viral community structures and the influence of environmental parameters on their distribution. We observed a high heterogeneity in virus signatures, independent of geographical proximity. Tailed bacteriophages were dominant in all samples, but the incidences of the affiliated families Siphoviridae and Myoviridae were inversely correlated, suggesting direct competition for hosts. Viruses of the families Phycodnaviridae and Mimiviridae were present at significant levels in high-diversity soil samples and were found to co-occur, implying little competition between them. Combinations of soil factors, including pH, calcium content, and site altitude, were found to be the main drivers of viral community structure. The pattern of viral community structure with higher levels of diversity at lower altitude and pH, and co-occurring viral families, suggests that these cold desert soil viruses interact with each other, the host, and the environment in an intricate manner, playing a potentially crucial role in maintaining host diversity and functioning of the microbial ecosystem in the extreme environments of Antarctic soil.

  16. Geospatial techniques to Identify the Location of Farmers Markets and Community Gardens within Food Deserts in Virginia

    Science.gov (United States)

    Sriharan, S.; Meekins, D.; Comar, M.; Bradshaw, S.; Jackson, L.

    2017-12-01

    Specifically, a food desert is defined as an area where populations live more than one mile from a supermarket or large grocery store if in an urban area or more than 10 miles from a supermarket or large grocery store if in a rural area (Ver Ploeg et al. 2012). According to the U.S. Department of Agriculture, a food desert is "an area in the United States with limited access to affordable and nutritious food, particularly such an area composed of predominately lower-income neighborhoods and communities" (110th Congress 2008). Three fourths of these food deserts are urban. In the Commonwealth of Virginia, Petersburg City is among the eight primary localities, where its population is living in a food desert. This project will compare those identified food deserts in Virginia (areas around Virginia State University) with focus to where farmers markets and community gardens are being established. The hypothesis of this study is that these minority groups do not get healthy food due to limited access to grocery stores and superstores. To address this problem, the community development activities should focus on partnering local Petersburg convenience stores with farmers and community gardeners to sell fresh produce. Existing data was collected on convenient stores and community gardens in Petersburg City and Chesterfield County. Rare data was generated for Emporia, Lynchburg and Hopewell. The data was compiled through field work and mapping with ArcGIS where markets and gardens are being established, and create a spatial analysis of their location We have localities that reflect both rural and urban areas. The project provides educational support for students who will find solution to community problems by developing activities to: (a) define and examine characteristics of food deserts, (b) identify causes and consequences of food deserts and determine if their community is a food desert, (c) research closest food desert to their school, and (d) design solutions to help

  17. Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

    Directory of Open Access Journals (Sweden)

    Hui-Qin Zou

    2015-12-01

    Full Text Available Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.

  18. Identifying the core microbial community in the gut of fungus-growing termites

    DEFF Research Database (Denmark)

    Otani, Saria; Mikaelyan, Aram; Nobre, Tânia

    2014-01-01

    Gut microbes play a crucial role in decomposing lignocellulose to fuel termite societies, with protists in the lower termites and prokaryotes in the higher termites providing these services. However, a single basal subfamily of the higher termites, the Macrotermitinae, also domesticated a plant......, and Synergistetes. A set of 42 genus-level taxa was present in all termite species and accounted for 56-68% of the species-specific reads. Gut communities of termites from the same genus were more similar than distantly related species, suggesting that phylogenetic ancestry matters, possibly in connection...... with specific termite genus-level ecological niches. Finally, we show that gut communities of fungus-growing termites are similar to cockroaches, both at the bacterial phylum level and in a comparison of the core Macrotermitinae taxa abundances with representative cockroach, lower termite, and higher non...

  19. Identifying Natural Alignments Between Ambulatory Surgery Centers and Local Health Systems: Building Broader Communities of Surgical Care.

    Science.gov (United States)

    Funk, Russell J; Owen-Smith, Jason; Landon, Bruce E; Birkmeyer, John D; Hollingsworth, John M

    2017-02-01

    To develop and compare methods for identifying natural alignments between ambulatory surgery centers (ASCs) and hospitals that anchor local health systems. Using all-payer data from Florida's State Ambulatory Surgery and Inpatient Databases (2005-2009), we developed 3 methods for identifying alignments between ASCS and hospitals. The first, a geographic proximity approach, used spatial data to assign an ASC to its nearest hospital neighbor. The second, a predominant affiliation approach, assigned an ASC to the hospital with which it shared a plurality of surgeons. The third, a network community approach, linked an ASC with a larger group of hospitals held together by naturally occurring physician networks. We compared each method in terms of its ability to capture meaningful and stable affiliations and its administrative simplicity. Although the proximity approach was simplest to implement and produced the most durable alignments, ASC surgeon's loyalty to the assigned hospital was low with this method. The predominant affiliation and network community approaches performed better and nearly equivalently on these metrics, capturing more meaningful affiliations between ASCs and hospitals. However, the latter's alignments were least durable, and it was complex to administer. We describe 3 methods for identifying natural alignments between ASCs and hospitals, each with strengths and weaknesses. These methods will help health system managers identify ASCs with which to partner. Moreover, health services researchers and policy analysts can use them to study broader communities of surgical care.

  20. Classification trees for identifying non-use of community-based long-term care services among older adults.

    Science.gov (United States)

    Penkunas, Michael James; Eom, Kirsten Yuna; Chan, Angelique Wei-Ming

    2017-10-01

    Home- and center-based long-term care (LTC) services allow older adults to remain in the community while simultaneously helping caregivers cope with the stresses associated with providing care. Despite these benefits, the uptake of community-based LTC services among older adults remains low. We analyzed data from a longitudinal study in Singapore to identify the characteristics of individuals with referrals to home-based LTC services or day rehabilitation services at the time of hospital discharge. Classification and regression tree analysis was employed to identify combinations of clinical and sociodemographic characteristics of patients and their caregivers for individuals who did not take up their referred services. Patients' level of limitation in activities of daily living (ADL) and caregivers' ethnicity and educational level were the most distinguishing characteristics for identifying older adults who failed to take up their referred home-based services. For day rehabilitation services, patients' level of ADL limitation, home size, age, and possession of a national medical savings account, as well as caregivers' education level, and gender were significant factors influencing service uptake. Identifying subgroups of patients with high rates of non-use can help clinicians target individuals who are need of community-based LTC services but unlikely to engage in formal treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. A New Feedback-Analysis based Reputation Algorithm for E-Commerce Communities

    Directory of Open Access Journals (Sweden)

    Hasnae Rahimi

    2014-12-01

    Full Text Available Dealing with the ever-growing content generated by users in the e-commerce applications, Trust Reputation Systems (TRS are widely used online to provide the trust reputation of each product using the customers’ ratings. However, there is also a good number of online customer reviews and feedback that must be used by the TRS. As a result, we propose in this work a new architecture for TRS in e-commerce application which includes feedback’ mining in order to calculate reputation scores. This architecture is based on an intelligent layer that proposes to each user (i.e. “feedback provider” who has already given his recommendation, a collection of prefabricated feedback to like or dislike. Then the proposed reputation algorithm calculates the trust degree of the user, the feedback’s trustworthiness and generates the global reputation score of the product according to his ‘likes’ and ‘dislikes’. In this work, we present also a state of the art of text mining tools and algorithms that can be used to generate the prefabricated feedback and to classify them into different categories.

  2. Identifying role of perceived quality and satisfaction on the utilization status of the community clinic services; Bangladesh context.

    Science.gov (United States)

    Karim, Rizwanul M; Abdullah, Mamun S; Rahman, Anisur M; Alam, Ashraful M

    2016-06-24

    Bangladesh is one among the few countries of the world that provides free medical services at the community level through various public health facilities. It is now evident that, clients' perceived quality of services and their expectations of service standards affect health service utilization to a great extent. The aim of the study was to develop and validate the measures for perception and satisfaction of primary health care quality in Bangladesh context and to identify their aspects on the utilization status of the Community Clinic services. This mixed method cross sectional survey was conducted from January to June 2012, in the catchment area of 12 community clinics. Since most of the outcome indicators focus mainly on women and children, women having children less than 2 years of age were randomly assigned and interviewed for the study purpose. Data were collected through FGD, Key informants interview and a pretested semi- structured questionnaire. About 95 % of the respondents were Muslims and 5 % were Hindus. The average age of the respondents was 23.38 (SD 4.15) and almost all of them are home makers. The average monthly expenditure of their family was 95US $ (SD 32US$). At the beginning of the study, two psychometric research instruments; 24 items perceived quality of primary care services PQPCS scale (chronbach's α = .89) and 22 items community clinic service satisfaction CCSS scale (chronbach's α = .97), were constructed and validated. This study showed less educated, poor, landless mothers utilized the community clinic services more than their educated and wealthier counterpart. Women who lived in their own residence used the community clinic services more frequently than those who lived in a rental house. Perceptions concerning skill and competence of the health care provider and satisfaction indicating interpersonal communication and attitude of the care provider were important predictors for community clinic service utilization

  3. SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree

    OpenAIRE

    Stevens, John R.; Jones, Todd R.; Lefevre, Michael; Ganesan, Balasubramanian; Weimer, Bart C.

    2017-01-01

    Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significan...

  4. Illicit Drug Use in a Community-Based Sample of Heterosexually Identified Emerging Adults

    Science.gov (United States)

    Halkitis, Perry N.; Manasse, Ashley N.; McCready, Karen C.

    2010-01-01

    In this study we assess lifetime and recent drug use patterns among 261 heterosexually identified 18- to 25-year-olds through brief street intercept surveys conducted in New York City. Marijuana, hallucinogens, powder cocaine, and ecstasy were the most frequently reported drugs for both lifetime and recent use. Findings further suggest significant…

  5. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

    Directory of Open Access Journals (Sweden)

    István A Kovács

    Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

  6. Primary factors identified in sport science students' coaching philosophies : sport education and community involvement

    OpenAIRE

    Liandi van den Berg

    2014-01-01

    Youth sport coaches have a great influence on the experiences and development of children who participate in organized sport. Given this influence of coaches on children and the huge participation numbers of children in sports, coach education programmes received increasing research attention over the past 30 years. Numerous important facets of coach educational programmes have been identified, of which the first key developmental domain as indicated by the President's Council on Fitness, Spo...

  7. Identifying community thresholds for lotic benthic diatoms in response to human disturbance.

    Science.gov (United States)

    Tang, Tao; Tang, Ting; Tan, Lu; Gu, Yuan; Jiang, Wanxiang; Cai, Qinghua

    2017-06-23

    Although human disturbance indirectly influences lotic assemblages through modifying physical and chemical conditions, identifying thresholds of human disturbance would provide direct evidence for preventing anthropogenic degradation of biological conditions. In the present study, we used data obtained from tributaries of the Three Gorges Reservoir in China to detect effects of human disturbance on streams and to identify disturbance thresholds for benthic diatoms. Diatom species composition was significantly affected by three in-stream stressors including TP, TN and pH. Diatoms were also influenced by watershed % farmland and natural environmental variables. Considering three in-stream stressors, TP was positively influenced by % farmland and % impervious surface area (ISA). In contrast, TN and pH were principally affected by natural environmental variables. Among measured natural environmental variables, average annual air temperature, average annual precipitation, and topsoil % CaCO 3 , % gravel, and total exchangeable bases had significant effects on study streams. When effects of natural variables were accounted for, substantial compositional changes in diatoms occurred when farmland or ISA land use exceeded 25% or 0.3%, respectively. Our study demonstrated the rationale for identifying thresholds of human disturbance for lotic assemblages and addressed the importance of accounting for effects of natural factors for accurate disturbance thresholds.

  8. Identifying Persuasive Public Health Messages to Change Community Knowledge and Attitudes About Bulimia Nervosa.

    Science.gov (United States)

    McLean, Siân A; Paxton, Susan J; Massey, Robin; Hay, Phillipa J; Mond, Jonathan M; Rodgers, Bryan

    2016-01-01

    Addressing stigma through social marketing campaigns has the potential to enhance currently low rates of treatment seeking and improve the well-being of individuals with the eating disorder bulimia nervosa. This study aimed to evaluate the persuasiveness of health messages designed to reduce stigma and improve mental health literacy about this disorder. A community sample of 1,936 adults (48.2% male, 51.8% female) from Victoria, Australia, provided (a) self-report information on knowledge and stigma about bulimia nervosa and (b) ratings of the persuasiveness of 9 brief health messages on dimensions of convincingness and likelihood of changing attitudes. Messages were rated moderately to very convincing and a little to moderately likely to change attitudes toward bulimia nervosa. The most persuasive messages were those that emphasized that bulimia nervosa is a serious mental illness and is not attributable to personal failings. Higher ratings of convincingness were associated with being female, with having more knowledge about bulimia nervosa, and with lower levels of stigma about bulimia nervosa. Higher ratings for likelihood of changing attitudes were associated with being female and with ratings of the convincingness of the corresponding message. This study provides direction for persuasive content to be included in social marketing campaigns to reduce stigma toward bulimia nervosa.

  9. Finding needles in a haystack: a methodology for identifying and sampling community-based youth smoking cessation programs.

    Science.gov (United States)

    Emery, Sherry; Lee, Jungwha; Curry, Susan J; Johnson, Tim; Sporer, Amy K; Mermelstein, Robin; Flay, Brian; Warnecke, Richard

    2010-02-01

    Surveys of community-based programs are difficult to conduct when there is virtually no information about the number or locations of the programs of interest. This article describes the methodology used by the Helping Young Smokers Quit (HYSQ) initiative to identify and profile community-based youth smoking cessation programs in the absence of a defined sample frame. We developed a two-stage sampling design, with counties as the first-stage probability sampling units. The second stage used snowball sampling to saturation, to identify individuals who administered youth smoking cessation programs across three economic sectors in each county. Multivariate analyses modeled the relationship between program screening, eligibility, and response rates and economic sector and stratification criteria. Cumulative logit models analyzed the relationship between the number of contacts in a county and the number of programs screened, eligible, or profiled in a county. The snowball process yielded 9,983 unique and traceable contacts. Urban and high-income counties yielded significantly more screened program administrators; urban counties produced significantly more eligible programs, but there was no significant association between the county characteristics and program response rate. There is a positive relationship between the number of informants initially located and the number of programs screened, eligible, and profiled in a county. Our strategy to identify youth tobacco cessation programs could be used to create a sample frame for other nonprofit organizations that are difficult to identify due to a lack of existing directories, lists, or other traditional sample frames.

  10. Identifying cost-effective treatment with raloxifene in postmenopausal women using risk algorithms for fractures and invasive breast cancer.

    Science.gov (United States)

    Ivergård, M; Ström, O; Borgström, F; Burge, R T; Tosteson, A N A; Kanis, J

    2010-11-01

    The National Osteoporosis Foundation (NOF) recommends considering treatment in women with a 20% or higher 10-year probability of a major fracture. However, raloxifene reduces both the risk of vertebral fractures and invasive breast cancer so that raloxifene treatment may be clinically appropriate and cost-effective in women who do not meet a 20% threshold risk. The aim of this study was to identify cost-effective scenarios of raloxifene treatment compared to no treatment in younger postmenopausal women at increased risk of invasive breast cancer and fracture risks below 20%. A micro-simulation model populated with data specific to American Caucasian women was used to quantify the costs and benefits of 5-year raloxifene treatment. The population evaluated was selected based on 10-year major fracture probability as estimated with FRAX® being below 20% and 5-year invasive breast cancer risk as estimated with the Gail risk model ranging from 1% to 5%. The cost per QALY gained ranged from US $22,000 in women age 55 with 5% invasive breast cancer risk and 15-19.9% fracture probability, to $110,000 in women age 55 with 1% invasive breast cancer risk and 5-9.9% fracture probability. Raloxifene was progressively cost-effective with increasing fracture risk and invasive breast cancer risk for a given age cohort. At lower fracture risk in combination with lower invasive breast cancer risk or when no preventive raloxifene effect on invasive breast cancer was assumed, the cost-effectiveness of raloxifene worsened markedly and was not cost-effective given a willingness-to-pay of US $50,000. At fracture risk of 15-19.9% raloxifene was cost-effective also in women at lower invasive breast cancer risk. Raloxifene is potentially cost-effective in cohorts of young postmenopausal women, who do not meet the suggested NOF 10-year fracture risk threshold. The cost-effectiveness is contingent on their 5-year invasive breast cancer risk. The result highlights the importance of considering

  11. A novel baiting microcosm approach used to identify the bacterial community associated with Penicillium bilaii hyphae in soil.

    Directory of Open Access Journals (Sweden)

    Behnoushsadat Ghodsalavi

    Full Text Available It is important to identify and recover bacteria associating with fungi under natural soil conditions to enable eco-physiological studies, and to facilitate the use of bacterial-fungal consortia in environmental biotechnology. We have developed a novel type of baiting microcosm, where fungal hyphae interact with bacteria under close-to-natural soil conditions; an advantage compared to model systems that determine fungal influences on bacterial communities in laboratory media. In the current approach, the hyphae are placed on a solid support, which enables the recovery of hyphae with associated bacteria in contrast to model systems that compare bulk soil and mycosphere soil. We used the baiting microcosm approach to determine, for the first time, the composition of the bacterial community associating in the soil with hyphae of the phosphate-solubilizer, Penicillium bilaii. By applying a cultivation-independent 16S rRNA gene-targeted amplicon sequencing approach, we found a hypha-associated bacterial community with low diversity compared to the bulk soil community and exhibiting massive dominance of Burkholderia OTUs. Burkholderia is known be abundant in soil environments affected by fungi, but the discovery of this massive dominance among bacteria firmly associating with hyphae in soil is novel and made possible by the current bait approach.

  12. Research on Community Structure in Bus Transport Networks

    International Nuclear Information System (INIS)

    Yang Xuhua; Wang Bo; Sun Youxian

    2009-01-01

    We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property. (general)

  13. The use of concept mapping to identify community-driven intervention strategies for physical and mental health.

    Science.gov (United States)

    Vaughn, Lisa M; Jacquez, Farrah; McLinden, Daniel

    2013-09-01

    Research that partners with youth and community stakeholders increases contextual relevance and community buy-in and therefore maximizes the chance for intervention success. Concept mapping is a mixed-method participatory research process that accesses the input of the community in a collaborative manner. After a school-wide health needs assessment at a low-income, minority/immigrant K-8 school identified bullying and obesity as the most important health issues, concept mapping was used to identify and prioritize specific strategies to address these two areas. Stakeholders including 160 K-8 students, 33 college students working in the school, 35 parents, 20 academic partners, and 22 teachers/staff brainstormed strategies to reduce and prevent obesity and bullying. A smaller group of stakeholders worked individually to complete an unstructured sorting of these strategies into groups of similar ideas, once for obesity and again for bullying. Multidimensional scaling and cluster analysis was applied to the sorting data to produce a series of maps that illustrated the stakeholders' conceptual thinking about obesity and bullying prevention strategies. The maps for both obesity and bullying organized specific strategies into themes that included education, parental role, teacher/school supervision, youth role, expert/professional role, and school structure/support.

  14. Identifying solutions to increase participation in physical activity interventions within a socio-economically disadvantaged community: a qualitative study.

    Science.gov (United States)

    Cleland, Claire L; Hunter, Ruth F; Tully, Mark A; Scott, David; Kee, Frank; Donnelly, Michael; Prior, Lindsay; Cupples, Margaret E

    2014-05-23

    There is an urgent need to increase population levels of physical activity, particularly amongst those who are socio-economically disadvantaged. Multiple factors influence physical activity behaviour but the generalisability of current evidence to such 'hard-to-reach' population subgroups is limited by difficulties in recruiting them into studies. Also, rigorous qualitative studies of lay perceptions and perceptions of community leaders about public health efforts to increase physical activity are sparse. We sought to explore, within a socio-economically disadvantaged community, residents' and community leaders' perceptions of physical activity (PA) interventions and issues regarding their implementation, in order to improve understanding of needs, expectations, and social/environmental factors relevant to future interventions. Within an ongoing regeneration project (Connswater Community Greenway), in a socio-economically disadvantaged community in Belfast, we collaborated with a Community Development Agency to purposively sample leaders from public- and voluntary-sector community groups and residents. Individual semi-structured interviews were conducted with 12 leaders. Residents (n = 113), of both genders and a range of ages (14 to 86 years) participated in focus groups (n = 14) in local facilities. Interviews and focus groups were recorded, transcribed verbatim and analysed using a thematic framework. Three main themes were identified: awareness of PA interventions; factors contributing to intervention effectiveness; and barriers to participation in PA interventions. Participants reported awareness only of interventions in which they were involved directly, highlighting a need for better communications, both inter- and intra-sectoral, and with residents. Meaningful engagement of residents in planning/organisation, tailoring to local context, supporting volunteers, providing relevant resources and an 'exit strategy' were perceived as important factors

  15. A novel baiting microcosm approach used to identify the bacterial community associated with Penicillium bilaii hyphae in soil

    DEFF Research Database (Denmark)

    Ghodsalavi, Behnoushsadat; Svenningsen, Nanna Bygvraa; Hao, Xiuli

    2017-01-01

    It is important to identify and recover bacteria associating with fungi under natural soil conditions to enable eco-physiological studies, and to facilitate the use of bacterial-fungal consortia in environmental biotechnology. We have developed a novel type of baiting microcosm, where fungal hyphae...... interact with bacteria under close-to-natural soil conditions; an advantage compared to model systems that determine fungal influences on bacterial communities in laboratory media. In the current approach, the hyphae are placed on a solid support, which enables the recovery of hyphae with associated...... bacteria in contrast to model systems that compare bulk soil and mycosphere soil. We used the baiting microcosm approach to determine, for the first time, the composition of the bacterial community associating in the soil with hyphae of the phosphate-solubilizer, Penicillium bilaii. By applying...

  16. Ethics in Community-University-Artist Partnered Research: Tensions, Contradictions and Gaps Identified in an 'Arts for Social Change' Project.

    Science.gov (United States)

    Yassi, Annalee; Spiegel, Jennifer Beth; Lockhart, Karen; Fels, Lynn; Boydell, Katherine; Marcuse, Judith

    Academics from diverse disciplines are recognizing not only the procedural ethical issues involved in research, but also the complexity of everyday "micro" ethical issues that arise. While ethical guidelines are being developed for research in aboriginal populations and low-and-middle-income countries, multi-partnered research initiatives examining arts-based interventions to promote social change pose a unique set of ethical dilemmas not yet fully explored. Our research team, comprising health, education, and social scientists, critical theorists, artists and community-activists launched a five-year research partnership on arts-for-social change. Funded by the Social Science and Humanities Research Council in Canada and based in six universities, including over 40 community-based collaborators, and informed by five main field projects (circus with street youth, theatre by people with disabilities, dance for people with Parkinson's disease, participatory theatre with refugees and artsinfused dialogue), we set out to synthesize existing knowledge and lessons we learned. We summarized these learnings into 12 key points for reflection, grouped into three categories: community-university partnership concerns ( n  = 3), dilemmas related to the arts ( n  = 5), and team issues ( n  = 4). In addition to addressing previous concerns outlined in the literature (e.g., related to consent, anonymity, dangerous emotional terrain, etc.), we identified power dynamics (visible and hidden) hindering meaningful participation of community partners and university-based teams that need to be addressed within a reflective critical framework of ethical practice. We present how our team has been addressing these issues, as examples of how such concerns could be approached in community-university partnerships in arts for social change.

  17. Conceptual and Operational Considerations in Identifying Socioenvironmental Factors Associated with Disability among Community-Dwelling Adults

    Directory of Open Access Journals (Sweden)

    Mathieu Philibert

    2015-04-01

    Full Text Available Disability is conceived as a person–context interaction. Physical and social environments are identified as intervention targets for improving social participation and independence. In comparison to the body of research on place and health, relatively few reports have been published on residential environments and disability in the health sciences literature. We reviewed studies evaluating the socioenvironmental correlates of disability. Searches were conducted in Medline, Embase and CINAHL databases for peer-reviewed articles published between 1997 and 2014. We found many environmental factors to be associated with disability, particularly area-level socioeconomic status and rurality. However, diversity in conceptual and methodological approaches to such research yields a limited basis for comparing studies. Conceptual inconsistencies in operational measures of disability and conceptual disagreement between studies potentially affect understanding of socioenvironmental influences. Similarly, greater precision in socioenvironmental measures and in study designs are likely to improve inference. Consistent and generalisable support for socioenvironmental influences on disability in the general adult population is scarce.

  18. Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

    Science.gov (United States)

    Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

    2015-01-01

    MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

  19. Structural Elements in a Persistent Identifier Infrastructure and Resulting Benefits for the Earth Science Community

    Science.gov (United States)

    Weigel, T.; Toussaiant, F.; Stockhause, M.; Höck, H.; Kindermann, S.; Lautenschlager, M.; Ludwig, T.

    2012-12-01

    We propose a wide adoption of structural elements (typed links, collections, trees) in the Handle System to improve identification and access of scientific data, metadata and software as well as traceability of data provenance. Typed links target the issue of data provenance as a means to assess the quality of scientific data. Data provenance is seen here as a directed acyclic graph with nodes representing data and vertices representing derivative operations (Moreau 2010). Landing pages can allow a human user to explore the provenance graph back to the primary unprocessed data, thereby also giving credit to the original data producer. As in Earth System Modeling no single infrastructure with complete data lifecycle coverage exists, we propose to split the problem domain in two parts. Project-specific infrastructures such as the German project C3-Grid or the Earth System Grid Federation (ESGF) for CMIP5 data are aware of data and data operations (Toussaint et al. 2012) and can thus detect and accumulate single nodes and vertices in the provenance graph, assigning Handles to data, metadata and software. With a common schema for typed links, the provenance graph is established as downstream infrastructures refer incoming Handles. Data in this context is for example hierarchically structured Earth System model output data, which receives DataCite DOIs only for the most coarse-granular elements. Using Handle tree structures, the lower levels of the hierarchy can also receive Handles, allowing authors to more precisely identify the data they used (Lawrence et al. 2011). We can e.g. define a DOI for just the 2m-temperature variable of CMIP5 data across many CMIP5 experiments or a DOI for model and observational data coming from different sources. The structural elements should be implemented through Handle values at the Handle infrastructure level for two reasons. Handle values are more durable than downstream websites or databases, and thus the provenance chain does not

  20. Using a Novel Evolutionary Algorithm to More Effectively Apply Community-Driven EcoHealth Interventions in Big Data with Application to Chagas Disease

    Science.gov (United States)

    Rizzo, D. M.; Hanley, J.; Monroy, C.; Rodas, A.; Stevens, L.; Dorn, P.

    2016-12-01

    Chagas disease is a deadly, neglected tropical disease that is endemic to every country in Central and South America. The principal insect vector of Chagas disease in Central America is Triatoma dimidiata. EcoHealth interventions are an environmentally friendly alternative that use local materials to lower household infestation, reduce the risk of infestation, and improve the quality of life. Our collaborators from La Universidad de San Carlos de Guatemala along with Ministry of Health Officials reach out to communities with high infestation and teach the community EcoHealth interventions. The process of identifying which interventions have the potential to be most effective as well as the houses that are most at risk is both expensive and time consuming. In order to better identify the risk factors associated with household infestation of T. dimidiata, a number of studies have conducted socioeconomic and entomologic surveys that contain numerous potential risk factors consisting of both nominal and ordinal data. Univariate logistic regression is one of the more popular methods for determining which risk factors are most closely associated with infestation. However, this tool has limitations, especially with the large amount and type of "Big Data" associated with our study sites (e.g., 5 villages comprise of socioeconomic, demographic, and entomologic data). The infestation of a household with T. dimidiata is a complex problem that is most likely not univariate in nature and is likely to contain higher order epistatic relationships that cannot be discovered using univariate logistic regression. Add to this, the problems raised with using p-values in traditional statistics. Also, our T. dimidiata infestation dataset is too large to exhaustively search. Therefore, we use a novel evolutionary algorithm to efficiently search for higher order interactions in surveys associated with households infested with T. dimidiata. In this study, we use our novel evolutionary

  1. REVIEW OF THE GOVERNING EQUATIONS, COMPUTATIONAL ALGORITHMS, AND OTHER COMPONENTS OF THE MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM

    Science.gov (United States)

    This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-of-the-science capabiliti...

  2. Using a service sector segmented approach to identify community stakeholders who can improve access to suicide prevention services for veterans.

    Science.gov (United States)

    Matthieu, Monica M; Gardiner, Giovanina; Ziegemeier, Ellen; Buxton, Miranda

    2014-04-01

    Veterans in need of social services may access many different community agencies within the public and private sectors. Each of these settings has the potential to be a pipeline for attaining needed health, mental health, and benefits services; however, many service providers lack information on how to conceptualize where Veterans go for services within their local community. This article describes a conceptual framework for outreach that uses a service sector segmented approach. This framework was developed to aid recruitment of a provider-based sample of stakeholders (N = 70) for a study on improving access to the Department of Veterans Affairs and community-based suicide prevention services. Results indicate that although there are statistically significant differences in the percent of Veterans served by the different service sectors (F(9, 55) = 2.71, p = 0.04), exposure to suicidal Veterans and providers' referral behavior is consistent across the sectors. Challenges to using this framework include isolating the appropriate sectors for targeted outreach efforts. The service sector segmented approach holds promise for identifying and referring at-risk Veterans in need of services. Reprint & Copyright © 2014 Association of Military Surgeons of the U.S.

  3. A rapid two-step algorithm detects and identifies clinical macrolide and beta-lactam antibiotic resistance in clinical bacterial isolates.

    Science.gov (United States)

    Lu, Xuedong; Nie, Shuping; Xia, Chengjing; Huang, Lie; He, Ying; Wu, Runxiang; Zhang, Li

    2014-07-01

    Aiming to identify macrolide and beta-lactam resistance in clinical bacterial isolates rapidly and accurately, a two-step algorithm was developed based on detection of eight antibiotic resistance genes. Targeting at genes linked to bacterial macrolide (msrA, ermA, ermB, and ermC) and beta-lactam (blaTEM, blaSHV, blaCTX-M-1, blaCTX-M-9) antibiotic resistances, this method includes a multiplex real-time PCR, a melting temperature profile analysis as well as a liquid bead microarray assay. Liquid bead microarray assay is applied only when indistinguishable Tm profile is observed. The clinical validity of this method was assessed on clinical bacterial isolates. Among the total 580 isolates that were determined by our diagnostic method, 75% of them were identified by the multiplex real-time PCR with melting temperature analysis alone, while the remaining 25% required both multiplex real-time PCR with melting temperature analysis and liquid bead microarray assay for identification. Compared with the traditional phenotypic antibiotic susceptibility test, an overall agreement of 81.2% (kappa=0.614, 95% CI=0.550-0.679) was observed, with a sensitivity and specificity of 87.7% and 73% respectively. Besides, the average test turnaround time is 3.9h, which is much shorter in comparison with more than 24h for the traditional phenotypic tests. Having the advantages of the shorter operating time and comparable high sensitivity and specificity with the traditional phenotypic test, our two-step algorithm provides an efficient tool for rapid determination of macrolide and beta-lactam antibiotic resistances in clinical bacterial isolates. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. IDENTIFYING CONCERNS OF POSTGRADUATES IN COMMUNITY MEDICINE USING A QUALITATIVE RESEARCH METHOD- VISUALISATION IN PARTICIPATORY PROGRAMMES (VIPP

    Directory of Open Access Journals (Sweden)

    Vinay Babu Koganti

    2017-06-01

    Full Text Available BACKGROUND Postgraduation in Community Medicine finds few takers and those who do take it up as a career option have many concerns regarding the course. To understand the issues involved, a qualitative method called VIPP was used, which is a people centered approach to identify issues from the perspectives of those involved. This study is set to identify the problems faced by postgraduate students in Community Medicine regarding their course of study. MATERIALS AND METHODS This study was conducted during a regional postgraduate CME of the NTR University of Health Sciences, Andhra Pradesh. Postgraduates and junior faculty from 5 medical colleges in the region were involved in the exercise after taking their informed consent. Visualisation in Participatory Programmes (VIPP, a qualitative method was used as a means of obtaining information followed by a discussion with visual display of all the mentioned items. RESULTS The themes that emerged are problems faced due to the student’s felt inadequacies, faculty shortcomings, issues regarding the department/college management and lacunae in the course structure and implementation. CONCLUSION In VIPP, sensitive issues are visually displayed for all to see and contemplate. Many of the student’s issues were actually brought on by poor curriculum planning and implementation. This was also undermining students’ self-esteem and causing anxiety about future career prospects.

  5. Identifying participation needs of people with acquired brain injury in the development of a collective community smart home.

    Science.gov (United States)

    Levasseur, Mélanie; Pigot, Hélène; Couture, Mélanie; Bier, Nathalie; Swaine, Bonnie; Therriault, Pierre-Yves; Giroux, Sylvain

    2016-11-01

    This study explored the personalized and collective participation needs of people with acquired brain injury (ABI) living in a future shared community smart home. An action research study was conducted with 16 persons, seven with ABI, four caregivers and five rehabilitation or smart home healthcare providers. Twelve interviews and two focus groups were conducted, audiotaped, transcribed and analyzed for content. Seventy personalized and 18 collective participation needs were reported related to daily and social activities. Personalized needs concerned interpersonal relationships, general organization of activities, leisure, housing, fitness and nutrition. Collective needs related mainly to housing, general organization of activities and nutrition. Personalized and collective participation needs of people with ABI planning to live in a community smart home are diverse and concern daily as well as social activities. Implications for Rehabilitation To meet participation needs of people with ABI, the design of smart homes must consider all categories of daily and social activities. Considering personalized and collective needs allowed identifying exclusive examples of each. As some persons with ABI had difficulty identifying their needs as well as accepting their limitations and the assistance required, rehabilitation professionals must be involved in needs identification.

  6. Identifying Psoriasis and Psoriatic Arthritis Patients in Retrospective Databases When Diagnosis Codes Are Not Available: A Validation Study Comparing Medication/Prescriber Visit-Based Algorithms with Diagnosis Codes.

    Science.gov (United States)

    Dobson-Belaire, Wendy; Goodfield, Jason; Borrelli, Richard; Liu, Fei Fei; Khan, Zeba M

    2018-01-01

    Using diagnosis code-based algorithms is the primary method of identifying patient cohorts for retrospective studies; nevertheless, many databases lack reliable diagnosis code information. To develop precise algorithms based on medication claims/prescriber visits (MCs/PVs) to identify psoriasis (PsO) patients and psoriatic patients with arthritic conditions (PsO-AC), a proxy for psoriatic arthritis, in Canadian databases lacking diagnosis codes. Algorithms were developed using medications with narrow indication profiles in combination with prescriber specialty to define PsO and PsO-AC. For a 3-year study period from July 1, 2009, algorithms were validated using the PharMetrics Plus database, which contains both adjudicated medication claims and diagnosis codes. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the developed algorithms were assessed using diagnosis code as the reference standard. Chosen algorithms were then applied to Canadian drug databases to profile the algorithm-identified PsO and PsO-AC cohorts. In the selected database, 183,328 patients were identified for validation. The highest PPVs for PsO (85%) and PsO-AC (65%) occurred when a predictive algorithm of two or more MCs/PVs was compared with the reference standard of one or more diagnosis codes. NPV and specificity were high (99%-100%), whereas sensitivity was low (≤30%). Reducing the number of MCs/PVs or increasing diagnosis claims decreased the algorithms' PPVs. We have developed an MC/PV-based algorithm to identify PsO patients with a high degree of accuracy, but accuracy for PsO-AC requires further investigation. Such methods allow researchers to conduct retrospective studies in databases in which diagnosis codes are absent. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  7. Heat transfer analysis of unsteady graphene oxide nanofluid flow using a fuzzy identifier evolved by genetically encoded mutable smart bee algorithm

    Directory of Open Access Journals (Sweden)

    Mohammadreza Azimi

    2015-03-01

    Full Text Available In the current research, the unsteady two dimensional Graphene Oxide water based nanofluid heat transfer between two moving parallel plates is analyzed using an intelligent black-box identifier. The developed intelligent tool is known as evolvable evolutionary fuzzy inference system (EE-FIS which is based on the integration of low-level fuzzy programming and hyper-level evolutionary computing concepts. Here, the authors propose the use of a modified evolutionary algorithm (EA which is called hybrid genetic mutable smart bee algorithm (HGMSBA. The proposed HGMSBA is used to evolve both antecedent and consequent parts of fuzzy rule base. Besides, it tries to prune the rule base of fuzzy inference system (FIS to decrease its computational complexity and increase its interpretability. By considering the prediction error of the fuzzy identifier as the objective function of HGMSBA, an automatic soft interpolation machine is developed which can intuitively increase the robustness and accuracy of the final model. Here, HGMSBA-FIS is used to provide a nonlinear map between inputs, i.e. nanoparticles solid volume fraction (ϕ, Eckert number (Ec and a moving parameter which describes the movements of plates (S, and output, i.e. Nusselt number (Nu. Prior to proceeding with the modeling process, a comprehensive numerical comparative study is performed to investigate the potentials of the proposed model for nonlinear system identification. After demonstrating the efficacy of HGMSBA for training the FIS, the system is applied to the considered problem. Based on the obtained results, it can be inferred that the developed HGMSBA-FIS black-box identifier can be used as a very authentic tool with respect to accuracy and robustness. Besides, as the proposed black-box is not a physics-based identifier, it frees experts from the cumbersome mathematical formulations, and can be used for advanced real-time applications such as model-based control. The simulations

  8. Community health center provider ability to identify, treat and account for the social determinants of health: a card study.

    Science.gov (United States)

    Lewis, Joy H; Whelihan, Kate; Navarro, Isaac; Boyle, Kimberly R

    2016-08-27

    The social determinants of health (SDH) are conditions that shape the overall health of an individual on a continuous basis. As momentum for addressing social factors in primary care settings grows, provider ability to identify, treat and assess these factors remains unknown. Community health centers care for over 20-million of America's highest risk populations. This study at three centers evaluates provider ability to identify, treat and code for the SDH. Investigators utilized a pre-study survey and a card study design to obtain evidence from the point of care. The survey assessed providers' perceptions of the SDH and their ability to address them. Then providers filled out one anonymous card per patient on four assigned days over a 4-week period, documenting social factors observed during encounters. The cards allowed providers to indicate if they were able to: provide counseling or other interventions, enter a diagnosis code and enter a billing code for identified factors. The results of the survey indicate providers were familiar with the SDH and were comfortable identifying social factors at the point of care. A total of 747 cards were completed. 1584 factors were identified and 31 % were reported as having a service provided. However, only 1.2 % of factors were associated with a billing code and 6.8 % received a diagnosis code. An obvious discrepancy exists between the number of identifiable social factors, provider ability to address them and documentation with billing and diagnosis codes. This disparity could be related to provider inability to code for social factors and bill for related time and services. Health care organizations should seek to implement procedures to document and monitor social factors and actions taken to address them. Results of this study suggest simple methods of identification may be sufficient. The addition of searchable codes and reimbursements may improve the way social factors are addressed for individuals and populations.

  9. Intersectionality in the Lives of LGBTQ Youth: Identifying as LGBTQ and Finding Community in Small Cities and Rural Towns.

    Science.gov (United States)

    Hulko, Wendy; Hovanes, Jessica

    2018-01-01

    This article presents an analysis of the views of younger bisexual and lesbian women and transgender youth living in a western Canadian small city on their sexual and gender identities. Data were collected through focus groups and interviews and analyzed thematically through an intersectional lens. The purposive sample was composed of 13 youth who identified as lesbian, gay, bisexual, transgender, or queer (LGBTQ) and whose average age was 19.8 years. The analytical themes of (1) living in a small town, (2) identifying and being identified, and (3) talking intersectionality indicate that the sexual identities and gender identities and expressions of LGBTQ youth change across time and context and are impacted by often overlooked factors including faith, Indigenous ancestry, disability, and class. Further, the size and character of the community significantly impacts LGBTQ youth identity development and expression. This research demonstrates the uniqueness of individual youth's experiences-opposing notions of milestone events as singularly important in queer youth identity development.

  10. Identifying Barriers and Facilitators at Affect Community Pharmacists' Ability to Engage Children in Medication Counseling: A Pilot Study

    Science.gov (United States)

    Alexander, Dayna S.; Schleiden, Loren J.; Carpenter, Delesha M.

    2017-01-01

    OBJECTIVES This study aimed to describe the barriers and facilitators that influence community pharmacists' ability to provide medication counseling to pediatric patients. METHODS Semistructured interviews (n = 16) were conducted with pharmacy staff at 3 community pharmacies in 2 Eastern states. The interview guide elicited pharmacy staff experiences interacting with children and their perceived barriers and facilitators to providing medication counseling. Transcripts were reviewed for accuracy and a codebook was developed for data analysis. NVivo 10 was used for content analysis and identifying relevant themes. RESULTS Ten pharmacists and 6 pharmacy technicians were interviewed. Most participants were female (69%), aged 30 to 49 years (56%), with ≥5 years of pharmacy practice experience. Eight themes emerged as barriers to pharmacists' engaging children in medication counseling, the most prevalent being the child's absence during medication pickup, the child appearing to be distracted or uninterested, and having an unconducive pharmacy environment. Pharmacy staff noted 7 common facilitators to engaging children, most importantly, availability of demonstrative and interactive devices/technology, pharmacist demeanor and communication approach, and having child-friendly educational materials. CONCLUSIONS Findings suggest that pharmacy personnel are rarely able to engage children in medication counseling because of the patient's absence during medication pickup; however, having child-friendly materials could facilitate interactions when the child is present. These findings can inform programs and interventions aimed at addressing the barriers pharmacists encounter while educating children about safe and appropriate use of medicines. PMID:29290741

  11. Using a community-driven approach to identify local forest and climate change priorities in Teslin, Yukon

    Directory of Open Access Journals (Sweden)

    Joleen Timko

    2015-12-01

    Full Text Available The likelihood of addressing the complex environmental, economic, and social/cultural issues associated with local climate change impacts is enhanced when collaborative partnerships with local people are established. Using a community-centered approach in the Teslin region of Canada’s Yukon Territory, we utilized our research skills to respond to local needs for information by facilitating both an internal community process to clarify traditional and local knowledge, values, and perceptions on locally identified priorities, while gathering external information to enable local people to make sound decisions. Specifically, we sought to clarify local perceptions surrounding climate change impacts on fire risk and wildlife habitat, and the potential adaptation strategies appropriate and feasible within the Teslin Tlingit Traditional Territory. This paper provides a characterization of the study region and our project team; provides background on the interview and data collection process; presents our key results; and discusses the importance of our findings and charts a way forward for our continued work with the people in the Teslin region. This approach presents an excellent opportunity to help people holistically connect a range of local values, including fire risk mitigation, habitat enhancement, economic development, and enhanced social health.

  12. Searching for Communities in Bipartite Networks

    OpenAIRE

    Barber, Michael J.; Faria, Margarida; Streit, Ludwig; Strogan, Oleg

    2008-01-01

    Bipartite networks are a useful tool for representing and investigating interaction networks. We consider methods for identifying communities in bipartite networks. Intuitive notions of network community groups are made explicit using Newman's modularity measure. A specialized version of the modularity, adapted to be appropriate for bipartite networks, is presented; a corresponding algorithm is described for identifying community groups through maximizing this measure. The algorithm is applie...

  13. Identifying community risk factors for HIV among South African adolescents with mental health problems: a qualitative study of parental perceptions.

    Science.gov (United States)

    Kagee, Ashraf; Donenberg, Geri; Davids, Alicia; Vermaak, Redwaan; Simbayi, Leickness; Ward, Catherine; Naidoo, Pamela; Mthembu, Jacky

    2014-01-01

    High risk sexual behaviour, alcohol and drug use, and mental health problems combine to yield high levels of HIV-risk behaviour among adolescents with mental health problems. In South Africa, little research has been conducted on parental perspectives of HIV-risk among this population. We conducted a series of focus group discussions with 28 mothers of adolescents receiving services at two mental health clinics in South Africa to identify, from their perspectives, the key community problems facing their children. Participants indicated that HIV remained a serious threat to their adolescent children's well-being, in addition to substance abuse, early sexual debut, and teenage pregnancy. These social problems were mentioned as external to their household dynamics, and thus seemingly beyond the purview of the parent-adolescent relationship. These data have implications for the design of family-based interventions to ameliorate the factors associated with HIV-risk among youth receiving mental health services.

  14. A retrospective observational analysis to identify patient and treatment-related predictors of outcomes in a community mental health programme.

    Science.gov (United States)

    Green, Stuart A; Honeybourne, Emmi; Chalkley, Sylvia R; Poots, Alan J; Woodcock, Thomas; Price, Geraint; Bell, Derek; Green, John

    2015-05-20

    This study aims to identify patient and treatment factors that affect clinical outcomes of community psychological therapy through the development of a predictive model using historic data from 2 services in London. In addition, the study aims to assess the completeness of data collection, explore how treatment outcomes are discriminated using current criteria for classifying recovery, and assess the feasibility and need for undertaking a future larger population analysis. Observational, retrospective discriminant analysis. 2 London community mental health services that provide psychological therapies for common mental disorders including anxiety and depression. A total of 7388 patients attended the services between February 2009 and May 2012, of which 4393 (59%) completed therapy, or there was an agreement to end therapy, and were included in the study. Different combinations of the clinical outcome scores for anxiety Generalised Anxiety Disorder-7 and depression Patient Health Questionnaire-9 were used to construct different treatment outcomes. The predictive models were able to assign a positive or negative clinical outcome to each patient based on 5 independent pre-treatment variables, with an accuracy of 69.4% and 79.3%, respectively: initial severity of anxiety and depression, ethnicity, deprivation and gender. The number of sessions attended/missed were also important factors identified in recovery. Predicting whether patients are likely to have a positive outcome following treatment at entry might allow suitable modification of scheduled treatment, possibly resulting in improvements in outcomes. The model also highlights factors not only associated with poorer outcomes but inextricably linked to prevalence of common mental disorders, emphasising the importance of social determinants not only in poor health but also poor recovery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

  15. Prevalence and Clinical Correlates of Sarcopenia in Community-Dwelling Older People: Application of the EWGSOP Definition and Diagnostic Algorithm

    Science.gov (United States)

    2014-01-01

    Background. Muscle impairment is a common condition in older people and a powerful risk factor for disability and mortality. The aim of this study was to apply the European Working Group on Sarcopenia in Older People criteria to estimate the prevalence and investigate the clinical correlates of sarcopenia, in a sample of Italian community-dwelling older people. Methods. Cross-sectional analysis of 730 participants (74% aged 65 years and older) enrolled in the InCHIANTI study. Sarcopenia was defined according to the European Working Group on Sarcopenia in Older People criteria using bioimpedance analysis for muscle mass assessment. Logistic regression analysis was used to identify the factors independently associated with sarcopenia. Results. Sarcopenia defined by the European Working Group on Sarcopenia in Older People criteria increased steeply with age (p Nutritional intake, physical activity, and level of comorbidity were not associated with sarcopenia. Conclusions. Sarcopenia identified by the European Working Group on Sarcopenia in Older People criteria is a relatively common condition in Italian octogenarians, and its prevalence increases with aging. Correlates of sarcopenia identified in this study might suggest new approaches for prevention and treatment of sarcopenia. PMID:24085400

  16. GIS-Mapping and Statistical Analyses to Identify Climate-Vulnerable Communities and Populations Exposed to Superfund Sites

    Science.gov (United States)

    Climate change-related cumulative health risks are expected to be disproportionately greater for overburdened communities, due to differential proximity and exposures to chemical sources and flood zones. Communities and populations vulnerable to climate change-associated impacts ...

  17. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  18. Karrikins Identified in Biochars Indicate Post-Fire Chemical Cues Can Influence Community Diversity and Plant Development.

    Directory of Open Access Journals (Sweden)

    Jitka Kochanek

    Full Text Available Karrikins are smoke-derived compounds that provide strong chemical cues to stimulate seed germination and seedling growth. The recent discovery in Arabidopsis that the karrikin perception system may be present throughout angiosperms implies a fundamental plant function. Here, we identify the most potent karrikin, karrikinolide (KAR1, in biochars and determine its role in species unique plant responses.Biochars were prepared by three distinct commercial-scale pyrolysis technologies using systematically selected source material and their chemical properties, including karrikinolide, were quantified. Dose-response assays determined the effects of biochar on seed germination for two model species that require karrikinolide to break dormancy (Solanum orbiculatum, Brassica tourneforttii and on seedling growth using two species that display plasticity to karrikins, biochar and phytotoxins (Lactuca sativa, Lycopersicon esculentum. Multivariate analysis examined relationships between biochar properties and the plant phenotype.Results showed that karrikin abundant biochars stimulated dormant seed germination and seedling growth via mechanisms analogous to post-fire chemical cues. The individual species response was associated with its sensitivity to karrikinolide and inhibitory compounds within the biochars. These findings are critical for understanding why biochar influences community composition and plant physiology uniquely for different species and reaffirms that future pyrolysis technologies promise by-products that concomitantly sequester carbon and enhance plant growth for ecological and broader plant related applications.

  19. An Exploration of Strategic Planning Perspectives and Processes within Community Colleges Identified as Being Distinctive in Their Strategic Planning Practices

    Science.gov (United States)

    Augustyniak, Lisa J.

    2015-01-01

    Community college leaders face unprecedented change, and some have begun reexamining their institutional strategic planning processes. Yet, studies in higher education strategic planning spend little time examining how community colleges formulate their strategic plans. This mixed-method qualitative study used an expert sampling method to identify…

  20. Combining the Power of Statistical Analyses and Community Interviews to Identify Adoption Barriers for Stormwater Best-Management Practices

    Science.gov (United States)

    Hoover, F. A.; Bowling, L. C.; Prokopy, L. S.

    2015-12-01

    Urban stormwater is an on-going management concern in municipalities of all sizes. In both combined or separated sewer systems, pollutants from stormwater runoff enter the natural waterway system during heavy rain events. Urban flooding during frequent and more intense storms are also a growing concern. Therefore, stormwater best-management practices (BMPs) are being implemented in efforts to reduce and manage stormwater pollution and overflow. The majority of BMP water quality studies focus on the small-scale, individual effects of the BMP, and the change in water quality directly from the runoff of these infrastructures. At the watershed scale, it is difficult to establish statistically whether or not these BMPs are making a difference in water quality, given that watershed scale monitoring is often costly and time consuming, relying on significant sources of funds, which a city may not have. Hence, there is a need to quantify the level of sampling needed to detect the water quality impact of BMPs at the watershed scale. In this study, a power analysis was performed on data from an urban watershed in Lafayette, Indiana, to determine the frequency of sampling required to detect a significant change in water quality measurements. Using the R platform, results indicate that detecting a significant change in watershed level water quality would require hundreds of weekly measurements, even when improvement is present. The second part of this study investigates whether the difficulty in demonstrating water quality change represents a barrier to adoption of stormwater BMPs. Semi-structured interviews of community residents and organizations in Chicago, IL are being used to investigate residents understanding of water quality and best management practices and identify their attitudes and perceptions towards stormwater BMPs. Second round interviews will examine how information on uncertainty in water quality improvements influences their BMP attitudes and perceptions.

  1. Identifying important and feasible policies and actions for health at community sports clubs: a consensus-generating approach.

    Science.gov (United States)

    Kelly, Bridget; King, Lesley; Bauman, Adrian E; Baur, Louise A; Macniven, Rona; Chapman, Kathy; Smith, Ben J

    2014-01-01

    Children's high participation in organised sport in Australia makes sport an ideal setting for health promotion. This study aimed to generate consensus on priority health promotion objectives for community sports clubs, based on informed expert judgements. Delphi survey using three structured questionnaires. Forty-six health promotion, nutrition, physical activity and sport management/delivery professionals were approached to participate in the survey. Questionnaires used an iterative process to determine aspects of sports clubs deemed necessary for developing healthy sporting environments for children. Initially, participants were provided with a list of potential standards for a range of health promotion areas and asked to rate standards based on their importance and feasibility, and any barriers to implementation. Subsequently, participants were provided with information that summarised ratings for each standard to indicate convergence of the group, and asked to review and potentially revise their responses where they diverged. In a third round, participants ranked confirmed standards by priority. 26 professionals completed round 1, 21 completed round 2, and 18 completed round 3. The highest ranked standards related to responsible alcohol practices, availability of healthy food and drinks at sports canteens, smoke-free club facilities, restricting the sale and consumption of alcohol during junior sporting activities, and restricting unhealthy food and beverage company sponsorship. Identifying and prioritising health promotion areas that are relevant to children's sports clubs assists in focusing public health efforts and may guide future engagement of sports clubs. Approaches for providing informational and financial support to clubs to operationalise these standards are proposed. Copyright © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  2. Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey.

    Directory of Open Access Journals (Sweden)

    Andrea A Kim

    Full Text Available A recent infection testing algorithm (RITA that can distinguish recent from long-standing HIV infection can be applied to nationally representative population-based surveys to characterize and identify risk factors for recent infection in a country.We applied a RITA using the Limiting Antigen Avidity Enzyme Immunoassay (LAg on stored HIV-positive samples from the 2007 Kenya AIDS Indicator Survey. The case definition for recent infection included testing recent on LAg and having no evidence of antiretroviral therapy use. Multivariate analysis was conducted to determine factors associated with recent and long-standing infection compared to HIV-uninfected persons. All estimates were weighted to adjust for sampling probability and nonresponse.Of 1,025 HIV-antibody-positive specimens, 64 (6.2% met the case definition for recent infection and 961 (93.8% met the case definition for long-standing infection. Compared to HIV-uninfected individuals, factors associated with higher adjusted odds of recent infection were living in Nairobi (adjusted odds ratio [AOR] 11.37; confidence interval [CI] 2.64-48.87 and Nyanza (AOR 4.55; CI 1.39-14.89 provinces compared to Western province; being widowed (AOR 8.04; CI 1.42-45.50 or currently married (AOR 6.42; CI 1.55-26.58 compared to being never married; having had ≥ 2 sexual partners in the last year (AOR 2.86; CI 1.51-5.41; not using a condom at last sex in the past year (AOR 1.61; CI 1.34-1.93; reporting a sexually transmitted infection (STI diagnosis or symptoms of STI in the past year (AOR 1.97; CI 1.05-8.37; and being aged <30 years with: 1 HSV-2 infection (AOR 8.84; CI 2.62-29.85, 2 male genital ulcer disease (AOR 8.70; CI 2.36-32.08, or 3 lack of male circumcision (AOR 17.83; CI 2.19-144.90. Compared to HIV-uninfected persons, factors associated with higher adjusted odds of long-standing infection included living in Coast (AOR 1.55; CI 1.04-2.32 and Nyanza (AOR 2.33; CI 1.67-3.25 provinces compared to

  3. Identifying influence of perceived quality and satisfaction on the utilization status of the community clinic services; Bangladesh context.

    Science.gov (United States)

    Karim, R M; Abdullah, M S; Rahman, A M; Alam, A M

    2015-04-01

    Bangladesh is one among the few countries of the world that provides free medical services at the community level through various public health facilities. It is now evident that, clients' perceived quality of services and their expectations of service standards affect health service utilization to a great extent. The aim of the study was to develop and validate the measures for perception and satisfaction of primary health care quality in Bangladesh context and to identify their aspects on the utilization status of the Community Clinic (CC) services. This mixed method cross sectional survey was conducted from January to June 2012, in the catchment area of 12 Community Clinics (CCs). Since most of the outcome indicators focus mainly on women and children, women having children less than two years of age were randomly assigned and interviewed for the study purpose. Data for the development of perceived service quality and satisfaction tools were collected through Focus Group Discussion (FGD), key informants interview and data for measuring the utilization status were collected by an interviewer administered pretested semi-structured questionnaire. About 95% of the respondents were Muslims and 5% were Hindus. The average age of the respondents was 23.38 (SD ± 4.15) years and almost all of them are home makers. The average monthly expenditure of their family was 7462.92 (SD ± 2545) BDT equivalent to 95 (SD ± 32) US$. To measure lay peoples' perception and satisfaction regarding primary health care service quality two scales e.g. Slim Haddad's 20-item scale for measuring perceived quality of primary health care services (PQPCS) validated in Guinea and Burkina Fuso and primary care satisfaction survey for women (PCSSW) developed by Scholle and colleagues 2004; is a 24-item survey tool validated in Turkey were chosen as a reference tools. Based on those, two psychometric research instruments; 24 items PQPCS scale (chronbach's α =0.89) and 22-items Community Clinic

  4. Measurement of the Jet Vertex Charge algorithm performance for identified $b$-jets in $t\\bar{t}$ events in $pp$ collisions with the ATLAS detector

    CERN Document Server

    The ATLAS collaboration

    2018-01-01

    The Jet Vertex Charge algorithm, developed recently within the ATLAS collaboration, discriminates between jets resulting from the hadronisation of a bottom quark or bottom antiquark. This note describes a measurement of the performance of the algorithm and the extraction of data-to-simulation scale factors, made using $b$-tagged jets in candidate single lepton $t\\bar{t}$ events. The data sample was collected by the ATLAS detector at the LHC using $pp$ collisions at $\\sqrt{s}$ = 13 TeV in 2015 and 2016 and corresponds to a total integrated luminosity of 36.1 fb$^{-1}$ . Overall, good agreement is found between data and the simulation.

  5. Decoding communities in networks

    Science.gov (United States)

    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.

  6. Decoding communities in networks.

    Science.gov (United States)

    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.

  7. Strength-based well-being indicators for Indigenous children and families: A literature review of Indigenous communities' identified well-being indicators.

    Science.gov (United States)

    Rountree, Jennifer; Smith, Addie

    2016-01-01

    Mainstream child and family well-being indicators frequently are based on measuring health, economic, and social deficits, and do not reflect Indigenous holistic and strength-based definitions of health and well-being. The present article is a review of literature that features Indigenous communities' self-identified strength-based indicators of child and family well-being. The literature search included Indigenous communities from across the world, incorporating findings from American Indians and Alaska Natives, First Nations, Native Hawaiians, Māori, Aboriginal Australians, and Sámi communities. Sorting the identified indicators into the quadrants of the Relational Worldview, an Indigenous framework for well-being based on medicine wheel teachings that views health and well-being as a balance among physical, mental, contextual, and spiritual factors, the authors discuss the findings.

  8. Is Telephone Screening Feasible? Accuracy and Cost-Effectiveness of Identifying People Medically Eligible for Home- And Community-Based Services.

    Science.gov (United States)

    Fries, Brant E.; James, Mary; Hammer, Susan S.; Shugarman, Lisa R.; Morris, John N.

    2004-01-01

    Purpose: To determine the accuracy of a telephone-screening system to identify persons eligible for home- and community-based long-term care. Design and Methods: Data from Michigan telephone screens were compared to data from in-person assessments using the Minimum Data Set for Home Care (MDS-HC). Weighted kappa statistics measured the level of…

  9. A risk profile for identifying community-dwelling elderly with a highrisk of recurrent falling: results of a 3-year prospective study

    NARCIS (Netherlands)

    Pluym, S.M.F.; Smit, J.H.; Tromp, A.M.; Stel, V.S.; Deeg, D.J.H.; Bouter, L.M.; Lips, P.T.A.M.

    2007-01-01

    Introduction: The aim of the prospective study reported here was to develop a risk profile that can be used to identify community-dwelling elderly at a high risk of recurrent falling. Materials and methods: The study was designed as a 3-year prospective cohort study. A total of 1365

  10. Community

    Science.gov (United States)

    stability Science & Innovation Collaboration Careers Community Environment Science & Innovation Recruitment Events Community Commitment Giving Campaigns, Drives Economic Development Employee Funded neighbor pledge: contribute to quality of life in Northern New Mexico through economic development

  11. Validation of a clinical practice-based algorithm for the diagnosis of autosomal recessive cerebellar ataxias based on NGS identified cases.

    Science.gov (United States)

    Mallaret, Martial; Renaud, Mathilde; Redin, Claire; Drouot, Nathalie; Muller, Jean; Severac, Francois; Mandel, Jean Louis; Hamza, Wahiba; Benhassine, Traki; Ali-Pacha, Lamia; Tazir, Meriem; Durr, Alexandra; Monin, Marie-Lorraine; Mignot, Cyril; Charles, Perrine; Van Maldergem, Lionel; Chamard, Ludivine; Thauvin-Robinet, Christel; Laugel, Vincent; Burglen, Lydie; Calvas, Patrick; Fleury, Marie-Céline; Tranchant, Christine; Anheim, Mathieu; Koenig, Michel

    2016-07-01

    Establishing a molecular diagnosis of autosomal recessive cerebellar ataxias (ARCA) is challenging due to phenotype and genotype heterogeneity. We report the validation of a previously published clinical practice-based algorithm to diagnose ARCA. Two assessors performed a blind analysis to determine the most probable mutated gene based on comprehensive clinical and paraclinical data, without knowing the molecular diagnosis of 23 patients diagnosed by targeted capture of 57 ataxia genes and high-throughput sequencing coming from a 145 patients series. The correct gene was predicted in 61 and 78 % of the cases by the two assessors, respectively. There was a high inter-rater agreement [K = 0.85 (0.55-0.98) p < 0.001] confirming the algorithm's reproducibility. Phenotyping patients with proper clinical examination, imaging, biochemical investigations and nerve conduction studies remain crucial for the guidance of molecular analysis and to interpret next generation sequencing results. The proposed algorithm should be helpful for diagnosing ARCA in clinical practice.

  12. Trait assembly of woody plants in communities across sub-alpine gradients: Identifying the role of limiting similarity

    NARCIS (Netherlands)

    Yan, B.; Zhang, J.; Liu, Y.; Li, Z.; Huang, X.; Yang, W.; Prinzing, A.

    2012-01-01

    Questions - Plant species can be assembled into communities through habitat filtering or species competition, but their relative roles are still debated. We do not know whether there is limited similarity between co-existing species when accounting for the parallel effect of abiotic habitat

  13. Deep Ion Torrent sequencing identifies soil fungal community shifts after frequent prescribed fires in a southeastern US forest ecosystem.

    Science.gov (United States)

    Brown, Shawn P; Callaham, Mac A; Oliver, Alena K; Jumpponen, Ari

    2013-12-01

    Prescribed burning is a common management tool to control fuel loads, ground vegetation, and facilitate desirable game species. We evaluated soil fungal community responses to long-term prescribed fire treatments in a loblolly pine forest on the Piedmont of Georgia and utilized deep Internal Transcribed Spacer Region 1 (ITS1) amplicon sequencing afforded by the recent Ion Torrent Personal Genome Machine (PGM). These deep sequence data (19,000 + reads per sample after subsampling) indicate that frequent fires (3-year fire interval) shift soil fungus communities, whereas infrequent fires (6-year fire interval) permit system resetting to a state similar to that without prescribed fire. Furthermore, in nonmetric multidimensional scaling analyses, primarily ectomycorrhizal taxa were correlated with axes associated with long fire intervals, whereas soil saprobes tended to be correlated with the frequent fire recurrence. We conclude that (1) multiplexed Ion Torrent PGM analyses allow deep cost effective sequencing of fungal communities but may suffer from short read lengths and inconsistent sequence quality adjacent to the sequencing adaptor; (2) frequent prescribed fires elicit a shift in soil fungal communities; and (3) such shifts do not occur when fire intervals are longer. Our results emphasize the general responsiveness of these forests to management, and the importance of fire return intervals in meeting management objectives. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

  14. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol.

    Science.gov (United States)

    Taljaard, Monica; Tuna, Meltem; Bennett, Carol; Perez, Richard; Rosella, Laura; Tu, Jack V; Sanmartin, Claudia; Hennessy, Deirdre; Tanuseputro, Peter; Lebenbaum, Michael; Manuel, Douglas G

    2014-10-23

    Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible

  15. The use of nominal group technique in identifying community health priorities in Moshi rural district, northern Tanzania

    DEFF Research Database (Denmark)

    Makundi, E A; Manongi, R; Mushi, A K

    2005-01-01

    in the list implying that priorities should not only be focused on diseases, but should also include health services and social cultural issues. Indeed, methods which are easily understood and applied thus able to give results close to those provided by the burden of disease approaches should be adopted....... The patients/caregivers, women's group representatives, youth leaders, religious leaders and community leaders/elders constituted the principal subjects. Emphasis was on providing qualitative data, which are of vital consideration in multi-disciplinary oriented studies, and not on quantitative information from....... It is the provision of ownership of the derived health priorities to partners including the community that enhances research utilization of the end results. In addition to disease-based methods, the Nominal Group Technique is being proposed as an important research tool for involving the non-experts in priority...

  16. Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

    International Nuclear Information System (INIS)

    Havemann, Frank; Heinz, Michael; Struck, Alexander; Gläser, Jochen

    2011-01-01

    We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similarly to GCE

  17. Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

    Science.gov (United States)

    Havemann, Frank; Heinz, Michael; Struck, Alexander; Gläser, Jochen

    2011-01-01

    We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similarly to GCE.

  18. Identifying Balance and Fall Risk in Community-Dwelling Older Women: The Effect of Executive Function on Postural Control

    OpenAIRE

    Muir-Hunter, Susan W.; Clark, Jennifer; McLean, Stephanie; Pedlow, Sam; Van Hemmen, Alysia; Montero Odasso, Manuel; Overend, Tom

    2014-01-01

    Purpose: The mechanisms linking cognition, balance function, and fall risk among older adults are not fully understood. An evaluation of the effect of cognition on balance tests commonly used in clinical practice to assess community-dwelling older adults could enhance the identification of at-risk individuals. The study aimed to determine (1) the association between cognition and clinical tests of balance and (2) the relationship between executive function (EF) and balance under single- and d...

  19. Community-level physiological profiling analyses show potential to identify the copiotrophic bacteria present in soil environments

    Czech Academy of Sciences Publication Activity Database

    Lladó, Salvador; Baldrian, Petr

    2017-01-01

    Roč. 12, č. 2 (2017), s. 1-9, č. článku e0171638. E-ISSN 1932-6203 R&D Projects: GA ČR(CZ) GP14-09040P; GA MŠk(CZ) LD15086 Institutional support: RVO:61388971 Keywords : SUBSTRATE UTILIZATION PATTERNS * CARBON -SOURCE UTILIZATION * MICROBIAL COMMUNITIES Subject RIV: EE - Microbiology, Virology OBOR OECD: Microbiology Impact factor: 2.806, year: 2016

  20. Biclique communities

    DEFF Research Database (Denmark)

    Jørgensen, Sune Lehmann; Hansen-Schwartz, Martin; Hansen, Lars Kai

    2008-01-01

    We present a method for detecting communities in bipartite networks. Based on an extension of the k-clique community detection algorithm, we demonstrate how modular structure in bipartite networks presents itself as overlapping bicliques. If bipartite information is available, the biclique...... community detection algorithm retains all of the advantages of the k-clique algorithm, but avoids discarding important structural information when performing a one-mode projection of the network. Further, the biclique community detection algorithm provides a level of flexibility by incorporating independent...

  1. Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

    Science.gov (United States)

    Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Annweiler, Cédric; Fantino, Bruno; Beauchet, Olivier

    2015-04-01

    Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults. Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis. Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI 4, vision score 9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP. NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

  2. New Genome-Wide Algorithm Identifies Novel In-Vivo Expressed Mycobacterium Tuberculosis Antigens Inducing Human T-Cell Responses with Classical and Unconventional Cytokine Profiles

    DEFF Research Database (Denmark)

    Coppola, Mariateresa; van Meijgaarden, Krista E.; Franken, Kees L. M. C.

    2016-01-01

    -wide transcriptomics of Mtb infected lungs we developed data sets and methods to identify IVE-TB (in-vivo expressed Mtb) antigens expressed in the lung. Quantitative expression analysis of 2,068 Mtb genes from the predicted first operons identified the most upregulated IVE-TB genes during in-vivo pulmonary infection...

  3. The closure of Trawsfynydd power station - effects on staff and the local community and identifying a strategy for decommissioning

    International Nuclear Information System (INIS)

    Kay, J.M.; Ellis, A.T.; Williams, T.W.

    1995-01-01

    The decision to close Trawsfynydd power station had implications for staff and the local community. It was necessary to take immediate steps to prepare for decommissioning the station and to devise an appropriate staff structure. At the same time, there was also a need for Nuclear Electric to adopt a clear and well defined decommissioning strategy. As the station is located within a National Park, as local employment opportunities are very limited and as the nuclear industry was approaching a Government Review, Nuclear Electric took steps to consult the staff and the local public on the options for decommissioning the station. This consultation influenced the decommissioning strategy chosen for Trawsfynydd. (Author)

  4. Referral outcomes of individuals identified at high risk of cardiovascular disease by community health workers in Bangladesh, Guatemala, Mexico, and South Africa.

    Science.gov (United States)

    Levitt, Naomi S; Puoane, Thandi; Denman, Catalina A; Abrahams-Gessel, Shafika; Surka, Sam; Mendoza, Carlos; Khanam, Masuma; Alam, Sartaj; Gaziano, Thomas A

    2015-01-01

    We have found that community health workers (CHWs) with appropriate training are able to accurately identify people at high cardiovascular disease (CVD) risk in the community who would benefit from the introduction of preventative management, in Bangladesh, Guatemala, Mexico, and South Africa. This paper examines the attendance pattern for those individuals who were so identified and referred to a health care facility for further assessment and management. Patient records from the health centres in each site were reviewed for data on diagnoses made and treatment commenced. Reasons for non-attendance were sought from participants who had not attended after being referred. Qualitative data were collected from study coordinators regarding their experiences in obtaining the records and conducting the record reviews. The perspectives of CHWs and community members, who were screened, were also obtained. Thirty-seven percent (96/263) of those referred attended follow-up: 36 of 52 (69%) were urgent and 60 of 211 (28.4%) were non-urgent referrals. A diagnosis of hypertension (HTN) was made in 69% of urgent referrals and 37% of non-urgent referrals with treatment instituted in all cases. Reasons for non-attendance included limited self-perception of risk, associated costs, health system obstacles, and lack of trust in CHWs to conduct CVD risk assessments and to refer community members into the health system. The existing barriers to referral in the health care systems negatively impact the gains to be had through screening by training CHWs in the use of a simple risk assessment tool. The new diagnoses of HTN and commencement on treatment in those that attended referrals underscores the value of having persons at the highest risk identified in the community setting and referred to a clinic for further evaluation and treatment.

  5. Referral outcomes of individuals identified at high risk of cardiovascular disease by community health workers in Bangladesh, Guatemala, Mexico, and South Africa

    Science.gov (United States)

    Levitt, Naomi S.; Puoane, Thandi; Denman, Catalina A.; Abrahams-Gessel, Shafika; Surka, Sam; Mendoza, Carlos; Khanam, Masuma; Alam, Sartaj; Gaziano, Thomas A.

    2015-01-01

    Background We have found that community health workers (CHWs) with appropriate training are able to accurately identify people at high cardiovascular disease (CVD) risk in the community who would benefit from the introduction of preventative management, in Bangladesh, Guatemala, Mexico, and South Africa. This paper examines the attendance pattern for those individuals who were so identified and referred to a health care facility for further assessment and management. Design Patient records from the health centres in each site were reviewed for data on diagnoses made and treatment commenced. Reasons for non-attendance were sought from participants who had not attended after being referred. Qualitative data were collected from study coordinators regarding their experiences in obtaining the records and conducting the record reviews. The perspectives of CHWs and community members, who were screened, were also obtained. Results Thirty-seven percent (96/263) of those referred attended follow-up: 36 of 52 (69%) were urgent and 60 of 211 (28.4%) were non-urgent referrals. A diagnosis of hypertension (HTN) was made in 69% of urgent referrals and 37% of non-urgent referrals with treatment instituted in all cases. Reasons for non-attendance included limited self-perception of risk, associated costs, health system obstacles, and lack of trust in CHWs to conduct CVD risk assessments and to refer community members into the health system. Conclusions The existing barriers to referral in the health care systems negatively impact the gains to be had through screening by training CHWs in the use of a simple risk assessment tool. The new diagnoses of HTN and commencement on treatment in those that attended referrals underscores the value of having persons at the highest risk identified in the community setting and referred to a clinic for further evaluation and treatment. PMID:25854780

  6. Community.

    Science.gov (United States)

    Grauer, Kit, Ed.

    1995-01-01

    Art in context of community is the theme of this newsletter. The theme is introduced in an editorial "Community-Enlarging the Definition" (Kit Grauer). Related articles include: (1) "The Children's Bridge is not Destroyed: Heart in the Middle of the World" (Emil Robert Tanay); (2) "Making Bridges: The Sock Doll…

  7. Identifying community healthcare supports for the elderly and the factors affecting their aging care model preference: evidence from three districts of Beijing

    Directory of Open Access Journals (Sweden)

    Tianyang Liu

    2016-11-01

    Full Text Available Abstract Background The Chinese tradition of filial piety, which prioritized family-based care for the elderly, is transitioning and elders can no longer necessarily rely on their children. The purpose of this study was to identify community support for the elderly, and analyze the factors that affect which model of old-age care elderly people dwelling in communities prefer. Methods We used the database “Health and Social Support of Elderly Population in Community”. Questionnaires were issued in 2013, covering 3 districts in Beijing. A group of 1036 people over 60 years in age were included in the study. The respondents’ profile variables were organized in Andersen’s Model and community healthcare resource factors were added. A multinomial logistic model was applied to analyze the factors associated with the desired aging care models. Results Cohabiting with children and relying on care from family was still the primary desired aging care model for seniors (78 %, followed by living in institutions (14.8 % and living at home independently while relying on community resources (7.2 %. The regression result indicated that predisposing, enabling and community factors were significantly associated with the aging care model preference. Specifically, compared with those who preferred to cohabit with children, those having higher education, fewer available family and friend helpers, and shorter distance to healthcare center were more likely to prefer to live independently and rely on community support. And compared with choosing to live in institutions, those having fewer available family and friend helpers and those living alone were more likely to prefer to live independently and rely on community. Need factors (health and disability condition were not significantly associated with desired aging care models, indicating that desired aging care models were passive choices resulted from the balancing of family and social caring resources

  8. Simplified clinical algorithm for identifying patients eligible for immediate initiation of antiretroviral therapy for HIV (SLATE): protocol for a randomised evaluation.

    Science.gov (United States)

    Rosen, Sydney; Fox, Matthew P; Larson, Bruce A; Brennan, Alana T; Maskew, Mhairi; Tsikhutsu, Isaac; Bii, Margaret; Ehrenkranz, Peter D; Venter, Wd Francois

    2017-05-28

    African countries are rapidly adopting guidelines to offer antiretroviral therapy (ART) to all HIV-infected individuals, regardless of CD4 count. For this policy of 'treat all' to succeed, millions of new patients must be initiated on ART as efficiently as possible. Studies have documented high losses of treatment-eligible patients from care before they receive their first dose of antiretrovirals (ARVs), due in part to a cumbersome, resource-intensive process for treatment initiation, requiring multiple clinic visits over a several-week period. The Simplified Algorithm for Treatment Eligibility (SLATE) study is an individually randomised evaluation of a simplified clinical algorithm for clinicians to reliably determine a patient's eligibility for immediate ART initiation without waiting for laboratory results or additional clinic visits. SLATE will enrol and randomise (1:1) 960 adult, HIV-positive patients who present for HIV testing or care and are not yet on ART in South Africa and Kenya. Patients randomised to the standard arm will receive routine, standard of care ART initiation from clinic staff. Patients randomised to the intervention arm will be administered a symptom report, medical history, brief physical exam and readiness assessment. Patients who have positive (satisfactory) results for all four components of SLATE will be dispensed ARVs immediately, at the same clinic visit. Patients who have any negative results will be referred for further clinical investigation, counselling, tests or other services prior to being dispensed ARVs. After the initial visit, follow-up will be by passive medical record review. The primary outcomes will be ART initiation ≤28 days and retention in care 8 months after study enrolment. Ethics approval has been provided by the Boston University Institutional Review Board, the University of the Witwatersrand Human Research Ethics Committee (Medical) and the KEMRI Scientific and Ethics Review Unit. Results will be published in

  9. The use of think-aloud protocols to identify a decision-making process of community pharmacists aimed at improving CMS Star Ratings scores.

    Science.gov (United States)

    George, David L; Smith, Michael J; Draugalis, JoLaine R; Tolma, Eleni L; Keast, Shellie L; Wilson, Justin B

    2018-03-01

    The Center for Medicare and Medicaid Services (CMS) created the Star Rating system based on multiple measures that indicate the overall quality of health plans. Community pharmacists can impact certain Star Ratings measure scores through medication adherence and patient safety interventions. To explore methods, needs, and workflow issues of community pharmacists to improve CMS Star Ratings measures. Think-aloud protocols (TAPs) were conducted with active community retail pharmacists in Oklahoma. Each TAP was audio recorded and transcribed to documents for analysis. Analysts agreed on common themes, illuminated differences in findings, and saturation of the data gathered. Methods, needs, and workflow themes of community pharmacists associated with improving Star Ratings measures were compiled and organized to exhibit a decision-making process. Five TAPs were performed among three independent pharmacy owners, one multi-store owner, and one chain-store administrator. A thematically common 4-step process to monitor and improve CMS Star Ratings scores among participants was identified. To improve Star Ratings measures, pharmacists: 1) used technology to access scores, 2) analyzed data to strategically set goals, 3) assessed individual patient information for comprehensive assessment, and 4) decided on interventions to best impact Star Ratings scores. Participants also shared common needs, workflow issues, and benefits associated with methods used in improving Star Ratings. TAPs were useful in exploring processes of pharmacists who improve CMS Star Ratings scores. Pharmacists demonstrated and verbalized their methods, workflow issues, needs, and benefits related to performing the task. The themes and decision-making process identified to improving CMS Star Ratings scores will assist in the development of training and education programs for pharmacists in the community setting. Published by Elsevier Inc.

  10. Identifying balance and fall risk in community-dwelling older women: the effect of executive function on postural control.

    Science.gov (United States)

    Muir-Hunter, Susan W; Clark, Jennifer; McLean, Stephanie; Pedlow, Sam; Van Hemmen, Alysia; Montero Odasso, Manuel; Overend, Tom

    2014-01-01

    The mechanisms linking cognition, balance function, and fall risk among older adults are not fully understood. An evaluation of the effect of cognition on balance tests commonly used in clinical practice to assess community-dwelling older adults could enhance the identification of at-risk individuals. The study aimed to determine (1) the association between cognition and clinical tests of balance and (2) the relationship between executive function (EF) and balance under single- and dual-task testing. Participants (24 women, mean age of 76.18 [SD 16.45] years) completed six clinical balance tests, four cognitive tests, and two measures of physical function. Poor balance function was associated with poor performance on cognitive testing of EF. In addition, the association with EF was strongest under the dual-task timed up-and-go (TUG) test and the Fullerton Advanced Balance Scale. Measures of global cognition were associated only with the dual-task performance of the TUG. Postural sway measured with the Standing Balance Test, under single- or dual-task test conditions, was not associated with cognition. Decreased EF was associated with worse performance on functional measures of balance. The relationship between EF and balance was more pronounced with dual-task testing using a complex cognitive task combined with the TUG.

  11. Identifying Balance and Fall Risk in Community-Dwelling Older Women: The Effect of Executive Function on Postural Control

    Science.gov (United States)

    Clark, Jennifer; McLean, Stephanie; Pedlow, Sam; Van Hemmen, Alysia; Montero Odasso, Manuel; Overend, Tom

    2014-01-01

    ABSTRACT Purpose: The mechanisms linking cognition, balance function, and fall risk among older adults are not fully understood. An evaluation of the effect of cognition on balance tests commonly used in clinical practice to assess community-dwelling older adults could enhance the identification of at-risk individuals. The study aimed to determine (1) the association between cognition and clinical tests of balance and (2) the relationship between executive function (EF) and balance under single- and dual-task testing. Methods: Participants (24 women, mean age of 76.18 [SD 16.45] years) completed six clinical balance tests, four cognitive tests, and two measures of physical function. Results: Poor balance function was associated with poor performance on cognitive testing of EF. In addition, the association with EF was strongest under the dual-task timed up-and-go (TUG) test and the Fullerton Advanced Balance Scale. Measures of global cognition were associated only with the dual-task performance of the TUG. Postural sway measured with the Standing Balance Test, under single- or dual-task test conditions, was not associated with cognition. Conclusions: Decreased EF was associated with worse performance on functional measures of balance. The relationship between EF and balance was more pronounced with dual-task testing using a complex cognitive task combined with the TUG. PMID:24799756

  12. Prevalence and Clinical Correlates of Sarcopenia, Identified According to the EWGSOP Definition and Diagnostic Algorithm, in Hospitalized Older People: The GLISTEN Study.

    Science.gov (United States)

    Bianchi, Lara; Abete, Pasquale; Bellelli, Giuseppe; Bo, Mario; Cherubini, Antonio; Corica, Francesco; Di Bari, Mauro; Maggio, Marcello; Manca, Giovanna Maria; Rizzo, Maria Rosaria; Rossi, Andrea P; Landi, Francesco; Volpato, Stefano

    2017-10-12

    Prevalence of sarcopenia is substantial in most geriatrics settings, but estimates vary greatly across studies because of difference in population characteristics, diagnostic criteria, and methods used to assess muscle mass, muscle strength, and physical performance. We investigated the feasibility of the European Working Group on Sarcopenia in Older People (EWGSOP) algorithm assessment in hospitalized older adults and analyzed prevalence and clinical correlates of sarcopenia. Cross-sectional analysis of 655 participants enrolled in a multicenter observational study of older adults admitted to 12 acute hospital wards in Italy. Sarcopenia was assessed as low skeletal mass index (kg/m2) plus either low handgrip strength or low walking speed (EWGSOP criteria). Skeletal muscle mass was estimated using bioimpedance analysis. Of the 655 patients (age 81.0 ± 6.8 years; women 51.9%) enrolled in the study, 275 (40.2%) were not able to perform the 4-m walking test because of medical problems. The overall prevalence of sarcopenia on hospital admission was 34.7% (95% confidence interval 28-37) and it steeply increased with aging (p sarcopenia on hospital admission were older and were more likely to be male and to have congestive heart failure, cerebrovascular disease, and severe basic activities of daily living disability. The prevalence of sarcopenia was inversely correlated with body mass index. Based on EWGSOP criteria, prevalence of sarcopenia is extremely high among older adults on admission to acute hospital wards. Older age, male gender, congestive heart failure, cerebrovascular disease, severe activities of daily living disability, and body mass index were the clinical variables significantly associated with the presence of sarcopenia. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. A 2-stage ovarian cancer screening strategy using the Risk of Ovarian Cancer Algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value.

    Science.gov (United States)

    Lu, Karen H; Skates, Steven; Hernandez, Mary A; Bedi, Deepak; Bevers, Therese; Leeds, Leroy; Moore, Richard; Granai, Cornelius; Harris, Steven; Newland, William; Adeyinka, Olasunkanmi; Geffen, Jeremy; Deavers, Michael T; Sun, Charlotte C; Horick, Nora; Fritsche, Herbert; Bast, Robert C

    2013-10-01

    A 2-stage ovarian cancer screening strategy was evaluated that incorporates change of carbohydrate antigen 125 (CA125) levels over time and age to estimate risk of ovarian cancer. Women with high-risk scores were referred for transvaginal ultrasound (TVS). A single-arm, prospective study of postmenopausal women was conducted. Participants underwent an annual CA125 blood test. Based on the Risk of Ovarian Cancer Algorithm (ROCA) result, women were triaged to next annual CA125 test (low risk), repeat CA125 test in 3 months (intermediate risk), or TVS and referral to a gynecologic oncologist (high risk). A total of 4051 women participated over 11 years. The average annual rate of referral to a CA125 test in 3 months was 5.8%, and the average annual referral rate to TVS and review by a gynecologic oncologist was 0.9%. Ten women underwent surgery on the basis of TVS, with 4 invasive ovarian cancers (1 with stage IA disease, 2 with stage IC disease, and 1 with stage IIB disease), 2 ovarian tumors of low malignant potential (both stage IA), 1 endometrial cancer (stage I), and 3 benign ovarian tumors, providing a positive predictive value of 40% (95% confidence interval = 12.2%, 73.8%) for detecting invasive ovarian cancer. The specificity was 99.9% (95% confidence interval = 99.7%, 100%). All 4 women with invasive ovarian cancer were enrolled in the study for at least 3 years with low-risk annual CA125 test values prior to rising CA125 levels. ROCA followed by TVS demonstrated excellent specificity and positive predictive value in a population of US women at average risk for ovarian cancer. Copyright © 2013 American Cancer Society.

  14. Identifying Likely Transmission Pathways within a 10-Year Community Outbreak of Tuberculosis by High-Depth Whole Genome Sequencing.

    Directory of Open Access Journals (Sweden)

    Alexander C Outhred

    Full Text Available Improved tuberculosis control and the need to contain the spread of drug-resistant strains provide a strong rationale for exploring tuberculosis transmission dynamics at the population level. Whole-genome sequencing provides optimal strain resolution, facilitating detailed mapping of potential transmission pathways.We sequenced 22 isolates from a Mycobacterium tuberculosis cluster in New South Wales, Australia, identified during routine 24-locus mycobacterial interspersed repetitive unit typing. Following high-depth paired-end sequencing using the Illumina HiSeq 2000 platform, two independent pipelines were employed for analysis, both employing read mapping onto reference genomes as well as de novo assembly, to control biases in variant detection. In addition to single-nucleotide polymorphisms, the analyses also sought to identify insertions, deletions and structural variants.Isolates were highly similar, with a distance of 13 variants between the most distant members of the cluster. The most sensitive analysis classified the 22 isolates into 18 groups. Four of the isolates did not appear to share a recent common ancestor with the largest clade; another four isolates had an uncertain ancestral relationship with the largest clade.Whole genome sequencing, with analysis of single-nucleotide polymorphisms, insertions, deletions, structural variants and subpopulations, enabled the highest possible level of discrimination between cluster members, clarifying likely transmission pathways and exposing the complexity of strain origin. The analysis provides a basis for targeted public health intervention and enhanced classification of future isolates linked to the cluster.

  15. Using the community pharmacy to identify patients at risk of poor asthma control and factors which contribute to this poor control.

    Science.gov (United States)

    Armour, Carol L; Lemay, Kate; Saini, Bandana; Reddel, Helen K; Bosnic-Anticevich, Sinthia Z; Smith, Lorraine D; Burton, Deborah; Song, Yun Ju Christine; Alles, Marie Chehani; Stewart, Kay; Emmerton, Lynne; Krass, Ines

    2011-11-01

    Although asthma can be well controlled by appropriate medication delivered in an appropriate way at an appropriate time, there is evidence that management is often suboptimal. This results in poor asthma control, poor quality of life, and significant morbidity. The objective of this study was to describe a population recruited in community pharmacy identified by trained community pharmacists as being at risk for poor asthma outcomes and to identify factors associated with poor asthma control. It used a cross-sectional design in 96 pharmacies in metropolitan and regional New South Wales, Victoria, Queensland, and Australian Capital Territory in Australia. Community pharmacists with specialized asthma training enrolled 570 patients aged ≥18 years with doctor-diagnosed asthma who were considered at risk of poor asthma outcomes and then conducted a comprehensive asthma assessment. In this assessment, asthma control was classified using a symptom and activity tool based on self-reported frequency of symptoms during the previous month and categorized as poor, fair, or good. Asthma history was discussed, and lung function and inhaler technique were also assessed by the pharmacist. Medication use/adherence was recorded from both pharmacy records and the Brief Medication Questionnaire (BMQ). The symptom and activity tool identified that 437 (77%) recruited patients had poor asthma control. Of the 570 patients, 117 (21%) smoked, 108 (19%) had an action plan, 372 (69%) used combination of inhaled corticosteroid (ICS)/long-acting β(2)-agonist (LABA) medications, and only 17-28% (depending on device) used their inhaler device correctly. In terms of adherence, 90% had their ICS or ICS/LABA dispensed <6 times in the previous 6 months, which is inconsistent with regular use; this low adherence was confirmed from the BMQ scores. A logistic regression model showed that patients who smoked had incorrect inhaler technique or low adherence (assessed by either dispensing history or

  16. Algorithming the Algorithm

    DEFF Research Database (Denmark)

    Mahnke, Martina; Uprichard, Emma

    2014-01-01

    Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...

  17. Community detection using preference networks

    Science.gov (United States)

    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.

  18. Identifying motivators and barriers to older community-dwelling people participating in resistance training: A cross-sectional study.

    Science.gov (United States)

    Burton, Elissa; Lewin, Gill; Pettigrew, Simone; Hill, Anne-Marie; Bainbridge, Liz; Farrier, Kaela; Langdon, Trish; Airey, Phil; Hill, Keith D

    2017-08-01

    Participation rates of older people in resistance training (RT) are low despite increasing research showing many health benefits. To increase the number of older people participating in RT it is important to know what would motivate people to become involved, what motivates those who participate to continue, and the factors preventing many older people from commencing participation. To investigate these issues, a questionnaire was mailed to three groups of older people: (1) those receiving home care services, (2) members of a peak non-government seniors' organisation and (3) those participating in a specific gym-based RT programme. In total, 1327 questionnaires were returned (response rate = 42.5%). To feel good physically and mentally were the main reasons motivating participation among all three groups, and falls prevention was identified as an important motivator for the home care respondents. Pain, injury and illness were the main barriers to participating, or continuing to participate. However, medical advice was a factor influencing participation commencement. The results suggest organisations providing RT programmes for older people should tailor the promotion and delivery of programmes to address key motivators and barriers specific to each group to increase the proportion of older people initiating and continuing to engage in RT.

  19. A search algorithm for identifying likely users and non-users of marijuana from the free text of the electronic medical record.

    Directory of Open Access Journals (Sweden)

    Salomeh Keyhani

    Full Text Available The harmful effects of marijuana on health and in particular cardiovascular health are understudied. To develop such knowledge, an efficient method of developing an informative cohort of marijuana users and non-users is needed.We identified patients with a diagnosis of coronary artery disease using ICD-9 codes who were seen in the San Francisco VA in 2015. We imported these patients' medical record notes into an informatics platform that facilitated text searches. We categorized patients into those with evidence of marijuana use in the past 12 months and patients with no such evidence, using the following text strings: "marijuana", "mjx", and "cannabis". We randomly selected 51 users and 51 non-users based on this preliminary classification, and sent a recruitment letter to 97 of these patients who had contact information available. Patients were interviewed on marijuana use and domains related to cardiovascular health. Data on marijuana use collected from the medical record was compared to data collected as part of the interview.The interview completion rate was 71%. Among the 35 patients identified by text strings as having used marijuana in the previous year, 15 had used marijuana in the past 30 days (positive predictive value = 42.9%. The probability of use in the past month increased from 8.8% to 42.9% in people who have these keywords in their medical record compared to those who did not have these terms in their medical record.Methods that combine text search strategies for participant recruitment with health interviews provide an efficient approach to developing prospective cohorts that can be used to study the health effects of marijuana.

  20. Utility of Neck Circumference for Identifying Metabolic Syndrome by Different Definitions in Chinese Subjects over 50 Years Old: A Community-Based Study

    Directory of Open Access Journals (Sweden)

    Shuo Lin

    2018-01-01

    Full Text Available Aims. Whether neck circumference (NC could be used as a valuable tool for identifying metabolic syndrome (MS by different criteria in Chinese is still unclear. Methods. We conducted a cross-sectional survey from October 2010 to January 2011 in Shipai community, Guangzhou, Guangdong Province, China. A total of 1473 subjects aged over 50 years were investigated. We measured height, weight, NC, waist circumference, blood pressure, blood glucose, and lipids in all subjects. MS was identified by criteria of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III, Chinese Diabetes Society (CDS, and International Diabetes Federation (IDF. Results. Mean NC was 38.0 ± 2.7 cm in men and 34.2 ± 2.5 cm in women. By using receiver operating characteristic curves, the area under the curve (AUC of NC for identifying MS (IDF was 0.823 in men and 0.777 in women, while for identifying MS (CDS, it was 0.788 in men and 0.762 in women. The AUC of NC for diagnosing MS (ATP III was 0.776 in men and 0.752 in women. The optimal cut points of NC for MS were 38.5 cm by three definitions in men, while those were 34.2 cm, 33.4 cm, and 34.0 cm in women by IDF, ATP III, and CDS definitions, respectively. No significant difference was observed between the AUC of NC and BMI for diagnosing MS by using different criteria (all p>0.05. Conclusions. NC is associated with MS by different definitions in Chinese subjects over 50 years old. It may be a useful tool to identify MS in a community population.

  1. Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.

    Science.gov (United States)

    Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George; Keane, John A; Nenadic, Goran

    2017-11-01

    De-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F 1 -scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information. Copyright © 2017. Published by Elsevier Inc.

  2. Interrelation between the changes of phase functions of cardiac muscle contraction and biochemical processes as an algorithm for identifying local pathologies in cardiovascular system

    Directory of Open Access Journals (Sweden)

    Yury V. Fedosov

    2012-11-01

    Full Text Available Aims The interrelation between hemodynamic changes, functions of the cardiovascular system and biochemical reactions in the cells of the heart muscle is investigated in the present paper. Materials and methods Several methods were used to influence the metabolism processes in the myocardium. The changes in the phase functions of contraction of different cardiac muscles were recorded. In order to have comprehensive influence on the metabolism processes, normalization of the acid-base balance was performed. L-carnitine and octolipen were used to affect the lipid metabolism. Results Phase blood volumes that are characteristic of hemodynamics changed in the course of treatment to reach their nornal values. The ECG shape during the heart cycle phases also changed to reach the norm. The initial ECG shape describing Brugada syndrome almost reached its normal value. Extrasystole disappeared therewith. Conclusion The method of the heart cycle phase analysis enables monitoring any changes in hemodynamics and functions of the cardiovascular system. The method can be used for identifying the original cause of pathologies and efficient monitoring of the treatment progress.

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

    Science.gov (United States)

    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.

  4. The Epidemiology of Hip and Major Osteoporotic Fractures in a Dutch Population of Community-Dwelling Elderly: Implications for the Dutch FRAX® Algorithm.

    Directory of Open Access Journals (Sweden)

    Corinne Klop

    Full Text Available Incidence rates of non-hip major osteoporotic fractures (MOF remain poorly characterized in the Netherlands. The Dutch FRAX® algorithm, which predicts 10-year probabilities of hip fracture and MOF (first of hip, humerus, forearm, clinical vertebral, therefore incorporates imputed MOF rates. Swedish incidence rate ratios for hip fracture to MOF (Malmo 1987-1996 were used to perform this imputation. However, equality of these ratios between countries is uncertain and recent evidence is scarce. Aims were to estimate incidence rates of hip fracture and MOF and to compare observed MOF rates to those predicted by the imputation method for the Netherlands.Using hospitalisation and general practitioner records from the Dutch PHARMO Database Network (2002-2011 we calculated age-and-sex-specific and age-standardized incidence rates (IRs of hip and other MOFs (humerus, forearm, clinical vertebral and as used in FRAX®. Observed MOF rates were compared to those predicted among community-dwelling individuals ≥50 years by the standardized incidence ratio (SIR; 95% CI.Age-standardized IRs (per 10,000 person-years of MOF among men and women ≥50 years were 25.9 and 77.0, respectively. These numbers were 9.3 and 24.0 for hip fracture. Among women 55-84 years, observed MOF rates were significantly higher than predicted (SIR ranged between 1.12-1.50, depending on age. In men, the imputation method performed reasonable.Observed MOF incidence was higher than predicted for community-dwelling women over a wide age-range, while it agreed reasonable for men. As miscalibration may influence treatment decisions, there is a need for confirmation of results in another data source. Until then, the Dutch FRAX® output should be interpreted with caution.

  5. Qualification, training, licensing/authorization and retraining of operating personnel in nuclear power plants. Noteworthy topics identified by evaluation of the practices in countries of the European Communities

    International Nuclear Information System (INIS)

    Kraut, A.; Pfeffer, W.

    1987-01-01

    In the report EUR 10118 '' Qualification, training, licensing and retraining of operating shift personnel in nuclear power plants'' the current practice in the countries of the European Communities as well as the procedures and programmes applied in Sweden, Switzerland and the USA are outlined and evaluated. The intent was to derive fundamental and generally valid concepts concerning shift-staff training and other relevant aspects. Those items were identified that seemed to be noteworthy because they give some guidance on how to achieve and maintain the qualification of the shift staff of NPPs or how to improve the staffing of the control room. These noteworthy topics identified by evaluation of the practice in countries of the European Communities and also elsewhere are presented in the publication at hand. The report addresses the following topics: tasks of the shift personnel, nomenclature for different grades of the personnel; shift staffing and staffing of the control room; criteria for personnel selection when recruiting new shift staff; personnel qualification necessary for recruitment; training of shift personnel; retraining and preservation of qualification standards; training facilities, especially simulators; responsibility for training; licensing/authorization; retirement from shift work. Consideration of these more general aspects and concepts may lead to improvement in training. The job descriptions given in the Annex to the document are only intended to give a general understanding of the typical designations, tasks and responsibilities of shift staff

  6. ERIC-PCR fingerprinting-based community DNA hybridization to pinpoint genome-specific fragments as molecular markers to identify and track populations common to healthy human guts.

    Science.gov (United States)

    Wei, Guifang; Pan, Li; Du, Huimin; Chen, Junyi; Zhao, Liping

    2004-10-01

    Bacterial populations common to healthy human guts may play important roles in human health. A new strategy for discovering genomic sequences as markers for these bacteria was developed using Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR fingerprinting. Structural features within microbial communities are compared with ERIC-PCR followed by DNA hybridization to identify genomic fragments shared by samples from healthy human individuals. ERIC-PCR profiles of fecal samples from 12 diseased or healthy human and piglet subjects demonstrated stable, unique banding patterns for each individual tested. Sequence homology of DNA fragments in bands of identical size was examined between samples by hybridization under high stringency conditions with DIG-labeled ERIC-PCR products derived from the fecal sample of one healthy child. Comparative analysis of the hybridization profiles with the original agarose fingerprints identified three predominant bands as signatures for populations associated with healthy human guts with sizes of 500, 800 and 1000 bp. Clone library profiling of the three bands produced 17 genome fragments, three of which showed high similarity only with regions of the Bacteroides thetaiotaomicron genome, while the remainder were orphan sequences. Association of these sequences with healthy guts was validated by sequence-selective PCR experiments, which showed that a single fragment was present in all 32 healthy humans and 13 healthy piglets tested. Two fragments were present in the healthy human group and in 18 children with non-infectious diarrhea but not in eight children with infectious diarrhea. Genome fragments identified with this novel strategy may be used as genome-specific markers for dynamic monitoring and sequence-guided isolation of functionally important bacterial populations in complex communities such as human gut microflora.

  7. Sound algorithms

    OpenAIRE

    De Götzen , Amalia; Mion , Luca; Tache , Olivier

    2007-01-01

    International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.

  8. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  9. A divisive spectral method for network community detection

    International Nuclear Information System (INIS)

    Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo

    2016-01-01

    Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)

  10. Store-Carry and Forward-Type M2M Communication Protocol Enabling Guide Robots to Work together and the Method of Identifying Malfunctioning Robots Using the Byzantine Algorithm

    Directory of Open Access Journals (Sweden)

    Yoshio Suga

    2016-11-01

    Full Text Available This paper concerns a service in which multiple guide robots in an area display arrows to guide individual users to their destinations. It proposes a method of identifying malfunctioning robots and robots that give wrong directions to users. In this method, users’ mobile terminals and robots form a store-carry and forward-type M2M communication network, and a distributed cooperative protocol is used to enable robots to share information and identify malfunctioning robots using the Byzantine algorithm. The robots do not directly communicate with each other, but through users’ mobile terminals. We have introduced the concept of the quasi-synchronous number, so whether a certain robot is malfunctioning can be determined even when items of information held by all of the robots are not synchronized. Using simulation, we have evaluated the proposed method in terms of the rate of identifying malfunctioning robots, the rate of reaching the destination and the average length of time to reach the destination.

  11. Algorithmic cryptanalysis

    CERN Document Server

    Joux, Antoine

    2009-01-01

    Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic

  12. Explaining algorithms using metaphors

    CERN Document Server

    Forišek, Michal

    2013-01-01

    There is a significant difference between designing a new algorithm, proving its correctness, and teaching it to an audience. When teaching algorithms, the teacher's main goal should be to convey the underlying ideas and to help the students form correct mental models related to the algorithm. This process can often be facilitated by using suitable metaphors. This work provides a set of novel metaphors identified and developed as suitable tools for teaching many of the 'classic textbook' algorithms taught in undergraduate courses worldwide. Each chapter provides exercises and didactic notes fo

  13. A similarity based agglomerative clustering algorithm in networks

    Science.gov (United States)

    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.

  14. Algorithmic mathematics

    CERN Document Server

    Hougardy, Stefan

    2016-01-01

    Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.

  15. Pandemic (H1N1 2009 influenza community transmission was established in one Australian state when the virus was first identified in North America.

    Directory of Open Access Journals (Sweden)

    Heath A Kelly

    Full Text Available BACKGROUND: In mid-June 2009 the State of Victoria in Australia appeared to have the highest notification rate of pandemic (H1N1 2009 influenza in the world. We hypothesise that this was because community transmission of pandemic influenza was already well established in Victoria at the time testing for the novel virus commenced. In contrast, this was not true for the pandemic in other parts of Australia, including Western Australia (WA. METHODS: We used data from detailed case follow-up of patients with confirmed infection in Victoria and WA to demonstrate the difference in the pandemic curve in two Australian states on opposite sides of the continent. We modelled the pandemic in both states, using a susceptible-infected-removed model with Bayesian inference accounting for imported cases. RESULTS: Epidemic transmission occurred earlier in Victoria and later in WA. Only 5% of the first 100 Victorian cases were not locally acquired and three of these were brothers in one family. By contrast, 53% of the first 102 cases in WA were associated with importation from Victoria. Using plausible model input data, estimation of the effective reproductive number for the Victorian epidemic required us to invoke an earlier date for commencement of transmission to explain the observed data. This was not required in modelling the epidemic in WA. CONCLUSION: Strong circumstantial evidence, supported by modelling, suggests community transmission of pandemic influenza was well established in Victoria, but not in WA, at the time testing for the novel virus commenced in Australia. The virus is likely to have entered Victoria and already become established around the time it was first identified in the US and Mexico.

  16. Total algorithms

    NARCIS (Netherlands)

    Tel, G.

    We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of

  17. A framework for detecting communities of unbalanced sizes in networks

    Science.gov (United States)

    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.

  18. Using the serious mental illness health improvement profile [HIP] to identify physical problems in a cohort of community patients: a pragmatic case series evaluation.

    Science.gov (United States)

    Shuel, Francis; White, Jacquie; Jones, Martin; Gray, Richard

    2010-02-01

    The physical health of people with serious mental illness is a cause of growing concern to clinicians. Life expectancy in this population may be reduced by up to 25 years and patients often live with considerable physical morbidity that can dramatically reduce quality of life and contribute to social exclusion. This study sought to determine whether the serious mental illness health improvement profile [HIP], facilitated by mental health nurses [MHNs], has the clinical potential to identify physical morbidity and inform future evidence-based care. Retrospective documentation audit and qualitative evaluation of patients' and clinicians' views about the use of the HIP in practice. A nurse-led outpatient medication management clinic, for community adult patients with serious mental illness in Scotland. 31 Community patients with serious mental illness seen in the clinic by 2 MHNs trained to use the HIP. All 31 patients, 9 MHNs, 4 consultant psychiatrists and 12 general practitioners [GPs] (primary care physicians) participated in the qualitative evaluation. A retrospective documentation audit of case notes for all patients where the HIP had been implemented. Semi-structured interviews with patients and their secondary care clinicians. Postal survey of GPs. 189 Physical health issues were identified (mean 6.1 per patient). Items most frequently flagged 'red', suggesting that intervention was required, were body mass index [BMI] (n=24), breast self-examination (n=23), waist circumference (n=21), pulse (n=14) and diet (n=13). Some rates of physical health problems observed were broadly similar to those reported in studies of patients receiving antipsychotics in primary care but much lower than those reported in epidemiological studies. Individualised care was planned and delivered with each patient based on the profile. 28 discreet interventions that included providing advice, promoting health behavioural change, performing an electrocardiogram and making a referral to

  19. Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

    Science.gov (United States)

    Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

    2015-01-01

    Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of

  20. Integrating national community-based health worker programmes into health systems: a systematic review identifying lessons learned from low-and middle-income countries.

    Science.gov (United States)

    Zulu, Joseph Mumba; Kinsman, John; Michelo, Charles; Hurtig, Anna-Karin

    2014-09-22

    Despite the development of national community-based health worker (CBHW) programmes in several low- and middle-income countries, their integration into health systems has not been optimal. Studies have been conducted to investigate the factors influencing the integration processes, but systematic reviews to provide a more comprehensive understanding are lacking. We conducted a systematic review of published research to understand factors that may influence the integration of national CBHW programmes into health systems in low- and middle-income countries. To be included in the study, CBHW programmes should have been developed by the government and have standardised training, supervision and incentive structures. A conceptual framework on the integration of health innovations into health systems guided the review. We identified 3410 records, of which 36 were finally selected, and on which an analysis was conducted concerning the themes and pathways associated with different factors that may influence the integration process. Four programmes from Brazil, Ethiopia, India and Pakistan met the inclusion criteria. Different aspects of each of these programmes were integrated in different ways into their respective health systems. Factors that facilitated the integration process included the magnitude of countries' human resources for health problems and the associated discourses about how to address these problems; the perceived relative advantage of national CBHWs with regard to delivering health services over training and retaining highly skilled health workers; and the participation of some politicians and community members in programme processes, with the result that they viewed the programmes as legitimate, credible and relevant. Finally, integration of programmes within the existing health systems enhanced programme compatibility with the health systems' governance, financing and training functions. Factors that inhibited the integration process included a rapid

  1. Community voices: barriers and opportunities for programmes to successfully prevent vertical transmission of HIV identified through consultations among people living with HIV.

    Science.gov (United States)

    Anderson, Ginna; Caswell, Georgina; Edwards, Olive; Hsieh, Amy; Hull, Beri; Mallouris, Christoforos; Mason, Naisiadet; Nöstlinger, Christiana

    2012-07-11

    In 2010, two global networks of people living with HIV, the International Community of Women Living with HIV (ICW Global) and the Global Network of People living with HIV (GNP+) were invited to review a draft strategic framework for the global scale up of prevention of vertical transmission (PVT) through the primary prevention of HIV and the prevention of unintended pregnancies among women living with HIV. In order to ensure recommendations were based on expressed needs of people living with HIV, GNP+ and ICW Global undertook a consultation amongst people living with HIV which highlighted both facilitators and barriers to prevention services. This commentary summarizes the results of that consultation. The consultation was comprised of an online consultation (moderated chat-forum with 36 participants from 16 countries), an anonymous online e-survey (601 respondents from 58 countries), and focus-group discussions with people living with HIV in Jamaica (27 participants). The consultation highlighted the discrepancies across regions with respect to access to essential packages of PVT services. However, the consultation participants also identified common barriers to access, including a lack of trustworthy sources of information, service providers' attitudes, and gender-based violence. In addition, participant responses revealed common facilitators of access, including quality counselling on reproductive choices, male involvement, and decentralized services. The consultation provided some understanding and insight into the participants' experiences with and recommendations for PVT strategies. Participants agreed that successful, comprehensive PVT programming require greater efforts to both prevent primary HIV infection among young women and girls and, in particular, targeted efforts to ensure that women living with HIV and their partners are supported to avoid unintended pregnancies and to have safe, healthy pregnancies instead. In addition to providing the insights

  2. Diagnostic stability of autism spectrum disorder in toddlers prospectively identified in a community-based setting: Behavioural characteristics and predictors of change over time.

    Science.gov (United States)

    Barbaro, Josephine; Dissanayake, Cheryl

    2017-10-01

    Autism spectrum disorder diagnoses in toddlers have been established as accurate and stable across time in high-risk siblings and clinic-referred samples. Few studies have investigated diagnostic stability in children prospective identified in community-based settings. Furthermore, there is a dearth of evidence on the individual behaviours that predict diagnostic change over time. The stability and change of autism spectrum disorder diagnoses were investigated from 24 to 48 months in 77 children drawn from the Social Attention and Communication Study. Diagnostic stability was high, with 88.3% overall stability and 85.5% autism spectrum disorder stability. The behavioural markers at 24 months that contributed to diagnostic shift off the autism spectrum by 48 months included better eye contact, more directed vocalisations, the integration of gaze and directed vocalisations/gestures and higher non-verbal developmental quotient. These four variables correctly predicted 88.7% of children into the autism spectrum disorder-stable and autism spectrum disorder-crossover groups overall, with excellent prediction for the stable group (96.2%) and modest prediction for the crossover group (44.4%). Furthermore, non-verbal developmental quotient at 24 months accounted for the significant improvement across time in 'Social Affect' scores on the Autism Diagnostic Observation Schedule for both groups and was the only unique predictor of diagnostic crossover. These findings contribute to the body of evidence on the feasibility of diagnoses at earlier ages to facilitate children's access to interventions to promote positive developmental outcomes.

  3. Impact of Soil Salinity on the Structure of the Bacterial Endophytic Community Identified from the Roots of Caliph Medic (Medicago truncatula).

    Science.gov (United States)

    Yaish, Mahmoud W; Al-Lawati, Abbas; Jana, Gerry Aplang; Vishwas Patankar, Himanshu; Glick, Bernard R

    2016-01-01

    In addition to being a forage crop, Caliph medic (Medicago truncatula) is also a model legume plant and is used for research focusing on the molecular characterization of the interaction between rhizobia and plants. However, the endophytic microbiome in this plant is poorly defined. Endophytic bacteria play a role in supplying plants with the basic requirements necessary for growth and development. Moreover, these bacteria also play a role in the mechanism of salinity stress adaptation in plants. As a prelude to the isolation and utilization of these bacteria in Caliph medic farming, 41 bacterial OTUs were identified in this project from within the interior of the roots of this plant by pyrosequencing of the small ribosomal subunit gene (16S rDNA) using a cultivation-independent approach. In addition, the differential abundance of these bacteria was studied following exposure of the plants to salinity stress. About 29,064 high-quality reads were obtained from the sequencing of six libraries prepared from control and salinity-treated tissues. Statistical analysis revealed that the abundance of ~70% of the OTUs was significantly (p ≤ 0.05) altered in roots that were exposed to salinity stress. Sequence analysis showed a similarity between some of the identified species and other, known, growth-promoting bacteria, marine and salt-stressed soil-borne bacteria, and nitrogen-fixing bacterial isolates. Determination of the amendments to the bacterial community due to salinity stress in Caliph medic provides a crucial step toward developing an understanding of the association of these endophytes, under salt stress conditions, in this model plant. To provide direct evidence regarding their growth promoting activity, a group of endophytic bacteria were isolated from inside of plant roots using a cultivation-dependent approach. Several of these isolates were able to produce ACC-deaminase, ammonia and IAA; and to solubilize Zn+2 and PO4-3. This data is consistent with the

  4. Impact of Soil Salinity on the Structure of the Bacterial Endophytic Community Identified from the Roots of Caliph Medic (Medicago truncatula.

    Directory of Open Access Journals (Sweden)

    Mahmoud W Yaish

    Full Text Available In addition to being a forage crop, Caliph medic (Medicago truncatula is also a model legume plant and is used for research focusing on the molecular characterization of the interaction between rhizobia and plants. However, the endophytic microbiome in this plant is poorly defined. Endophytic bacteria play a role in supplying plants with the basic requirements necessary for growth and development. Moreover, these bacteria also play a role in the mechanism of salinity stress adaptation in plants. As a prelude to the isolation and utilization of these bacteria in Caliph medic farming, 41 bacterial OTUs were identified in this project from within the interior of the roots of this plant by pyrosequencing of the small ribosomal subunit gene (16S rDNA using a cultivation-independent approach. In addition, the differential abundance of these bacteria was studied following exposure of the plants to salinity stress. About 29,064 high-quality reads were obtained from the sequencing of six libraries prepared from control and salinity-treated tissues. Statistical analysis revealed that the abundance of ~70% of the OTUs was significantly (p ≤ 0.05 altered in roots that were exposed to salinity stress. Sequence analysis showed a similarity between some of the identified species and other, known, growth-promoting bacteria, marine and salt-stressed soil-borne bacteria, and nitrogen-fixing bacterial isolates. Determination of the amendments to the bacterial community due to salinity stress in Caliph medic provides a crucial step toward developing an understanding of the association of these endophytes, under salt stress conditions, in this model plant. To provide direct evidence regarding their growth promoting activity, a group of endophytic bacteria were isolated from inside of plant roots using a cultivation-dependent approach. Several of these isolates were able to produce ACC-deaminase, ammonia and IAA; and to solubilize Zn+2 and PO4-3. This data is

  5. Fast unfolding of communities in large networks

    International Nuclear Information System (INIS)

    Blondel, Vincent D; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne

    2008-01-01

    We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks

  6. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  7. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  8. Race Differences: Use of Walking Speed to Identify Community-Dwelling Women at Risk for Poor Health Outcomes--Osteoarthritis Initiative Study.

    Science.gov (United States)

    Kirkness, Carmen S; Ren, Jinma

    2015-07-01

    of less than 1.0 m/s in community-dwelling women who had or were at risk for osteoarthritis, with African American women having 3 times the risk for slow walking as white American women. This finding suggests that middle-aged African American women have an increased risk for poor health outcomes. Further longitudinal evaluations are needed to confirm the long-term health outcomes in a middle-aged population and to establish walking speed as a useful tool for identifying middle-aged women at high risk for poor health outcomes. © 2015 American Physical Therapy Association.

  9. Metaproteomics: Harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities

    Science.gov (United States)

    Hettich, Robert L.; Pan, Chongle; Chourey, Karuna; Giannone, Richard J.

    2013-01-01

    Summary The availability of extensive genome information for many different microbes, including unculturable species in mixed communities from environmental samples, has enabled systems-biology interrogation by providing a means to access genomic, transcriptomic, and proteomic information. To this end, metaproteomics exploits the power of high performance mass spectrometry for extensive characterization of the complete suite of proteins expressed by a microbial community in an environmental sample. PMID:23469896

  10. Identifying the sources of nitrate contamination of groundwater in an agricultural area (Haean basin, Korea) using isotope and microbial community analyses

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Heejung [School of Earth and Environmental Sciences (BK21 SEES), Seoul National University, Seoul 151–747 (Korea, Republic of); Kaown, Dugin, E-mail: dugin1@snu.ac.kr [School of Earth and Environmental Sciences (BK21 SEES), Seoul National University, Seoul 151–747 (Korea, Republic of); Mayer, Bernhard [Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary T2N 1N4, Alberta (Canada); Lee, Jin-Yong [Department of Geology, Kangwon National University, Chuncheon 200–701 (Korea, Republic of); Hyun, Yunjung [Planning and Management Group, Korea Environment Institute, Sejong 339-007 (Korea, Republic of); Lee, Kang-Kun [School of Earth and Environmental Sciences (BK21 SEES), Seoul National University, Seoul 151–747 (Korea, Republic of)

    2015-11-15

    } and SO{sub 4}{sup 2−} in groundwater in areas with intensive agricultural land use. - Highlights: • Dual isotope analyses identified contaminant sources. • Aquifer contamination was affected by land use. • Microbial community in groundwater reflects land use. • Approach is promising for managing water quality in agricultural areas.

  11. Identifying the sources of nitrate contamination of groundwater in an agricultural area (Haean basin, Korea) using isotope and microbial community analyses

    International Nuclear Information System (INIS)

    Kim, Heejung; Kaown, Dugin; Mayer, Bernhard; Lee, Jin-Yong; Hyun, Yunjung; Lee, Kang-Kun

    2015-01-01

    An integrated study based on hydrogeochemical, microbiological and dual isotopic approaches for nitrate and sulfate was conducted to elucidate sources and biogeochemical reactions governing groundwater contaminants in different seasons and under different land use in a basin of Korea. The land use in the study area is comprised of forests (58.0%), vegetable fields (27.6%), rice paddy fields (11.4%) and others (3.0%). The concentrations of NO 3 –N and SO 4 2− in groundwater in vegetable fields were highest with 4.2–15.2 mg L −1 and 1.6–19.7 mg L −1 respectively, whereas under paddy fields NO 3 –N concentrations ranged from 0 to 10.7 mg L −1 and sulfate concentrations were ~ 15 mg L −1 . Groundwater with high NO 3 –N concentrations of > 10 mg L −1 had δ 15 N–NO 3 − values ranging from 5.2 to 5.9‰ and δ 18 O values of nitrate between 2.7 and 4.6‰ suggesting that the nitrate was mineralized from soil organic matter that was amended by fertilizer additions. Elevated concentrations of SO 4 2− with δ 34 S–SO 4 2− values between 1 and 6‰ in aquifers in vegetable fields indicated that a mixture of sulfate from atmospheric deposition, mineralization of soil organic matter and from synthetic fertilizers is the source of groundwater sulfate. Elevated δ 18 O–NO 3 − and δ 18 O–SO 4 2− values in samples collected from the paddy fields indicated that denitrification and bacterial sulfate reduction are actively occurring removing sulfate and nitrate from the groundwater. This was supported by high occurrences of denitrifying and sulfate reducing bacteria in groundwater of the paddy fields as evidenced by 16S rRNA pyrosequencing analysis. This study shows that dual isotope techniques combined with microbial data can be a powerful tool for identification of sources and microbial processes affecting NO 3 − and SO 4 2− in groundwater in areas with intensive agricultural land use. - Highlights: • Dual isotope analyses identified

  12. [Diagnostic value of serum procalcitonin in identifying the etiology of non-responding community-acquired pneumonia after initial antibiotic therapy].

    Science.gov (United States)

    Wang, Zheng; Zhang, Xiaoju; Wu, Jizhen; Zhang, Wenping; Kuang, Hongyan; Li, Xiao; Xuan, Weixia; Wang, Kai; Ma, Lijun

    2014-11-01

    This study was to investigate the diagnostic value of serum procalcitonin(PCT) in identifying the etiology of non-responding community-acquired pneumonia (CAP) after initial antibiotic therapy. A retrospective analysis was performed for 232 hospitalized CAP patients admitted to the People's Hospital of Zhengzhou University during June 2013 and January 2014. Early treatment failure was defined as the presence of persistent fever (>38 °C) and/or clinical symptoms (malaise, cough, expectoration, dyspnea) or deterioration after at least 72 h of initial antimicrobial treatment, or development of respiratory failure requiring mechanical ventilation, or septic shock. Bronchoscopy or transthoracic lung biopsy was performed in case of early treatment failure when indicated. Serum level of PCT was detected by double antibody sandwich method. The differences between 2 or more groups were compared using 2-independent student t test, one-way ANOVA; Mann-Whitney U test, Kruskal-Wallis rank sum test, or χ(2) test. Risk factors and odds ratios for nonresponsiveness were analyzed by setting up a Logistic regression model. The diagnostic values of PCT were determined by receiver operating characteristic curves (ROC curves). Of the 232 CAP patients enrolled, 124 were male and 108 were female, with an average age of (46 ± 20) years. Thirty-six patients failed to respond to the initial antibiotic therapy. As shown by Logistic regression analysis, the risk factors for treatment failure included hypoalbuminemia, type 2 diabetes, previous history of splenectomy , PSI 4-5 grade, and lung infiltration ≥ 3 lobes. The most common causes of non-responsiveness were antimicrobial insufficiency (n = 23), and misdiagnosis of noninfectious mimics of pneumonia (n = 11), with 2 cases of unidentified etiology. The serum PCT level in admission was 0.19 (0.07-0.66) µg/L in the antimicrobial insufficiency subgroup, which was significantly higher than that in the misdiagnosis subgroup [0

  13. Inclusive Flavour Tagging Algorithm

    International Nuclear Information System (INIS)

    Likhomanenko, Tatiana; Derkach, Denis; Rogozhnikov, Alex

    2016-01-01

    Identifying the flavour of neutral B mesons production is one of the most important components needed in the study of time-dependent CP violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of the algorithms currently used by the LHCb experiment. Specifically, a probabilistic model which efficiently combines information from reconstructed vertices and tracks using machine learning is proposed. The algorithm does not use information about underlying physics process. It reduces the dependence on the performance of lower level identification capacities and thus increases the overall performance. The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of B mesons in any proton-proton experiment. (paper)

  14. A novel community detection method in bipartite networks

    Science.gov (United States)

    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.

  15. Identifying the Value of the ACT Score as a Predictor of Student Success in Respiratory Care, Radiography, and Nursing at Southeast Kentucky Community and Technical College

    Science.gov (United States)

    Parrott-Robbins, Rebecca Jon

    2010-01-01

    The purpose of this study was to investigate--by utilizing data obtained from the Kentucky Community and Technical College System (KCTCS) PeopleSoft database-- whether the American College Testing (ACT) assessment was a predictor of student success for students who had graduated from respiratory, radiography, and nursing programs at Southeast…

  16. A new cluster algorithm for graphs

    NARCIS (Netherlands)

    S. van Dongen

    1998-01-01

    textabstractA new cluster algorithm for graphs called the emph{Markov Cluster algorithm ($MCL$ algorithm) is introduced. The graphs may be both weighted (with nonnegative weight) and directed. Let~$G$~be such a graph. The $MCL$ algorithm simulates flow in $G$ by first identifying $G$ in a

  17. Evaluation of an algorithm for switching from IV to PO therapy in clinical practice in patients with community-acquired pneumonia

    NARCIS (Netherlands)

    van der Eerden, Menno M.; de Graaff, Casper S.; Vlaspolder, Fer; Bronsveld, Willem; Jansen, Henk M.; Boersma, Wim G.

    2004-01-01

    Background: in patients with community-acquired pneumonia (CAP), switching from IV to PO antibiotics offers advantages over IV therapy alone, including improved cost-effectiveness through reductions in the length of hospital stay and treatment costs. Objective: The aim of this study was to determine

  18. Identifying Low pH Active and Lactate-Utilizing Taxa within Oral Microbiome Communities from Healthy Children Using Stable Isotope Probing Techniques

    Energy Technology Data Exchange (ETDEWEB)

    McLean, Jeffrey S.; Fansler, Sarah J.; Majors, Paul D.; Mcateer, Kathleen; Allen, Lisa Z.; Shirtliff, Mark E.; Lux, Renate; Shi, Wenyuan

    2012-03-05

    Many human microbial infectious diseases including dental caries are polymicrobial in nature and how these complex multi-species communities evolve from a healthy to a diseased state is not well understood. Although many health- or disease-associated oral microbes have been characterized in vitro, their physiology in vivo in the presence of the complex oral microbiome is difficult to determine with current approaches. In addition, about half of these oral species remain uncultivated to date and little is known except their 16S rRNA sequence. Lacking culture-based physiological analyses, the functional roles of uncultivated microorganisms will remain enigmatic despite their apparent disease correlation. To start addressing these knowledge gaps, we applied a novel combination of in vivo Magnetic Resonance Spectroscopy (MRS) with RNA and DNA based Stable Isotope Probing (SIP) to oral plaque communities from healthy children for temporal monitoring of carbohydrate utilization, organic acid production and identification of metabolically active and inactive bacterial species.

  19. Eigenspaces of networks reveal the overlapping and hierarchical community structure more precisely

    International Nuclear Information System (INIS)

    Ma, Xiaoke; Gao, Lin; Yong, Xuerong

    2010-01-01

    Identifying community structure is fundamental for revealing the structure–functionality relationship in complex networks, and spectral algorithms have been shown to be powerful for this purpose. In a traditional spectral algorithm, each vertex of a network is embedded into a spectral space by making use of the eigenvectors of the adjacency matrix or Laplacian matrix of the graph. In this paper, a novel spectral approach for revealing the overlapping and hierarchical community structure of complex networks is proposed by not only using the eigenvalues and eigenvectors but also the properties of eigenspaces of the networks involved. This gives us a better characterization of community. We first show that the communicability between a pair of vertices can be rewritten in term of eigenspaces of a network. An agglomerative clustering algorithm is then presented to discover the hierarchical communities using the communicability matrix. Finally, these overlapping vertices are discovered with the corresponding eigenspaces, based on the fact that the vertices more densely connected amongst one another are more likely to be linked through short cycles. Compared with the traditional spectral algorithms, our algorithm can identify both the overlapping and hierarchical community without increasing the time complexity O(n 3 ), where n is the size of the network. Furthermore, our algorithm can also distinguish the overlapping vertices from bridges. The method is tested by applying it to some computer-generated and real-world networks. The experimental results indicate that our algorithm can reveal community structure more precisely than the traditional spectral approaches

  20. Polynucleotide probes that target a hypervariable region of 16S rRNA genes to identify bacterial isolates corresponding to bands of community fingerprints.

    Science.gov (United States)

    Heuer, H; Hartung, K; Wieland, G; Kramer, I; Smalla, K

    1999-03-01

    Temperature gradient gel electrophoresis (TGGE) is well suited for fingerprinting bacterial communities by separating PCR-amplified fragments of 16S rRNA genes (16S ribosomal DNA [rDNA]). A strategy was developed and was generally applicable for linking 16S rDNA from community fingerprints to pure culture isolates from the same habitat. For this, digoxigenin-labeled polynucleotide probes were generated by PCR, using bands excised from TGGE community fingerprints as a template, and applied in hybridizations with dot blotted 16S rDNA amplified from bacterial isolates. Within 16S rDNA, the hypervariable V6 region, corresponding to positions 984 to 1047 (Escherichia coli 16S rDNA sequence), which is a subset of the region used for TGGE (positions 968 to 1401), best met the criteria of high phylogenetic variability, required for sufficient probe specificity, and closely flanking conserved priming sites for amplification. Removal of flanking conserved bases was necessary to enable the differentiation of closely related species. This was achieved by 5' exonuclease digestion, terminated by phosphorothioate bonds which were synthesized into the primers. The remaining complementary strand was removed by single-strand-specific digestion. Standard hybridization with truncated probes allowed differentiation of bacteria which differed by only two bases within the probe target site and 1.2% within the complete 16S rDNA. However, a truncated probe, derived from an excised TGGE band of a rhizosphere community, hybridized with three phylogenetically related isolates with identical V6 sequences. Only one of the isolates comigrated with the excised band in TGGE, which was shown to be due to identical sequences, demonstrating the utility of a combined TGGE and V6 probe approach.

  1. Algorithmic alternatives

    International Nuclear Information System (INIS)

    Creutz, M.

    1987-11-01

    A large variety of Monte Carlo algorithms are being used for lattice gauge simulations. For purely bosonic theories, present approaches are generally adequate; nevertheless, overrelaxation techniques promise savings by a factor of about three in computer time. For fermionic fields the situation is more difficult and less clear. Algorithms which involve an extrapolation to a vanishing step size are all quite closely related. Methods which do not require such an approximation tend to require computer time which grows as the square of the volume of the system. Recent developments combining global accept/reject stages with Langevin or microcanonical updatings promise to reduce this growth to V/sup 4/3/

  2. Combinatorial algorithms

    CERN Document Server

    Hu, T C

    2002-01-01

    Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9

  3. The psychometric properties of the Chinese version-reintegration to normal living index (C-RNLI) for identifying participation restriction among community-dwelling frail older people.

    Science.gov (United States)

    Liu, Justina Yat-Wa; Ma, Ka Wai

    2017-01-31

    The Reintegration to Normal Living Index (RNLI) was developed to measure reintegration to normal living after major traumas/illnesses. Its psychometric properties remain unknown when used to measure participation restriction under the World Health Organization's International Classification of Functioning, Disability, and Health (WHO-ICF) framework. This study examines the psychometric properties of the Chinese version-RNLI to measure WHO-ICF participation restriction among community-dwelling pre-frail and frail older people. A cross-sectional study was conducted in community and day-care centres in Hong Kong between May 2015 and January 2016. Through face-to-face interviews, information was collected on the participants' demographic background, medical history, frailty status, depressive mood, functional performance in daily activities, and participation restriction. The internal consistency, test-retest reliability, and construct and convergent validity of the C-RNLI were assessed. Two hundred and ninety-nine pre-frail or frail community-dwelling older people with a mean age of 79.53 were recruited. A confirmatory factor analysis showed that the C-RNLI has a two-factor structure comprised of "participation in physical activities" and "participation in social events". The test-retest coefficient was 0.71. The Cronbach's alpha of the total C-RNLI score, and those of the factors "participation in physical activities" and "participation in social events" were 0.88, 0.82 and 0.84, respectively. Pre-frail older people had significantly higher scores for the factors "participation in physical activities" (z = -5.05, older people. Older people from community centres had significantly higher scores for the factors "participation in physical activities" (z = -4.48, older people from day-care centres. The factors "participation in physical activities" and "participation in social events" of the C-RNLI were significantly convergent with depressive mood (r s  = -0

  4. Autodriver algorithm

    Directory of Open Access Journals (Sweden)

    Anna Bourmistrova

    2011-02-01

    Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.

  5. Risk assessment and HbA1c measurement in Norwegian community pharmacies to identify people with undiagnosed type 2 diabetes – A feasibility study

    Science.gov (United States)

    Kjome, Reidun Lisbet Skeide; Sandberg, Sverre; Sølvik, Una Ørvim

    2018-01-01

    Objectives Determine the feasibility of using a diabetes risk assessment tool followed by HbA1c-measurement in a community-pharmacy setting in Norway. Methods In this longitudinal study two pharmacists in each of three community pharmacies were trained to perform risk assessments, HbA1c-measurements and counselling. Pharmacy customers who were > 18 years old and could understand and speak Norwegian or English were recruited in the pharmacies during a two-months-period. Information about the service was presented in local newspapers, social media, leaflets and posters at the pharmacy. Customers wishing to participate contacted the pharmacy staff. Participants completed a validated diabetes risk test and a background questionnaire including a validated instrument for self-rated health. A HbA1c measurement was performed for individuals with a moderate to high risk of developing diabetes. If HbA1c ≥ 6.5% they were recommended to visit their general practitioner for follow-up. The pharmacies performed internal and external quality control of the HbA1c instrument. Results Of the 211 included participants 97 (46%) were > 50 years old. HbA1c was measured for the 47 participants (22%) with high risk. Thirty-two (15%) had HbA1c values internal and external quality control for HbA1c were within set limits. Conclusion The pharmacists were able to perform the risk assessment and measurement of HbA1c, and pharmacy customers were willing to participate. The HbA1c measurements fulfilled the requirements for analytical quality. Thus, it is feasible to implement this service in community pharmacies in Norway. In a large-scale study the inclusion criteria should be increased to 45 years in accordance with the population the risk test has been validated for. PMID:29474501

  6. Risk assessment and HbA1c measurement in Norwegian community pharmacies to identify people with undiagnosed type 2 diabetes - A feasibility study.

    Science.gov (United States)

    Risøy, Aslaug Johanne; Kjome, Reidun Lisbet Skeide; Sandberg, Sverre; Sølvik, Una Ørvim

    2018-01-01

    Determine the feasibility of using a diabetes risk assessment tool followed by HbA1c-measurement in a community-pharmacy setting in Norway. In this longitudinal study two pharmacists in each of three community pharmacies were trained to perform risk assessments, HbA1c-measurements and counselling. Pharmacy customers who were > 18 years old and could understand and speak Norwegian or English were recruited in the pharmacies during a two-months-period. Information about the service was presented in local newspapers, social media, leaflets and posters at the pharmacy. Customers wishing to participate contacted the pharmacy staff. Participants completed a validated diabetes risk test and a background questionnaire including a validated instrument for self-rated health. A HbA1c measurement was performed for individuals with a moderate to high risk of developing diabetes. If HbA1c ≥ 6.5% they were recommended to visit their general practitioner for follow-up. The pharmacies performed internal and external quality control of the HbA1c instrument. Of the 211 included participants 97 (46%) were > 50 years old. HbA1c was measured for the 47 participants (22%) with high risk. Thirty-two (15%) had HbA1c values HbA1c ≥ 6.5%. Two participants with HbA1 ≥ 6.5% were diagnosed with diabetes by their general practitioner. The third was lost to follow-up. Results from internal and external quality control for HbA1c were within set limits. The pharmacists were able to perform the risk assessment and measurement of HbA1c, and pharmacy customers were willing to participate. The HbA1c measurements fulfilled the requirements for analytical quality. Thus, it is feasible to implement this service in community pharmacies in Norway. In a large-scale study the inclusion criteria should be increased to 45 years in accordance with the population the risk test has been validated for.

  7. Algorithmic Self

    DEFF Research Database (Denmark)

    Markham, Annette

    This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....

  8. Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

    Science.gov (United States)

    Hsu, Amy T; Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

    2016-12-01

    Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. NCT02779309, Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

  9. Overlapping communities from dense disjoint and high total degree clusters

    Science.gov (United States)

    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.

  10. Detection of algorithmic trading

    Science.gov (United States)

    Bogoev, Dimitar; Karam, Arzé

    2017-10-01

    We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.

  11. Application of Microarrays and qPCR to Identify Phylogenetic and Functional Biomarkers Diagnostic of Microbial Communities that Biodegrade Chlorinated Solvents to Ethene

    Science.gov (United States)

    2012-01-01

    appropriate and cost - effective biomarkers to assess, monitor, and optimize performance. Commonly, biomarker development has focused on identifying...field sites. Firmicutes (Mostly Clostridium spp.), Bacteroidetes (Mostly Bacteroides spp.), as well as Proteobacteria (Mostly sulfate-reducer, i.e...continuous-flow chemostat, and environmental samples from contaminated field sites. Firmicutes (Mostly Clostridium spp.), Bacteroidetes (Mostly

  12. Economic Education within the BME Research Community: Rejoinder to "Identifying Research Topic Development in Business and Management Education Research Using Legitimation Code Theory"

    Science.gov (United States)

    Asarta, Carlos J.

    2016-01-01

    Carlos Asarta comments here that Arbaugh, Fornaciari, and Hwang (2016) are to be commended for their work ("Identifying Research Topic Development in Business and Management Education Research Using Legitimation Code Theory" "Journal of Management Education," Dec 2016, see EJ1118407). Asarta says that they make several…

  13. An Algorithmic Diversity Diet?

    DEFF Research Database (Denmark)

    Sørensen, Jannick Kirk; Schmidt, Jan-Hinrik

    2016-01-01

    With the growing influence of personalized algorithmic recommender systems on the exposure of media content to users, the relevance of discussing the diversity of recommendations increases, particularly as far as public service media (PSM) is concerned. An imagined implementation of a diversity...... diet system however triggers not only the classic discussion of the reach – distinctiveness balance for PSM, but also shows that ‘diversity’ is understood very differently in algorithmic recommender system communities than it is editorially and politically in the context of PSM. The design...... of a diversity diet system generates questions not just about editorial power, personal freedom and techno-paternalism, but also about the embedded politics of recommender systems as well as the human skills affiliated with PSM editorial work and the nature of PSM content....

  14. Named Entity Linking Algorithm

    Directory of Open Access Journals (Sweden)

    M. F. Panteleev

    2017-01-01

    Full Text Available In the tasks of processing text in natural language, Named Entity Linking (NEL represents the task to define and link some entity, which is found in the text, with some entity in the knowledge base (for example, Dbpedia. Currently, there is a diversity of approaches to solve this problem, but two main classes can be identified: graph-based approaches and machine learning-based ones. Graph and Machine Learning approaches-based algorithm is proposed accordingly to the stated assumptions about the interrelations of named entities in a sentence and in general.In the case of graph-based approaches, it is necessary to solve the problem of identifying an optimal set of the related entities according to some metric that characterizes the distance between these entities in a graph built on some knowledge base. Due to limitations in processing power, to solve this task directly is impossible. Therefore, its modification is proposed. Based on the algorithms of machine learning, an independent solution cannot be built due to small volumes of training datasets relevant to NEL task. However, their use can contribute to improving the quality of the algorithm. The adaptation of the Latent Dirichlet Allocation model is proposed in order to obtain a measure of the compatibility of attributes of various entities encountered in one context.The efficiency of the proposed algorithm was experimentally tested. A test dataset was independently generated. On its basis the performance of the model was compared using the proposed algorithm with the open source product DBpedia Spotlight, which solves the NEL problem.The mockup, based on the proposed algorithm, showed a low speed as compared to DBpedia Spotlight. However, the fact that it has shown higher accuracy, stipulates the prospects for work in this direction.The main directions of development were proposed in order to increase the accuracy of the system and its productivity.

  15. Multisensor data fusion algorithm development

    Energy Technology Data Exchange (ETDEWEB)

    Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.

    1995-12-01

    This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.

  16. Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample.

    Science.gov (United States)

    Illeghems, Koen; Weckx, Stefan; De Vuyst, Luc

    2015-09-01

    A high-resolution functional metagenomic analysis of a representative single sample of a Brazilian spontaneous cocoa bean fermentation process was carried out to gain insight into its bacterial community functioning. By reconstruction of microbial meta-pathways based on metagenomic data, the current knowledge about the metabolic capabilities of bacterial members involved in the cocoa bean fermentation ecosystem was extended. Functional meta-pathway analysis revealed the distribution of the metabolic pathways between the bacterial members involved. The metabolic capabilities of the lactic acid bacteria present were most associated with the heterolactic fermentation and citrate assimilation pathways. The role of Enterobacteriaceae in the conversion of substrates was shown through the use of the mixed-acid fermentation and methylglyoxal detoxification pathways. Furthermore, several other potential functional roles for Enterobacteriaceae were indicated, such as pectinolysis and citrate assimilation. Concerning acetic acid bacteria, metabolic pathways were partially reconstructed, in particular those related to responses toward stress, explaining their metabolic activities during cocoa bean fermentation processes. Further, the in-depth metagenomic analysis unveiled functionalities involved in bacterial competitiveness, such as the occurrence of CRISPRs and potential bacteriocin production. Finally, comparative analysis of the metagenomic data with bacterial genomes of cocoa bean fermentation isolates revealed the applicability of the selected strains as functional starter cultures. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Parallel algorithms

    CERN Document Server

    Casanova, Henri; Robert, Yves

    2008-01-01

    ""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi

  18. Algorithm 865

    DEFF Research Database (Denmark)

    Gustavson, Fred G.; Reid, John K.; Wasniewski, Jerzy

    2007-01-01

    We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real...... variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n2 variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel...

  19. Measuring Health Information Dissemination and Identifying Target Interest Communities on Twitter: Methods Development and Case Study of the @SafetyMD Network.

    Science.gov (United States)

    Kandadai, Venk; Yang, Haodong; Jiang, Ling; Yang, Christopher C; Fleisher, Linda; Winston, Flaura Koplin

    2016-05-05

    Little is known about the ability of individual stakeholder groups to achieve health information dissemination goals through Twitter. This study aimed to develop and apply methods for the systematic evaluation and optimization of health information dissemination by stakeholders through Twitter. Tweet content from 1790 followers of @SafetyMD (July-November 2012) was examined. User emphasis, a new indicator of Twitter information dissemination, was defined and applied to retweets across two levels of retweeters originating from @SafetyMD. User interest clusters were identified based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) of a random sample of 170 followers. User emphasis of keywords remained across levels but decreased by 9.5 percentage points. PCA and HCA identified 12 statistically unique clusters of followers within the @SafetyMD Twitter network. This study is one of the first to develop methods for use by stakeholders to evaluate and optimize their use of Twitter to disseminate health information. Our new methods provide preliminary evidence that individual stakeholders can evaluate the effectiveness of health information dissemination and create content-specific clusters for more specific targeted messaging.

  20. Community Detection for Large Graphs

    KAUST Repository

    Peng, Chengbin

    2014-05-04

    Many real world networks have inherent community structures, including social networks, transportation networks, biological networks, etc. For large scale networks with millions or billions of nodes in real-world applications, accelerating current community detection algorithms is in demand, and we present two approaches to tackle this issue -A K-core based framework that can accelerate existing community detection algorithms significantly; -A parallel inference algorithm via stochastic block models that can distribute the workload.

  1. Using geographic information systems (GIS) to identify communities in need of health insurance outreach: An OCHIN practice-based research network (PBRN) report.

    Science.gov (United States)

    Angier, Heather; Likumahuwa, Sonja; Finnegan, Sean; Vakarcs, Trisha; Nelson, Christine; Bazemore, Andrew; Carrozza, Mark; DeVoe, Jennifer E

    2014-01-01

    Our practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients. Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n = 228,284). Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates. EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions. © Copyright 2014 by the American Board of Family Medicine.

  2. Science for Managing Riverine Ecosystems: Actions for the USGS Identified in the Workshop "Analysis of Flow and Habitat for Instream Aquatic Communities"

    Science.gov (United States)

    Bencala, Kenneth E.; Hamilton, David B.; Petersen, James H.

    2006-01-01

    Federal and state agencies need improved scientific analysis to support riverine ecosystem management. The ability of the USGS to integrate geologic, hydrologic, chemical, geographic, and biological data into new tools and models provides unparalleled opportunities to translate the best riverine science into useful approaches and usable information to address issues faced by river managers. In addition to this capability to provide integrated science, the USGS has a long history of providing long-term and nationwide information about natural resources. The USGS is now in a position to advance its ability to provide the scientific support for the management of riverine ecosystems. To address this need, the USGS held a listening session in Fort Collins, Colorado in April 2006. Goals of the workshop were to: 1) learn about the key resource issues facing DOI, other Federal, and state resource management agencies; 2) discuss new approaches and information needs for addressing these issues; and 3) outline a strategy for the USGS role in supporting riverine ecosystem management. Workshop discussions focused on key components of a USGS strategy: Communications, Synthesis, and Research. The workshop identified 3 priority actions the USGS can initiate now to advance its capabilities to support integrated science for resource managers in partner government agencies and non-governmental organizations: 1) Synthesize the existing science of riverine ecosystem processes to produce broadly applicable conceptual models, 2) Enhance selected ongoing instream flow projects with complementary interdisciplinary studies, and 3) Design a long-term, watershed-scale research program that will substantively reinvent riverine ecosystem science. In addition, topical discussion groups on hydrology, geomorphology, aquatic habitat and populations, and socio-economic analysis and negotiation identified eleven important complementary actions required to advance the state of the science and to

  3. Could a brief assessment of negative emotions and self-esteem identify adolescents at current and future risk of self-harm in the community? A prospective cohort analysis.

    Science.gov (United States)

    Phillips, Rhiannon; Spears, Melissa R; Montgomery, Alan A; Millings, Abigail; Sayal, Kapil; Stallard, Paul

    2013-06-22

    Self-harm is common in adolescents, but it is often unreported and undetected. Available screening tools typically ask directly about self-harm and suicidal ideation. Although in an ideal world, direct enquiry and open discussion around self-harm would be advocated, non-psychiatric professionals in community settings are often reluctant to ask about this directly and disclosure can be met with feeling of intense anxiety. Training non-specialist staff to directly ask about self-harm has limited effects suggesting that alternative approaches are required. This study investigated whether a targeted analysis of negative emotions and self-esteem could identify young adolescents at risk of self-harm in community settings. Data were collected as part of a clinical trial from young people in school years 8-11 (aged 12-16) at eight UK secondary schools (N = 4503 at baseline, N = 3263 in prospective analysis). The Short Mood and Feelings Questionnaire, Revised Child Anxiety and Depression Scale, Rosenberg Self-Esteem Scale, personal failure (Children's Automatic Thoughts Scale), and two items on self-harm were completed at baseline, 6 and 12 months. Following a process of Principal Components Analysis, item reduction, and logistic regression analysis, three internally reliable factors were identified from the original measures that were independently associated with current and future self-harm; personal failure (3 items), physical symptoms of depression/anxiety (6 items), positive self-esteem (5 items). The summed score of these 14 items had good accuracy in identifying current self-harm (AUC 0.87 girls, 0.81 boys) and at six months for girls (0.81), and fair accuracy at six months for boys (AUC 0.74) and 12 months for girls (AUC 0.77). A brief and targeted assessment of negative emotions and self-esteem, focusing on factors that are strongly associated with current and future self-harm, could potentially be used to help identify adolescents who are at risk in

  4. A population-based matched cohort study examining the mortality and costs of patients with community-onset Clostridium difficile infection identified using emergency department visits and hospital admissions.

    Science.gov (United States)

    Nanwa, Natasha; Sander, Beate; Krahn, Murray; Daneman, Nick; Lu, Hong; Austin, Peter C; Govindarajan, Anand; Rosella, Laura C; Cadarette, Suzanne M; Kwong, Jeffrey C

    2017-01-01

    Few studies have evaluated the mortality or quantified the economic burden of community-onset Clostridium difficile infection (CDI). We estimated the attributable mortality and costs of community-onset CDI. We conducted a population-based matched cohort study. We identified incident subjects with community-onset CDI using health administrative data (emergency department visits and hospital admissions) in Ontario, Canada between January 1, 2003 and December 31, 2010. We propensity-score matched each infected subject to one uninfected subject and followed subjects in the cohort until December 31, 2011. We evaluated all-cause mortality and costs (unadjusted and adjusted for survival) from the healthcare payer perspective (2014 Canadian dollars). During our study period, we identified 7,950 infected subjects. The mean age was 63.5 years (standard deviation = 22.0), 62.7% were female, and 45.0% were very high users of the healthcare system. The relative risk for 30-day, 180-day, and 1-year mortality were 7.32 (95% confidence interval [CI], 5.94-9.02), 3.55 (95%CI, 3.17-3.97), and 2.59 (95%CI, 2.37-2.83), respectively. Mean attributable cumulative 30-day, 180-day, and 1-year costs (unadjusted for survival) were $7,434 (95%CI, $7,122-$7,762), $12,517 (95%CI, $11,687-$13,366), and $13,217 (95%CI, $12,062-$14,388). Mean attributable cumulative 1-, 2-, and 3-year costs (adjusted for survival) were $10,700 (95%CI, $9,811-$11,645), $13,312 (95%CI, $12,024-$14,682), and $15,812 (95%CI, $14,159-$17,571). Infected subjects had considerably higher risk of all-cause mortality and costs compared with uninfected subjects. This study provides insight on an understudied patient group. Our study findings will facilitate assessment of interventions to prevent community-onset CDI.

  5. FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS

    Directory of Open Access Journals (Sweden)

    G. Sithole

    2015-05-01

    Full Text Available The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and error. Automating the selection of segmentation algorithms and their parameters first requires methods to evaluate segmentations. Three common approaches for evaluating segmentation algorithms are ‘goodness methods’, ‘discrepancy methods’ and ‘benchmarks’. Benchmarks are considered the most comprehensive method of evaluation. This paper shortcomings in current benchmark methods are identified and a framework is proposed that permits both a visual and numerical evaluation of segmentations for different algorithms, algorithm parameters and evaluation metrics. The concept of the framework is demonstrated on a real point cloud. Current results are promising and suggest that it can be used to predict the performance of segmentation algorithms.

  6. Relationship of drinking motives with alcohol consumption and alcohol-related problems identified in a representative community-based study from Ningxia, China.

    Science.gov (United States)

    Cheng, Hui G; Phillips, Michael R; Zhang, Yuhong; Wang, Zhizhong

    2017-11-01

    Drinking motives have been linked to alcohol consumption and drinking-related problems in western countries, but evidence about this relationship is largely lacking for Asian countries. We aim to assess the relationship between drinking motives and drinking-related outcomes in China, where alcohol use disorders are an increasingly important contributor to the overall burden of illness. Validated Chinese versions of the Drinking Motives Questionnaire-Revised (DMQ-R) and the Alcohol Use Disorder Identification Test (AUDIT) were used to assess drinking motives and drinking-related outcomes among 612 current drinkers identified from a cross-sectional survey of a representative sample of 2425 adults living in Ningxia Province in 2013. Structural equation modeling was used to estimate the relationships linking specific drinking motives ('enhancement', 'conformity', 'social' and 'coping') to drinking-related outcomes ('level of alcohol consumption', 'alcohol dependence' and 'adverse consequences'). The enhancement motive is significantly associated with the level of alcohol consumption (β=0.52, 95% CI=0.27, 0.78). The conformity motive is associated with higher levels of alcohol dependence (β=0.74, 95% CI=0.50, 0.98) and adverse consequences of drinking (β=0.43, 95% CI=0.04, 0.81). The social motive and drinking to cope motive are not significantly associated with any of the three drinking outcomes. The relationships between drinking motives and drinking-related outcomes in China are quite different from those reported in western countries. This study highlights the need to consider local context when adapting prevention or intervention strategies developed in western countries to address the problem of the harmful use of alcohol in China. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Comprehensive eye evaluation algorithm

    Science.gov (United States)

    Agurto, C.; Nemeth, S.; Zamora, G.; Vahtel, M.; Soliz, P.; Barriga, S.

    2016-03-01

    In recent years, several research groups have developed automatic algorithms to detect diabetic retinopathy (DR) in individuals with diabetes (DM), using digital retinal images. Studies have indicated that diabetics have 1.5 times the annual risk of developing primary open angle glaucoma (POAG) as do people without DM. Moreover, DM patients have 1.8 times the risk for age-related macular degeneration (AMD). Although numerous investigators are developing automatic DR detection algorithms, there have been few successful efforts to create an automatic algorithm that can detect other ocular diseases, such as POAG and AMD. Consequently, our aim in the current study was to develop a comprehensive eye evaluation algorithm that not only detects DR in retinal images, but also automatically identifies glaucoma suspects and AMD by integrating other personal medical information with the retinal features. The proposed system is fully automatic and provides the likelihood of each of the three eye disease. The system was evaluated in two datasets of 104 and 88 diabetic cases. For each eye, we used two non-mydriatic digital color fundus photographs (macula and optic disc centered) and, when available, information about age, duration of diabetes, cataracts, hypertension, gender, and laboratory data. Our results show that the combination of multimodal features can increase the AUC by up to 5%, 7%, and 8% in the detection of AMD, DR, and glaucoma respectively. Marked improvement was achieved when laboratory results were combined with retinal image features.

  8. An approach of community evolution based on gravitational relationship refactoring in dynamic networks

    International Nuclear Information System (INIS)

    Yin, Guisheng; Chi, Kuo; Dong, Yuxin; Dong, Hongbin

    2017-01-01

    In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks. - Highlights: • The proposed approach considers both the static community detection and dynamic community evolution. • The approach of community evolution can identify the whole 6 common evolution events. • The proposed approach can judge the evolutionary events according to the variations of the core nodes chains.

  9. Algorithmic chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Fontana, W.

    1990-12-13

    In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.

  10. Involving the Community

    International Development Research Centre (IDRC) Digital Library (Canada)

    Step 3: Identifying the different community groups and other stakeholders concerned .... How can two-way communication enhance community participation in ...... for maintenance and the rights of specific community groups to drinkable water.

  11. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  12. Leveraging disjoint communities for detecting overlapping community structure

    International Nuclear Information System (INIS)

    Chakraborty, Tanmoy

    2015-01-01

    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. (paper)

  13. Helping air quality managers identify vulnerable communities

    CSIR Research Space (South Africa)

    Wright, C

    2008-10-01

    Full Text Available population exposure and vulnerability risk prioritisation model is proposed for potential use by air quality managers in conjunction with their air quality management plans. The model includes factors such as vulnerability caused by poverty, respiratory...

  14. A population-based matched cohort study examining the mortality and costs of patients with community-onset Clostridium difficile infection identified using emergency department visits and hospital admissions

    Science.gov (United States)

    Nanwa, Natasha; Sander, Beate; Krahn, Murray; Daneman, Nick; Lu, Hong; Austin, Peter C.; Govindarajan, Anand; Rosella, Laura C.; Cadarette, Suzanne M.; Kwong, Jeffrey C.

    2017-01-01

    Few studies have evaluated the mortality or quantified the economic burden of community-onset Clostridium difficile infection (CDI). We estimated the attributable mortality and costs of community-onset CDI. We conducted a population-based matched cohort study. We identified incident subjects with community-onset CDI using health administrative data (emergency department visits and hospital admissions) in Ontario, Canada between January 1, 2003 and December 31, 2010. We propensity-score matched each infected subject to one uninfected subject and followed subjects in the cohort until December 31, 2011. We evaluated all-cause mortality and costs (unadjusted and adjusted for survival) from the healthcare payer perspective (2014 Canadian dollars). During our study period, we identified 7,950 infected subjects. The mean age was 63.5 years (standard deviation = 22.0), 62.7% were female, and 45.0% were very high users of the healthcare system. The relative risk for 30-day, 180-day, and 1-year mortality were 7.32 (95% confidence interval [CI], 5.94–9.02), 3.55 (95%CI, 3.17–3.97), and 2.59 (95%CI, 2.37–2.83), respectively. Mean attributable cumulative 30-day, 180-day, and 1-year costs (unadjusted for survival) were $7,434 (95%CI, $7,122-$7,762), $12,517 (95%CI, $11,687-$13,366), and $13,217 (95%CI, $12,062-$14,388). Mean attributable cumulative 1-, 2-, and 3-year costs (adjusted for survival) were $10,700 (95%CI, $9,811-$11,645), $13,312 (95%CI, $12,024-$14,682), and $15,812 (95%CI, $14,159-$17,571). Infected subjects had considerably higher risk of all-cause mortality and costs compared with uninfected subjects. This study provides insight on an understudied patient group. Our study findings will facilitate assessment of interventions to prevent community-onset CDI. PMID:28257438

  15. A qualitative study to identify community structures for management of severe malaria: a basis for introducing rectal artesunate in the under five years children in Nakonde District of Zambia.

    Science.gov (United States)

    Kaona, Frederick A D; Tuba, Mary

    2005-03-25

    Malaria is a serious illness among children aged 5 years and below in Zambia, which carries with it many adverse effects including anemia and high parasites exposure that lead to infant and childhood mortality. Due to poor accessibility to modern health facilities, malaria is normally managed at home using indigenous and cosmopolitan medicines. In view of problems and implications associated with management of severe malaria at home, rectal artesunate is being proposed as a first aid drug to slow down multiplication of parasites in children before accessing appropriate treatment. A qualitative study using standardised in-depth and Focus Group Discussions (FGDs) guides to collect information from four (4) villages in Nakonde district, was conducted between February and March 2004. The guides were administered on 29 key informants living in the community and those whose children were admitted in the health facility. Participants in the 12 FGDs came from the 4 participating villages. Participants and key informants were fathers, younger and older mothers including grandmothers and other influential people at household level. Others were traditional healers, headmen, village secretaries, traditional birth attendants, church leaders and blacksmiths. FGDs and interview transcriptions were coded to identify common themes that were related to recognition, classification and naming of malaria illness, care-seeking behaviour and community treatment practices for severe malaria. Parental prior knowledge of the disease was important as the majority of informants (23 out of 29) and participants (69 out of 97) mentioned four combined symptoms that were used to recognise severe malaria. The symptoms were excessive body hotness, convulsions, vomiting yellow things and bulging of the fontanelle. On the other hand, all informants mentioned two or more of symptoms associated with severe malaria. In all 12 FGDs, participants reported that treatment of severe malaria commenced with the

  16. The Top Ten Algorithms in Data Mining

    CERN Document Server

    Wu, Xindong

    2009-01-01

    From classification and clustering to statistical learning, association analysis, and link mining, this book covers the most important topics in data mining research. It presents the ten most influential algorithms used in the data mining community today. Each chapter provides a detailed description of the algorithm, a discussion of available software implementation, advanced topics, and exercises. With a simple data set, examples illustrate how each algorithm works and highlight the overall performance of each algorithm in a real-world application. Featuring contributions from leading researc

  17. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique.

    Science.gov (United States)

    Jia, Cangzhi; Zuo, Yun; Zou, Quan; Hancock, John

    2018-02-06

    Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation causes many diseases, such as cancer and various neurodegenerative diseases. With the available protein O-G1cNAcylation sites experimentally verified, it is highly desired to develop automated methods to rapidly and effectively identify O-G1cNAcylation sites. Although some computational methods have been proposed, their performance has been unsatisfactory, particularly in terms of prediction sensitivity. In this study, we developed an ensemble model O-GlcNAcPRED-II to identify potential O-G1cNAcylation sites. A K-means principal component analysis oversampling technique (KPCA) and fuzzy undersampling method (FUS) were first proposed and incorporated to reduce the proportion of the original positive and negative training samples. Then, rotation forest, a type of classifier-integrated system, was adopted to divide the eight types of feature space into several subsets using four sub-classifiers: random forest, k-nearest neighbour, naive Bayesian and support vector machine. We observed that O-GlcNAcPRED-II achieved a sensitivity of 81.05%, specificity of 95.91%, accuracy of 91.43% and Matthew's correlation coefficient of 0.7928 for five-fold cross-validation run 10 times. Additionally, the results obtained by O-GlcNAcPRED-II on two independent datasets also indicated that the proposed predictor outperformed five published prediction tools. http://121.42.167.206/OGlcPred/. cangzhijia@dlmu.edu.cn or zouquan@nclab.net. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  18. Community Mining Method of Label Propagation Based on Dense Pairs

    Directory of Open Access Journals (Sweden)

    WENG Wei

    2014-03-01

    Full Text Available In recent years, with the popularity of handheld Internet equipments like mobile phones, increasing numbers of people are becoming involved in the virtual social network. Because of its large amount of data and complex structure, the network faces new challenges of community mining. A label propagation algorithm with low time complexity and without prior parameters deals easily with a large networks. This study explored a new method of community mining, based on label propagation with two stages. The first stage involved identifying closely linked nodes according to their local adjacency relations that gave rise to a micro-community. The second stage involved expanding and adjusting this community through a label propagation algorithm (LPA to finally obtain the community structure of the entire social network. This algorithm reduced the number of initial labels and avoided the merging of small communities in general LPAs. Thus, the quality of community discovery was improved, and the linear time complexity of the LPA was maintained.

  19. Community detection by graph Voronoi diagrams

    Science.gov (United States)

    Deritei, Dávid; Lázár, Zsolt I.; Papp, István; Járai-Szabó, Ferenc; Sumi, Róbert; Varga, Levente; Ravasz Regan, Erzsébet; Ercsey-Ravasz, Mária

    2014-06-01

    Accurate and efficient community detection in networks is a key challenge for complex network theory and its applications. The problem is analogous to cluster analysis in data mining, a field rich in metric space-based methods. Common to these methods is a geometric, distance-based definition of clusters or communities. Here we propose a new geometric approach to graph community detection based on graph Voronoi diagrams. Our method serves as proof of principle that the definition of appropriate distance metrics on graphs can bring a rich set of metric space-based clustering methods to network science. We employ a simple edge metric that reflects the intra- or inter-community character of edges, and a graph density-based rule to identify seed nodes of Voronoi cells. Our algorithm outperforms most network community detection methods applicable to large networks on benchmark as well as real-world networks. In addition to offering a computationally efficient alternative for community detection, our method opens new avenues for adapting a wide range of data mining algorithms to complex networks from the class of centroid- and density-based clustering methods.

  20. Research on Algorithms of Identifying Map-sheet Number of Cartographic Area%求解制图区域的地图图幅编号的算法研究

    Institute of Scientific and Technical Information of China (English)

    吴曜宏; 乔俊军; 胡冯伟

    2017-01-01

    基于地理格网理论,从点、线、面三个方面,提出了归原法、斜率分段-同异侧判别法和投影反算-图廓内外判别法,这些算法可准确映射各种投影后制图区域所对应的基础地理信息数据范围,实现了制图范围内地理信息数据所属地图图幅编号的可视化查询.%This artical provides three methods for identifying the map-sheet number for the drawing area. The methods are returning home position, segementing slope-assessment with the same side, projection inversion-assessment of inside map neat line based on the theoretical geographic grid. The cartographic drawing corresponding to a certain projection can be mapping correctly by these methods. The methods can be used to query the map sheet number of the geographic information data within the drawing area.

  1. Pseudo-deterministic Algorithms

    OpenAIRE

    Goldwasser , Shafi

    2012-01-01

    International audience; In this talk we describe a new type of probabilistic algorithm which we call Bellagio Algorithms: a randomized algorithm which is guaranteed to run in expected polynomial time, and to produce a correct and unique solution with high probability. These algorithms are pseudo-deterministic: they can not be distinguished from deterministic algorithms in polynomial time by a probabilistic polynomial time observer with black box access to the algorithm. We show a necessary an...

  2. [Algorithms based on medico-administrative data in the field of endocrine, nutritional and metabolic diseases, especially diabetes].

    Science.gov (United States)

    Fosse-Edorh, S; Rigou, A; Morin, S; Fezeu, L; Mandereau-Bruno, L; Fagot-Campagna, A

    2017-10-01

    Medico-administrative databases represent a very interesting source of information in the field of endocrine, nutritional and metabolic diseases. The objective of this article is to describe the early works of the Redsiam working group in this field. Algorithms developed in France in the field of diabetes, the treatment of dyslipidemia, precocious puberty, and bariatric surgery based on the National Inter-schema Information System on Health Insurance (SNIIRAM) data were identified and described. Three algorithms for identifying people with diabetes are available in France. These algorithms are based either on full insurance coverage for diabetes or on claims of diabetes treatments, or on the combination of these two methods associated with hospitalizations related to diabetes. Each of these algorithms has a different purpose, and the choice should depend on the goal of the study. Algorithms for identifying people treated for dyslipidemia or precocious puberty or who underwent bariatric surgery are also available. Early work from the Redsiam working group in the field of endocrine, nutritional and metabolic diseases produced an inventory of existing algorithms in France, linked with their goals, together with a presentation of their limitations and advantages, providing useful information for the scientific community. This work will continue with discussions about algorithms on the incidence of diabetes in children, thyroidectomy for thyroid nodules, hypothyroidism, hypoparathyroidism, and amyloidosis. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  3. Computational geometry algorithms and applications

    CERN Document Server

    de Berg, Mark; Overmars, Mark; Schwarzkopf, Otfried

    1997-01-01

    Computational geometry emerged from the field of algorithms design and anal­ ysis in the late 1970s. It has grown into a recognized discipline with its own journals, conferences, and a large community of active researchers. The suc­ cess of the field as a research discipline can on the one hand be explained from the beauty of the problems studied and the solutions obtained, and, on the other hand, by the many application domains--computer graphics, geographic in­ formation systems (GIS), robotics, and others-in which geometric algorithms play a fundamental role. For many geometric problems the early algorithmic solutions were either slow or difficult to understand and implement. In recent years a number of new algorithmic techniques have been developed that improved and simplified many of the previous approaches. In this textbook we have tried to make these modem algorithmic solutions accessible to a large audience. The book has been written as a textbook for a course in computational geometry, but it can ...

  4. A qualitative study to identify community structures for management of severe malaria: a basis for introducing rectal artesunate in the under five years children in Nakonde District of Zambia

    Directory of Open Access Journals (Sweden)

    Tuba Mary

    2005-03-01

    Full Text Available Abstract Background Malaria is a serious illness among children aged 5 years and below in Zambia, which carries with it many adverse effects including anemia and high parasites exposure that lead to infant and childhood mortality. Due to poor accessibility to modern health facilities, malaria is normally managed at home using indigenous and cosmopolitan medicines. In view of problems and implications associated with management of severe malaria at home, rectal artesunate is being proposed as a first aid drug to slow down multiplication of parasites in children before accessing appropriate treatment. Methods A qualitative study using standardised in-depth and Focuss Group Discussions (FGDs guides to collect information from four (4 villages in Nakonde district, was conducted between February and March 2004. The guides were administered on 29 key informants living in the community and those whose children were admitted in the health facility. Participants in the 12 FGDs came from the 4 participating villages. Participants and key informants were fathers, younger and older mothers including grandmothers and other influential people at household level. Others were traditional healers, headmen, village secretaries, tradtional birth attendants, church leaders and black smiths. FGDs and interview transcriptions were coded to identify common themes that were related to recognition, classification and naming of malaria illness, care-seeking behaviour and community treatment practices for severe malaria. Results Parental prior knowledge of the disease was important as the majority of informants (23 out of 29 and participants (69 out of 97 mentioned four combined symptoms that were used to recognise severe malaria. The symptoms were excessive body hotness, convulsions, vomiting yellow things and bulging of the fontanelle. On the other hand, all informants mentioned two or more of symptoms associated with severe malaria. In all 12 FGDs, participants

  5. Community Detection for Correlation Matrices

    Directory of Open Access Journals (Sweden)

    Mel MacMahon

    2015-04-01

    Full Text Available 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.

  6. Community Detection for Correlation Matrices

    Science.gov (United States)

    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.

  7. Hamiltonian Algorithm Sound Synthesis

    OpenAIRE

    大矢, 健一

    2013-01-01

    Hamiltonian Algorithm (HA) is an algorithm for searching solutions is optimization problems. This paper introduces a sound synthesis technique using Hamiltonian Algorithm and shows a simple example. "Hamiltonian Algorithm Sound Synthesis" uses phase transition effect in HA. Because of this transition effect, totally new waveforms are produced.

  8. Progressive geometric algorithms

    NARCIS (Netherlands)

    Alewijnse, S.P.A.; Bagautdinov, T.M.; de Berg, M.T.; Bouts, Q.W.; ten Brink, Alex P.; Buchin, K.A.; Westenberg, M.A.

    2015-01-01

    Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms

  9. Progressive geometric algorithms

    NARCIS (Netherlands)

    Alewijnse, S.P.A.; Bagautdinov, T.M.; Berg, de M.T.; Bouts, Q.W.; Brink, ten A.P.; Buchin, K.; Westenberg, M.A.

    2014-01-01

    Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms

  10. The Algorithmic Imaginary

    DEFF Research Database (Denmark)

    Bucher, Taina

    2017-01-01

    the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself...... of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops...

  11. The BR eigenvalue algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Geist, G.A. [Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.; Howell, G.W. [Florida Inst. of Tech., Melbourne, FL (United States). Dept. of Applied Mathematics; Watkins, D.S. [Washington State Univ., Pullman, WA (United States). Dept. of Pure and Applied Mathematics

    1997-11-01

    The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrowband, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.

  12. Algorithmically specialized parallel computers

    CERN Document Server

    Snyder, Lawrence; Gannon, Dennis B

    1985-01-01

    Algorithmically Specialized Parallel Computers focuses on the concept and characteristics of an algorithmically specialized computer.This book discusses the algorithmically specialized computers, algorithmic specialization using VLSI, and innovative architectures. The architectures and algorithms for digital signal, speech, and image processing and specialized architectures for numerical computations are also elaborated. Other topics include the model for analyzing generalized inter-processor, pipelined architecture for search tree maintenance, and specialized computer organization for raster

  13. Citation algorithms for identifying research milestones driving biomedical innovation

    NARCIS (Netherlands)

    Comins, J.A.; Leydesdorff, L.

    Scientific activity plays a major role in innovation for biomedicine and healthcare. For instance, fundamental research on disease pathologies and mechanisms can generate potential targets for drug therapy. This co-evolution is punctuated by papers which provide new perspectives and open new

  14. Identifying spam e-mail messages using an intelligence algorithm

    Directory of Open Access Journals (Sweden)

    Parichehr Ghaedi

    2014-06-01

    Full Text Available During the past few years, there have been growing interests in using email for delivering various types of messages such as social, financial, etc. There are also people who use email messages to promote products and services or even to do criminal activities called Spam email. These unwanted messages are sent to different target population for different purposes and there is a growing interest to develop methods to filter such email messages. This paper presents a method to filter Spam email messages based on the keyword pattern. In this article, a multi-agent filter trade based on the Bayes rule, which has benefit of using the users’ interest, keywords and investigation the message content according to its topic, has been used. Then Nested Neural Network has been used to detect the spam messages. To check the authenticity of this proposed method, we test it for a couple of email messages, so that it could determine spams and hams from each other, effectively. The result shows the superiority of this method over the previous ones including filters with Multi-Layer Perceptron that detect spams.

  15. Comparison of correlation algorithms for identifying ultrasonic seals

    International Nuclear Information System (INIS)

    Beer, C.L.; McKenzie, J.M.

    1991-01-01

    Ultrasonic seals are used on reactor fuel assemblies for international safeguards applications. The seals are read by a Seal Pattern Reader to obtain a discrete digital signature that is unique to each seal. The signature is used to determine the identity and integrity of the seals such that accountability and integrity of the nuclear fuel assemblies can be addressed. A correlation coefficient is calculated between the signature obtained at the time of inspection and a stored reference signature, yielding a number between negative one and one. Numbers close to one indicate a high probability that the two signatures represent the same seal. Data from two seals were studied, the Atomic Energy of Canada Limited Random Coil (ARC) seal and the JRC-Ispra VAK-III seal. Currently, correlation coefficients are obtained using data in the time or spatial domain, respectively. An approach is proposed in which the correlation coefficients are obtained from the Fourier transforms of the data. This paper reports that the objective of the study was to perform independent experiments on available ARC and VAK-III data to determine the advantages, if any, of transforming the data to the frequency spectrum prior to performing the correlation calculation. The results indicate definite advantages can be obtained

  16. A Markov random walk under constraint for discovering overlapping communities in complex networks

    International Nuclear Information System (INIS)

    Jin, Di; Yang, Bo; Liu, Dayou; He, Dongxiao; Liu, Jie; Baquero, Carlos

    2011-01-01

    The detection of overlapping communities in complex networks has motivated recent research in relevant fields. Aiming to address this problem, we propose a Markov-dynamics-based algorithm, called UEOC, which means 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge of the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and has been compared with a set of competing algorithms. The experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities

  17. Application of Genetic Algorithms in Seismic Tomography

    Science.gov (United States)

    Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos

    2010-05-01

    In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the

  18. Fast compact algorithms and software for spline smoothing

    CERN Document Server

    Weinert, Howard L

    2012-01-01

    Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.

  19. Community detection with consideration of non-topological information

    International Nuclear Information System (INIS)

    Zou Sheng-Rong; Peng Yu-Jing; Liu Ai-Fen; Xu Xiu-Lian; He Da-Ren

    2011-01-01

    In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly from topological information. This paper shows, by a simple example where scientists in three research groups and one external group form four communities, that in some real world networks non-topological information (in this example, the research group affiliation) dominates community division. If the information has some influence on the network topological structure, the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology. We show that weighted Newman algorithm may be the best choice for this example. We believe that this idea is general for real-world complex networks. (interdisciplinary physics and related areas of science and technology)

  20. Marshall Rosenbluth and the Metropolis algorithm

    International Nuclear Information System (INIS)

    Gubernatis, J.E.

    2005-01-01

    The 1953 publication, 'Equation of State Calculations by Very Fast Computing Machines' by N. Metropolis, A. W. Rosenbluth and M. N. Rosenbluth, and M. Teller and E. Teller [J. Chem. Phys. 21, 1087 (1953)] marked the beginning of the use of the Monte Carlo method for solving problems in the physical sciences. The method described in this publication subsequently became known as the Metropolis algorithm, undoubtedly the most famous and most widely used Monte Carlo algorithm ever published. As none of the authors made subsequent use of the algorithm, they became unknown to the large simulation physics community that grew from this publication and their roles in its development became the subject of mystery and legend. At a conference marking the 50th anniversary of the 1953 publication, Marshall Rosenbluth gave his recollections of the algorithm's development. The present paper describes the algorithm, reconstructs the historical context in which it was developed, and summarizes Marshall's recollections

  1. Generalization of Risch's algorithm to special functions

    International Nuclear Information System (INIS)

    Raab, Clemens G.

    2013-05-01

    Symbolic integration deals with the evaluation of integrals in closed form. We present an overview of Risch's algorithm including recent developments. The algorithms discussed are suited for both indefinite and definite integration. They can also be used to compute linear relations among integrals and to find identities for special functions given by parameter integrals. The aim of this presentation is twofold: to introduce the reader to some basic ideas of differential algebra in the context of integration and to raise awareness in the physics community of computer algebra algorithms for indefinite and definite integration.

  2. Alternative confidence measure for local matching stereo algorithms

    CSIR Research Space (South Africa)

    Ndhlovu, T

    2009-11-01

    Full Text Available The authors present a confidence measure applied to individual disparity estimates in local matching stereo correspondence algorithms. It aims at identifying textureless areas, where most local matching algorithms fail. The confidence measure works...

  3. New calibration algorithms for dielectric-based microwave moisture sensors

    Science.gov (United States)

    New calibration algorithms for determining moisture content in granular and particulate materials from measurement of the dielectric properties at a single microwave frequency are proposed. The algorithms are based on identifying empirically correlations between the dielectric properties and the par...

  4. Detecting community structure using label propagation with consensus weight in complex network

    International Nuclear Information System (INIS)

    Liang Zong-Wen; Li Jian-Ping; Yang Fan; Petropulu Athina

    2014-01-01

    Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm (LPA) is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight (LPAcw), and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions. (interdisciplinary physics and related areas of science and technology)

  5. Quantum Computation and Algorithms

    International Nuclear Information System (INIS)

    Biham, O.; Biron, D.; Biham, E.; Grassi, M.; Lidar, D.A.

    1999-01-01

    It is now firmly established that quantum algorithms provide a substantial speedup over classical algorithms for a variety of problems, including the factorization of large numbers and the search for a marked element in an unsorted database. In this talk I will review the principles of quantum algorithms, the basic quantum gates and their operation. The combination of superposition and interference, that makes these algorithms efficient, will be discussed. In particular, Grover's search algorithm will be presented as an example. I will show that the time evolution of the amplitudes in Grover's algorithm can be found exactly using recursion equations, for any initial amplitude distribution

  6. Effects of multi-state links in network community detection

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

    2017-01-01

    A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

  7. Algorithms and Data Structures (lecture 1)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Algorithms have existed, in one form or another, for as long as humanity has. During the second half of the 20th century, the field was revolutionised with the introduction of ever faster computers. In these lectures we discuss how algorithms are designed, how to evaluate their speed, and how to identify areas of improvement in existing algorithms. An algorithm consists of more than just a series of instructions; almost as important is the memory structure of the data on which it operates. A part of the lectures will be dedicated to a discussion of the various ways one can store data in memory, and their advantages and disadvantages.

  8. Algorithms and Data Structures (lecture 2)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Algorithms have existed, in one form or another, for as long as humanity has. During the second half of the 20th century, the field was revolutionised with the introduction of ever faster computers. In these lectures we discuss how algorithms are designed, how to evaluate their speed, and how to identify areas of improvement in existing algorithms. An algorithm consists of more than just a series of instructions; almost as important is the memory structure of the data on which it operates. A part of the lectures will be dedicated to a discussion of the various ways one can store data in memory, and their advantages and disadvantages.

  9. Next Generation Suspension Dynamics Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Schunk, Peter Randall [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Higdon, Jonathon [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Chen, Steven [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-12-01

    This research project has the objective to extend the range of application, improve the efficiency and conduct simulations with the Fast Lubrication Dynamics (FLD) algorithm for concentrated particle suspensions in a Newtonian fluid solvent. The research involves a combination of mathematical development, new computational algorithms, and application to processing flows of relevance in materials processing. The mathematical developments clarify the underlying theory, facilitate verification against classic monographs in the field and provide the framework for a novel parallel implementation optimized for an OpenMP shared memory environment. The project considered application to consolidation flows of major interest in high throughput materials processing and identified hitherto unforeseen challenges in the use of FLD in these applications. Extensions to the algorithm have been developed to improve its accuracy in these applications.

  10. De-identifying an EHR Database

    DEFF Research Database (Denmark)

    Lauesen, Søren; Pantazos, Kostas; Lippert, Søren

    2011-01-01

    -identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect...... lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree...... of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database....

  11. Structure-Based Algorithms for Microvessel Classification

    KAUST Repository

    Smith, Amy F.; Secomb, Timothy W.; Pries, Axel R.; Smith, Nicolas P.; Shipley, Rebecca J.

    2015-01-01

    algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules

  12. Learning from nature: Nature-inspired algorithms

    DEFF Research Database (Denmark)

    Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin

    2016-01-01

    .), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...

  13. Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

    Science.gov (United States)

    Salameh Shreem, Salam; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad

    2016-04-01

    Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.

  14. Fermion cluster algorithms

    International Nuclear Information System (INIS)

    Chandrasekharan, Shailesh

    2000-01-01

    Cluster algorithms have been recently used to eliminate sign problems that plague Monte-Carlo methods in a variety of systems. In particular such algorithms can also be used to solve sign problems associated with the permutation of fermion world lines. This solution leads to the possibility of designing fermion cluster algorithms in certain cases. Using the example of free non-relativistic fermions we discuss the ideas underlying the algorithm

  15. Autonomous Star Tracker Algorithms

    DEFF Research Database (Denmark)

    Betto, Maurizio; Jørgensen, John Leif; Kilsgaard, Søren

    1998-01-01

    Proposal, in response to an ESA R.f.P., to design algorithms for autonomous star tracker operations.The proposal also included the development of a star tracker breadboard to test the algorithms performances.......Proposal, in response to an ESA R.f.P., to design algorithms for autonomous star tracker operations.The proposal also included the development of a star tracker breadboard to test the algorithms performances....

  16. Comparing Whole Building Energy Implications of Sidelighting Systems with Alternate Manual Blind Control Algorithms

    Directory of Open Access Journals (Sweden)

    Christopher Dyke

    2015-05-01

    Full Text Available Currently, there is no manual blind control guideline used consistently throughout the energy modeling community. This paper identifies and compares five manual blind control algorithms with unique control patterns and reports blind occlusion, rate of change data, and annual building energy consumption. The blind control schemes detailed here represent five reasonable candidates for use in lighting and energy simulation based on difference driving factors. This study was performed on a medium-sized office building using EnergyPlus with the internal daylight harvesting engine. Results show that applying manual blind control algorithms affects the total annual consumption of the building by as much as 12.5% and 11.5% for interior and exterior blinds respectively, compared to the Always Retracted blinds algorithm. Peak demand was also compared showing blind algorithms affected zone load sizing by as much as 9.8%. The alternate algorithms were tested for their impact on American Society of Heating, Refrigeration and Air-Conditioning Engineers (ASHRAE Guideline 14 calibration metrics and all models were found to differ from the original calibrated baseline by more than the recommended ±15% for coefficient of variance of the mean square error (CVRMSE and ±5% for normalized mean bias error (NMBE. The paper recommends that energy modelers use one or more manual blind control algorithms during design stages when making decisions about energy efficiency and other design alternatives.

  17. A verified LLL algorithm

    NARCIS (Netherlands)

    Divasón, Jose; Joosten, Sebastiaan; Thiemann, René; Yamada, Akihisa

    2018-01-01

    The Lenstra-Lenstra-Lovász basis reduction algorithm, also known as LLL algorithm, is an algorithm to find a basis with short, nearly orthogonal vectors of an integer lattice. Thereby, it can also be seen as an approximation to solve the shortest vector problem (SVP), which is an NP-hard problem,

  18. Overlapping community detection based on link graph using distance dynamics

    Science.gov (United States)

    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.

  19. Data clustering algorithms and applications

    CERN Document Server

    Aggarwal, Charu C

    2013-01-01

    Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea

  20. Nature-inspired optimization algorithms

    CERN Document Server

    Yang, Xin-She

    2014-01-01

    Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

  1. VISUALIZATION OF PAGERANK ALGORITHM

    OpenAIRE

    Perhaj, Ervin

    2013-01-01

    The goal of the thesis is to develop a web application that help users understand the functioning of the PageRank algorithm. The thesis consists of two parts. First we develop an algorithm to calculate PageRank values of web pages. The input of algorithm is a list of web pages and links between them. The user enters the list through the web interface. From the data the algorithm calculates PageRank value for each page. The algorithm repeats the process, until the difference of PageRank va...

  2. Parallel sorting algorithms

    CERN Document Server

    Akl, Selim G

    1985-01-01

    Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the

  3. Modified Clipped LMS Algorithm

    Directory of Open Access Journals (Sweden)

    Lotfizad Mojtaba

    2005-01-01

    Full Text Available Abstract A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.

  4. Approximate Computing Techniques for Iterative Graph Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Panyala, Ajay R.; Subasi, Omer; Halappanavar, Mahantesh; Kalyanaraman, Anantharaman; Chavarria Miranda, Daniel G.; Krishnamoorthy, Sriram

    2017-12-18

    Approximate computing enables processing of large-scale graphs by trading off quality for performance. Approximate computing techniques have become critical not only due to the emergence of parallel architectures but also the availability of large scale datasets enabling data-driven discovery. Using two prototypical graph algorithms, PageRank and community detection, we present several approximate computing heuristics to scale the performance with minimal loss of accuracy. We present several heuristics including loop perforation, data caching, incomplete graph coloring and synchronization, and evaluate their efficiency. We demonstrate performance improvements of up to 83% for PageRank and up to 450x for community detection, with low impact of accuracy for both the algorithms. We expect the proposed approximate techniques will enable scalable graph analytics on data of importance to several applications in science and their subsequent adoption to scale similar graph algorithms.

  5. Semioptimal practicable algorithmic cooling

    International Nuclear Information System (INIS)

    Elias, Yuval; Mor, Tal; Weinstein, Yossi

    2011-01-01

    Algorithmic cooling (AC) of spins applies entropy manipulation algorithms in open spin systems in order to cool spins far beyond Shannon's entropy bound. Algorithmic cooling of nuclear spins was demonstrated experimentally and may contribute to nuclear magnetic resonance spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semioptimal practicable AC (SOPAC), wherein a few cycles (typically two to six) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than PAC and are much more efficient than the exhaustive algorithms. These algorithms are shown to bridge the gap between PAC and exhaustive AC. In addition, we calculated the number of spins required by SOPAC in order to purify qubits for quantum computation. As few as 12 and 7 spins are required (in an ideal scenario) to yield a mildly pure spin (60% polarized) from initial polarizations of 1% and 10%, respectively. In the latter case, about five more spins are sufficient to produce a highly pure spin (99.99% polarized), which could be relevant for fault-tolerant quantum computing.

  6. Evaluation Of Algorithms Of Anti- HIV Antibody Tests

    Directory of Open Access Journals (Sweden)

    Paranjape R.S

    1997-01-01

    Full Text Available Research question: Can alternate algorithms be used in place of conventional algorithm for epidemiological studies of HIV infection with less expenses? Objective: To compare the results of HIV sero- prevalence as determined by test algorithms combining three kits with conventional test algorithm. Study design: Cross â€" sectional. Participants: 282 truck drivers. Statistical analysis: Sensitivity and specificity analysis and predictive values. Results: Three different algorithms that do not include Western Blot (WB were compared with the conventional algorithm, in a truck driver population with 5.6% prevalence of HIV â€"I infection. Algorithms with one EIA (Genetic Systems or Biotest and a rapid test (immunocomb or with two EIAs showed 100% positive predictive value in relation to the conventional algorithm. Using an algorithm with EIA as screening test and a rapid test as a confirmatory test was 50 to 70% less expensive than the conventional algorithm per positive scrum sample. These algorithms obviate the interpretation of indeterminate results and also give differential diagnosis of HIV-2 infection. Alternate algorithms are ideally suited for community based control programme in developing countries. Application of these algorithms in population with low prevalence should also be studied in order to evaluate universal applicability.

  7. Categorizing segmentation quality using a quantitative quality assurance algorithm

    International Nuclear Information System (INIS)

    Rodrigues, George; Louie, Alexander; Best, Lara

    2012-01-01

    Obtaining high levels of contouring consistency is a major limiting step in optimizing the radiotherapeutic ratio. We describe a novel quantitative methodology for the quality assurance (QA) of contour compliance referenced against a community set of contouring experts. Two clinical tumour site scenarios (10 lung cases and one prostate case) were used with QA algorithm. For each case, multiple physicians (lung: n = 6, prostate: n = 25) segmented various target/organ at risk (OAR) structures to define a set of community reference contours. For each set of community contours, a consensus contour (Simultaneous Truth and Performance Level Estimation (STAPLE)) was created. Differences between each individual community contour versus the group consensus contour were quantified by consensus-based contouring penalty metric (PM) scores. New observers segmented these same cases to calculate individual PM scores (for each unique target/OAR) for each new observer–STAPLE pair for comparison against the community and consensus contours. Four physicians contoured the 10 lung cases for a total of 72 contours for quality assurance evaluation against the previously derived community consensus contours. A total of 16 outlier contours were identified by the QA system of which 11 outliers were due to over-contouring discrepancies, three were due to over-/under-contouring discrepancies, and two were due to missing/incorrect nodal contours. In the prostate scenario involving six physicians, the QA system detected a missing penile bulb contour, systematic inner-bladder contouring, and under-contouring of the upper/anterior rectum. A practical methodology for QA has been demonstrated with future clinical trial credentialing, medical education and auto-contouring assessment applications.

  8. Applying Kitaev's algorithm in an ion trap quantum computer

    International Nuclear Information System (INIS)

    Travaglione, B.; Milburn, G.J.

    2000-01-01

    Full text: Kitaev's algorithm is a method of estimating eigenvalues associated with an operator. Shor's factoring algorithm, which enables a quantum computer to crack RSA encryption codes, is a specific example of Kitaev's algorithm. It has been proposed that the algorithm can also be used to generate eigenstates. We extend this proposal for small quantum systems, identifying the conditions under which the algorithm can successfully generate eigenstates. We then propose an implementation scheme based on an ion trap quantum computer. This scheme allows us to illustrate a simple example, in which the algorithm effectively generates eigenstates

  9. A Performance Evaluation of Lightning-NO Algorithms in CMAQ

    Science.gov (United States)

    In the Community Multiscale Air Quality (CMAQv5.2) model, we have implemented two algorithms for lightning NO production; one algorithm is based on the hourly observed cloud-to-ground lightning strike data from National Lightning Detection Network (NLDN) to replace the previous m...

  10. Introduction to Evolutionary Algorithms

    CERN Document Server

    Yu, Xinjie

    2010-01-01

    Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

  11. Recursive forgetting algorithms

    DEFF Research Database (Denmark)

    Parkum, Jens; Poulsen, Niels Kjølstad; Holst, Jan

    1992-01-01

    In the first part of the paper, a general forgetting algorithm is formulated and analysed. It contains most existing forgetting schemes as special cases. Conditions are given ensuring that the basic convergence properties will hold. In the second part of the paper, the results are applied...... to a specific algorithm with selective forgetting. Here, the forgetting is non-uniform in time and space. The theoretical analysis is supported by a simulation example demonstrating the practical performance of this algorithm...

  12. Overlapping community detection in networks with positive and negative links

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Yuan, B; Tang, B Z

    2014-01-01

    Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks. (paper)

  13. Algorithms in Algebraic Geometry

    CERN Document Server

    Dickenstein, Alicia; Sommese, Andrew J

    2008-01-01

    In the last decade, there has been a burgeoning of activity in the design and implementation of algorithms for algebraic geometric computation. Some of these algorithms were originally designed for abstract algebraic geometry, but now are of interest for use in applications and some of these algorithms were originally designed for applications, but now are of interest for use in abstract algebraic geometry. The workshop on Algorithms in Algebraic Geometry that was held in the framework of the IMA Annual Program Year in Applications of Algebraic Geometry by the Institute for Mathematics and Its

  14. Shadow algorithms data miner

    CERN Document Server

    Woo, Andrew

    2012-01-01

    Digital shadow generation continues to be an important aspect of visualization and visual effects in film, games, simulations, and scientific applications. This resource offers a thorough picture of the motivations, complexities, and categorized algorithms available to generate digital shadows. From general fundamentals to specific applications, it addresses shadow algorithms and how to manage huge data sets from a shadow perspective. The book also examines the use of shadow algorithms in industrial applications, in terms of what algorithms are used and what software is applicable.

  15. Spectral Decomposition Algorithm (SDA)

    Data.gov (United States)

    National Aeronautics and Space Administration — Spectral Decomposition Algorithm (SDA) is an unsupervised feature extraction technique similar to PCA that was developed to better distinguish spectral features in...

  16. Quick fuzzy backpropagation algorithm.

    Science.gov (United States)

    Nikov, A; Stoeva, S

    2001-03-01

    A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

  17. Portfolios of quantum algorithms.

    Science.gov (United States)

    Maurer, S M; Hogg, T; Huberman, B A

    2001-12-17

    Quantum computation holds promise for the solution of many intractable problems. However, since many quantum algorithms are stochastic in nature they can find the solution of hard problems only probabilistically. Thus the efficiency of the algorithms has to be characterized by both the expected time to completion and the associated variance. In order to minimize both the running time and its uncertainty, we show that portfolios of quantum algorithms analogous to those of finance can outperform single algorithms when applied to the NP-complete problems such as 3-satisfiability.

  18. Tag SNP selection via a genetic algorithm.

    Science.gov (United States)

    Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

    2010-10-01

    Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

  19. Evolutionary algorithms for mobile ad hoc networks

    CERN Document Server

    Dorronsoro, Bernabé; Danoy, Grégoire; Pigné, Yoann; Bouvry, Pascal

    2014-01-01

    Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking. Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms topology management, and mobility models to address challenges in the ...

  20. Quantum algorithms for testing Boolean functions

    Directory of Open Access Journals (Sweden)

    Erika Andersson

    2010-06-01

    Full Text Available We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.

  1. Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

    OpenAIRE

    Havemann, Frank; Heinz, Michael; Struck, Alexander; Gläser, Jochen

    2010-01-01

    We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This ana...

  2. Algorithm 426 : Merge sort algorithm [M1

    NARCIS (Netherlands)

    Bron, C.

    1972-01-01

    Sorting by means of a two-way merge has a reputation of requiring a clerically complicated and cumbersome program. This ALGOL 60 procedure demonstrates that, using recursion, an elegant and efficient algorithm can be designed, the correctness of which is easily proved [2]. Sorting n objects gives

  3. Genetic algorithms for protein threading.

    Science.gov (United States)

    Yadgari, J; Amir, A; Unger, R

    1998-01-01

    Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

  4. Composite Differential Search Algorithm

    Directory of Open Access Journals (Sweden)

    Bo Liu

    2014-01-01

    Full Text Available Differential search algorithm (DS is a relatively new evolutionary algorithm inspired by the Brownian-like random-walk movement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.

  5. Algorithms and Their Explanations

    NARCIS (Netherlands)

    Benini, M.; Gobbo, F.; Beckmann, A.; Csuhaj-Varjú, E.; Meer, K.

    2014-01-01

    By analysing the explanation of the classical heapsort algorithm via the method of levels of abstraction mainly due to Floridi, we give a concrete and precise example of how to deal with algorithmic knowledge. To do so, we introduce a concept already implicit in the method, the ‘gradient of

  6. Finite lattice extrapolation algorithms

    International Nuclear Information System (INIS)

    Henkel, M.; Schuetz, G.

    1987-08-01

    Two algorithms for sequence extrapolation, due to von den Broeck and Schwartz and Bulirsch and Stoer are reviewed and critically compared. Applications to three states and six states quantum chains and to the (2+1)D Ising model show that the algorithm of Bulirsch and Stoer is superior, in particular if only very few finite lattice data are available. (orig.)

  7. Recursive automatic classification algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Bauman, E V; Dorofeyuk, A A

    1982-03-01

    A variational statement of the automatic classification problem is given. The dependence of the form of the optimal partition surface on the form of the classification objective functional is investigated. A recursive algorithm is proposed for maximising a functional of reasonably general form. The convergence problem is analysed in connection with the proposed algorithm. 8 references.

  8. Graph Colouring Algorithms

    DEFF Research Database (Denmark)

    Husfeldt, Thore

    2015-01-01

    This chapter presents an introduction to graph colouring algorithms. The focus is on vertex-colouring algorithms that work for general classes of graphs with worst-case performance guarantees in a sequential model of computation. The presentation aims to demonstrate the breadth of available...

  9. 8. Algorithm Design Techniques

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 8. Algorithms - Algorithm Design Techniques. R K Shyamasundar. Series Article Volume 2 ... Author Affiliations. R K Shyamasundar1. Computer Science Group, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India ...

  10. Geometric approximation algorithms

    CERN Document Server

    Har-Peled, Sariel

    2011-01-01

    Exact algorithms for dealing with geometric objects are complicated, hard to implement in practice, and slow. Over the last 20 years a theory of geometric approximation algorithms has emerged. These algorithms tend to be simple, fast, and more robust than their exact counterparts. This book is the first to cover geometric approximation algorithms in detail. In addition, more traditional computational geometry techniques that are widely used in developing such algorithms, like sampling, linear programming, etc., are also surveyed. Other topics covered include approximate nearest-neighbor search, shape approximation, coresets, dimension reduction, and embeddings. The topics covered are relatively independent and are supplemented by exercises. Close to 200 color figures are included in the text to illustrate proofs and ideas.

  11. Group leaders optimization algorithm

    Science.gov (United States)

    Daskin, Anmer; Kais, Sabre

    2011-03-01

    We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.

  12. Fast geometric algorithms

    International Nuclear Information System (INIS)

    Noga, M.T.

    1984-01-01

    This thesis addresses a number of important problems that fall within the framework of the new discipline of Computational Geometry. The list of topics covered includes sorting and selection, convex hull algorithms, the L 1 hull, determination of the minimum encasing rectangle of a set of points, the Euclidean and L 1 diameter of a set of points, the metric traveling salesman problem, and finding the superrange of star-shaped and monotype polygons. The main theme of all the work was to develop a set of very fast state-of-the-art algorithms that supersede any rivals in terms of speed and ease of implementation. In some cases existing algorithms were refined; for others new techniques were developed that add to the present database of fast adaptive geometric algorithms. What emerges is a collection of techniques that is successful at merging modern tools developed in analysis of algorithms with those of classical geometry

  13. Totally parallel multilevel algorithms

    Science.gov (United States)

    Frederickson, Paul O.

    1988-01-01

    Four totally parallel algorithms for the solution of a sparse linear system have common characteristics which become quite apparent when they are implemented on a highly parallel hypercube such as the CM2. These four algorithms are Parallel Superconvergent Multigrid (PSMG) of Frederickson and McBryan, Robust Multigrid (RMG) of Hackbusch, the FFT based Spectral Algorithm, and Parallel Cyclic Reduction. In fact, all four can be formulated as particular cases of the same totally parallel multilevel algorithm, which are referred to as TPMA. In certain cases the spectral radius of TPMA is zero, and it is recognized to be a direct algorithm. In many other cases the spectral radius, although not zero, is small enough that a single iteration per timestep keeps the local error within the required tolerance.

  14. Governance by algorithms

    Directory of Open Access Journals (Sweden)

    Francesca Musiani

    2013-08-01

    Full Text Available Algorithms are increasingly often cited as one of the fundamental shaping devices of our daily, immersed-in-information existence. Their importance is acknowledged, their performance scrutinised in numerous contexts. Yet, a lot of what constitutes 'algorithms' beyond their broad definition as “encoded procedures for transforming input data into a desired output, based on specified calculations” (Gillespie, 2013 is often taken for granted. This article seeks to contribute to the discussion about 'what algorithms do' and in which ways they are artefacts of governance, providing two examples drawing from the internet and ICT realm: search engine queries and e-commerce websites’ recommendations to customers. The question of the relationship between algorithms and rules is likely to occupy an increasingly central role in the study and the practice of internet governance, in terms of both institutions’ regulation of algorithms, and algorithms’ regulation of our society.

  15. Where genetic algorithms excel.

    Science.gov (United States)

    Baum, E B; Boneh, D; Garrett, C

    2001-01-01

    We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve "implicit parallelism" in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.

  16. Network-Oblivious Algorithms

    DEFF Research Database (Denmark)

    Bilardi, Gianfranco; Pietracaprina, Andrea; Pucci, Geppino

    2016-01-01

    A framework is proposed for the design and analysis of network-oblivious algorithms, namely algorithms that can run unchanged, yet efficiently, on a variety of machines characterized by different degrees of parallelism and communication capabilities. The framework prescribes that a network......-oblivious algorithm be specified on a parallel model of computation where the only parameter is the problem’s input size, and then evaluated on a model with two parameters, capturing parallelism granularity and communication latency. It is shown that for a wide class of network-oblivious algorithms, optimality...... of cache hierarchies, to the realm of parallel computation. Its effectiveness is illustrated by providing optimal network-oblivious algorithms for a number of key problems. Some limitations of the oblivious approach are also discussed....

  17. Vectorised Spreading Activation algorithm for centrality measurement

    Directory of Open Access Journals (Sweden)

    Alexander Troussov

    2011-01-01

    Full Text Available Spreading Activation is a family of graph-based algorithms widely used in areas such as information retrieval, epidemic models, and recommender systems. In this paper we introduce a novel Spreading Activation (SA method that we call Vectorised Spreading Activation (VSA. VSA algorithms, like “traditional” SA algorithms, iteratively propagate the activation from the initially activated set of nodes to the other nodes in a network through outward links. The level of the node’s activation could be used as a centrality measurement in accordance with dynamic model-based view of centrality that focuses on the outcomes for nodes in a network where something is flowing from node to node across the edges. Representing the activation by vectors allows the use of the information about various dimensionalities of the flow and the dynamic of the flow. In this capacity, VSA algorithms can model multitude of complex multidimensional network flows. We present the results of numerical simulations on small synthetic social networks and multi­dimensional network models of folksonomies which show that the results of VSA propagation are more sensitive to the positions of the initial seed and to the community structure of the network than the results produced by traditional SA algorithms. We tentatively conclude that the VSA methods could be instrumental to develop scalable and computationally efficient algorithms which could achieve synergy between computation of centrality indexes with detection of community structures in networks. Based on our preliminary results and on improvements made over previous studies, we foresee advances and applications in the current state of the art of this family of algorithms and their applications to centrality measurement.

  18. Identifying individual fires from satellite-derived burned area data

    CSIR Research Space (South Africa)

    Archibald, S

    2009-07-01

    Full Text Available An algorithm for identifying individual fires from the Modis burned area data product is introduced for southern Africa. This algorithm gives the date of burning, size of fire, and location of the centroid for all fires identified over 8 years...

  19. Results of Evolution Supervised by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Lorentz JÄNTSCHI

    2010-09-01

    Full Text Available The efficiency of a genetic algorithm is frequently assessed using a series of operators of evolution like crossover operators, mutation operators or other dynamic parameters. The present paper aimed to review the main results of evolution supervised by genetic algorithms used to identify solutions to agricultural and horticultural hard problems and to discuss the results of using a genetic algorithms on structure-activity relationships in terms of behavior of evolution supervised by genetic algorithms. A genetic algorithm had been developed and implemented in order to identify the optimal solution in term of estimation power of a multiple linear regression approach for structure-activity relationships. Three survival and three selection strategies (proportional, deterministic and tournament were investigated in order to identify the best survival-selection strategy able to lead to the model with higher estimation power. The Molecular Descriptors Family for structure characterization of a sample of 206 polychlorinated biphenyls with measured octanol-water partition coefficients was used as case study. Evolution using different selection and survival strategies proved to create populations of genotypes living in the evolution space with different diversity and variability. Under a series of criteria of comparisons these populations proved to be grouped and the groups were showed to be statistically different one to each other. The conclusions about genetic algorithm evolution according to a number of criteria were also highlighted.

  20. Algorithms in Singular

    Directory of Open Access Journals (Sweden)

    Hans Schonemann

    1996-12-01

    Full Text Available Some algorithms for singularity theory and algebraic geometry The use of Grobner basis computations for treating systems of polynomial equations has become an important tool in many areas. This paper introduces of the concept of standard bases (a generalization of Grobner bases and the application to some problems from algebraic geometry. The examples are presented as SINGULAR commands. A general introduction to Grobner bases can be found in the textbook [CLO], an introduction to syzygies in [E] and [St1]. SINGULAR is a computer algebra system for computing information about singularities, for use in algebraic geometry. The basic algorithms in SINGULAR are several variants of a general standard basis algorithm for general monomial orderings (see [GG]. This includes wellorderings (Buchberger algorithm ([B1], [B2] and tangent cone orderings (Mora algorithm ([M1], [MPT] as special cases: It is able to work with non-homogeneous and homogeneous input and also to compute in the localization of the polynomial ring in 0. Recent versions include algorithms to factorize polynomials and a factorizing Grobner basis algorithm. For a complete description of SINGULAR see [Si].

  1. A New Modified Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Medha Gupta

    2016-07-01

    Full Text Available Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.

  2. Magnet sorting algorithms

    International Nuclear Information System (INIS)

    Dinev, D.

    1996-01-01

    Several new algorithms for sorting of dipole and/or quadrupole magnets in synchrotrons and storage rings are described. The algorithms make use of a combinatorial approach to the problem and belong to the class of random search algorithms. They use an appropriate metrization of the state space. The phase-space distortion (smear) is used as a goal function. Computational experiments for the case of the JINR-Dubna superconducting heavy ion synchrotron NUCLOTRON have shown a significant reduction of the phase-space distortion after the magnet sorting. (orig.)

  3. Scalable Static and Dynamic Community Detection Using Grappolo

    Energy Technology Data Exchange (ETDEWEB)

    Halappanavar, Mahantesh; Lu, Hao; Kalyanaraman, Anantharaman; Tumeo, Antonino

    2017-09-12

    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 detection 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.

  4. Algorithms for parallel computers

    International Nuclear Information System (INIS)

    Churchhouse, R.F.

    1985-01-01

    Until relatively recently almost all the algorithms for use on computers had been designed on the (usually unstated) assumption that they were to be run on single processor, serial machines. With the introduction of vector processors, array processors and interconnected systems of mainframes, minis and micros, however, various forms of parallelism have become available. The advantage of parallelism is that it offers increased overall processing speed but it also raises some fundamental questions, including: (i) which, if any, of the existing 'serial' algorithms can be adapted for use in the parallel mode. (ii) How close to optimal can such adapted algorithms be and, where relevant, what are the convergence criteria. (iii) How can we design new algorithms specifically for parallel systems. (iv) For multi-processor systems how can we handle the software aspects of the interprocessor communications. Aspects of these questions illustrated by examples are considered in these lectures. (orig.)

  5. Fluid structure coupling algorithm

    International Nuclear Information System (INIS)

    McMaster, W.H.; Gong, E.Y.; Landram, C.S.; Quinones, D.F.

    1980-01-01

    A fluid-structure-interaction algorithm has been developed and incorporated into the two-dimensional code PELE-IC. This code combines an Eulerian incompressible fluid algorithm with a Lagrangian finite element shell algorithm and incorporates the treatment of complex free surfaces. The fluid structure and coupling algorithms have been verified by the calculation of solved problems from the literature and from air and steam blowdown experiments. The code has been used to calculate loads and structural response from air blowdown and the oscillatory condensation of steam bubbles in water suppression pools typical of boiling water reactors. The techniques developed have been extended to three dimensions and implemented in the computer code PELE-3D

  6. Algorithmic phase diagrams

    Science.gov (United States)

    Hockney, Roger

    1987-01-01

    Algorithmic phase diagrams are a neat and compact representation of the results of comparing the execution time of several algorithms for the solution of the same problem. As an example, the recent results are shown of Gannon and Van Rosendale on the solution of multiple tridiagonal systems of equations in the form of such diagrams. The act of preparing these diagrams has revealed an unexpectedly complex relationship between the best algorithm and the number and size of the tridiagonal systems, which was not evident from the algebraic formulae in the original paper. Even so, for a particular computer, one diagram suffices to predict the best algorithm for all problems that are likely to be encountered the prediction being read directly from the diagram without complex calculation.

  7. Diagnostic Algorithm Benchmarking

    Science.gov (United States)

    Poll, Scott

    2011-01-01

    A poster for the NASA Aviation Safety Program Annual Technical Meeting. It describes empirical benchmarking on diagnostic algorithms using data from the ADAPT Electrical Power System testbed and a diagnostic software framework.

  8. Unsupervised learning algorithms

    CERN Document Server

    Aydin, Kemal

    2016-01-01

    This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...

  9. Sensor and ad-hoc networks theoretical and algorithmic aspects

    CERN Document Server

    Makki, S Kami; Pissinou, Niki; Makki, Shamila; Karimi, Masoumeh; Makki, Kia

    2008-01-01

    This book brings together leading researchers and developers in the field of wireless sensor networks to explain the special problems and challenges of the algorithmic aspects of sensor and ad-hoc networks. The book also fosters communication not only between the different sensor and ad-hoc communities, but also between those communities and the distributed systems and information systems communities. The topics addressed pertain to the sensors and mobile environment.

  10. Vector Network Coding Algorithms

    OpenAIRE

    Ebrahimi, Javad; Fragouli, Christina

    2010-01-01

    We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algori...

  11. Optimization algorithms and applications

    CERN Document Server

    Arora, Rajesh Kumar

    2015-01-01

    Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc

  12. 76 FR 11433 - Federal Transition To Secure Hash Algorithm (SHA)-256

    Science.gov (United States)

    2011-03-02

    ... ADMINISTRATION [FAR-N-2011-01; Docket No. 2011-0083; Sequence 1] Federal Transition To Secure Hash Algorithm (SHA... acquisition community to transition to Secure Hash Algorithm SHA-256. SHA-256 is a cryptographic hash function... persons attending. Please cite ``Federal Transition to Secure Hash Algorithm SHA-256'' in all...

  13. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    Genetic algorithms (GAs) are computational optimisation schemes with an ... The algorithms solve optimisation problems ..... Genetic Algorithms in Search, Optimisation and Machine. Learning, Addison-Wesley Publishing Company, Inc. 1989.

  14. Algorithmic Principles of Mathematical Programming

    NARCIS (Netherlands)

    Faigle, Ulrich; Kern, Walter; Still, Georg

    2002-01-01

    Algorithmic Principles of Mathematical Programming investigates the mathematical structures and principles underlying the design of efficient algorithms for optimization problems. Recent advances in algorithmic theory have shown that the traditionally separate areas of discrete optimization, linear

  15. RFID Location Algorithm

    Directory of Open Access Journals (Sweden)

    Wang Zi Min

    2016-01-01

    Full Text Available With the development of social services, people’s living standards improve further requirements, there is an urgent need for a way to adapt to the complex situation of the new positioning technology. In recent years, RFID technology have a wide range of applications in all aspects of life and production, such as logistics tracking, car alarm, security and other items. The use of RFID technology to locate, it is a new direction in the eyes of the various research institutions and scholars. RFID positioning technology system stability, the error is small and low-cost advantages of its location algorithm is the focus of this study.This article analyzes the layers of RFID technology targeting methods and algorithms. First, RFID common several basic methods are introduced; Secondly, higher accuracy to political network location method; Finally, LANDMARC algorithm will be described. Through this it can be seen that advanced and efficient algorithms play an important role in increasing RFID positioning accuracy aspects.Finally, the algorithm of RFID location technology are summarized, pointing out the deficiencies in the algorithm, and put forward a follow-up study of the requirements, the vision of a better future RFID positioning technology.

  16. Modified Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Surafel Luleseged Tilahun

    2012-01-01

    Full Text Available Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

  17. Bio-inspired algorithms applied to molecular docking simulations.

    Science.gov (United States)

    Heberlé, G; de Azevedo, W F

    2011-01-01

    Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

  18. Improved multivariate polynomial factoring algorithm

    International Nuclear Information System (INIS)

    Wang, P.S.

    1978-01-01

    A new algorithm for factoring multivariate polynomials over the integers based on an algorithm by Wang and Rothschild is described. The new algorithm has improved strategies for dealing with the known problems of the original algorithm, namely, the leading coefficient problem, the bad-zero problem and the occurrence of extraneous factors. It has an algorithm for correctly predetermining leading coefficients of the factors. A new and efficient p-adic algorithm named EEZ is described. Bascially it is a linearly convergent variable-by-variable parallel construction. The improved algorithm is generally faster and requires less store then the original algorithm. Machine examples with comparative timing are included

  19. Algorithms for optimizing drug therapy

    Directory of Open Access Journals (Sweden)

    Martin Lene

    2004-07-01

    Full Text Available Abstract Background Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. Methods One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface and JavaScript (program logic. Results Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs

  20. Anaphora Resolution Algorithm for Sanskrit

    Science.gov (United States)

    Pralayankar, Pravin; Devi, Sobha Lalitha

    This paper presents an algorithm, which identifies different types of pronominal and its antecedents in Sanskrit, an Indo-European language. The computational grammar implemented here uses very familiar concepts such as clause, subject, object etc., which are identified with the help of morphological information and concepts such as precede and follow. It is well known that natural languages contain anaphoric expressions, gaps and elliptical constructions of various kinds and that understanding of natural languages involves assignment of interpretations to these elements. Therefore, it is only to be expected that natural language understanding systems must have the necessary mechanism to resolve the same. The method we adopt here for resolving the anaphors is by exploiting the morphological richness of the language. The system is giving encouraging results when tested with a small corpus.

  1. A Parallel Butterfly Algorithm

    KAUST Repository

    Poulson, Jack; Demanet, Laurent; Maxwell, Nicholas; Ying, Lexing

    2014-01-01

    The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

  2. A Parallel Butterfly Algorithm

    KAUST Repository

    Poulson, Jack

    2014-02-04

    The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

  3. Agency and Algorithms

    Directory of Open Access Journals (Sweden)

    Hanns Holger Rutz

    2016-11-01

    Full Text Available Although the concept of algorithms has been established a long time ago, their current topicality indicates a shift in the discourse. Classical definitions based on logic seem to be inadequate to describe their aesthetic capabilities. New approaches stress their involvement in material practices as well as their incompleteness. Algorithmic aesthetics can no longer be tied to the static analysis of programs, but must take into account the dynamic and experimental nature of coding practices. It is suggested that the aesthetic objects thus produced articulate something that could be called algorithmicity or the space of algorithmic agency. This is the space or the medium – following Luhmann’s form/medium distinction – where human and machine undergo mutual incursions. In the resulting coupled “extimate” writing process, human initiative and algorithmic speculation cannot be clearly divided out any longer. An observation is attempted of defining aspects of such a medium by drawing a trajectory across a number of sound pieces. The operation of exchange between form and medium I call reconfiguration and it is indicated by this trajectory. 

  4. Algebraic dynamics algorithm: Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG ShunJin; ZHANG Hua

    2007-01-01

    Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

  5. Algebraic dynamics algorithm:Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

  6. Evidence-based algorithm for heparin dosing before cardiopulmonary bypass. Part 1: Development of the algorithm.

    Science.gov (United States)

    McKinney, Mark C; Riley, Jeffrey B

    2007-12-01

    The incidence of heparin resistance during adult cardiac surgery with cardiopulmonary bypass has been reported at 15%-20%. The consistent use of a clinical decision-making algorithm may increase the consistency of patient care and likely reduce the total required heparin dose and other problems associated with heparin dosing. After a directed survey of practicing perfusionists regarding treatment of heparin resistance and a literature search for high-level evidence regarding the diagnosis and treatment of heparin resistance, an evidence-based decision-making algorithm was constructed. The face validity of the algorithm decisive steps and logic was confirmed by a second survey of practicing perfusionists. The algorithm begins with review of the patient history to identify predictors for heparin resistance. The definition for heparin resistance contained in the algorithm is an activated clotting time 450 IU/kg heparin loading dose. Based on the literature, the treatment for heparin resistance used in the algorithm is anti-thrombin III supplement. The algorithm seems to be valid and is supported by high-level evidence and clinician opinion. The next step is a human randomized clinical trial to test the clinical procedure guideline algorithm vs. current standard clinical practice.

  7. Handbook of Memetic Algorithms

    CERN Document Server

    Cotta, Carlos; Moscato, Pablo

    2012-01-01

    Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems.  The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes.   “Handbook of Memetic Algorithms” organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now.  A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem,  memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, ...

  8. Algorithms in invariant theory

    CERN Document Server

    Sturmfels, Bernd

    2008-01-01

    J. Kung and G.-C. Rota, in their 1984 paper, write: "Like the Arabian phoenix rising out of its ashes, the theory of invariants, pronounced dead at the turn of the century, is once again at the forefront of mathematics". The book of Sturmfels is both an easy-to-read textbook for invariant theory and a challenging research monograph that introduces a new approach to the algorithmic side of invariant theory. The Groebner bases method is the main tool by which the central problems in invariant theory become amenable to algorithmic solutions. Students will find the book an easy introduction to this "classical and new" area of mathematics. Researchers in mathematics, symbolic computation, and computer science will get access to a wealth of research ideas, hints for applications, outlines and details of algorithms, worked out examples, and research problems.

  9. The Retina Algorithm

    CERN Multimedia

    CERN. Geneva; PUNZI, Giovanni

    2015-01-01

    Charge particle reconstruction is one of the most demanding computational tasks found in HEP, and it becomes increasingly important to perform it in real time. We envision that HEP would greatly benefit from achieving a long-term goal of making track reconstruction happen transparently as part of the detector readout ("detector-embedded tracking"). We describe here a track-reconstruction approach based on a massively parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature ('RETINA algorithm'). It turns out that high-quality tracking in large HEP detectors is possible with very small latencies, when this algorithm is implemented in specialized processors, based on current state-of-the-art, high-speed/high-bandwidth digital devices.

  10. Online Community Transition Detection

    DEFF Research Database (Denmark)

    Tan, Biying; Zhu, Feida; Qu, Qiang

    2014-01-01

    communities over time. How to automatically detect the online community transitions of individual users is a research problem of immense practical value yet with great technical challenges. In this paper, we propose an algorithm based on the Minimum Description Length (MDL) principle to trace the evolution......Mining user behavior patterns in social networks is of great importance in user behavior analysis, targeted marketing, churn prediction and other applications. However, less effort has been made to study the evolution of user behavior in social communities. In particular, users join and leave...... of community transition of individual users, adaptive to the noisy behavior. Experiments on real data sets demonstrate the efficiency and effectiveness of our proposed method....

  11. Law and Order in Algorithmics

    NARCIS (Netherlands)

    Fokkinga, M.M.

    1992-01-01

    An algorithm is the input-output effect of a computer program; mathematically, the notion of algorithm comes close to the notion of function. Just as arithmetic is the theory and practice of calculating with numbers, so is ALGORITHMICS the theory and practice of calculating with algorithms. Just as

  12. A cluster algorithm for graphs

    NARCIS (Netherlands)

    S. van Dongen

    2000-01-01

    textabstractA cluster algorithm for graphs called the emph{Markov Cluster algorithm (MCL~algorithm) is introduced. The algorithm provides basically an interface to an algebraic process defined on stochastic matrices, called the MCL~process. The graphs may be both weighted (with nonnegative weight)

  13. Algorithms for Reinforcement Learning

    CERN Document Server

    Szepesvari, Csaba

    2010-01-01

    Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

  14. Animation of planning algorithms

    OpenAIRE

    Sun, Fan

    2014-01-01

    Planning is the process of creating a sequence of steps/actions that will satisfy a goal of a problem. The partial order planning (POP) algorithm is one of Artificial Intelligence approach for problem planning. By learning G52PAS module, I find that it is difficult for students to understand this planning algorithm by just reading its pseudo code and doing some exercise in writing. Students cannot know how each actual step works clearly and might miss some steps because of their confusion. ...

  15. Secondary Vertex Finder Algorithm

    CERN Document Server

    Heer, Sebastian; The ATLAS collaboration

    2017-01-01

    If a jet originates from a b-quark, a b-hadron is formed during the fragmentation process. In its dominant decay modes, the b-hadron decays into a c-hadron via the electroweak interaction. Both b- and c-hadrons have lifetimes long enough, to travel a few millimetres before decaying. Thus displaced vertices from b- and subsequent c-hadron decays provide a strong signature for a b-jet. Reconstructing these secondary vertices (SV) and their properties is the aim of this algorithm. The performance of this algorithm is studied with tt̄ events, requiring at least one lepton, simulated at 13 TeV.

  16. Parallel Algorithms and Patterns

    Energy Technology Data Exchange (ETDEWEB)

    Robey, Robert W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-06-16

    This is a powerpoint presentation on parallel algorithms and patterns. A parallel algorithm is a well-defined, step-by-step computational procedure that emphasizes concurrency to solve a problem. Examples of problems include: Sorting, searching, optimization, matrix operations. A parallel pattern is a computational step in a sequence of independent, potentially concurrent operations that occurs in diverse scenarios with some frequency. Examples are: Reductions, prefix scans, ghost cell updates. We only touch on parallel patterns in this presentation. It really deserves its own detailed discussion which Gabe Rockefeller would like to develop.

  17. Randomized Filtering Algorithms

    DEFF Research Database (Denmark)

    Katriel, Irit; Van Hentenryck, Pascal

    2008-01-01

    of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...

  18. An Ordering Linear Unification Algorithm

    Institute of Scientific and Technical Information of China (English)

    胡运发

    1989-01-01

    In this paper,we present an ordering linear unification algorithm(OLU).A new idea on substituteion of the binding terms is introduced to the algorithm,which is able to overcome some drawbacks of other algorithms,e.g.,MM algorithm[1],RG1 and RG2 algorithms[2],Particularly,if we use the directed eyclie graphs,the algoritm needs not check the binding order,then the OLU algorithm can also be aplied to the infinite tree data struceture,and a higher efficiency can be expected.The paper focuses upon the discussion of OLU algorithm and a partial order structure with respect to the unification algorithm.This algorithm has been implemented in the GKD-PROLOG/VAX 780 interpreting system.Experimental results have shown that the algorithm is very simple and efficient.

  19. A Record Linkage Protocol for a Diabetes Registry at Ethnically Diverse Community Health Centers

    OpenAIRE

    Maizlish, Neil A.; Herrera, Linda

    2005-01-01

    Community health centers serve ethnically diverse populations that may pose challenges for record linkage based on name and date of birth. The objective was to identify an optimal deterministic algorithm to link patient encounters and laboratory results for hemoglobin A1c testing and examine its variability by health center site, patient ethnicity, and other variables. Based on data elements of last name, first name, date of birth, gender, and health center site, matches with ≥50% to < 100% o...

  20. The health informatics cohort enhancement project (HICE: using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder

    Directory of Open Access Journals (Sweden)

    Economou Alexis

    2012-02-01

    Full Text Available Abstract Background We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered. Methods We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT. Case notes of 100 patients from a community mental health team (CMHT in Swansea were studied of whom 80 had matched GP records. Results The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7 and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9. Conclusions With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies.