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Sample records for multiresolution community detection

  1. Adaptive multi-resolution Modularity for detecting communities in networks

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  4. A Biologically Motivated Multiresolution Approach to Contour Detection

    Directory of Open Access Journals (Sweden)

    Alessandro Neri

    2007-01-01

    Full Text Available Standard edge detectors react to all local luminance changes, irrespective of whether they are due to the contours of the objects represented in a scene or due to natural textures like grass, foliage, water, and so forth. Moreover, edges due to texture are often stronger than edges due to object contours. This implies that further processing is needed to discriminate object contours from texture edges. In this paper, we propose a biologically motivated multiresolution contour detection method using Bayesian denoising and a surround inhibition technique. Specifically, the proposed approach deploys computation of the gradient at different resolutions, followed by Bayesian denoising of the edge image. Then, a biologically motivated surround inhibition step is applied in order to suppress edges that are due to texture. We propose an improvement of the surround suppression used in previous works. Finally, a contour-oriented binarization algorithm is used, relying on the observation that object contours lead to long connected components rather than to short rods obtained from textures. Experimental results show that our contour detection method outperforms standard edge detectors as well as other methods that deploy inhibition.

  5. Accuracy assessment of tree crown detection using local maxima and multi-resolution segmentation

    International Nuclear Information System (INIS)

    Khalid, N; Hamid, J R A; Latif, Z A

    2014-01-01

    Diversity of trees forms an important component in the forest ecosystems and needs proper inventories to assist the forest personnel in their daily activities. However, tree parameter measurements are often constrained by physical inaccessibility to site locations, high costs, and time. With the advancement in remote sensing technology, such as the provision of higher spatial and spectral resolution of imagery, a number of developed algorithms fulfil the needs of accurate tree inventories information in a cost effective and timely manner over larger forest areas. This study intends to generate tree distribution map in Ampang Forest Reserve using the Local Maxima and Multi-Resolution image segmentation algorithm. The utilization of recent worldview-2 imagery with Local Maxima and Multi-Resolution image segmentation proves to be capable of detecting and delineating the tree crown in its accurate standing position

  6. Real-time Multiresolution Crosswalk Detection with Walk Light Recognition for the Blind

    Directory of Open Access Journals (Sweden)

    ROMIC, K.

    2018-02-01

    Full Text Available Real-time image processing and object detection techniques have a great potential to be applied in digital assistive tools for the blind and visually impaired persons. In this paper, algorithm for crosswalk detection and walk light recognition is proposed with the main aim to help blind person when crossing the road. The proposed algorithm is optimized to work in real-time on portable devices using standard cameras. Images captured by camera are processed while person is moving and decision about detected crosswalk is provided as an output along with the information about walk light if one is present. Crosswalk detection method is based on multiresolution morphological image processing, while the walk light recognition is performed by proposed 6-stage algorithm. The main contributions of this paper are accurate crosswalk detection with small processing time due to multiresolution processing and the recognition of the walk lights covering only small amount of pixels in image. The experiment is conducted using images from video sequences captured in realistic situations on crossings. The results show 98.3% correct crosswalk detections and 89.5% correct walk lights recognition with average processing speed of about 16 frames per second.

  7. Detection of pulmonary nodules on lung X-ray images. Studies on multi-resolutional filter and energy subtraction images

    International Nuclear Information System (INIS)

    Sawada, Akira; Sato, Yoshinobu; Kido, Shoji; Tamura, Shinichi

    1999-01-01

    The purpose of this work is to prove the effectiveness of an energy subtraction image for the detection of pulmonary nodules and the effectiveness of multi-resolutional filter on an energy subtraction image to detect pulmonary nodules. Also we study influential factors to the accuracy of detection of pulmonary nodules from viewpoints of types of images, types of digital filters and types of evaluation methods. As one type of images, we select an energy subtraction image, which removes bones such as ribs from the conventional X-ray image by utilizing the difference of X-ray absorption ratios at different energy between bones and soft tissue. Ribs and vessels are major causes of CAD errors in detection of pulmonary nodules and many researches have tried to solve this problem. So we select conventional X-ray images and energy subtraction X-ray images as types of images, and at the same time select ∇ 2 G (Laplacian of Guassian) filter, Min-DD (Minimum Directional Difference) filter and our multi-resolutional filter as types of digital filters. Also we select two evaluation methods and prove the effectiveness of an energy subtraction image, the effectiveness of Min-DD filter on a conventional X-ray image and the effectiveness of multi-resolutional filter on an energy subtraction image. (author)

  8. Automatic multiresolution age-related macular degeneration detection from fundus images

    Science.gov (United States)

    Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

    2014-03-01

    Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

  9. 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.

  10. 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.

  11. Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

    Energy Technology Data Exchange (ETDEWEB)

    Tsantis, Stavros [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Spiliopoulos, Stavros; Karnabatidis, Dimitrios [Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Skouroliakou, Aikaterini [Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210 (Greece); Hazle, John D. [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Kagadis, George C., E-mail: gkagad@gmail.com, E-mail: George.Kagadis@med.upatras.gr, E-mail: GKagadis@mdanderson.org [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)

    2014-07-15

    Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

  12. Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

    International Nuclear Information System (INIS)

    Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios; Skouroliakou, Aikaterini; Hazle, John D.; Kagadis, George C.

    2014-01-01

    Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

  13. Multi-resolution anisotropy studies of ultrahigh-energy cosmic rays detected at the Pierre Auger Observatory

    Energy Technology Data Exchange (ETDEWEB)

    Aab, A.; Abreu, P.; Aglietta, M.; Samarai, I. Al; Albuquerque, I. F. M.; Allekotte, I.; Almela, A.; Castillo, J. Alvarez; Alvarez-Muñiz, J.; Anastasi, G. A.; Anchordoqui, L.; Andrada, B.; Andringa, S.; Aramo, C.; Arqueros, F.; Arsene, N.; Asorey, H.; Assis, P.; Aublin, J.; Avila, G.; Badescu, A. M.; Balaceanu, A.; Luz, R. J. Barreira; Baus, C.; Beatty, J. J.; Becker, K. H.; Bellido, J. A.; Berat, C.; Bertaina, M. E.; Bertou, X.; Biermann, P. L.; Billoir, P.; Biteau, J.; Blaess, S. G.; Blanco, A.; Blazek, J.; Bleve, C.; Boháčová, M.; Boncioli, D.; Bonifazi, C.; Borodai, N.; Botti, A. M.; Brack, J.; Brancus, I.; Bretz, T.; Bridgeman, A.; Briechle, F. L.; Buchholz, P.; Bueno, A.; Buitink, S.; Buscemi, M.; Caballero-Mora, K. S.; Caccianiga, L.; Cancio, A.; Canfora, F.; Caramete, L.; Caruso, R.; Castellina, A.; Cataldi, G.; Cazon, L.; Chavez, A. G.; Chinellato, J. A.; Chudoba, J.; Clay, R. W.; Colalillo, R.; Coleman, A.; Collica, L.; Coluccia, M. R.; Conceição, R.; Contreras, F.; Cooper, M. J.; Coutu, S.; Covault, C. E.; Cronin, J.; D' Amico, S.; Daniel, B.; Dasso, S.; Daumiller, K.; Dawson, B. R.; de Almeida, R. M.; de Jong, S. J.; Mauro, G. De; Neto, J. R. T. de Mello; Mitri, I. De; de Oliveira, J.; de Souza, V.; Debatin, J.; Deligny, O.; Giulio, C. Di; Matteo, A. Di; Castro, M. L. Díaz; Diogo, F.; Dobrigkeit, C.; D' Olivo, J. C.; Anjos, R. C. dos; Dova, M. T.; Dundovic, A.; Ebr, J.; Engel, R.; Erdmann, M.; Erfani, M.; Escobar, C. O.; Espadanal, J.; Etchegoyen, A.; Falcke, H.; Farrar, G.; Fauth, A. C.; Fazzini, N.; Fick, B.; Figueira, J. M.; Filipčič, A.; Fratu, O.; Freire, M. M.; Fujii, T.; Fuster, A.; Gaior, R.; García, B.; Garcia-Pinto, D.; Gaté, F.; Gemmeke, H.; Gherghel-Lascu, A.; Ghia, P. L.; Giaccari, U.; Giammarchi, M.; Giller, M.; Głas, D.; Glaser, C.; Golup, G.; Berisso, M. Gómez; Vitale, P. F. Gómez; González, N.; Gorgi, A.; Gorham, P.; Gouffon, P.; Grillo, A. F.; Grubb, T. D.; Guarino, F.; Guedes, G. P.; Hampel, M. R.; Hansen, P.; Harari, D.; Harrison, T. A.; Harton, J. L.; Hasankiadeh, Q.; Haungs, A.; Hebbeker, T.; Heck, D.; Heimann, P.; Herve, A. E.; Hill, G. C.; Hojvat, C.; Holt, E.; Homola, P.; Hörandel, J. R.; Horvath, P.; Hrabovský, M.; Huege, T.; Hulsman, J.; Insolia, A.; Isar, P. G.; Jandt, I.; Jansen, S.; Johnsen, J. A.; Josebachuili, M.; Kääpä, A.; Kambeitz, O.; Kampert, K. H.; Katkov, I.; Keilhauer, B.; Kemp, E.; Kemp, J.; Kieckhafer, R. M.; Klages, H. O.; Kleifges, M.; Kleinfeller, J.; Krause, R.; Krohm, N.; Kuempel, D.; Mezek, G. Kukec; Kunka, N.; Awad, A. Kuotb; LaHurd, D.; Lauscher, M.; Legumina, R.; de Oliveira, M. A. Leigui; Letessier-Selvon, A.; Lhenry-Yvon, I.; Link, K.; Lopes, L.; López, R.; Casado, A. López; Luce, Q.; Lucero, A.; Malacari, M.; Mallamaci, M.; Mandat, D.; Mantsch, P.; Mariazzi, A. G.; Mariş, I. C.; Marsella, G.; Martello, D.; Martinez, H.; Bravo, O. Martínez; Meza, J. J. Masías; Mathes, H. J.; Mathys, S.; Matthews, J.; Matthews, J. A. J.; Matthiae, G.; Mayotte, E.; Mazur, P. O.; Medina, C.; Medina-Tanco, G.; Melo, D.; Menshikov, A.; Messina, S.; Micheletti, M. I.; Middendorf, L.; Minaya, I. A.; Miramonti, L.; Mitrica, B.; Mockler, D.; Mollerach, S.; Montanet, F.; Morello, C.; Mostafá, M.; Müller, A. L.; Müller, G.; Muller, M. A.; Müller, S.; Mussa, R.; Naranjo, I.; Nellen, L.; Nguyen, P. H.; Niculescu-Oglinzanu, M.; Niechciol, M.; Niemietz, L.; Niggemann, T.; Nitz, D.; Nosek, D.; Novotny, V.; Nožka, H.; Núñez, L. A.; Ochilo, L.; Oikonomou, F.; Olinto, A.; Selmi-Dei, D. Pakk; Palatka, M.; Pallotta, J.; Papenbreer, P.; Parente, G.; Parra, A.; Paul, T.; Pech, M.; Pedreira, F.; Pȩkala, J.; Pelayo, R.; Peña-Rodriguez, J.; Pereira, L. A. S.; Perlín, M.; Perrone, L.; Peters, C.; Petrera, S.; Phuntsok, J.; Piegaia, R.; Pierog, T.; Pieroni, P.; Pimenta, M.; Pirronello, V.; Platino, M.; Plum, M.; Porowski, C.; Prado, R. R.; Privitera, P.; Prouza, M.; Quel, E. J.; Querchfeld, S.; Quinn, S.; Ramos-Pollan, R.; Rautenberg, J.; Ravignani, D.; Revenu, B.; Ridky, J.; Risse, M.; Ristori, P.; Rizi, V.; de Carvalho, W. Rodrigues; Fernandez, G. Rodriguez; Rojo, J. Rodriguez; Rogozin, D.; Roncoroni, M. J.; Roth, M.; Roulet, E.; Rovero, A. C.; Ruehl, P.; Saffi, S. J.; Saftoiu, A.; Salazar, H.; Saleh, A.; Greus, F. Salesa; Salina, G.; Sánchez, F.; Sanchez-Lucas, P.; Santos, E. M.; Santos, E.; Sarazin, F.; Sarmento, R.; Sarmiento, C. A.; Sato, R.; Schauer, M.; Scherini, V.; Schieler, H.; Schimp, M.; Schmidt, D.; Scholten, O.; Schovánek, P.; Schröder, F. G.; Schulz, A.; Schulz, J.; Schumacher, J.; Sciutto, S. J.; Segreto, A.; Settimo, M.; Shadkam, A.; Shellard, R. C.; Sigl, G.; Silli, G.; Sima, O.; Śmiałkowski, A.; Šmída, R.; Snow, G. R.; Sommers, P.; Sonntag, S.; Sorokin, J.; Squartini, R.; Stanca, D.; Stanič, S.; Stasielak, J.; Stassi, P.; Strafella, F.; Suarez, F.; Durán, M. Suarez; Sudholz, T.; Suomijärvi, T.; Supanitsky, A. D.; Swain, J.; Szadkowski, Z.; Taboada, A.; Taborda, O. A.; Tapia, A.; Theodoro, V. M.; Timmermans, C.; Peixoto, C. J. Todero; Tomankova, L.; Tomé, B.; Elipe, G. Torralba; Torri, M.; Travnicek, P.; Trini, M.; Ulrich, R.; Unger, M.; Urban, M.; Galicia, J. F. Valdés; Valiño, I.; Valore, L.; Aar, G. van; Bodegom, P. van; Berg, A. M. van den; Vliet, A. van; Varela, E.; Cárdenas, B. Vargas; Varner, G.; Vázquez, J. R.; Vázquez, R. A.; Veberič, D.; Quispe, I. D. Vergara; Verzi, V.; Vicha, J.; Villaseñor, L.; Vorobiov, S.; Wahlberg, H.; Wainberg, O.; Walz, D.; Watson, A. A.; Weber, M.; Weindl, A.; Wiencke, L.; Wilczyński, H.; Winchen, T.; Wittkowski, D.; Wundheiler, B.; Yang, L.; Yelos, D.; Yushkov, A.; Zas, E.; Zavrtanik, D.; Zavrtanik, M.; Zepeda, A.; Zimmermann, B.; Ziolkowski, M.; Zong, Z.; Zuccarello, F.

    2017-06-01

    We report a multi-resolution search for anisotropies in the arrival directions of cosmic rays detected at the Pierre Auger Observatory with local zenith angles up to 80(o) and energies in excess of 4 EeV (4 × 1018 eV). This search is conducted by measuring the angular power spectrum and performing a needlet wavelet analysis in two independent energy ranges. Both analyses are complementary since the angular power spectrum achieves a better performance in identifying large-scale patterns while the needlet wavelet analysis, considering the parameters used in this work, presents a higher efficiency in detecting smaller-scale anisotropies, potentially providing directional information on any observed anisotropies. No deviation from isotropy is observed on any angular scale in the energy range between 4 and 8 EeV. Above 8 EeV, an indication for a dipole moment is captured, while no other deviation from isotropy is observed for moments beyond the dipole one. The corresponding p-values obtained after accounting for searches blindly performed at several angular scales, are 1.3 × 10-5 in the case of the angular power spectrum, and 2.5 × 10-3 in the case of the needlet analysis. While these results are consistent with previous reports making use of the same data set, they provide extensions of the previous works through the thorough scans of the angular scales.

  14. Multi-resolution anisotropy studies of ultrahigh-energy cosmic rays detected at the Pierre Auger Observatory

    Energy Technology Data Exchange (ETDEWEB)

    Aab, A. [Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud Universiteit, Nijmegen (Netherlands); Abreu, P.; Andringa, S. [Laboratório de Instrumentação e Física Experimental de Partículas—LIP and Instituto Superior Técnico—IST, Universidade de Lisboa—UL (Portugal); Aglietta, M. [Osservatorio Astrofisico di Torino (INAF), Torino (Italy); Samarai, I. Al [Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), Universités Paris 6 et Paris 7, CNRS-IN2P3 (France); Albuquerque, I.F.M. [Universidade de São Paulo, Inst. de Física, São Paulo (Brazil); Allekotte, I. [Centro Atómico Bariloche and Instituto Balseiro (CNEA-UNCuyo-CONICET) (Argentina); Almela, A.; Andrada, B. [Instituto de Tecnologías en Detección y Astropartículas (CNEA, CONICET, UNSAM), Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica (Argentina); Castillo, J. Alvarez [Universidad Nacional Autónoma de México, México (Mexico); Alvarez-Muñiz, J. [Universidad de Santiago de Compostela (Spain); Anastasi, G.A. [Gran Sasso Science Institute (INFN), L' Aquila (Italy); Anchordoqui, L., E-mail: auger_spokespersons@fnal.gov [Department of Physics and Astronomy, Lehman College, City University of New York (United States); and others

    2017-06-01

    We report a multi-resolution search for anisotropies in the arrival directions of cosmic rays detected at the Pierre Auger Observatory with local zenith angles up to 80{sup o} and energies in excess of 4 EeV (4 × 10{sup 18} eV). This search is conducted by measuring the angular power spectrum and performing a needlet wavelet analysis in two independent energy ranges. Both analyses are complementary since the angular power spectrum achieves a better performance in identifying large-scale patterns while the needlet wavelet analysis, considering the parameters used in this work, presents a higher efficiency in detecting smaller-scale anisotropies, potentially providing directional information on any observed anisotropies. No deviation from isotropy is observed on any angular scale in the energy range between 4 and 8 EeV. Above 8 EeV, an indication for a dipole moment is captured; while no other deviation from isotropy is observed for moments beyond the dipole one. The corresponding p -values obtained after accounting for searches blindly performed at several angular scales, are 1.3 × 10{sup −5} in the case of the angular power spectrum, and 2.5 × 10{sup −3} in the case of the needlet analysis. While these results are consistent with previous reports making use of the same data set, they provide extensions of the previous works through the thorough scans of the angular scales.

  15. Community detection for fluorescent lifetime microscopy image segmentation

    Science.gov (United States)

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

    2014-03-01

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

  16. Bayesian community detection

    DEFF Research Database (Denmark)

    Mørup, Morten; Schmidt, Mikkel N

    2012-01-01

    Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....

  17. On frame multiresolution analysis

    DEFF Research Database (Denmark)

    Christensen, Ole

    2003-01-01

    We use the freedom in frame multiresolution analysis to construct tight wavelet frames (even in the case where the refinable function does not generate a tight frame). In cases where a frame multiresolution does not lead to a construction of a wavelet frame we show how one can nevertheless...

  18. 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....

  19. Multiresolution signal decomposition schemes

    NARCIS (Netherlands)

    J. Goutsias (John); H.J.A.M. Heijmans (Henk)

    1998-01-01

    textabstract[PNA-R9810] Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This report proposes a general axiomatic pyramid decomposition scheme for signal analysis

  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. An introductory analysis of digital infrared thermal imaging guided oral cancer detection using multiresolution rotation invariant texture features

    Science.gov (United States)

    Chakraborty, M.; Das Gupta, R.; Mukhopadhyay, S.; Anjum, N.; Patsa, S.; Ray, J. G.

    2017-03-01

    This manuscript presents an analytical treatment on the feasibility of multi-scale Gabor filter bank response for non-invasive oral cancer pre-screening and detection in the long infrared spectrum. Incapability of present healthcare technology to detect oral cancer in budding stage manifests in high mortality rate. The paper contributes a step towards automation in non-invasive computer-aided oral cancer detection using an amalgamation of image processing and machine intelligence paradigms. Previous works have shown the discriminative difference of facial temperature distribution between a normal subject and a patient. The proposed work, for the first time, exploits this difference further by representing the facial Region of Interest(ROI) using multiscale rotation invariant Gabor filter bank responses followed by classification using Radial Basis Function(RBF) kernelized Support Vector Machine(SVM). The proposed study reveals an initial increase in classification accuracy with incrementing image scales followed by degradation of performance; an indication that addition of more and more finer scales tend to embed noisy information instead of discriminative texture patterns. Moreover, the performance is consistently better for filter responses from profile faces compared to frontal faces.This is primarily attributed to the ineptness of Gabor kernels to analyze low spatial frequency components over a small facial surface area. On our dataset comprising of 81 malignant, 59 pre-cancerous, and 63 normal subjects, we achieve state-of-the-art accuracy of 85.16% for normal v/s precancerous and 84.72% for normal v/s malignant classification. This sets a benchmark for further investigation of multiscale feature extraction paradigms in IR spectrum for oral cancer detection.

  2. Signal and image multiresolution analysis

    CERN Document Server

    Ouahabi, Abdelialil

    2012-01-01

    Multiresolution analysis using the wavelet transform has received considerable attention in recent years by researchers in various fields. It is a powerful tool for efficiently representing signals and images at multiple levels of detail with many inherent advantages, including compression, level-of-detail display, progressive transmission, level-of-detail editing, filtering, modeling, fractals and multifractals, etc.This book aims to provide a simple formalization and new clarity on multiresolution analysis, rendering accessible obscure techniques, and merging, unifying or completing

  3. 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.

  4. Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

    Directory of Open Access Journals (Sweden)

    Peng Shao

    2014-08-01

    Full Text Available The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

  5. Multiresolution analysis applied to text-independent phone segmentation

    International Nuclear Information System (INIS)

    Cherniz, AnalIa S; Torres, MarIa E; Rufiner, Hugo L; Esposito, Anna

    2007-01-01

    Automatic speech segmentation is of fundamental importance in different speech applications. The most common implementations are based on hidden Markov models. They use a statistical modelling of the phonetic units to align the data along a known transcription. This is an expensive and time-consuming process, because of the huge amount of data needed to train the system. Text-independent speech segmentation procedures have been developed to overcome some of these problems. These methods detect transitions in the evolution of the time-varying features that represent the speech signal. Speech representation plays a central role is the segmentation task. In this work, two new speech parameterizations based on the continuous multiresolution entropy, using Shannon entropy, and the continuous multiresolution divergence, using Kullback-Leibler distance, are proposed. These approaches have been compared with the classical Melbank parameterization. The proposed encodings increase significantly the segmentation performance. Parameterization based on the continuous multiresolution divergence shows the best results, increasing the number of correctly detected boundaries and decreasing the amount of erroneously inserted points. This suggests that the parameterization based on multiresolution information measures provide information related to acoustic features that take into account phonemic transitions

  6. 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)

  7. Community Detection for Large Graphs

    KAUST Repository

    Peng, Chengbin; Kolda, Tamara G.; Pinar, Ali; Zhang, Zhihua; Keyes, David E.

    2014-01-01

    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

  8. 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.

  9. Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

    KAUST Repository

    Sicat, Ronell Barrera; Kruger, Jens; Moller, Torsten; Hadwiger, Markus

    2014-01-01

    This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined

  10. A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ model v4.7 wet deposition estimates for 2002–2006

    Directory of Open Access Journals (Sweden)

    K. W. Appel

    2011-05-01

    Full Text Available This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ model simulations for 2002–2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO4=, ammonium (NH4+ and nitrate (NO3. Performance of the wet deposition estimates from the model is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP, specifically the National Trends Network (NTN. For SO4= wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO4= wet deposition than the 36-km simulation on average. The result is a slightly larger normalized mean bias (NMB for the 12-km simulation; however both simulations had annual biases that were less than ±15 % for each of the five years. The model estimated SO4= wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH4+ wet deposition over the eastern US, with a slightly larger underestimation in the 36-km simulation. The largest underestimations occur in the winter and spring periods, while the summer and fall have slightly smaller underestimations of NH4+ wet deposition. The underestimation in NH4+ wet deposition is likely due in part to the poor temporal and spatial representation of ammonia (NH3 emissions, particularly those emissions associated with fertilizer applications and NH3 bi-directional exchange. The model performance for estimates of NO3 wet deposition are

  11. Multiresolution Analysis Adapted to Irregularly Spaced Data

    Directory of Open Access Journals (Sweden)

    Anissa Mokraoui

    2009-01-01

    Full Text Available This paper investigates the mathematical background of multiresolution analysis in the specific context where the signal is represented by irregularly sampled data at known locations. The study is related to the construction of nested piecewise polynomial multiresolution spaces represented by their corresponding orthonormal bases. Using simple spline basis orthonormalization procedures involves the construction of a large family of orthonormal spline scaling bases defined on consecutive bounded intervals. However, if no more additional conditions than those coming from multiresolution are imposed on each bounded interval, the orthonormal basis is represented by a set of discontinuous scaling functions. The spline wavelet basis also has the same problem. Moreover, the dimension of the corresponding wavelet basis increases with the spline degree. An appropriate orthonormalization procedure of the basic spline space basis, whatever the degree of the spline, allows us to (i provide continuous scaling and wavelet functions, (ii reduce the number of wavelets to only one, and (iii reduce the complexity of the filter bank. Examples of the multiresolution implementations illustrate that the main important features of the traditional multiresolution are also satisfied.

  12. Community detection in networks with unequal groups.

    Science.gov (United States)

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

    2016-01-01

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

  13. (Automated) software modularization using community detection

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius; Manikas, Konstantinos

    2015-01-01

    The modularity of a software system is known to have an effect on, among other, development effort, change impact, and technical debt. Modularizing a specific system and evaluating this modularization is, however, challenging. In this paper, we apply community detection methods to the graph...... of class dependencies in software systems to find optimal modularizations through communities. We evaluate this approach through a study of 111 Java systems contained in the Qualitas Corpus. We found that using the modularity function of Newman with an Erdős-Rényi null-model and using the community...... detection algorithm of Reichardt and Bornholdt improved community quality for all systems, that coupling decreased for 99 of the systems, and that coherence increased for 102 of the systems. Furthermore, the modularity function correlates with existing metrics for coupling and coherence....

  14. Dynamic graphs, community detection, and Riemannian geometry

    Energy Technology Data Exchange (ETDEWEB)

    Bakker, Craig; Halappanavar, Mahantesh; Visweswara Sathanur, Arun

    2018-03-29

    A community is a subset of a wider network where the members of that subset are more strongly connected to each other than they are to the rest of the network. In this paper, we consider the problem of identifying and tracking communities in graphs that change over time {dynamic community detection} and present a framework based on Riemannian geometry to aid in this task. Our framework currently supports several important operations such as interpolating between and averaging over graph snapshots. We compare these Riemannian methods with entry-wise linear interpolation and that the Riemannian methods are generally better suited to dynamic community detection. Next steps with the Riemannian framework include developing higher-order interpolation methods (e.g. the analogues of polynomial and spline interpolation) and a Riemannian least-squares regression method for working with noisy data.

  15. Network Community Detection on Metric Space

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2015-08-01

    Full Text Available Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

  16. A Novel Vertex Affinity for Community Detection

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-05

    We propose a novel vertex affinity measure in this paper. The new vertex affinity quantifies the proximity between two vertices in terms of their clustering strength and is ideal for such graph analytics applications as community detection. We also developed a framework that combines simple graph searches and resistance circuit formulas to compute the vertex affinity efficiently. We study the properties of the new affinity measure empirically in comparison to those of other popular vertex proximity metrics. Our results show that the existing metrics are ill-suited for community detection due to their lack of fundamental properties that are essential for correctly capturing inter- and intra-cluster vertex proximity.

  17. An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis

    Directory of Open Access Journals (Sweden)

    Seyed Alireza Ravanfar

    2015-09-01

    Full Text Available This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE. The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.

  18. Semisupervised Community Detection by Voltage Drops

    Directory of Open Access Journals (Sweden)

    Min Ji

    2016-01-01

    Full Text Available Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is OV+E for the sparse network with V vertices and E edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

  19. A multiresolution model of rhythmic expectancy

    NARCIS (Netherlands)

    Smith, L.M.; Honing, H.; Miyazaki, K.; Hiraga, Y.; Adachi, M.; Nakajima, Y.; Tsuzaki, M.

    2008-01-01

    We describe a computational model of rhythmic cognition that predicts expected onset times. A dynamic representation of musical rhythm, the multiresolution analysis using the continuous wavelet transform is used. This representation decomposes the temporal structure of a musical rhythm into time

  20. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

    This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

  1. A Posteriori Approach for Community Detection

    Institute of Scientific and Technical Information of China (English)

    Chuan Shi; Zhen-Yu Yan; Xin Pan; Ya-Nan Cai; Bin Wu

    2011-01-01

    Conventional community detection approaches in complex network are based on the optimization of a priori decision,i.e.,a single quality function designed beforehand.This paper proposes a posteriori decision approach for community detection.The approach includes two phases:in the search phase,a special multi-objective evolutionary algorithm is designed to search for a set of tradeoff partitions that reveal the community structure at different scales in one run; in the decision phase,three model selection criteria and the Possibility Matrix method are proposed to aid decision makers to select the preferable solutions through differentiating the set of optimal solutions according to their qualities.The experiments in five synthetic and real social networks illustrate that,in one run,our method is able to obtain many candidate solutions,which effectively avoids the resolution limit existing in priori decision approaches.In addition,our method can discover more authentic and comprehensive community structures than those priori decision approaches.

  2. Dynamic social community detection and its applications.

    Directory of Open Access Journals (Sweden)

    Nam P Nguyen

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

  3. Dynamic social community detection and its applications.

    Science.gov (United States)

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

    2014-01-01

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

  4. Spatial Quality of Manually Geocoded Multispectral and Multiresolution Mosaics

    Directory of Open Access Journals (Sweden)

    Andrija Krtalić

    2008-05-01

    Full Text Available The digital airborne multisensor and multiresolution system for collection of information (images about mine suspected area was created, within European commission project Airborne Minefield Area Reduction (ARC, EC IST-2000-25300, http://www.arc.vub.ac.be to gain a better perspective in mine suspected areas (MSP in the Republic of Croatia. The system consists of a matrix camera (visible and near infrared range of electromagnetic spectrum, 0.4-1.1 µm, thermal (thermal range of electromagnetic spectrum, 8-14 µm and a hyperspectral linear scanner. Because of a specific purpose and seeking object on the scene, the flights for collecting the images took place at heights from 130 m to 900 m above the ground. The result of a small relative flight height and large MSPs was a large number of images which cover MSPs. Therefore, the need for merging images in largest parts, for a better perspective in whole MSPs and the interaction of detected object influences on the scene appeared. The mentioned system did not dispose of the module for automatic mosaicking and geocoding, so mosaicking and after that geocoding were done manually. This process made the classification of the scene (better distinguishing of objects on the scene and fusion of multispectral and multiresolution images after that possible. Classification and image fusion can be even done by manually mosaicking and geocoding. This article demonstrated this claim.

  5. Interactive indirect illumination using adaptive multiresolution splatting.

    Science.gov (United States)

    Nichols, Greg; Wyman, Chris

    2010-01-01

    Global illumination provides a visual richness not achievable with the direct illumination models used by most interactive applications. To generate global effects, numerous approximations attempt to reduce global illumination costs to levels feasible in interactive contexts. One such approximation, reflective shadow maps, samples a shadow map to identify secondary light sources whose contributions are splatted into eye space. This splatting introduces significant overdraw that is usually reduced by artificially shrinking each splat's radius of influence. This paper introduces a new multiresolution approach for interactively splatting indirect illumination. Instead of reducing GPU fill rate by reducing splat size, we reduce fill rate by rendering splats into a multiresolution buffer. This takes advantage of the low-frequency nature of diffuse and glossy indirect lighting, allowing rendering of indirect contributions at low resolution where lighting changes slowly and at high-resolution near discontinuities. Because this multiresolution rendering occurs on a per-splat basis, we can significantly reduce fill rate without arbitrarily clipping splat contributions below a given threshold-those regions simply are rendered at a coarse resolution.

  6. Multiresolution approximation for volatility processes

    NARCIS (Netherlands)

    E. Capobianco (Enrico)

    2002-01-01

    textabstractWe present an application of wavelet techniques to non-stationary time series with the aim of detecting the dependence structure which is typically found to characterize intraday stock index financial returns. It is particularly important to identify what components truly belong to the

  7. A new class of morphological pyramids for multiresolution image analysis

    NARCIS (Netherlands)

    Roerdink, Jos B.T.M.; Asano, T; Klette, R; Ronse, C

    2003-01-01

    We study nonlinear multiresolution signal decomposition based on morphological pyramids. Motivated by a problem arising in multiresolution volume visualization, we introduce a new class of morphological pyramids. In this class the pyramidal synthesis operator always has the same form, i.e. a

  8. 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...

  9. Wireless Falling Detection System Based on Community.

    Science.gov (United States)

    Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

    2015-06-01

    The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

  10. Multiresolution signal decomposition transforms, subbands, and wavelets

    CERN Document Server

    Akansu, Ali N

    1992-01-01

    This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source.n The first book to give a unified and coherent exposition of multiresolutional signal decompos

  11. Multiresolution Computation of Conformal Structures of Surfaces

    Directory of Open Access Journals (Sweden)

    Xianfeng Gu

    2003-10-01

    Full Text Available An efficient multiresolution method to compute global conformal structures of nonzero genus triangle meshes is introduced. The homology, cohomology groups of meshes are computed explicitly, then a basis of harmonic one forms and a basis of holomorphic one forms are constructed. A progressive mesh is generated to represent the original surface at different resolutions. The conformal structure is computed for the coarse level first, then used as the estimation for that of the finer level, by using conjugate gradient method it can be refined to the conformal structure of the finer level.

  12. Enhancing community detection by using local structural information

    International Nuclear Information System (INIS)

    Xiang, Ju; Bao, Mei-Hua; Tang, Liang; Li, Jian-Ming; Hu, Ke; Chen, Benyan; Hu, Jing-Bo; Zhang, Yan; Tang, Yan-Ni; Gao, Yuan-Yuan

    2016-01-01

    Many real-world networks, such as gene networks, protein–protein interaction networks and metabolic networks, exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have a positive effect on community detection in the networks. Here, various local similarity measures are used to extract local structural information, which is then applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial for the improvement of community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and applied community detection methods. (paper: interdisciplinary statistical mechanics)

  13. 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.

  14. 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.

  15. Overlapping community detection in weighted networks via a Bayesian approach

    Science.gov (United States)

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

    2017-02-01

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

  16. 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)

  17. 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.

  18. 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)

  19. Network coding for multi-resolution multicast

    DEFF Research Database (Denmark)

    2013-01-01

    A method, apparatus and computer program product for utilizing network coding for multi-resolution multicast is presented. A network source partitions source content into a base layer and one or more refinement layers. The network source receives a respective one or more push-back messages from one...... or more network destination receivers, the push-back messages identifying the one or more refinement layers suited for each one of the one or more network destination receivers. The network source computes a network code involving the base layer and the one or more refinement layers for at least one...... of the one or more network destination receivers, and transmits the network code to the one or more network destination receivers in accordance with the push-back messages....

  20. 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.

  1. Ensemble method: Community detection based on game theory

    Science.gov (United States)

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

    2014-08-01

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

  2. Enhancing Community Detection By Affinity-based Edge Weighting Scheme

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-05

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

  3. Overlapping community detection using weighted consensus ...

    Indian Academy of Sciences (India)

    2016-09-21

    Sep 21, 2016 ... Complex networks; overlapping community; consensus clustering. PACS Nos 89.75 ... networks, a person may be in several social groups like family, friends ..... the social interactions between individuals in a karate club in an.

  4. Multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation

    Science.gov (United States)

    Ortiz-Jaramillo, B.; Fandiño Toro, H. A.; Benitez-Restrepo, H. D.; Orjuela-Vargas, S. A.; Castellanos-Domínguez, G.; Philips, W.

    2012-03-01

    Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art.

  5. Incorporating profile information in community detection for online social networks

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2014-07-01

    Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

  6. Morphological pyramids in multiresolution MIP rendering of large volume data : Survey and new results

    NARCIS (Netherlands)

    Roerdink, J.B.T.M.

    We survey and extend nonlinear signal decompositions based on morphological pyramids, and their application to multiresolution maximum intensity projection (MIP) volume rendering with progressive refinement and perfect reconstruction. The structure of the resulting multiresolution rendering

  7. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  8. Detection of communities with Naming Game-based methods

    Science.gov (United States)

    Ribeiro, Carlos Henrique Costa

    2017-01-01

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

  9. Overlapping communities detection based on spectral analysis of line graphs

    Science.gov (United States)

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

    2018-05-01

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

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

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2015-03-01

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

  11. Telescopic multi-resolution augmented reality

    Science.gov (United States)

    Jenkins, Jeffrey; Frenchi, Christopher; Szu, Harold

    2014-05-01

    To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as `make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

  12. Multiresolution molecular mechanics: Implementation and efficiency

    Energy Technology Data Exchange (ETDEWEB)

    Biyikli, Emre; To, Albert C., E-mail: albertto@pitt.edu

    2017-01-01

    Atomistic/continuum coupling methods combine accurate atomistic methods and efficient continuum methods to simulate the behavior of highly ordered crystalline systems. Coupled methods utilize the advantages of both approaches to simulate systems at a lower computational cost, while retaining the accuracy associated with atomistic methods. Many concurrent atomistic/continuum coupling methods have been proposed in the past; however, their true computational efficiency has not been demonstrated. The present work presents an efficient implementation of a concurrent coupling method called the Multiresolution Molecular Mechanics (MMM) for serial, parallel, and adaptive analysis. First, we present the features of the software implemented along with the associated technologies. The scalability of the software implementation is demonstrated, and the competing effects of multiscale modeling and parallelization are discussed. Then, the algorithms contributing to the efficiency of the software are presented. These include algorithms for eliminating latent ghost atoms from calculations and measurement-based dynamic balancing of parallel workload. The efficiency improvements made by these algorithms are demonstrated by benchmark tests. The efficiency of the software is found to be on par with LAMMPS, a state-of-the-art Molecular Dynamics (MD) simulation code, when performing full atomistic simulations. Speed-up of the MMM method is shown to be directly proportional to the reduction of the number of the atoms visited in force computation. Finally, an adaptive MMM analysis on a nanoindentation problem, containing over a million atoms, is performed, yielding an improvement of 6.3–8.5 times in efficiency, over the full atomistic MD method. For the first time, the efficiency of a concurrent atomistic/continuum coupling method is comprehensively investigated and demonstrated.

  13. Large Scale Community Detection Using a Small World Model

    Directory of Open Access Journals (Sweden)

    Ranjan Kumar Behera

    2017-11-01

    Full Text Available In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones.

  14. 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.

  15. Deep learning for classification of islanding and grid disturbance based on multi-resolution singular spectrum entropy

    Science.gov (United States)

    Li, Tie; He, Xiaoyang; Tang, Junci; Zeng, Hui; Zhou, Chunying; Zhang, Nan; Liu, Hui; Lu, Zhuoxin; Kong, Xiangrui; Yan, Zheng

    2018-02-01

    Forasmuch as the distinguishment of islanding is easy to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of photovoltaic out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, deep learning is utilized to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the photovoltaic system mistakenly withdrawing from power grids can be avoided.

  16. Distributed detection of communities in complex networks using synthetic coordinates

    International Nuclear Information System (INIS)

    Papadakis, H; Fragopoulou, P; Panagiotakis, C

    2014-01-01

    Various applications like finding Web communities, detecting the structure of social networks, and even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, on the basis of local interactions between neighboring nodes and an unsupervised distributed hierarchical clustering algorithm. The novelty of the proposed approach, named SCCD (standing for synthetic coordinate community detection), lies in the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived from varying a number of graph parameters and real data set graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme. (paper)

  17. 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.

  18. An ROI multi-resolution compression method for 3D-HEVC

    Science.gov (United States)

    Ti, Chunli; Guan, Yudong; Xu, Guodong; Teng, Yidan; Miao, Xinyuan

    2017-09-01

    3D High Efficiency Video Coding (3D-HEVC) provides a significant potential on increasing the compression ratio of multi-view RGB-D videos. However, the bit rate still rises dramatically with the improvement of the video resolution, which will bring challenges to the transmission network, especially the mobile network. This paper propose an ROI multi-resolution compression method for 3D-HEVC to better preserve the information in ROI on condition of limited bandwidth. This is realized primarily through ROI extraction and compression multi-resolution preprocessed video as alternative data according to the network conditions. At first, the semantic contours are detected by the modified structured forests to restrain the color textures inside objects. The ROI is then determined utilizing the contour neighborhood along with the face region and foreground area of the scene. Secondly, the RGB-D videos are divided into slices and compressed via 3D-HEVC under different resolutions for selection by the audiences and applications. Afterwards, the reconstructed low-resolution videos from 3D-HEVC encoder are directly up-sampled via Laplace transformation and used to replace the non-ROI areas of the high-resolution videos. Finally, the ROI multi-resolution compressed slices are obtained by compressing the ROI preprocessed videos with 3D-HEVC. The temporal and special details of non-ROI are reduced in the low-resolution videos, so the ROI will be better preserved by the encoder automatically. Experiments indicate that the proposed method can keep the key high-frequency information with subjective significance while the bit rate is reduced.

  19. 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.

  20. A multiresolution remeshed Vortex-In-Cell algorithm using patches

    DEFF Research Database (Denmark)

    Rasmussen, Johannes Tophøj; Cottet, Georges-Henri; Walther, Jens Honore

    2011-01-01

    We present a novel multiresolution Vortex-In-Cell algorithm using patches of varying resolution. The Poisson equation relating the fluid vorticity and velocity is solved using Fast Fourier Transforms subject to free space boundary conditions. Solid boundaries are implemented using the semi...

  1. 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)

  2. Study on spillover effect of copper futures between LME and SHFE using wavelet multiresolution analysis

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Research on information spillover effects between financial markets remains active in the economic community. A Granger-type model has recently been used to investigate the spillover between London Metal Exchange (LME) and Shanghai Futures Exchange (SHFE), however, possible correlation between the future price and return on different time scales have been ignored. In this paper, wavelet multiresolution decomposition is used to investigate the spillover effects of copper future returns between the two markets. The daily return time series are decomposed on 2n (n=1, ..., 6) frequency bands through wavelet multiresolution analysis. The correlation between the two markets is studied with decomposed data. It is shown that high frequency detail components represent much more energy than low-frequency smooth components. The relation between copper future daily returns in LME and that in SHFE are different on different time scales. The fluctuations of the copper future daily returns in LME have large effect on that in SHFE in 32-day scale, but small effect in high frequency scales. It also has evidence that strong effects exist between LME and SHFE for monthly responses of the copper futures but not for daily responses.

  3. Community structures and role detection in music networks

    Science.gov (United States)

    Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

    2008-12-01

    We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

  4. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

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

    2018-04-01

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

  5. 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)

  6. 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.

  7. Community detection in complex networks using proximate support vector clustering

    Science.gov (United States)

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

    2018-03-01

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

  8. 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.

  9. Accounting for Incomplete Species Detection in Fish Community Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    McManamay, Ryan A [ORNL; Orth, Dr. Donald J [Virginia Polytechnic Institute and State University; Jager, Yetta [ORNL

    2013-01-01

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

  10. Multiresolution and Explicit Methods for Vector Field Analysis and Visualization

    Science.gov (United States)

    Nielson, Gregory M.

    1997-01-01

    This is a request for a second renewal (3d year of funding) of a research project on the topic of multiresolution and explicit methods for vector field analysis and visualization. In this report, we describe the progress made on this research project during the second year and give a statement of the planned research for the third year. There are two aspects to this research project. The first is concerned with the development of techniques for computing tangent curves for use in visualizing flow fields. The second aspect of the research project is concerned with the development of multiresolution methods for curvilinear grids and their use as tools for visualization, analysis and archiving of flow data. We report on our work on the development of numerical methods for tangent curve computation first.

  11. Multiresolution analysis of Bursa Malaysia KLCI time series

    Science.gov (United States)

    Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

    2017-05-01

    In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

  12. Multiresolution persistent homology for excessively large biomolecular datasets

    Energy Technology Data Exchange (ETDEWEB)

    Xia, Kelin; Zhao, Zhixiong [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Wei, Guo-Wei, E-mail: wei@math.msu.edu [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824 (United States)

    2015-10-07

    Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

  13. Multiresolution with Hierarchical Modulations for Long Term Evolution of UMTS

    Directory of Open Access Journals (Sweden)

    Soares Armando

    2009-01-01

    Full Text Available In the Long Term Evolution (LTE of UMTS the Interactive Mobile TV scenario is expected to be a popular service. By using multiresolution with hierarchical modulations this service is expected to be broadcasted to larger groups achieving significant reduction in power transmission or increasing the average throughput. Interactivity in the uplink direction will not be affected by multiresolution in the downlink channels, since it will be supported by dedicated uplink channels. The presence of interactivity will allow for a certain amount of link quality feedback for groups or individuals. As a result, an optimization of the achieved throughput will be possible. In this paper system level simulations of multi-cellular networks considering broadcast/multicast transmissions using the OFDM/OFDMA based LTE technology are presented to evaluate the capacity, in terms of number of TV channels with given bit rates or total spectral efficiency and coverage. multiresolution with hierarchical modulations is presented to evaluate the achievable throughput gain compared to single resolution systems of Multimedia Broadcast/Multicast Service (MBMS standardised in Release 6.

  14. Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

    KAUST Repository

    Sicat, Ronell Barrera

    2014-12-31

    This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

  15. 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.

  16. Consensus-based methodology for detection communities in multilayered networks

    Science.gov (United States)

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

    2018-03-01

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

  17. 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.

  18. Active link selection for efficient semi-supervised community detection

    Science.gov (United States)

    Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

    2015-01-01

    Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

  19. Weighted community detection and data clustering using message passing

    Science.gov (United States)

    Shi, Cheng; Liu, Yanchen; Zhang, Pan

    2018-03-01

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

  20. Stochastic fluctuations and the detectability limit of network communities.

    Science.gov (United States)

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

    2013-12-01

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

  1. Phase transitions in community detection: A solvable toy model

    Science.gov (United States)

    Ver Steeg, Greg; Moore, Cristopher; Galstyan, Aram; Allahverdyan, Armen

    2014-05-01

    Recently, it was shown that there is a phase transition in the community detection problem. This transition was first computed using the cavity method, and has been proved rigorously in the case of q = 2 groups. However, analytic calculations using the cavity method are challenging since they require us to understand probability distributions of messages. We study analogous transitions in the so-called “zero-temperature inference” model, where this distribution is supported only on the most likely messages. Furthermore, whenever several messages are equally likely, we break the tie by choosing among them with equal probability, corresponding to an infinitesimal random external field. While the resulting analysis overestimates the thresholds, it reproduces some of the qualitative features of the system. It predicts a first-order detectability transition whenever q > 2 (as opposed to q > 4 according to the finite-temperature cavity method). It also has a regime analogous to the “hard but detectable” phase, where the community structure can be recovered, but only when the initial messages are sufficiently accurate. Finally, we study a semisupervised setting where we are given the correct labels for a fraction ρ of the nodes. For q > 2, we find a regime where the accuracy jumps discontinuously at a critical value of ρ.

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

    Science.gov (United States)

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

    2017-04-01

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

  3. A multiresolution method for solving the Poisson equation using high order regularization

    DEFF Research Database (Denmark)

    Hejlesen, Mads Mølholm; Walther, Jens Honore

    2016-01-01

    We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches and regulari......We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches...... and regularized Green's functions corresponding to the difference in the spatial resolution between the patches. The full solution is obtained utilizing the linearity of the Poisson equation enabling super-position of solutions. We show that the multiresolution Poisson solver produces convergence rates...

  4. MR-CDF: Managing multi-resolution scientific data

    Science.gov (United States)

    Salem, Kenneth

    1993-01-01

    MR-CDF is a system for managing multi-resolution scientific data sets. It is an extension of the popular CDF (Common Data Format) system. MR-CDF provides a simple functional interface to client programs for storage and retrieval of data. Data is stored so that low resolution versions of the data can be provided quickly. Higher resolutions are also available, but not as quickly. By managing data with MR-CDF, an application can be relieved of the low-level details of data management, and can easily trade data resolution for improved access time.

  5. Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter

    Science.gov (United States)

    Joseph, Kenneth; Carley, Kathleen M.

    2017-01-01

    The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets. PMID:29194446

  6. Multisensor multiresolution data fusion for improvement in classification

    Science.gov (United States)

    Rubeena, V.; Tiwari, K. C.

    2016-04-01

    The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

  7. Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

    Directory of Open Access Journals (Sweden)

    Peng Ge

    2013-01-01

    Full Text Available Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and the ω path of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

  8. Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy

    Directory of Open Access Journals (Sweden)

    Liyan Zhang

    2017-01-01

    Full Text Available The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following. (1 Volume-density relationships of three submodels are unanimous with detector data. (2 When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger. (3 Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

  9. 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

  10. 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

  11. Extended generalized Lagrangian multipliers for magnetohydrodynamics using adaptive multiresolution methods

    Directory of Open Access Journals (Sweden)

    Domingues M. O.

    2013-12-01

    Full Text Available We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution.

  12. Multiresolution forecasting for futures trading using wavelet decompositions.

    Science.gov (United States)

    Zhang, B L; Coggins, R; Jabri, M A; Dersch, D; Flower, B

    2001-01-01

    We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades.

  13. Multiresolution Motion Estimation for Low-Rate Video Frame Interpolation

    Directory of Open Access Journals (Sweden)

    Hezerul Abdul Karim

    2004-09-01

    Full Text Available Interpolation of video frames with the purpose of increasing the frame rate requires the estimation of motion in the image so as to interpolate pixels along the path of the objects. In this paper, the specific challenges of low-rate video frame interpolation are illustrated by choosing one well-performing algorithm for high-frame-rate interpolation (Castango 1996 and applying it to low frame rates. The degradation of performance is illustrated by comparing the original algorithm, the algorithm adapted to low frame rate, and simple averaging. To overcome the particular challenges of low-frame-rate interpolation, two algorithms based on multiresolution motion estimation are developed and compared on objective and subjective basis and shown to provide an elegant solution to the specific challenges of low-frame-rate video interpolation.

  14. Multiresolution 3-D reconstruction from side-scan sonar images.

    Science.gov (United States)

    Coiras, Enrique; Petillot, Yvan; Lane, David M

    2007-02-01

    In this paper, a new method for the estimation of seabed elevation maps from side-scan sonar images is presented. The side-scan image formation process is represented by a Lambertian diffuse model, which is then inverted by a multiresolution optimization procedure inspired by expectation-maximization to account for the characteristics of the imaged seafloor region. On convergence of the model, approximations for seabed reflectivity, side-scan beam pattern, and seabed altitude are obtained. The performance of the system is evaluated against a real structure of known dimensions. Reconstruction results for images acquired by different sonar sensors are presented. Applications to augmented reality for the simulation of targets in sonar imagery are also discussed.

  15. Multiresolution strategies for the numerical solution of optimal control problems

    Science.gov (United States)

    Jain, Sachin

    There exist many numerical techniques for solving optimal control problems but less work has been done in the field of making these algorithms run faster and more robustly. The main motivation of this work is to solve optimal control problems accurately in a fast and efficient way. Optimal control problems are often characterized by discontinuities or switchings in the control variables. One way of accurately capturing the irregularities in the solution is to use a high resolution (dense) uniform grid. This requires a large amount of computational resources both in terms of CPU time and memory. Hence, in order to accurately capture any irregularities in the solution using a few computational resources, one can refine the mesh locally in the region close to an irregularity instead of refining the mesh uniformly over the whole domain. Therefore, a novel multiresolution scheme for data compression has been designed which is shown to outperform similar data compression schemes. Specifically, we have shown that the proposed approach results in fewer grid points in the grid compared to a common multiresolution data compression scheme. The validity of the proposed mesh refinement algorithm has been verified by solving several challenging initial-boundary value problems for evolution equations in 1D. The examples have demonstrated the stability and robustness of the proposed algorithm. The algorithm adapted dynamically to any existing or emerging irregularities in the solution by automatically allocating more grid points to the region where the solution exhibited sharp features and fewer points to the region where the solution was smooth. Thereby, the computational time and memory usage has been reduced significantly, while maintaining an accuracy equivalent to the one obtained using a fine uniform mesh. Next, a direct multiresolution-based approach for solving trajectory optimization problems is developed. The original optimal control problem is transcribed into a

  16. Adaptive multiresolution method for MAP reconstruction in electron tomography

    Energy Technology Data Exchange (ETDEWEB)

    Acar, Erman, E-mail: erman.acar@tut.fi [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland); Peltonen, Sari; Ruotsalainen, Ulla [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland)

    2016-11-15

    3D image reconstruction with electron tomography holds problems due to the severely limited range of projection angles and low signal to noise ratio of the acquired projection images. The maximum a posteriori (MAP) reconstruction methods have been successful in compensating for the missing information and suppressing noise with their intrinsic regularization techniques. There are two major problems in MAP reconstruction methods: (1) selection of the regularization parameter that controls the balance between the data fidelity and the prior information, and (2) long computation time. One aim of this study is to provide an adaptive solution to the regularization parameter selection problem without having additional knowledge about the imaging environment and the sample. The other aim is to realize the reconstruction using sequences of resolution levels to shorten the computation time. The reconstructions were analyzed in terms of accuracy and computational efficiency using a simulated biological phantom and publically available experimental datasets of electron tomography. The numerical and visual evaluations of the experiments show that the adaptive multiresolution method can provide more accurate results than the weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), and sequential MAP expectation maximization (sMAPEM) method. The method is superior to sMAPEM also in terms of computation time and usability since it can reconstruct 3D images significantly faster without requiring any parameter to be set by the user. - Highlights: • An adaptive multiresolution reconstruction method is introduced for electron tomography. • The method provides more accurate results than the conventional reconstruction methods. • The missing wedge and noise problems can be compensated by the method efficiently.

  17. Detecting the overlapping and hierarchical community structure in complex networks

    International Nuclear Information System (INIS)

    Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

    2009-01-01

    Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

  18. The multi-resolution capability of Tchebichef moments and its applications to the analysis of fluorescence excitation-emission spectra

    Science.gov (United States)

    Li, Bao Qiong; Wang, Xue; Li Xu, Min; Zhai, Hong Lin; Chen, Jing; Liu, Jin Jin

    2018-01-01

    Fluorescence spectroscopy with an excitation-emission matrix (EEM) is a fast and inexpensive technique and has been applied to the detection of a very wide range of analytes. However, serious scattering and overlapping signals hinder the applications of EEM spectra. In this contribution, the multi-resolution capability of Tchebichef moments was investigated in depth and applied to the analysis of two EEM data sets (data set 1 consisted of valine-tyrosine-valine, tryptophan-glycine and phenylalanine, and data set 2 included vitamin B1, vitamin B2 and vitamin B6) for the first time. By means of the Tchebichef moments with different orders, the different information in the EEM spectra can be represented. It is owing to this multi-resolution capability that the overlapping problem was solved, and the information of chemicals and scatterings were separated. The obtained results demonstrated that the Tchebichef moment method is very effective, which provides a promising tool for the analysis of EEM spectra. It is expected that the applications of Tchebichef moment method could be developed and extended in complex systems such as biological fluids, food, environment and others to deal with the practical problems (overlapped peaks, unknown interferences, baseline drifts, and so on) with other spectra.

  19. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2015-01-01

    Full Text Available The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI. In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII. The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

  20. High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a novel, high-accuracy, high-fidelity, multiresolution (MRES), wavelet-based framework for efficient prediction of airframe noise sources and...

  1. Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

    KAUST Repository

    Sicat, Ronell Barrera

    2015-01-01

    approach is to employ output-sensitive operations on multi-resolution data representations. Output-sensitive operations facilitate interactive applications since their required computations are proportional only to the size of the data that is visible, i

  2. Layout Optimization of Structures with Finite-size Features using Multiresolution Analysis

    DEFF Research Database (Denmark)

    Chellappa, S.; Diaz, A. R.; Bendsøe, Martin P.

    2004-01-01

    A scheme for layout optimization in structures with multiple finite-sized heterogeneities is presented. Multiresolution analysis is used to compute reduced operators (stiffness matrices) representing the elastic behavior of material distributions with heterogeneities of sizes that are comparable...

  3. Evolved Multiresolution Transforms for Optimized Image Compression and Reconstruction Under Quantization

    National Research Council Canada - National Science Library

    Moore, Frank

    2005-01-01

    ...) First, this research demonstrates that a GA can evolve a single set of coefficients describing a single matched forward and inverse transform pair that can be used at each level of a multiresolution...

  4. Homogeneous hierarchies: A discrete analogue to the wavelet-based multiresolution approximation

    Energy Technology Data Exchange (ETDEWEB)

    Mirkin, B. [Rutgers Univ., Piscataway, NJ (United States)

    1996-12-31

    A correspondence between discrete binary hierarchies and some orthonormal bases of the n-dimensional Euclidean space can be applied to such problems as clustering, ordering, identifying/testing in very large data bases, or multiresolution image/signal processing. The latter issue is considered in the paper. The binary hierarchy based multiresolution theory is expected to lead to effective methods for data processing because of relaxing the regularity restrictions of the classical theory.

  5. Static multiresolution grids with inline hierarchy information for cosmic ray propagation

    Energy Technology Data Exchange (ETDEWEB)

    Müller, Gero, E-mail: gero.mueller@physik.rwth-aachen.de [III. Physikalisches Institut A, RWTH Aachen University, D-52056 Aachen (Germany)

    2016-08-01

    For numerical simulations of cosmic-ray propagation fast access to static magnetic field data is required. We present a data structure for multiresolution vector grids which is optimized for fast access, low overhead and shared memory use. The hierarchy information is encoded into the grid itself, reducing the memory overhead. Benchmarks show that in certain scenarios the differences in deflections introduced by sampling the magnetic field model can be significantly reduced when using the multiresolution approach.

  6. Pharmacy student driven detection of adverse drug reactions in the community pharmacy setting

    DEFF Research Database (Denmark)

    Christensen, Søren Troels; Søndergaard, Birthe; Honoré, Per Hartvig

    2011-01-01

    of pharmacists in ADR reporting, although varies significantly among countries. Pharmacists in community pharmacies are in a unique position for detection of experienced ADRs by the drug users. The study reports from a study on community pharmacy internship students' proactive role in ADR detection through...

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

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  9. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

    Directory of Open Access Journals (Sweden)

    Dane Taylor

    2017-09-01

    Full Text Available Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection and which occur for a given community provided its size surpasses a detectability limit K^{*}. When layers are aggregated via a summation, we obtain K^{*}∝O(sqrt[NL]/T, where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than O(L^{-1/2}. Moreover, we find that thresholding the summation can, in some cases, cause K^{*} to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  10. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

    Science.gov (United States)

    Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

    2017-01-01

    Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  11. Global Multi-Resolution Topography (GMRT) Synthesis - Recent Updates and Developments

    Science.gov (United States)

    Ferrini, V. L.; Morton, J. J.; Celnick, M.; McLain, K.; Nitsche, F. O.; Carbotte, S. M.; O'hara, S. H.

    2017-12-01

    The Global Multi-Resolution Topography (GMRT, http://gmrt.marine-geo.org) synthesis is a multi-resolution compilation of elevation data that is maintained in Mercator, South Polar, and North Polar Projections. GMRT consists of four independently curated elevation components: (1) quality controlled multibeam data ( 100m res.), (2) contributed high-resolution gridded bathymetric data (0.5-200 m res.), (3) ocean basemap data ( 500 m res.), and (4) variable resolution land elevation data (to 10-30 m res. in places). Each component is managed and updated as new content becomes available, with two scheduled releases each year. The ocean basemap content for GMRT includes the International Bathymetric Chart of the Arctic Ocean (IBCAO), the International Bathymetric Chart of the Southern Ocean (IBCSO), and the GEBCO 2014. Most curatorial effort for GMRT is focused on the swath bathymetry component, with an emphasis on data from the US Academic Research Fleet. As of July 2017, GMRT includes data processed and curated by the GMRT Team from 974 research cruises, covering over 29 million square kilometers ( 8%) of the seafloor at 100m resolution. The curated swath bathymetry data from GMRT is routinely contributed to international data synthesis efforts including GEBCO and IBCSO. Additional curatorial effort is associated with gridded data contributions from the international community and ensures that these data are well blended in the synthesis. Significant new additions to the gridded data component this year include the recently released data from the search for MH370 (Geoscience Australia) as well as a large high-resolution grid from the Gulf of Mexico derived from 3D seismic data (US Bureau of Ocean Energy Management). Recent developments in functionality include the deployment of a new Polar GMRT MapTool which enables users to export custom grids and map images in polar projection for their selected area of interest at the resolution of their choosing. Available for both

  12. 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.

  13. 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.

  14. Multiresolution molecular mechanics: Surface effects in nanoscale materials

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Qingcheng, E-mail: qiy9@pitt.edu; To, Albert C., E-mail: albertto@pitt.edu

    2017-05-01

    Surface effects have been observed to contribute significantly to the mechanical response of nanoscale structures. The newly proposed energy-based coarse-grained atomistic method Multiresolution Molecular Mechanics (MMM) (Yang, To (2015), ) is applied to capture surface effect for nanosized structures by designing a surface summation rule SR{sup S} within the framework of MMM. Combined with previously proposed bulk summation rule SR{sup B}, the MMM summation rule SR{sup MMM} is completed. SR{sup S} and SR{sup B} are consistently formed within SR{sup MMM} for general finite element shape functions. Analogous to quadrature rules in finite element method (FEM), the key idea to the good performance of SR{sup MMM} lies in that the order or distribution of energy for coarse-grained atomistic model is mathematically derived such that the number, position and weight of quadrature-type (sampling) atoms can be determined. Mathematically, the derived energy distribution of surface area is different from that of bulk region. Physically, the difference is due to the fact that surface atoms lack neighboring bonding. As such, SR{sup S} and SR{sup B} are employed for surface and bulk domains, respectively. Two- and three-dimensional numerical examples using the respective 4-node bilinear quadrilateral, 8-node quadratic quadrilateral and 8-node hexahedral meshes are employed to verify and validate the proposed approach. It is shown that MMM with SR{sup MMM} accurately captures corner, edge and surface effects with less 0.3% degrees of freedom of the original atomistic system, compared against full atomistic simulation. The effectiveness of SR{sup MMM} with respect to high order element is also demonstrated by employing the 8-node quadratic quadrilateral to solve a beam bending problem considering surface effect. In addition, the introduced sampling error with SR{sup MMM} that is analogous to numerical integration error with quadrature rule in FEM is very small. - Highlights:

  15. Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.

    Science.gov (United States)

    Zhao, Yize; Kang, Jian; Long, Qi

    2018-01-01

    Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov chain Monte Carlo (MCMC) algorithms. The proposed algorithms are computationally feasible for ultra-high dimensional data. Also, our model incorporates two levels of structural information into variable selection using Ising priors: the spatial dependence between voxels and the functional connectivity between anatomical brain regions. Applied to the resting state functional magnetic resonance imaging (R-fMRI) data in the ABIDE study, our methods identify voxel-level imaging biomarkers highly predictive of the ASD, which are biologically meaningful and interpretable. Extensive simulations also show that our methods achieve better performance in variable selection compared to existing methods.

  16. Detecting changes in insect herbivore communities along a pollution gradient

    International Nuclear Information System (INIS)

    Eatough Jones, Michele; Paine, Timothy D.

    2006-01-01

    The forests surrounding the urban areas of the Los Angeles basin are impacted by ozone and nitrogen pollutants arising from urban areas. We examined changes in the herbivore communities of three prominent plant species (ponderosa pine, California black oak and bracken fern) at six sites along an air pollution gradient. Insects were extracted from foliage samples collected in spring, as foliage reached full expansion. Community differences were evaluated using total herbivore abundance, richness, Shannon-Weiner diversity, and discriminant function analysis. Even without conspicuous changes in total numbers, diversity or richness of herbivores, herbivore groups showed patterns of change that followed the air pollution gradient that were apparent through discriminant function analysis. For bracken fern and oak, chewing insects were more dominant at high pollution sites. Oak herbivore communities showed the strongest effect. These changes in herbivore communities may affect nutrient cycling in forest systems. - Differences in insect herbivore communities were associated with an ambient air pollution gradient in the mixed conifer forest outside the Los Angeles area

  17. Detecting changes in insect herbivore communities along a pollution gradient

    Energy Technology Data Exchange (ETDEWEB)

    Eatough Jones, Michele [Department of Entomology, University of California Riverside, Riverside, CA 92521 (United States)]. E-mail: michele.eatough@ucr.edu; Paine, Timothy D. [Department of Entomology, University of California Riverside, Riverside, CA 92521 (United States)]. E-mail: timothy.paine@ucr.edu

    2006-10-15

    The forests surrounding the urban areas of the Los Angeles basin are impacted by ozone and nitrogen pollutants arising from urban areas. We examined changes in the herbivore communities of three prominent plant species (ponderosa pine, California black oak and bracken fern) at six sites along an air pollution gradient. Insects were extracted from foliage samples collected in spring, as foliage reached full expansion. Community differences were evaluated using total herbivore abundance, richness, Shannon-Weiner diversity, and discriminant function analysis. Even without conspicuous changes in total numbers, diversity or richness of herbivores, herbivore groups showed patterns of change that followed the air pollution gradient that were apparent through discriminant function analysis. For bracken fern and oak, chewing insects were more dominant at high pollution sites. Oak herbivore communities showed the strongest effect. These changes in herbivore communities may affect nutrient cycling in forest systems. - Differences in insect herbivore communities were associated with an ambient air pollution gradient in the mixed conifer forest outside the Los Angeles area.

  18. 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)

  19. 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.

  20. 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.

  1. Universal phase transition in community detectability under a stochastic block model.

    Science.gov (United States)

    Chen, Pin-Yu; Hero, Alfred O

    2015-03-01

    We prove the existence of an asymptotic phase-transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. Natl. Acad. Sci. (USA) 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the intercommunity edge connection probability p grows. This phase transition separates a subcritical regime of small p, where modularity-based community detection successfully identifies the communities, from a supercritical regime of large p where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase-transition threshold p* is equal to √[p1p2], where pi(i=1,2) is the within-community edge connection probability. Thus the phase-transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase-transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase-transition threshold, we propose an empirical method for estimating this threshold from real-world data.

  2. On analysis of electroencephalogram by multiresolution-based energetic approach

    Science.gov (United States)

    Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

    2013-10-01

    Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

  3. A fast multi-resolution approach to tomographic PIV

    Science.gov (United States)

    Discetti, Stefano; Astarita, Tommaso

    2012-03-01

    Tomographic particle image velocimetry (Tomo-PIV) is a recently developed three-component, three-dimensional anemometric non-intrusive measurement technique, based on an optical tomographic reconstruction applied to simultaneously recorded images of the distribution of light intensity scattered by seeding particles immersed into the flow. Nowadays, the reconstruction process is carried out mainly by iterative algebraic reconstruction techniques, well suited to handle the problem of limited number of views, but computationally intensive and memory demanding. The adoption of the multiplicative algebraic reconstruction technique (MART) has become more and more accepted. In the present work, a novel multi-resolution approach is proposed, relying on the adoption of a coarser grid in the first step of the reconstruction to obtain a fast estimation of a reliable and accurate first guess. A performance assessment, carried out on three-dimensional computer-generated distributions of particles, shows a substantial acceleration of the reconstruction process for all the tested seeding densities with respect to the standard method based on 5 MART iterations; a relevant reduction in the memory storage is also achieved. Furthermore, a slight accuracy improvement is noticed. A modified version, improved by a multiplicative line of sight estimation of the first guess on the compressed configuration, is also tested, exhibiting a further remarkable decrease in both memory storage and computational effort, mostly at the lowest tested seeding densities, while retaining the same performances in terms of accuracy.

  4. A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

    Science.gov (United States)

    Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

    2016-04-01

    Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

  5. 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.

  6. Early detection and assertive community treatment of young psychotics

    DEFF Research Database (Denmark)

    Jørgensen, P; Nordentoft, M; Abel, M B

    2000-01-01

    Recent research indicates that early detection of young persons suffering from psychosis and subsequent intensive intervention enhances treatment response and prognosis, but the data are only preliminary and suggestive....

  7. 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.

  8. Community detection for networks with unipartite and bipartite structure

    Science.gov (United States)

    Chang, Chang; Tang, Chao

    2014-09-01

    Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

  9. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    Directory of Open Access Journals (Sweden)

    Maria Isabel Sobral Escada

    2012-01-01

    Full Text Available Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163, west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5 image (30m spatial resolution. High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera images (5 m merged with CBERS-CCD (Charge Coupled Device images (20 m were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.

  10. EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

    Directory of Open Access Journals (Sweden)

    S. Arivazhagan

    2011-11-01

    Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

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

    Science.gov (United States)

    Zhao, Xiang; Li, Yantao; Qu, Zehui

    2018-04-01

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

  12. A one-time truncate and encode multiresolution stochastic framework

    Energy Technology Data Exchange (ETDEWEB)

    Abgrall, R.; Congedo, P.M.; Geraci, G., E-mail: gianluca.geraci@inria.fr

    2014-01-15

    In this work a novel adaptive strategy for stochastic problems, inspired from the classical Harten's framework, is presented. The proposed algorithm allows building, in a very general manner, stochastic numerical schemes starting from a whatever type of deterministic schemes and handling a large class of problems, from unsteady to discontinuous solutions. Its formulations permits to recover the same results concerning the interpolation theory of the classical multiresolution approach, but with an extension to uncertainty quantification problems. The present strategy permits to build numerical scheme with a higher accuracy with respect to other classical uncertainty quantification techniques, but with a strong reduction of the numerical cost and memory requirements. Moreover, the flexibility of the proposed approach allows to employ any kind of probability density function, even discontinuous and time varying, without introducing further complications in the algorithm. The advantages of the present strategy are demonstrated by performing several numerical problems where different forms of uncertainty distributions are taken into account, such as discontinuous and unsteady custom-defined probability density functions. In addition to algebraic and ordinary differential equations, numerical results for the challenging 1D Kraichnan–Orszag are reported in terms of accuracy and convergence. Finally, a two degree-of-freedom aeroelastic model for a subsonic case is presented. Though quite simple, the model allows recovering some physical key aspect, on the fluid/structure interaction, thanks to the quasi-steady aerodynamic approximation employed. The injection of an uncertainty is chosen in order to obtain a complete parameterization of the mass matrix. All the numerical results are compared with respect to classical Monte Carlo solution and with a non-intrusive Polynomial Chaos method.

  13. On-the-Fly Decompression and Rendering of Multiresolution Terrain

    Energy Technology Data Exchange (ETDEWEB)

    Lindstrom, P; Cohen, J D

    2009-04-02

    We present a streaming geometry compression codec for multiresolution, uniformly-gridded, triangular terrain patches that supports very fast decompression. Our method is based on linear prediction and residual coding for lossless compression of the full-resolution data. As simplified patches on coarser levels in the hierarchy already incur some data loss, we optionally allow further quantization for more lossy compression. The quantization levels are adaptive on a per-patch basis, while still permitting seamless, adaptive tessellations of the terrain. Our geometry compression on such a hierarchy achieves compression ratios of 3:1 to 12:1. Our scheme is not only suitable for fast decompression on the CPU, but also for parallel decoding on the GPU with peak throughput over 2 billion triangles per second. Each terrain patch is independently decompressed on the fly from a variable-rate bitstream by a GPU geometry program with no branches or conditionals. Thus we can store the geometry compressed on the GPU, reducing storage and bandwidth requirements throughout the system. In our rendering approach, only compressed bitstreams and the decoded height values in the view-dependent 'cut' are explicitly stored on the GPU. Normal vectors are computed in a streaming fashion, and remaining geometry and texture coordinates, as well as mesh connectivity, are shared and re-used for all patches. We demonstrate and evaluate our algorithms on a small prototype system in which all compressed geometry fits in the GPU memory and decompression occurs on the fly every rendering frame without any cache maintenance.

  14. Applying multi-resolution numerical methods to geodynamics

    Science.gov (United States)

    Davies, David Rhodri

    Computational models yield inaccurate results if the underlying numerical grid fails to provide the necessary resolution to capture a simulation's important features. For the large-scale problems regularly encountered in geodynamics, inadequate grid resolution is a major concern. The majority of models involve multi-scale dynamics, being characterized by fine-scale upwelling and downwelling activity in a more passive, large-scale background flow. Such configurations, when coupled to the complex geometries involved, present a serious challenge for computational methods. Current techniques are unable to resolve localized features and, hence, such models cannot be solved efficiently. This thesis demonstrates, through a series of papers and closely-coupled appendices, how multi-resolution finite-element methods from the forefront of computational engineering can provide a means to address these issues. The problems examined achieve multi-resolution through one of two methods. In two-dimensions (2-D), automatic, unstructured mesh refinement procedures are utilized. Such methods improve the solution quality of convection dominated problems by adapting the grid automatically around regions of high solution gradient, yielding enhanced resolution of the associated flow features. Thermal and thermo-chemical validation tests illustrate that the technique is robust and highly successful, improving solution accuracy whilst increasing computational efficiency. These points are reinforced when the technique is applied to geophysical simulations of mid-ocean ridge and subduction zone magmatism. To date, successful goal-orientated/error-guided grid adaptation techniques have not been utilized within the field of geodynamics. The work included herein is therefore the first geodynamical application of such methods. In view of the existing three-dimensional (3-D) spherical mantle dynamics codes, which are built upon a quasi-uniform discretization of the sphere and closely coupled

  15. RAPID DETECTION OF PNEUMOCOCCAL ANTIGEN IN PLEURAL FLUID OF PATIENTS WITH COMMUNITY ACQUIRED PNEUMONIA

    NARCIS (Netherlands)

    BOERSMA, WG; LOWENBERG, A; HOLLOWAY, Y; KUTTSCHRUTTER, H; SNIJDER, JAM; KOETER, GH

    Background Detection of pneumococcal antigen may help to increase the rate of diagnosis of pneumococcal pneumonia. This study was designed to determine the value of rapid detection of pneumococcal antigen in pleural fluid from patients with community acquired pneumonia. Methods Thoracentesis was

  16. Evaluating Classifiers in Detecting 419 Scams in Bilingual Cybercriminal Communities

    OpenAIRE

    Mbaziira, Alex V.; Abozinadah, Ehab; Jones Jr, James H.

    2015-01-01

    Incidents of organized cybercrime are rising because of criminals are reaping high financial rewards while incurring low costs to commit crime. As the digital landscape broadens to accommodate more internet-enabled devices and technologies like social media, more cybercriminals who are not native English speakers are invading cyberspace to cash in on quick exploits. In this paper we evaluate the performance of three machine learning classifiers in detecting 419 scams in a bilingual Nigerian c...

  17. Detecting Network Communities: An Application to Phylogenetic Analysis

    Science.gov (United States)

    Andrade, Roberto F. S.; Rocha-Neto, Ivan C.; Santos, Leonardo B. L.; de Santana, Charles N.; Diniz, Marcelo V. C.; Lobão, Thierry Petit; Goés-Neto, Aristóteles; Pinho, Suani T. R.; El-Hani, Charbel N.

    2011-01-01

    This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis. PMID:21573202

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

    Science.gov (United States)

    Westlake, Bryce G; Bouchard, Martin

    2016-09-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

    Darmon, David; Omodei, Elisa; Garland, Joshua

    2015-01-01

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

  1. A cooperative game framework for detecting overlapping communities in social networks

    Science.gov (United States)

    Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

    2018-02-01

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

  2. Multi-scale analysis of the European airspace using network community detection.

    Directory of Open Access Journals (Sweden)

    Gérald Gurtner

    Full Text Available We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

  3. A multi-resolution approach to heat kernels on discrete surfaces

    KAUST Repository

    Vaxman, Amir; Ben-Chen, Mirela; Gotsman, Craig

    2010-01-01

    process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel

  4. Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis

    NARCIS (Netherlands)

    Gu, Y.H.; Katsaggelos, A.K.

    1994-01-01

    A new multiresolution image analysis approach using adaptive Hermite-Binomial filters is presented in this paper. According to the local image structural and textural properties, the analysis filter kernels are made adaptive both in their scales and orders. Applications of such an adaptive filtering

  5. Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

    KAUST Repository

    Sicat, Ronell B.

    2015-11-25

    The resolutions of acquired image and volume data are ever increasing. However, the resolutions of commodity display devices remain limited. This leads to an increasing gap between data and display resolutions. To bridge this gap, the standard approach is to employ output-sensitive operations on multi-resolution data representations. Output-sensitive operations facilitate interactive applications since their required computations are proportional only to the size of the data that is visible, i.e., the output, and not the full size of the input. Multi-resolution representations, such as image mipmaps, and volume octrees, are crucial in providing these operations direct access to any subset of the data at any resolution corresponding to the output. Despite its widespread use, this standard approach has some shortcomings in three important application areas, namely non-linear image operations, multi-resolution volume rendering, and large-scale image exploration. This dissertation presents new multi-resolution representations for large-scale images and volumes that address these shortcomings. Standard multi-resolution representations require low-pass pre-filtering for anti- aliasing. However, linear pre-filters do not commute with non-linear operations. This becomes problematic when applying non-linear operations directly to any coarse resolution levels in standard representations. Particularly, this leads to inaccurate output when applying non-linear image operations, e.g., color mapping and detail-aware filters, to multi-resolution images. Similarly, in multi-resolution volume rendering, this leads to inconsistency artifacts which manifest as erroneous differences in rendering outputs across resolution levels. To address these issues, we introduce the sparse pdf maps and sparse pdf volumes representations for large-scale images and volumes, respectively. These representations sparsely encode continuous probability density functions (pdfs) of multi-resolution pixel

  6. Characterizing and understanding the climatic determinism of high- to low-frequency variations in precipitation in northwestern France using a coupled wavelet multiresolution/statistical downscaling approach

    Science.gov (United States)

    Massei, Nicolas; Dieppois, Bastien; Hannah, David; Lavers, David; Fossa, Manuel; Laignel, Benoit; Debret, Maxime

    2017-04-01

    Geophysical signals oscillate over several time-scales that explain different amount of their overall variability and may be related to different physical processes. Characterizing and understanding such variabilities in hydrological variations and investigating their determinism is one important issue in a context of climate change, as these variabilities can be occasionally superimposed to long-term trend possibly due to climate change. It is also important to refine our understanding of time-scale dependent linkages between large-scale climatic variations and hydrological responses on the regional or local-scale. Here we investigate such links by conducting a wavelet multiresolution statistical dowscaling approach of precipitation in northwestern France (Seine river catchment) over 1950-2016 using sea level pressure (SLP) and sea surface temperature (SST) as indicators of atmospheric and oceanic circulations, respectively. Previous results demonstrated that including multiresolution decomposition in a statistical downscaling model (within a so-called multiresolution ESD model) using SLP as large-scale predictor greatly improved simulation of low-frequency, i.e. interannual to interdecadal, fluctuations observed in precipitation. Building on these results, continuous wavelet transform of simulated precipiation using multiresolution ESD confirmed the good performance of the model to better explain variability at all time-scales. A sensitivity analysis of the model to the choice of the scale and wavelet function used was also tested. It appeared that whatever the wavelet used, the model performed similarly. The spatial patterns of SLP found as the best predictors for all time-scales, which resulted from the wavelet decomposition, revealed different structures according to time-scale, showing possible different determinisms. More particularly, some low-frequency components ( 3.2-yr and 19.3-yr) showed a much wide-spread spatial extentsion across the Atlantic

  7. Topic-oriented community detection of rating-based social networks

    Directory of Open Access Journals (Sweden)

    Ali Reihanian

    2016-07-01

    Full Text Available Nowadays, real world social networks contain a vast range of information including shared objects, comments, following information, etc. Finding meaningful communities in this kind of networks is an interesting research area and has attracted the attention of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structure of the network without performing any content analysis. In recent years, a number of researches have proposed approaches which consider both the contents that are interchanged in networks, and the topological structures of the networks in order to find more meaningful communities. In this research, the effect of topic analysis in finding more meaningful communities in social networking sites in which the users express their feelings toward different objects (like movies by means of rating is demonstrated by performing extensive experiments.

  8. Early detection of breast and cervical cancer among indigenous communities in Morelos, Mexico.

    Directory of Open Access Journals (Sweden)

    Lourdes Campero

    2014-09-01

    Full Text Available Objective. To analyze the perception in relation to when and how to perform actions for the early detection of breast and cervical cancer among women and health care providers in communities with a high percentage of indigenous population in Morelos, Mexico. Materials and methods. Ten health providers and 58 women users of health services were interviewed which have a first level of attention in five communities. The analysis was developed under the approach of the Grounded Theory. Results. Providers are poorly informed about current regulations and specific clinical indications for the detection of cervical and breast cancer. Few propitiate health literacy under intercultural sensitization. The users have imprecise or wrong notions of the early detection. Conclusions. The need for training in adherence to norms is evident. It is urgent to assume a culturally relevant approach to enable efficient communication and promote health literacy for early detection of these two cancers.

  9. Global multi-resolution terrain elevation data 2010 (GMTED2010)

    Science.gov (United States)

    Danielson, Jeffrey J.; Gesch, Dean B.

    2011-01-01

    -second DTEDRegistered level 0, the USGS and the National Geospatial-Intelligence Agency (NGA) have collaborated to produce an enhanced replacement for GTOPO30, the Global Land One-km Base Elevation (GLOBE) model and other comparable 30-arc-second-resolution global models, using the best available data. The new model is called the Global Multi-resolution Terrain Elevation Data 2010, or GMTED2010 for short. This suite of products at three different resolutions (approximately 1,000, 500, and 250 meters) is designed to support many applications directly by providing users with generic products (for example, maximum, minimum, and median elevations) that have been derived directly from the raw input data that would not be available to the general user or would be very costly and time-consuming to produce for individual applications. The source of all the elevation data is captured in metadata for reference purposes. It is also hoped that as better data become available in the future, the GMTED2010 model will be updated.

  10. Spectral methods for the detection of network community structure: a comparative analysis

    International Nuclear Information System (INIS)

    Shen, Hua-Wei; Cheng, Xue-Qi

    2010-01-01

    Spectral analysis has been successfully applied to the detection of community structure of networks, respectively being based on the adjacency matrix, the standard Laplacian matrix, the normalized Laplacian matrix, the modularity matrix, the correlation matrix and several other variants of these matrices. However, the comparison between these spectral methods is less reported. More importantly, it is still unclear which matrix is more appropriate for the detection of community structure. This paper answers the question by evaluating the effectiveness of these five matrices against benchmark networks with heterogeneous distributions of node degree and community size. Test results demonstrate that the normalized Laplacian matrix and the correlation matrix significantly outperform the other three matrices at identifying the community structure of networks. This indicates that it is crucial to take into account the heterogeneous distribution of node degree when using spectral analysis for the detection of community structure. In addition, to our surprise, the modularity matrix exhibits very similar performance to the adjacency matrix, which indicates that the modularity matrix does not gain benefits from using the configuration model as a reference network with the consideration of the node degree heterogeneity

  11. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...

  12. Health-related hot topic detection in online communities using text clustering.

    Directory of Open Access Journals (Sweden)

    Yingjie Lu

    Full Text Available Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients' needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.

  13. Metabarcoding improves detection of eukaryotes from early biofouling communities: implications for pest monitoring and pathway management.

    Science.gov (United States)

    Zaiko, Anastasija; Schimanski, Kate; Pochon, Xavier; Hopkins, Grant A; Goldstien, Sharyn; Floerl, Oliver; Wood, Susanna A

    2016-07-01

    In this experimental study the patterns in early marine biofouling communities and possible implications for surveillance and environmental management were explored using metabarcoding, viz. 18S ribosomal RNA gene barcoding in combination with high-throughput sequencing. The community structure of eukaryotic assemblages and the patterns of initial succession were assessed from settlement plates deployed in a busy port for one, five and 15 days. The metabarcoding results were verified with traditional morphological identification of taxa from selected experimental plates. Metabarcoding analysis identified > 400 taxa at a comparatively low taxonomic level and morphological analysis resulted in the detection of 25 taxa at varying levels of resolution. Despite the differences in resolution, data from both methods were consistent at high taxonomic levels and similar patterns in community shifts were observed. A high percentage of sequences belonging to genera known to contain non-indigenous species (NIS) were detected after exposure for only one day.

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

    Science.gov (United States)

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

    2018-06-01

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

  15. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

    Science.gov (United States)

    Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

    2018-04-15

    In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  16. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

    Directory of Open Access Journals (Sweden)

    Vasileios Karyotis

    2018-04-01

    Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  19. Costs of a community-based glaucoma detection programme: analysis of the Philadelphia Glaucoma Detection and Treatment Project.

    Science.gov (United States)

    Pizzi, Laura T; Waisbourd, Michael; Hark, Lisa; Sembhi, Harjeet; Lee, Paul; Crews, John E; Saaddine, Jinan B; Steele, Deon; Katz, L Jay

    2018-02-01

    Glaucoma is the foremost cause of irreversible blindness, and more than 50% of cases remain undiagnosed. Our objective was to report the costs of a glaucoma detection programme operationalised through Philadelphia community centres. The analysis was performed using a healthcare system perspective in 2013 US dollars. Costs of examination and educational workshops were captured. Measures were total programme costs, cost/case of glaucoma detected and cost/case of any ocular disease detected (including glaucoma). Diagnoses are reported at the individual level (therefore representing a diagnosis made in one or both eyes). Staff time was captured during site visits to 15 of 43 sites and included time to deliver examinations and workshops, supervision, training and travel. Staff time was converted to costs by applying wage and fringe benefit costs from the US Bureau of Labor Statistics. Non-staff costs (equipment and mileage) were collected using study logs. Participants with previously diagnosed glaucoma were excluded. 1649 participants were examined. Mean total per-participant examination time was 56 min (SD 4). Mean total examination cost/participant was $139. The cost/case of glaucoma newly identified (open-angle glaucoma, angle-closure glaucoma, glaucoma suspect, or primary angle closure) was $420 and cost/case for any ocular disease identified was $273. Glaucoma examinations delivered through this programme provided significant health benefit to hard-to-reach communities. On a per-person basis, examinations were fairly low cost, though opportunities exist to improve efficiency. Findings serve as an important benchmark for planning future community-based glaucoma examination programmes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  20. Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2016-03-01

    Full Text Available The objective of this article is to bridge the gap between two important research directions: (1 nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2 complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time.

  1. A multi-resolution envelope-power based model for speech intelligibility

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Ewert, Stephan D.; Dau, Torsten

    2013-01-01

    The speech-based envelope power spectrum model (sEPSM) presented by Jørgensen and Dau [(2011). J. Acoust. Soc. Am. 130, 1475-1487] estimates the envelope power signal-to-noise ratio (SNRenv) after modulation-frequency selective processing. Changes in this metric were shown to account well...... to conditions with stationary interferers, due to the long-term integration of the envelope power, and cannot account for increased intelligibility typically obtained with fluctuating maskers. Here, a multi-resolution version of the sEPSM is presented where the SNRenv is estimated in temporal segments...... with a modulation-filter dependent duration. The multi-resolution sEPSM is demonstrated to account for intelligibility obtained in conditions with stationary and fluctuating interferers, and noisy speech distorted by reverberation or spectral subtraction. The results support the hypothesis that the SNRenv...

  2. Efficient Human Action and Gait Analysis Using Multiresolution Motion Energy Histogram

    Directory of Open Access Journals (Sweden)

    Kuo-Chin Fan

    2010-01-01

    Full Text Available Average Motion Energy (AME image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH. To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

  3. A multiresolution approach to iterative reconstruction algorithms in X-ray computed tomography.

    Science.gov (United States)

    De Witte, Yoni; Vlassenbroeck, Jelle; Van Hoorebeke, Luc

    2010-09-01

    In computed tomography, the application of iterative reconstruction methods in practical situations is impeded by their high computational demands. Especially in high resolution X-ray computed tomography, where reconstruction volumes contain a high number of volume elements (several giga voxels), this computational burden prevents their actual breakthrough. Besides the large amount of calculations, iterative algorithms require the entire volume to be kept in memory during reconstruction, which quickly becomes cumbersome for large data sets. To overcome this obstacle, we present a novel multiresolution reconstruction, which greatly reduces the required amount of memory without significantly affecting the reconstructed image quality. It is shown that, combined with an efficient implementation on a graphical processing unit, the multiresolution approach enables the application of iterative algorithms in the reconstruction of large volumes at an acceptable speed using only limited resources.

  4. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    Directory of Open Access Journals (Sweden)

    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  5. Pathfinder: multiresolution region-based searching of pathology images using IRM.

    OpenAIRE

    Wang, J. Z.

    2000-01-01

    The fast growth of digitized pathology slides has created great challenges in research on image database retrieval. The prevalent retrieval technique involves human-supplied text annotations to describe slide contents. These pathology images typically have very high resolution, making it difficult to search based on image content. In this paper, we present Pathfinder, an efficient multiresolution region-based searching system for high-resolution pathology image libraries. The system uses wave...

  6. Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

    Science.gov (United States)

    2002-01-01

    their expression profile and for classification of cells into tumerous and non- tumerous classes. Then we will present a parallel tree method for... cancerous cells. We will use the same dataset and use tree structured classifiers with multi-resolution analysis for classifying cancerous from non- cancerous ...cells. We have the expressions of 4096 genes from 98 different cell types. Of these 98, 72 are cancerous while 26 are non- cancerous . We are interested

  7. A MULTIRESOLUTION METHOD FOR THE SIMULATION OF SEDIMENTATION IN INCLINED CHANNELS

    OpenAIRE

    Buerger, Raimund; Ruiz-Baier, Ricardo; Schneider, Kai; Torres, Hector

    2012-01-01

    An adaptive multiresolution scheme is proposed for the numerical solution of a spatially two-dimensional model of sedimentation of suspensions of small solid particles dispersed in a viscous fluid. This model consists in a version of the Stokes equations for incompressible fluid flow coupled with a hyperbolic conservation law for the local solids concentration. We study the process in an inclined, rectangular closed vessel, a configuration that gives rise a well-known increase of settling rat...

  8. Adaptive Multiresolution Methods: Practical issues on Data Structures, Implementation and Parallelization*

    Directory of Open Access Journals (Sweden)

    Bachmann M.

    2011-12-01

    Full Text Available The concept of fully adaptive multiresolution finite volume schemes has been developed and investigated during the past decade. Here grid adaptation is realized by performing a multiscale decomposition of the discrete data at hand. By means of hard thresholding the resulting multiscale data are compressed. From the remaining data a locally refined grid is constructed. The aim of the present work is to give a self-contained overview on the construction of an appropriate multiresolution analysis using biorthogonal wavelets, its efficient realization by means of hash maps using global cell identifiers and the parallelization of the multiresolution-based grid adaptation via MPI using space-filling curves. Le concept des schémas de volumes finis multi-échelles et adaptatifs a été développé et etudié pendant les dix dernières années. Ici le maillage adaptatif est réalisé en effectuant une décomposition multi-échelle des données discrètes proches. En les tronquant à l’aide d’une valeur seuil fixée, les données multi-échelles obtenues sont compressées. A partir de celles-ci, le maillage est raffiné localement. Le but de ce travail est de donner un aperçu concis de la construction d’une analyse appropriée de multiresolution utilisant les fonctions ondelettes biorthogonales, de son efficacité d’application en terme de tables de hachage en utilisant des identification globales de cellule et de la parallélisation du maillage adaptatif multirésolution via MPI à l’aide des courbes remplissantes.

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

    Science.gov (United States)

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

    2018-04-01

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

  10. Combining nonlinear multiresolution system and vector quantization for still image compression

    Energy Technology Data Exchange (ETDEWEB)

    Wong, Y.

    1993-12-17

    It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge-preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized to the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding.

  11. Video Classification and Adaptive QoP/QoS Control for Multiresolution Video Applications on IPTV

    Directory of Open Access Journals (Sweden)

    Huang Shyh-Fang

    2012-01-01

    Full Text Available With the development of heterogeneous networks and video coding standards, multiresolution video applications over networks become important. It is critical to ensure the service quality of the network for time-sensitive video services. Worldwide Interoperability for Microwave Access (WIMAX is a good candidate for delivering video signals because through WIMAX the delivery quality based on the quality-of-service (QoS setting can be guaranteed. The selection of suitable QoS parameters is, however, not trivial for service users. Instead, what a video service user really concerns with is the video quality of presentation (QoP which includes the video resolution, the fidelity, and the frame rate. In this paper, we present a quality control mechanism in multiresolution video coding structures over WIMAX networks and also investigate the relationship between QoP and QoS in end-to-end connections. Consequently, the video presentation quality can be simply mapped to the network requirements by a mapping table, and then the end-to-end QoS is achieved. We performed experiments with multiresolution MPEG coding over WIMAX networks. In addition to the QoP parameters, the video characteristics, such as, the picture activity and the video mobility, also affect the QoS significantly.

  12. LOD map--A visual interface for navigating multiresolution volume visualization.

    Science.gov (United States)

    Wang, Chaoli; Shen, Han-Wei

    2006-01-01

    In multiresolution volume visualization, a visual representation of level-of-detail (LOD) quality is important for us to examine, compare, and validate different LOD selection algorithms. While traditional methods rely on ultimate images for quality measurement, we introduce the LOD map--an alternative representation of LOD quality and a visual interface for navigating multiresolution data exploration. Our measure for LOD quality is based on the formulation of entropy from information theory. The measure takes into account the distortion and contribution of multiresolution data blocks. A LOD map is generated through the mapping of key LOD ingredients to a treemap representation. The ordered treemap layout is used for relative stable update of the LOD map when the view or LOD changes. This visual interface not only indicates the quality of LODs in an intuitive way, but also provides immediate suggestions for possible LOD improvement through visually-striking features. It also allows us to compare different views and perform rendering budget control. A set of interactive techniques is proposed to make the LOD adjustment a simple and easy task. We demonstrate the effectiveness and efficiency of our approach on large scientific and medical data sets.

  13. A DTM MULTI-RESOLUTION COMPRESSED MODEL FOR EFFICIENT DATA STORAGE AND NETWORK TRANSFER

    Directory of Open Access Journals (Sweden)

    L. Biagi

    2012-08-01

    Full Text Available In recent years the technological evolution of terrestrial, aerial and satellite surveying, has considerably increased the measurement accuracy and, consequently, the quality of the derived information. At the same time, the smaller and smaller limitations on data storage devices, in terms of capacity and cost, has allowed the storage and the elaboration of a bigger number of instrumental observations. A significant example is the terrain height surveyed by LIDAR (LIght Detection And Ranging technology where several height measurements for each square meter of land can be obtained. The availability of such a large quantity of observations is an essential requisite for an in-depth knowledge of the phenomena under study. But, at the same time, the most common Geographical Information Systems (GISs show latency in visualizing and analyzing these kind of data. This problem becomes more evident in case of Internet GIS. These systems are based on the very frequent flow of geographical information over the internet and, for this reason, the band-width of the network and the size of the data to be transmitted are two fundamental factors to be considered in order to guarantee the actual usability of these technologies. In this paper we focus our attention on digital terrain models (DTM's and we briefly analyse the problems about the definition of the minimal necessary information to store and transmit DTM's over network, with a fixed tolerance, starting from a huge number of observations. Then we propose an innovative compression approach for sparse observations by means of multi-resolution spline functions approximation. The method is able to provide metrical accuracy at least comparable to that provided by the most common deterministic interpolation algorithms (inverse distance weighting, local polynomial, radial basis functions. At the same time it dramatically reduces the number of information required for storing or for transmitting and rebuilding a

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

    Science.gov (United States)

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

    2016-06-03

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

  15. Complex networks from experimental horizontal oil–water flows: Community structure detection versus flow pattern discrimination

    International Nuclear Information System (INIS)

    Gao, Zhong-Ke; Fang, Peng-Cheng; Ding, Mei-Shuang; Yang, Dan; Jin, Ning-De

    2015-01-01

    We propose a complex network-based method to distinguish complex patterns arising from experimental horizontal oil–water two-phase flow. We first use the adaptive optimal kernel time–frequency representation (AOK TFR) to characterize flow pattern behaviors from the energy and frequency point of view. Then, we infer two-phase flow complex networks from experimental measurements and detect the community structures associated with flow patterns. The results suggest that the community detection in two-phase flow complex network allows objectively discriminating complex horizontal oil–water flow patterns, especially for the segregated and dispersed flow patterns, a task that existing method based on AOK TFR fails to work. - Highlights: • We combine time–frequency analysis and complex network to identify flow patterns. • We explore the transitional flow behaviors in terms of betweenness centrality. • Our analysis provides a novel way for recognizing complex flow patterns. • Broader applicability of our method is demonstrated and articulated

  16. A complex network based model for detecting isolated communities in water distribution networks

    Science.gov (United States)

    Sheng, Nan; Jia, Youwei; Xu, Zhao; Ho, Siu-Lau; Wai Kan, Chi

    2013-12-01

    Water distribution network (WDN) is a typical real-world complex network of major infrastructure that plays an important role in human's daily life. In this paper, we explore the formation of isolated communities in WDN based on complex network theory. A graph-algebraic model is proposed to effectively detect the potential communities due to pipeline failures. This model can properly illustrate the connectivity and evolution of WDN during different stages of contingency events, and identify the emerging isolated communities through spectral analysis on Laplacian matrix. A case study on a practical urban WDN in China is conducted, and the consistency between the simulation results and the historical data are reported to showcase the feasibility and effectiveness of the proposed model.

  17. 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.

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

    Science.gov (United States)

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

    2018-07-01

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

  19. A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data

    KAUST Repository

    Castruccio, Stefano

    2018-01-23

    Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.

  20. An improved cone-beam filtered backprojection reconstruction algorithm based on x-ray angular correction and multiresolution analysis

    International Nuclear Information System (INIS)

    Sun, Y.; Hou, Y.; Yan, Y.

    2004-01-01

    With the extensive application of industrial computed tomography in the field of non-destructive testing, how to improve the quality of the reconstructed image is receiving more and more concern. It is well known that in the existing cone-beam filtered backprojection reconstruction algorithms the cone angle is controlled within a narrow range. The reason of this limitation is the incompleteness of projection data when the cone angle increases. Thus the size of the tested workpiece is limited. Considering the characteristic of X-ray cone angle, an improved cone-beam filtered back-projection reconstruction algorithm taking account of angular correction is proposed in this paper. The aim of our algorithm is to correct the cone-angle effect resulted from the incompleteness of projection data in the conventional algorithm. The basis of the correction is the angular relationship among X-ray source, tested workpiece and the detector. Thus the cone angle is not strictly limited and this algorithm may be used to detect larger workpiece. Further more, adaptive wavelet filter is used to make multiresolution analysis, which can modify the wavelet decomposition series adaptively according to the demand for resolution of local reconstructed area. Therefore the computation and the time of reconstruction can be reduced, and the quality of the reconstructed image can also be improved. (author)

  1. Factors influencing the detection rate of drug-related problems in community pharmacy

    DEFF Research Database (Denmark)

    Westerlund, T; Almarsdóttir, Anna Birna; Melander, A

    1999-01-01

    This study analyzes relationships between the number of drug-related problems detected in community pharmacy practice and the educational level and other characteristics of pharmacy personnel and their work sites. Random samples of pharmacists, prescriptionists and pharmacy technicians were drawn...... by each professional. The regression analysis showed the educational level of the professional to have a statistically significant effect on the detection rate, with pharmacists finding on average 2.5 more drug-related problems per 100 patients than prescriptionists and about 3.6 more than technicians....... The results of this study indicate the importance of education and training of pharmacy personnel in detection of drug-related problems. This findings speaks in favor of increasing the pharmacist to other personnel ratio, provided the higher costs will be offset by societal benefits....

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

    Science.gov (United States)

    Yu, Han; Hageman Blair, Rachael

    2016-01-01

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

  3. Detection of memory impairment in a community-based system: a collaborative study.

    Science.gov (United States)

    Kiral, Kahraman; Ozge, Aynur; Sungur, Mehmet Ali; Tasdelen, Bahar

    2013-05-01

    The ability to distinguish between older people with cognitive impairment and those who age in a healthy manner is crucial because cognitive impairment may be a precursor to full-blown dementia. Therefore, an early diagnosis of cognitive impairment is important. However, patients are often admitted to a hospital only when they already have a serious cognitive impairment. Consequently, cooperative studies between clinics and community-based organizations may assist hospitals in detecting early cognitive impairment. This article examines how community-based organizations can contribute to the early diagnosis of dementia. A cooperation model between the Neurology Department of Mersin University Hospital and the Mersin branch of the Alzheimer's Association was developed. Trained professionals used a neuropsychological battery to evaluate 50 individuals at the Mersin branch of the Alzheimer's Association in Turkey. Individuals whose performance fell below the average (1 standard deviation or less) were subsequently referred to the hospital. On the basis of the neurological and neuropsychological assessments, 11 participants were placed in the mild cognitive impairment group and 39 were placed in the healthy group. The results suggest that the Standardized Mini-Mental State Examination and the Three Words-Three Shapes Test are useful tools for detecting early memory impairments in a community-based setting.

  4. A mesocosm approach for detecting stream invertebrate community responses to treated wastewater effluent

    International Nuclear Information System (INIS)

    Grantham, Theodore E.; Cañedo-Argüelles, Miguel; Perrée, Isabelle; Rieradevall, Maria; Prat, Narcís

    2012-01-01

    The discharge of wastewater from sewage treatment plants is one of the most common forms of pollution to river ecosystems, yet the effects on aquatic invertebrate assemblages have not been investigated in a controlled experimental setting. Here, we use a mesocosm approach to evaluate community responses to exposure to different concentrations of treated wastewater effluents over a two week period. Multivariate analysis using Principal Response Curves indicated a clear, dose-effect response to the treatments, with significant changes in macroinvertebrate assemblages after one week when exposed to 30% effluent, and after two weeks in the 15% and 30% effluent treatments. Treatments were associated with an increase in nutrient concentrations (ammonium, sulfate, and phosphate) and reduction of dissolved oxygen. These findings indicate that exposure to wastewater effluent cause significant changes in abundance and composition of macroinvertebrate taxa and that effluent concentration as low as 5% can have detectable ecological effects. - Highlights: ► Stream invertebrate communities are altered by exposure to wastewater effluent. ► Principal Response Curves indicate a dose-effect response to effluent treatment. ► Biotic quality indices decline with increasing effluent concentration and exposure time. ► Effluent concentrations as low as 5% have detectable ecological effects. - Exposure to treated effluent in a stream mesocosm caused a dose-dependent response in the aquatic invertebrate community and led to declines in biological quality indices.

  5. Label Propagation with α-Degree Neighborhood Impact for Network Community Detection

    Directory of Open Access Journals (Sweden)

    Heli Sun

    2014-01-01

    Full Text Available Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

  6. Spatial correlation analysis of urban traffic state under a perspective of community detection

    Science.gov (United States)

    Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

    2018-05-01

    Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

  7. Detection of memory impairment among community-dwelling elderly by using the Rivermead Behavioural Memory Test

    International Nuclear Information System (INIS)

    Shinagawa, Shunichiro; Toyota, Yasutaka; Matsumoto, Teruhisa; Sonobe, Naomi; Adachi, Hiroyoshi; Mori, Takaaki; Ishikawa, Tomohisa; Fukuhara, Ryuji; Ikeda, Manabu

    2010-01-01

    The aim of this study was to use the Rivermead Behavioural Memory Test (RBMT) to evaluate everyday memory impairment among community-dwelling elderly who had normal cognitive function and performed daily activities normally but displayed memory impairments, and to diagnose the condition as either mild cognitive impairment or dementia. Among the 1,290 community-dwelling elderly persons who participated in the study, 72 subjects scored higher than 24 on the Mini-Mental State Examination (MMSE): these subjects performed daily activities normally, but their family members reported that they showed memory impairments. Fifty-two subjects completed RBMT, Clinical Dementia Rating, and brain computed tomography, and a final diagnosis was established. The mean standard profile score was 15.1±5.0 and mean screening score was 6.4±3.0. RBMT score was correlated with the MMSE score. Nine of the subjects were diagnosed with dementia and 26 of them were found to be normal. RBMT achieved 100% sensitivity and specificity with regard to the differentiation of subjects with Alzheimer's disease. However, some subjects were diagnosed with dementia even though their RBMT score was higher than the cut-off score. RBMT was useful in detecting memory impairments of Alzheimer's disease (AD) subjects in community-based surveys. However, some subjects were diagnosed with dementia because of the existence of other cognitive impairments among community-dwelling elderly. (author)

  8. 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.

  9. 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)

  10. Communities detection as a tool to assess a reform of the Italian interlocking directorship network

    Science.gov (United States)

    Drago, Carlo; Ricciuti, Roberto

    2017-01-01

    Interlocking directorships are important communication channels among companies and may have anticompetitive effect. A corporate governance reform was introduced in 2011 to prevent interlocking directorships in the financial sector. We apply community detection techniques to the analysis of the networks in 2009 and 2012 to ascertain the effect of such reform on the Italian directorship network. We find that, although the number of interlocking directorships decreases in 2012, the reduction takes place mainly at the periphery of the network. The network core is stable, allowing the most connected companies to keep their strategic position.

  11. W-transform method for feature-oriented multiresolution image retrieval

    Energy Technology Data Exchange (ETDEWEB)

    Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

    1995-07-01

    Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

  12. A morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes

    Science.gov (United States)

    Zheng, Xianwei; Xiong, Hanjiang; Gong, Jianya; Yue, Linwei

    2017-07-01

    Virtual globes play an important role in representing three-dimensional models of the Earth. To extend the functioning of a virtual globe beyond that of a "geobrowser", the accuracy of the geospatial data in the processing and representation should be of special concern for the scientific analysis and evaluation. In this study, we propose a method for the processing of large-scale terrain data for virtual globe visualization and analysis. The proposed method aims to construct a morphologically preserved multi-resolution triangulated irregular network (TIN) pyramid for virtual globes to accurately represent the landscape surface and simultaneously satisfy the demands of applications at different scales. By introducing cartographic principles, the TIN model in each layer is controlled with a data quality standard to formulize its level of detail generation. A point-additive algorithm is used to iteratively construct the multi-resolution TIN pyramid. The extracted landscape features are also incorporated to constrain the TIN structure, thus preserving the basic morphological shapes of the terrain surface at different levels. During the iterative construction process, the TIN in each layer is seamlessly partitioned based on a virtual node structure, and tiled with a global quadtree structure. Finally, an adaptive tessellation approach is adopted to eliminate terrain cracks in the real-time out-of-core spherical terrain rendering. The experiments undertaken in this study confirmed that the proposed method performs well in multi-resolution terrain representation, and produces high-quality underlying data that satisfy the demands of scientific analysis and evaluation.

  13. A multi-resolution HEALPix data structure for spherically mapped point data

    Directory of Open Access Journals (Sweden)

    Robert W. Youngren

    2017-06-01

    Full Text Available Data describing entities with locations that are points on a sphere are described as spherically mapped. Several data structures designed for spherically mapped data have been developed. One of them, known as Hierarchical Equal Area iso-Latitude Pixelization (HEALPix, partitions the sphere into twelve diamond-shaped equal-area base cells and then recursively subdivides each cell into four diamond-shaped subcells, continuing to the desired level of resolution. Twelve quadtrees, one associated with each base cell, store the data records associated with that cell and its subcells.HEALPix has been used successfully for numerous applications, notably including cosmic microwave background data analysis. However, for applications involving sparse point data HEALPix has possible drawbacks, including inefficient memory utilization, overwriting of proximate points, and return of spurious points for certain queries.A multi-resolution variant of HEALPix specifically optimized for sparse point data was developed. The new data structure allows different areas of the sphere to be subdivided at different levels of resolution. It combines HEALPix positive features with the advantages of multi-resolution, including reduced memory requirements and improved query performance.An implementation of the new Multi-Resolution HEALPix (MRH data structure was tested using spherically mapped data from four different scientific applications (warhead fragmentation trajectories, weather station locations, galaxy locations, and synthetic locations. Four types of range queries were applied to each data structure for each dataset. Compared to HEALPix, MRH used two to four orders of magnitude less memory for the same data, and on average its queries executed 72% faster. Keywords: Computer science

  14. Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

    Science.gov (United States)

    Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

    2018-01-01

    This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

  15. Multiresolution approach to processing images for different applications interaction of lower processing with higher vision

    CERN Document Server

    Vujović, Igor

    2015-01-01

    This book presents theoretical and practical aspects of the interaction between low and high level image processing. Multiresolution analysis owes its popularity mostly to wavelets and is widely used in a variety of applications. Low level image processing is important for the performance of many high level applications. The book includes examples from different research fields, i.e. video surveillance; biomedical applications (EMG and X-ray); improved communication, namely teleoperation, telemedicine, animation, augmented/virtual reality and robot vision; monitoring of the condition of ship systems and image quality control.

  16. Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology

    Science.gov (United States)

    Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.

    1998-02-01

    We applied multiresolution wavelet analysis to the sequence of times between human heartbeats ( R-R intervals) and have found a scale window, between 16 and 32 heartbeat intervals, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as belonging either to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of the presence of heart failure from the R-R intervals alone. Comparison is made with previous approaches, which have provided only statistically significant measures.

  17. Multiresolution wavelet analysis of heartbeat intervals discriminates healthy patients from those with cardiac pathology

    OpenAIRE

    Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.

    1997-01-01

    We applied multiresolution wavelet analysis to the sequence of times between human heartbeats (R-R intervals) and have found a scale window, between 16 and 32 heartbeats, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as either belonging to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of...

  18. Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

    Science.gov (United States)

    Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

    2012-01-01

    The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for

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

    Science.gov (United States)

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

    2016-06-01

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

  20. An efficient multi-resolution GA approach to dental image alignment

    Science.gov (United States)

    Nassar, Diaa Eldin; Ogirala, Mythili; Adjeroh, Donald; Ammar, Hany

    2006-02-01

    Automating the process of postmortem identification of individuals using dental records is receiving an increased attention in forensic science, especially with the large volume of victims encountered in mass disasters. Dental radiograph alignment is a key step required for automating the dental identification process. In this paper, we address the problem of dental radiograph alignment using a Multi-Resolution Genetic Algorithm (MR-GA) approach. We use location and orientation information of edge points as features; we assume that affine transformations suffice to restore geometric discrepancies between two images of a tooth, we efficiently search the 6D space of affine parameters using GA progressively across multi-resolution image versions, and we use a Hausdorff distance measure to compute the similarity between a reference tooth and a query tooth subject to a possible alignment transform. Testing results based on 52 teeth-pair images suggest that our algorithm converges to reasonable solutions in more than 85% of the test cases, with most of the error in the remaining cases due to excessive misalignments.

  1. A general CFD framework for fault-resilient simulations based on multi-resolution information fusion

    Science.gov (United States)

    Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

    2017-10-01

    We develop a general CFD framework for multi-resolution simulations to target multiscale problems but also resilience in exascale simulations, where faulty processors may lead to gappy, in space-time, simulated fields. We combine approximation theory and domain decomposition together with statistical learning techniques, e.g. coKriging, to estimate boundary conditions and minimize communications by performing independent parallel runs. To demonstrate this new simulation approach, we consider two benchmark problems. First, we solve the heat equation (a) on a small number of spatial "patches" distributed across the domain, simulated by finite differences at fine resolution and (b) on the entire domain simulated at very low resolution, thus fusing multi-resolution models to obtain the final answer. Second, we simulate the flow in a lid-driven cavity in an analogous fashion, by fusing finite difference solutions obtained with fine and low resolution assuming gappy data sets. We investigate the influence of various parameters for this framework, including the correlation kernel, the size of a buffer employed in estimating boundary conditions, the coarseness of the resolution of auxiliary data, and the communication frequency across different patches in fusing the information at different resolution levels. In addition to its robustness and resilience, the new framework can be employed to generalize previous multiscale approaches involving heterogeneous discretizations or even fundamentally different flow descriptions, e.g. in continuum-atomistic simulations.

  2. Multi-Resolution Multimedia QoE Models for IPTV Applications

    Directory of Open Access Journals (Sweden)

    Prasad Calyam

    2012-01-01

    Full Text Available Internet television (IPTV is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.

  3. A VIRTUAL GLOBE-BASED MULTI-RESOLUTION TIN SURFACE MODELING AND VISUALIZETION METHOD

    Directory of Open Access Journals (Sweden)

    X. Zheng

    2016-06-01

    Full Text Available The integration and visualization of geospatial data on a virtual globe play an significant role in understanding and analysis of the Earth surface processes. However, the current virtual globes always sacrifice the accuracy to ensure the efficiency for global data processing and visualization, which devalue their functionality for scientific applications. In this article, we propose a high-accuracy multi-resolution TIN pyramid construction and visualization method for virtual globe. Firstly, we introduce the cartographic principles to formulize the level of detail (LOD generation so that the TIN model in each layer is controlled with a data quality standard. A maximum z-tolerance algorithm is then used to iteratively construct the multi-resolution TIN pyramid. Moreover, the extracted landscape features are incorporated into each-layer TIN, thus preserving the topological structure of terrain surface at different levels. In the proposed framework, a virtual node (VN-based approach is developed to seamlessly partition and discretize each triangulation layer into tiles, which can be organized and stored with a global quad-tree index. Finally, the real time out-of-core spherical terrain rendering is realized on a virtual globe system VirtualWorld1.0. The experimental results showed that the proposed method can achieve an high-fidelity terrain representation, while produce a high quality underlying data that satisfies the demand for scientific analysis.

  4. A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

    Science.gov (United States)

    Sawall, Mathias; Kubis, Christoph; Börner, Armin; Selent, Detlef; Neymeyr, Klaus

    2015-09-03

    Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements. The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Long-range force and moment calculations in multiresolution simulations of molecular systems

    International Nuclear Information System (INIS)

    Poursina, Mohammad; Anderson, Kurt S.

    2012-01-01

    Multiresolution simulations of molecular systems such as DNAs, RNAs, and proteins are implemented using models with different resolutions ranging from a fully atomistic model to coarse-grained molecules, or even to continuum level system descriptions. For such simulations, pairwise force calculation is a serious bottleneck which can impose a prohibitive amount of computational load on the simulation if not performed wisely. Herein, we approximate the resultant force due to long-range particle-body and body-body interactions applicable to multiresolution simulations. Since the resultant force does not necessarily act through the center of mass of the body, it creates a moment about the mass center. Although this potentially important torque is neglected in many coarse-grained models which only use particle dynamics to formulate the dynamics of the system, it should be calculated and used when coarse-grained simulations are performed in a multibody scheme. Herein, the approximation for this moment due to far-field particle-body and body-body interactions is also provided.

  6. Single-resolution and multiresolution extended-Kalman-filter-based reconstruction approaches to optical refraction tomography.

    Science.gov (United States)

    Naik, Naren; Vasu, R M; Ananthasayanam, M R

    2010-02-20

    The problem of reconstruction of a refractive-index distribution (RID) in optical refraction tomography (ORT) with optical path-length difference (OPD) data is solved using two adaptive-estimation-based extended-Kalman-filter (EKF) approaches. First, a basic single-resolution EKF (SR-EKF) is applied to a state variable model describing the tomographic process, to estimate the RID of an optically transparent refracting object from noisy OPD data. The initialization of the biases and covariances corresponding to the state and measurement noise is discussed. The state and measurement noise biases and covariances are adaptively estimated. An EKF is then applied to the wavelet-transformed state variable model to yield a wavelet-based multiresolution EKF (MR-EKF) solution approach. To numerically validate the adaptive EKF approaches, we evaluate them with benchmark studies of standard stationary cases, where comparative results with commonly used efficient deterministic approaches can be obtained. Detailed reconstruction studies for the SR-EKF and two versions of the MR-EKF (with Haar and Daubechies-4 wavelets) compare well with those obtained from a typically used variant of the (deterministic) algebraic reconstruction technique, the average correction per projection method, thus establishing the capability of the EKF for ORT. To the best of our knowledge, the present work contains unique reconstruction studies encompassing the use of EKF for ORT in single-resolution and multiresolution formulations, and also in the use of adaptive estimation of the EKF's noise covariances.

  7. Inferring species richness and turnover by statistical multiresolution texture analysis of satellite imagery.

    Directory of Open Access Journals (Sweden)

    Matteo Convertino

    Full Text Available BACKGROUND: The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. METHODOLOGY/PRINCIPAL FINDINGS: We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL. Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species

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

    Science.gov (United States)

    Kawamoto, Tatsuro; Kabashima, Yoshiyuki

    2018-02-01

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

  9. Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization

    Directory of Open Access Journals (Sweden)

    Bruno M. Brentan

    2017-01-01

    Full Text Available Management of large water distribution systems can be improved by dividing their networks into so-called district metered areas (DMAs. However, such divisions must be based on appropriated technical criteria. Considering the importance of deeply understanding the relationship between DMA creation and these criteria, this work proposes a performance analysis of DMA generation that takes into account such indicators as resilience index, demand similarity, pressure uniformity, water age (and thus water quality, solution implantation costs, and electrical consumption. To cope with the complexity of the problem, suitable mathematical techniques are proposed in this paper. We use a social community detection technique to define the sectors, and then a multilevel particle swarm optimization approach is applied to find the optimal placement and operating point of the necessary devices. The results obtained by implementing the methodology in a real water supply network show its validity and the meaningful influence on the final result of, especially, elevation and pipe length.

  10. Detection of Spiroplasma and Wolbachia in the bacterial gonad community of Chorthippus parallelus.

    Science.gov (United States)

    Martínez-Rodríguez, P; Hernández-Pérez, M; Bella, J L

    2013-07-01

    We have recently detected the endosymbiont Wolbachia in multiple individuals and populations of the grasshopper Chorthippus parallelus (Orthoptera: acrididae). This bacterium induces reproductive anomalies, including cytoplasmic incompatibility. Such incompatibilities may help explain the maintenance of two distinct subspecies of this grasshopper, C. parallelus parallelus and C. parallelus erythropus, which are involved in a Pyrenean hybrid zone that has been extensively studied for the past 20 years, becoming a model system for the study of genetic divergence and speciation. To evaluate whether Wolbachia is the sole bacterial infection that might induce reproductive anomalies, the gonadal bacterial community of individuals from 13 distinct populations of C. parallelus was determined by denaturing gradient gel electrophoresis analysis of bacterial 16S rRNA gene fragments and sequencing. The study revealed low bacterial diversity in the gonads: a persistent bacterial trio consistent with Spiroplasma sp. and the two previously described supergroups of Wolbachia (B and F) dominated the gonad microbiota. A further evaluation of the composition of the gonad bacterial communities was carried out by whole cell hybridization. Our results confirm previous studies of the cytological distribution of Wolbachia in C. parallelus gonads and show a homogeneous infection by Spiroplasma. Spiroplasma and Wolbachia cooccurred in some individuals, but there was no significant association of Spiroplasma with a grasshopper's sex or with Wolbachia infection, although subtle trends might be detected with a larger sample size. This information, together with previous experimental crosses of this grasshopper, suggests that Spiroplasma is unlikely to contribute to sex-specific reproductive anomalies; instead, they implicate Wolbachia as the agent of the observed anomalies in C. parallelus.

  11. The Detection of Spotted Fever Group Rickettsia DNA in Tick Samples From Pastoral Communities in Kenya.

    Science.gov (United States)

    Koka, Hellen; Sang, Rosemary; Kutima, Helen Lydia; Musila, Lillian

    2017-05-01

    In this study, ticks from pastoral communities in Kenya were tested for Rickettsia spp. infections in geographical regions where the presence of tick-borne arboviruses had previously been reported. Rickettsial and arbovirus infections have similar clinical features which makes differential diagnosis challenging when both diseases occur. The tick samples were tested for Rickettsia spp. by conventional PCR using three primer sets targeting the gltA, ompA, and ompB genes followed by amplicon sequencing. Of the tick pools screened, 25% (95/380) were positive for Rickettsia spp. DNA using the gltA primer set. Of the tick-positive pools, 60% were ticks collected from camels. Rickettsia aeschlimannii and R. africae were the main Rickettsia spp. detected in the tick pools sequenced. The findings of this study indicate that multiple Rickettsia species are circulating in ticks from pastoral communities in Kenya and could contribute to the etiology of febrile illness in these areas. Diagnosis and treatment of rickettsial infections should be a public health priority in these regions. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. 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

  13. Adaptive multi-resolution 3D Hartree-Fock-Bogoliubov solver for nuclear structure

    Science.gov (United States)

    Pei, J. C.; Fann, G. I.; Harrison, R. J.; Nazarewicz, W.; Shi, Yue; Thornton, S.

    2014-08-01

    Background: Complex many-body systems, such as triaxial and reflection-asymmetric nuclei, weakly bound halo states, cluster configurations, nuclear fragments produced in heavy-ion fusion reactions, cold Fermi gases, and pasta phases in neutron star crust, are all characterized by large sizes and complex topologies in which many geometrical symmetries characteristic of ground-state configurations are broken. A tool of choice to study such complex forms of matter is an adaptive multi-resolution wavelet analysis. This method has generated much excitement since it provides a common framework linking many diversified methodologies across different fields, including signal processing, data compression, harmonic analysis and operator theory, fractals, and quantum field theory. Purpose: To describe complex superfluid many-fermion systems, we introduce an adaptive pseudospectral method for solving self-consistent equations of nuclear density functional theory in three dimensions, without symmetry restrictions. Methods: The numerical method is based on the multi-resolution and computational harmonic analysis techniques with a multi-wavelet basis. The application of state-of-the-art parallel programming techniques include sophisticated object-oriented templates which parse the high-level code into distributed parallel tasks with a multi-thread task queue scheduler for each multi-core node. The internode communications are asynchronous. The algorithm is variational and is capable of solving coupled complex-geometric systems of equations adaptively, with functional and boundary constraints, in a finite spatial domain of very large size, limited by existing parallel computer memory. For smooth functions, user-defined finite precision is guaranteed. Results: The new adaptive multi-resolution Hartree-Fock-Bogoliubov (HFB) solver madness-hfb is benchmarked against a two-dimensional coordinate-space solver hfb-ax that is based on the B-spline technique and a three-dimensional solver

  14. A parameter-free community detection method based on centrality and dispersion of nodes in complex networks

    Science.gov (United States)

    Li, Yafang; Jia, Caiyan; Yu, Jian

    2015-11-01

    K-means is a simple and efficient clustering algorithm to detect communities in networks. However, it may suffer from a bad choice of initial seeds (also called centers) that seriously affect the clustering accuracy and the convergence rate. Additionally, in K-means, the number of communities should be specified in advance. Till now, it is still an open problem on how to select initial seeds and how to determine the number of communities. In this study, a new parameter-free community detection method (named K-rank-D) was proposed. First, based on the fact that good initial seeds usually have high importance and are dispersedly located in a network, we proposed a modified PageRank centrality to evaluate the importance of a node, and drew a decision graph to depict the importance and the dispersion of nodes. Then, the initial seeds and the number of communities were selected from the decision graph actively and intuitively as the 'start' parameter of K-means. Experimental results on synthetic and real-world networks demonstrate the superior performance of our approach over competing methods for community detection.

  15. 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.

  16. Sparse PDF maps for non-linear multi-resolution image operations

    KAUST Repository

    Hadwiger, Markus

    2012-11-01

    We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters. © 2012 ACM.

  17. Investigations of homologous disaccharides by elastic incoherent neutron scattering and wavelet multiresolution analysis

    Energy Technology Data Exchange (ETDEWEB)

    Magazù, S.; Migliardo, F. [Dipartimento di Fisica e di Scienze della Terra dell’, Università degli Studi di Messina, Viale F. S. D’Alcontres 31, 98166 Messina (Italy); Vertessy, B.G. [Institute of Enzymology, Hungarian Academy of Science, Budapest (Hungary); Caccamo, M.T., E-mail: maccamo@unime.it [Dipartimento di Fisica e di Scienze della Terra dell’, Università degli Studi di Messina, Viale F. S. D’Alcontres 31, 98166 Messina (Italy)

    2013-10-16

    Highlights: • Innovative multiresolution wavelet analysis of elastic incoherent neutron scattering. • Elastic Incoherent Neutron Scattering measurements on homologues disaccharides. • EINS wavevector analysis. • EINS temperature analysis. - Abstract: In the present paper the results of a wavevector and thermal analysis of Elastic Incoherent Neutron Scattering (EINS) data collected on water mixtures of three homologous disaccharides through a wavelet approach are reported. The wavelet analysis allows to compare both the spatial properties of the three systems in the wavevector range of Q = 0.27 Å{sup −1} ÷ 4.27 Å{sup −1}. It emerges that, differently from previous analyses, for trehalose the scalograms are constantly lower and sharper in respect to maltose and sucrose, giving rise to a global spectral density along the wavevector range markedly less extended. As far as the thermal analysis is concerned, the global scattered intensity profiles suggest a higher thermal restrain of trehalose in respect to the other two homologous disaccharides.

  18. Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

    Science.gov (United States)

    Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

    2016-09-01

    Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

  19. A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data

    Science.gov (United States)

    Chen, Chuanfa; Li, Yanyan; Li, Wei; Dai, Honglei

    2013-08-01

    We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating ground from non-ground LiDAR point cloud based on point residuals from the interpolated raster surface. MHC includes three levels of hierarchy, with the simultaneous increase of cell resolution and residual threshold from the low to the high level of the hierarchy. At each level, the surface is iteratively interpolated towards the ground using thin plate spline (TPS) until no ground points are classified, and the classified ground points are used to update the surface in the next iteration. 15 groups of benchmark dataset, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were used to compare the performance of MHC with those of the 17 other publicized filtering methods. Results indicated that MHC with the average total error and average Cohen’s kappa coefficient of 4.11% and 86.27% performs better than all other filtering methods.

  20. Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

    Science.gov (United States)

    Lacaze, Alberto; Meystel, Michael; Meystel, Alex

    1994-01-01

    This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.

  1. Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation.

    Directory of Open Access Journals (Sweden)

    Najah Alsubaie

    Full Text Available Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.

  2. A multi-resolution approach to heat kernels on discrete surfaces

    KAUST Repository

    Vaxman, Amir

    2010-07-26

    Studying the behavior of the heat diffusion process on a manifold is emerging as an important tool for analyzing the geometry of the manifold. Unfortunately, the high complexity of the computation of the heat kernel - the key to the diffusion process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel approximation method for the heat kernel at short times results in an efficient and robust algorithm for computing the heat kernels of detailed models. We show experimentally that our method can achieve good approximations in a fraction of the time required by traditional algorithms. Finally, we demonstrate how these heat kernels can be used to improve a diffusion-based feature extraction algorithm. © 2010 ACM.

  3. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Science.gov (United States)

    Zhu, Aichun; Wang, Tian; Snoussi, Hichem

    2018-03-01

    This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  4. Hybrid Multiscale Finite Volume method for multiresolution simulations of flow and reactive transport in porous media

    Science.gov (United States)

    Barajas-Solano, D. A.; Tartakovsky, A. M.

    2017-12-01

    We present a multiresolution method for the numerical simulation of flow and reactive transport in porous, heterogeneous media, based on the hybrid Multiscale Finite Volume (h-MsFV) algorithm. The h-MsFV algorithm allows us to couple high-resolution (fine scale) flow and transport models with lower resolution (coarse) models to locally refine both spatial resolution and transport models. The fine scale problem is decomposed into various "local'' problems solved independently in parallel and coordinated via a "global'' problem. This global problem is then coupled with the coarse model to strictly ensure domain-wide coarse-scale mass conservation. The proposed method provides an alternative to adaptive mesh refinement (AMR), due to its capacity to rapidly refine spatial resolution beyond what's possible with state-of-the-art AMR techniques, and the capability to locally swap transport models. We illustrate our method by applying it to groundwater flow and reactive transport of multiple species.

  5. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Directory of Open Access Journals (Sweden)

    Aichun Zhu

    2018-03-01

    Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  6. Using wavelet multi-resolution nature to accelerate the identification of fractional order system

    International Nuclear Information System (INIS)

    Li Yuan-Lu; Meng Xiao; Ding Ya-Qing

    2017-01-01

    Because of the fractional order derivatives, the identification of the fractional order system (FOS) is more complex than that of an integral order system (IOS). In order to avoid high time consumption in the system identification, the least-squares method is used to find other parameters by fixing the fractional derivative order. Hereafter, the optimal parameters of a system will be found by varying the derivative order in an interval. In addition, the operational matrix of the fractional order integration combined with the multi-resolution nature of a wavelet is used to accelerate the FOS identification, which is achieved by discarding wavelet coefficients of high-frequency components of input and output signals. In the end, the identifications of some known fractional order systems and an elastic torsion system are used to verify the proposed method. (paper)

  7. Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

    KAUST Repository

    She, James

    2009-11-01

    Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

  8. Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

    KAUST Repository

    She, James; Ho, Pin-Han; Shihada, Basem

    2009-01-01

    Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

  9. Terascale Visualization: Multi-resolution Aspirin for Big-Data Headaches

    Science.gov (United States)

    Duchaineau, Mark

    2001-06-01

    Recent experience on the Accelerated Strategic Computing Initiative (ASCI) computers shows that computational physicists are successfully producing a prodigious collection of numbers on several thousand processors. But with this wealth of numbers comes an unprecedented difficulty in processing and moving them to provide useful insight and analysis. In this talk, a few simulations are highlighted where recent advancements in multiple-resolution mathematical representations and algorithms have provided some hope of seeing most of the physics of interest while keeping within the practical limits of the post-simulation storage and interactive data-exploration resources. A whole host of visualization research activities was spawned by the 1999 Gordon Bell Prize-winning computation of a shock-tube experiment showing Richtmyer-Meshkov turbulent instabilities. This includes efforts for the entire data pipeline from running simulation to interactive display: wavelet compression of field data, multi-resolution volume rendering and slice planes, out-of-core extraction and simplification of mixing-interface surfaces, shrink-wrapping to semi-regularize the surfaces, semi-structured surface wavelet compression, and view-dependent display-mesh optimization. More recently on the 12 TeraOps ASCI platform, initial results from a 5120-processor, billion-atom molecular dynamics simulation showed that 30-to-1 reductions in storage size can be achieved with no human-observable errors for the analysis required in simulations of supersonic crack propagation. This made it possible to store the 25 trillion bytes worth of simulation numbers in the available storage, which was under 1 trillion bytes. While multi-resolution methods and related systems are still in their infancy, for the largest-scale simulations there is often no other choice should the science require detailed exploration of the results.

  10. Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals.

    Science.gov (United States)

    Verma, Gyanendra K; Tiwary, Uma Shanker

    2014-11-15

    The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for 'Depressing' with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Exploring a Multiresolution Modeling Approach within the Shallow-Water Equations

    Energy Technology Data Exchange (ETDEWEB)

    Ringler, Todd D.; Jacobsen, Doug; Gunzburger, Max; Ju, Lili; Duda, Michael; Skamarock, William

    2011-11-01

    The ability to solve the global shallow-water equations with a conforming, variable-resolution mesh is evaluated using standard shallow-water test cases. While the long-term motivation for this study is the creation of a global climate modeling framework capable of resolving different spatial and temporal scales in different regions, the process begins with an analysis of the shallow-water system in order to better understand the strengths and weaknesses of the approach developed herein. The multiresolution meshes are spherical centroidal Voronoi tessellations where a single, user-supplied density function determines the region(s) of fine- and coarsemesh resolution. The shallow-water system is explored with a suite of meshes ranging from quasi-uniform resolution meshes, where the grid spacing is globally uniform, to highly variable resolution meshes, where the grid spacing varies by a factor of 16 between the fine and coarse regions. The potential vorticity is found to be conserved to within machine precision and the total available energy is conserved to within a time-truncation error. This result holds for the full suite of meshes, ranging from quasi-uniform resolution and highly variable resolution meshes. Based on shallow-water test cases 2 and 5, the primary conclusion of this study is that solution error is controlled primarily by the grid resolution in the coarsest part of the model domain. This conclusion is consistent with results obtained by others.When these variable-resolution meshes are used for the simulation of an unstable zonal jet, the core features of the growing instability are found to be largely unchanged as the variation in the mesh resolution increases. The main differences between the simulations occur outside the region of mesh refinement and these differences are attributed to the additional truncation error that accompanies increases in grid spacing. Overall, the results demonstrate support for this approach as a path toward

  12. Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

    Science.gov (United States)

    Wang, Limin; Guo, Sheng; Huang, Weilin; Xiong, Yuanjun; Qiao, Yu

    2017-04-01

    Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity. (i) We exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category. (ii) We utilize the knowledge of extra networks to produce a soft label for each image. Then the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7\\%) and SUN397 (72.0\\%). We release the code and models at~\\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.

  13. 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…

  14. Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

    Energy Technology Data Exchange (ETDEWEB)

    Purohit, Sumit; Choudhury, Sutanay; Holder, Lawrence B.

    2017-12-11

    Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). We explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.

  15. Interventions to increase tuberculosis case detection at primary healthcare or community-level services.

    Science.gov (United States)

    Mhimbira, Francis A; Cuevas, Luis E; Dacombe, Russell; Mkopi, Abdallah; Sinclair, David

    2017-11-28

    Pulmonary tuberculosis is usually diagnosed when symptomatic individuals seek care at healthcare facilities, and healthcare workers have a minimal role in promoting the health-seeking behaviour. However, some policy specialists believe the healthcare system could be more active in tuberculosis diagnosis to increase tuberculosis case detection. To evaluate the effectiveness of different strategies to increase tuberculosis case detection through improving access (geographical, financial, educational) to tuberculosis diagnosis at primary healthcare or community-level services. We searched the following databases for relevant studies up to 19 December 2016: the Cochrane Infectious Disease Group Specialized Register; the Cochrane Central Register of Controlled Trials (CENTRAL), published in the Cochrane Library, Issue 12, 2016; MEDLINE; Embase; Science Citation Index Expanded, Social Sciences Citation Index; BIOSIS Previews; and Scopus. We also searched the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, and the metaRegister of Controlled Trials (mRCT) for ongoing trials. Randomized and non-randomized controlled studies comparing any intervention that aims to improve access to a tuberculosis diagnosis, with no intervention or an alternative intervention. Two review authors independently assessed trials for eligibility and risk of bias, and extracted data. We compared interventions using risk ratios (RR) and 95% confidence intervals (CI). We assessed the certainty of the evidence using the GRADE approach. We included nine cluster-randomized trials, one individual randomized trial, and seven non-randomized controlled studies. Nine studies were conducted in sub-Saharan Africa (Ethiopia, Nigeria, South Africa, Zambia, and Zimbabwe), six in Asia (Bangladesh, Cambodia, India, Nepal, and Pakistan), and two in South America (Brazil and Colombia); which are all high tuberculosis prevalence areas.Tuberculosis outreach

  16. Multi-resolution simulation of focused ultrasound propagation through ovine skull from a single-element transducer

    Science.gov (United States)

    Yoon, Kyungho; Lee, Wonhye; Croce, Phillip; Cammalleri, Amanda; Yoo, Seung-Schik

    2018-05-01

    Transcranial focused ultrasound (tFUS) is emerging as a non-invasive brain stimulation modality. Complicated interactions between acoustic pressure waves and osseous tissue introduce many challenges in the accurate targeting of an acoustic focus through the cranium. Image-guidance accompanied by a numerical simulation is desired to predict the intracranial acoustic propagation through the skull; however, such simulations typically demand heavy computation, which warrants an expedited processing method to provide on-site feedback for the user in guiding the acoustic focus to a particular brain region. In this paper, we present a multi-resolution simulation method based on the finite-difference time-domain formulation to model the transcranial propagation of acoustic waves from a single-element transducer (250 kHz). The multi-resolution approach improved computational efficiency by providing the flexibility in adjusting the spatial resolution. The simulation was also accelerated by utilizing parallelized computation through the graphic processing unit. To evaluate the accuracy of the method, we measured the actual acoustic fields through ex vivo sheep skulls with different sonication incident angles. The measured acoustic fields were compared to the simulation results in terms of focal location, dimensions, and pressure levels. The computational efficiency of the presented method was also assessed by comparing simulation speeds at various combinations of resolution grid settings. The multi-resolution grids consisting of 0.5 and 1.0 mm resolutions gave acceptable accuracy (under 3 mm in terms of focal position and dimension, less than 5% difference in peak pressure ratio) with a speed compatible with semi real-time user feedback (within 30 s). The proposed multi-resolution approach may serve as a novel tool for simulation-based guidance for tFUS applications.

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

    Science.gov (United States)

    Huang, Yishuo

    2015-09-01

    regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005-2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.

  18. Crack Identification in CFRP Laminated Beams Using Multi-Resolution Modal Teager–Kaiser Energy under Noisy Environments

    Science.gov (United States)

    Xu, Wei; Cao, Maosen; Ding, Keqin; Radzieński, Maciej; Ostachowicz, Wiesław

    2017-01-01

    Carbon fiber reinforced polymer laminates are increasingly used in the aerospace and civil engineering fields. Identifying cracks in carbon fiber reinforced polymer laminated beam components is of considerable significance for ensuring the integrity and safety of the whole structures. With the development of high-resolution measurement technologies, mode-shape-based crack identification in such laminated beam components has become an active research focus. Despite its sensitivity to cracks, however, this method is susceptible to noise. To address this deficiency, this study proposes a new concept of multi-resolution modal Teager–Kaiser energy, which is the Teager–Kaiser energy of a mode shape represented in multi-resolution, for identifying cracks in carbon fiber reinforced polymer laminated beams. The efficacy of this concept is analytically demonstrated by identifying cracks in Timoshenko beams with general boundary conditions; and its applicability is validated by diagnosing cracks in a carbon fiber reinforced polymer laminated beam, whose mode shapes are precisely acquired via non-contact measurement using a scanning laser vibrometer. The analytical and experimental results show that multi-resolution modal Teager–Kaiser energy is capable of designating the presence and location of cracks in these beams under noisy environments. This proposed method holds promise for developing crack identification systems for carbon fiber reinforced polymer laminates. PMID:28773016

  19. Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks.

    Directory of Open Access Journals (Sweden)

    Kathleen M Gates

    Full Text Available Clinical investigations of many neuropsychiatric disorders rely on the assumption that diagnostic categories and typical control samples each have within-group homogeneity. However, research using human neuroimaging has revealed that much heterogeneity exists across individuals in both clinical and control samples. This reality necessitates that researchers identify and organize the potentially varied patterns of brain physiology. We introduce an analytical approach for arriving at subgroups of individuals based entirely on their brain physiology. The method begins with Group Iterative Multiple Model Estimation (GIMME to assess individual directed functional connectivity maps. GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity. Individuals are then grouped based on similarities in their connectivity patterns using a modularity approach for community detection. Monte Carlo simulations demonstrate that using GIMME in combination with the modularity algorithm works exceptionally well--on average over 97% of simulated individuals are placed in the accurate subgroup with no prior information on functional architecture or group identity. Having demonstrated reliability, we examine resting-state data of fronto-parietal regions drawn from a sample (N = 80 of typically developing and attention-deficit/hyperactivity disorder (ADHD -diagnosed children. Here, we find 5 subgroups. Two subgroups were predominantly comprised of ADHD, suggesting that more than one biological marker exists that can be used to identify children with ADHD based from their brain physiology. Empirical evidence presented here supports notions that heterogeneity exists in brain physiology within ADHD and control samples. This type of information gained from the approach presented here can assist in

  20. Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

    Science.gov (United States)

    Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

    2018-04-01

    This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent

  1. Accurate convolution/superposition for multi-resolution dose calculation using cumulative tabulated kernels

    International Nuclear Information System (INIS)

    Lu Weiguo; Olivera, Gustavo H; Chen Mingli; Reckwerdt, Paul J; Mackie, Thomas R

    2005-01-01

    Convolution/superposition (C/S) is regarded as the standard dose calculation method in most modern radiotherapy treatment planning systems. Different implementations of C/S could result in significantly different dose distributions. This paper addresses two major implementation issues associated with collapsed cone C/S: one is how to utilize the tabulated kernels instead of analytical parametrizations and the other is how to deal with voxel size effects. Three methods that utilize the tabulated kernels are presented in this paper. These methods differ in the effective kernels used: the differential kernel (DK), the cumulative kernel (CK) or the cumulative-cumulative kernel (CCK). They result in slightly different computation times but significantly different voxel size effects. Both simulated and real multi-resolution dose calculations are presented. For simulation tests, we use arbitrary kernels and various voxel sizes with a homogeneous phantom, and assume forward energy transportation only. Simulations with voxel size up to 1 cm show that the CCK algorithm has errors within 0.1% of the maximum gold standard dose. Real dose calculations use a heterogeneous slab phantom, both the 'broad' (5 x 5 cm 2 ) and the 'narrow' (1.2 x 1.2 cm 2 ) tomotherapy beams. Various voxel sizes (0.5 mm, 1 mm, 2 mm, 4 mm and 8 mm) are used for dose calculations. The results show that all three algorithms have negligible difference (0.1%) for the dose calculation in the fine resolution (0.5 mm voxels). But differences become significant when the voxel size increases. As for the DK or CK algorithm in the broad (narrow) beam dose calculation, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 10% (7%) of the maximum dose. As for the broad (narrow) beam dose calculation using the CCK algorithm, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 1% of the maximum dose. Among all three methods, the CCK algorithm

  2. A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

    KAUST Repository

    Tagle, Felipe

    2017-12-06

    Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.

  3. Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech.

    Science.gov (United States)

    Campo, D.; Quintero, O. L.; Bastidas, M.

    2016-04-01

    We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform - was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.

  4. Developing a real-time emulation of multiresolutional control architectures for complex, discrete-event systems

    Energy Technology Data Exchange (ETDEWEB)

    Davis, W.J.; Macro, J.G.; Brook, A.L. [Univ. of Illinois, Urbana, IL (United States)] [and others

    1996-12-31

    This paper first discusses an object-oriented, control architecture and then applies the architecture to produce a real-time software emulator for the Rapid Acquisition of Manufactured Parts (RAMP) flexible manufacturing system (FMS). In specifying the control architecture, the coordinated object is first defined as the primary modeling element. These coordinated objects are then integrated into a Recursive, Object-Oriented Coordination Hierarchy. A new simulation methodology, the Hierarchical Object-Oriented Programmable Logic Simulator, is then employed to model the interactions among the coordinated objects. The final step in implementing the emulator is to distribute the models of the coordinated objects over a network of computers and to synchronize their operation to a real-time clock. The paper then introduces the Hierarchical Subsystem Controller as an intelligent controller for the coordinated object. The proposed approach to intelligent control is then compared to the concept of multiresolutional semiosis that has been developed by Dr. Alex Meystel. Finally, the plans for implementing an intelligent controller for the RAMP FMS are discussed.

  5. OpenCL-based vicinity computation for 3D multiresolution mesh compression

    Science.gov (United States)

    Hachicha, Soumaya; Elkefi, Akram; Ben Amar, Chokri

    2017-03-01

    3D multiresolution mesh compression systems are still widely addressed in many domains. These systems are more and more requiring volumetric data to be processed in real-time. Therefore, the performance is becoming constrained by material resources usage and an overall reduction in the computational time. In this paper, our contribution entirely lies on computing, in real-time, triangles neighborhood of 3D progressive meshes for a robust compression algorithm based on the scan-based wavelet transform(WT) technique. The originality of this latter algorithm is to compute the WT with minimum memory usage by processing data as they are acquired. However, with large data, this technique is considered poor in term of computational complexity. For that, this work exploits the GPU to accelerate the computation using OpenCL as a heterogeneous programming language. Experiments demonstrate that, aside from the portability across various platforms and the flexibility guaranteed by the OpenCL-based implementation, this method can improve performance gain in speedup factor of 5 compared to the sequential CPU implementation.

  6. Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Hoa T. [Univ. of Utah, Salt Lake City, UT (United States); Stone, Daithi [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2016-01-01

    An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

  7. Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

    Directory of Open Access Journals (Sweden)

    Hae-Gil Hwang

    2005-01-01

    Full Text Available Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS, and invasive ductal carcinoma (CA. To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each, respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

  8. Multiresolution analysis over graphs for a motor imagery based online BCI game.

    Science.gov (United States)

    Asensio-Cubero, Javier; Gan, John Q; Palaniappan, Ramaswamy

    2016-01-01

    Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Multi-resolution voxel phantom modeling: a high-resolution eye model for computational dosimetry.

    Science.gov (United States)

    Caracappa, Peter F; Rhodes, Ashley; Fiedler, Derek

    2014-09-21

    Voxel models of the human body are commonly used for simulating radiation dose with a Monte Carlo radiation transport code. Due to memory limitations, the voxel resolution of these computational phantoms is typically too large to accurately represent the dimensions of small features such as the eye. Recently reduced recommended dose limits to the lens of the eye, which is a radiosensitive tissue with a significant concern for cataract formation, has lent increased importance to understanding the dose to this tissue. A high-resolution eye model is constructed using physiological data for the dimensions of radiosensitive tissues, and combined with an existing set of whole-body models to form a multi-resolution voxel phantom, which is used with the MCNPX code to calculate radiation dose from various exposure types. This phantom provides an accurate representation of the radiation transport through the structures of the eye. Two alternate methods of including a high-resolution eye model within an existing whole-body model are developed. The accuracy and performance of each method is compared against existing computational phantoms.

  10. On the use of adaptive multiresolution method with time-varying tolerance for compressible fluid flows

    Science.gov (United States)

    Soni, V.; Hadjadj, A.; Roussel, O.

    2017-12-01

    In this paper, a fully adaptive multiresolution (MR) finite difference scheme with a time-varying tolerance is developed to study compressible fluid flows containing shock waves in interaction with solid obstacles. To ensure adequate resolution near rigid bodies, the MR algorithm is combined with an immersed boundary method based on a direct-forcing approach in which the solid object is represented by a continuous solid-volume fraction. The resulting algorithm forms an efficient tool capable of solving linear and nonlinear waves on arbitrary geometries. Through a one-dimensional scalar wave equation, the accuracy of the MR computation is, as expected, seen to decrease in time when using a constant MR tolerance considering the accumulation of error. To overcome this problem, a variable tolerance formulation is proposed, which is assessed through a new quality criterion, to ensure a time-convergence solution for a suitable quality resolution. The newly developed algorithm coupled with high-resolution spatial and temporal approximations is successfully applied to shock-bluff body and shock-diffraction problems solving Euler and Navier-Stokes equations. Results show excellent agreement with the available numerical and experimental data, thereby demonstrating the efficiency and the performance of the proposed method.

  11. Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

    Science.gov (United States)

    Haque, Md. Enamul; Al-Ramadan, Baqer; Johnson, Brian A.

    2016-07-01

    Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.

  12. Community Awareness - A Key to the Early Detection of Head and Neck Cancer

    Directory of Open Access Journals (Sweden)

    Ajay Manickam

    2016-04-01

    Full Text Available INTRODUCTION Mortality and morbidity associated with head and neck cancers have decreased to a great extent in many developed countries of the world due to early diagnosis and treatment with advances in surgical techniques and better availability of radiotherapist and oncologists. But the situation in developing countries like India is quite different. AIMS AND OBJECTIVES This study was conducted amongst the patients attending the Otolaryngology department of a teaching hospital in Kolkata to find the incidence of various types and sites of head and neck cancer, to assess time delay from the date of onset of symptom to the final disease confirmation in relation to patients’ demographic profile, to assess time delay in histopathological diagnosis after reaching a tertiary care setup and also to correlate tobacco consumption and alcohol intake as risk factors for head and neck cancer and to note the reasons for late presentation, as described by the patient. MATERIALS AND METHODS The descriptive study was conducted at a tertiary level teaching hospital, in the Department of Otorhinolaryngology for a period from August 2013 to August 2015 with a study population of 133.  OBSERVATIONS  An average time lag from the onset of symptom to final diagnosis as malignancy was found to be 6 months to one year in nearly 72% of cases. Most of the patients were uneducated males of more than 50 years of age, hailing from rural areas. Cancer larynx was found to be the commonest of all head and neck cancers (31.6%. More than 65% of the patients were addicted to tobacco chewing or smoking or consumption of alcohol.  DISCUSSION  Poverty, lack of education, poor communication, lack of health care infrastructure in rural areas, community awareness about various risk factors, lack of effective health policy to achieve early diagnosis of head neck cancer were common factors related to delay in diagnosis.  CONCLUSION  Tobacco use and alcohol intake are the

  13. Betweenness centrality and its applications from modeling traffic flows to network community detection

    Science.gov (United States)

    Ren, Yihui

    network and we demonstrate that the changes can propagate globally, affecting traffic several hundreds of miles away. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. In the second part of the thesis we focus on network deconstruction and community detection problems, both intensely studied topics in network science, using a weighted betweenness centrality approach. We present an algorithm that solves both problems efficiently and accurately and demonstrate that on both benchmark networks and data networks.

  14. The importance of scaling for detecting community patterns: success and failure in assemblages of introduced species

    Science.gov (United States)

    Allen, Craig R.; Angeler, David G.; Moulton, Michael P.; Holling, Crawford S.

    2015-01-01

    Community saturation can help to explain why biological invasions fail. However, previous research has documented inconsistent relationships between failed invasions (i.e., an invasive species colonizes but goes extinct) and the number of species present in the invaded community. We use data from bird communities of the Hawaiian island of Oahu, which supports a community of 38 successfully established introduced birds and where 37 species were introduced but went extinct (failed invasions). We develop a modified approach to evaluate the effects of community saturation on invasion failure. Our method accounts (1) for the number of species present (NSP) when the species goes extinct rather than during its introduction; and (2) scaling patterns in bird body mass distributions that accounts for the hierarchical organization of ecosystems and the fact that interaction strength amongst species varies with scale. We found that when using NSP at the time of extinction, NSP was higher for failed introductions as compared to successful introductions, supporting the idea that increasing species richness and putative community saturation mediate invasion resistance. Accounting for scale-specific patterns in body size distributions further improved the relationship between NSP and introduction failure. Results show that a better understanding of invasion outcomes can be obtained when scale-specific community structure is accounted for in the analysis.

  15. Statistical power to detect change in a mangrove shoreline fish community adjacent to a nuclear power plant.

    Science.gov (United States)

    Dolan, T E; Lynch, P D; Karazsia, J L; Serafy, J E

    2016-03-01

    An expansion is underway of a nuclear power plant on the shoreline of Biscayne Bay, Florida, USA. While the precise effects of its construction and operation are unknown, impacts on surrounding marine habitats and biota are considered by experts to be likely. The objective of the present study was to determine the adequacy of an ongoing monitoring survey of fish communities associated with mangrove habitats directly adjacent to the power plant to detect fish community changes, should they occur, at three spatial scales. Using seasonally resolved data recorded during 532 fish surveys over an 8-year period, power analyses were performed for four mangrove fish metrics (fish diversity, fish density, and the occurrence of two ecologically important fish species: gray snapper (Lutjanus griseus) and goldspotted killifish (Floridichthys carpio). Results indicated that the monitoring program at current sampling intensity allows for detection of <33% changes in fish density and diversity metrics in both the wet and the dry season in the two larger study areas. Sampling effort was found to be insufficient in either season to detect changes at this level (<33%) in species-specific occurrence metrics for the two fish species examined. The option of supplementing ongoing, biological monitoring programs for improved, focused change detection deserves consideration from both ecological and cost-benefit perspectives.

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

    Science.gov (United States)

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

    2013-10-01

    In the past 15 years mortality rates from liver disease have doubled in the UK. Brief alcohol advice is cost effective, but clinically meaningful reductions in alcohol consumption only occur in around 1 in 10 individuals. To provide evidence that detecting early liver disease in the community is feasible, practical, and that feedback of liver risk can increase the proportion of subjects reducing alcohol consumption. A community feasibility study in nine general practice sites in Hampshire. Hazardous and harmful drinkers were identified by WHO AUDIT questionnaire and offered screening for liver fibrosis. In total, 4630 individuals responded, of whom 1128 (24%) hazardous or harmful drinkers were offered a liver fibrosis check using the Southampton Traffic Light (STL) test; 393 (38%) attended and test results were returned by post. The STL has a low threshold for liver fibrosis with 45 (11%) red, 157 (40%) amber, and 191 (49%) green results. Follow-up AUDIT data was obtained for 303/393 (77%) and 76/153 (50%) subjects with evidence of liver damage reduced drinking by at least one AUDIT category (harmful to hazardous, or hazardous to low risk) compared with 52/150 (35%, PAUDIT >15), 22/34 (65%) of STL positives, reduced drinking compared with 10/29 (35%, PDetection of liver disease in the community is feasible, and feedback of liver risk may reduce harmful drinking.

  17. High prevalence of tuberculosis and insufficient case detection in two communities in the Western Cape, South Africa.

    Directory of Open Access Journals (Sweden)

    Mareli Claassens

    Full Text Available In South Africa the estimated incidence of all forms of tuberculosis (TB for 2008 was 960/100000. It was reported that all South Africans lived in districts with Directly Observed Therapy, Short-course. However, the 2011 WHO report indicated South Africa as the only country in the world where the TB incidence is still rising.To report the results of a TB prevalence survey and to determine the speed of TB case detection in the study communities.In 2005 a TB prevalence survey was done to inform the sample size calculation for the ZAMSTAR (Zambia South Africa TB and AIDS Reduction trial. It was a cluster survey with clustering by enumeration area; all households were visited within enumeration areas and informed consent obtained from eligible adults. A questionnaire was completed and a sputum sample collected from each adult. Samples were inoculated on both liquid mycobacterium growth indicator tube (MGIT and Löwenstein-Jensen media. A follow-up HIV prevalence survey was done in 2007.In Community A, the adjusted prevalence of culture positive TB was 32/1000 (95%CI 25-41/1000 and of smear positive TB 8/1000 (95%CI 5-13/1000. In Community B, the adjusted prevalence of culture positive TB was 24/1000 (95%CI 17-32/1000 and of smear positive TB 9/1000 (95%CI 6-15/1000. In Community A the patient diagnostic rate was 0.38/person-year while in community B it was 0.30/person-year. In both communities the adjusted HIV prevalence was 25% (19-30%.In both communities a higher TB prevalence than national estimates and a low patient diagnostic rate was calculated, suggesting that cases are not detected at a sufficient rate to interrupt transmission. These findings may contribute to the rising TB incidence in South Africa. The TB epidemic should therefore be addressed rapidly and effectively, especially in the presence of the concurrently high HIV prevalence.

  18. A multiresolution image based approach for correction of partial volume effects in emission tomography

    International Nuclear Information System (INIS)

    Boussion, N; Hatt, M; Lamare, F; Bizais, Y; Turzo, A; Rest, C Cheze-Le; Visvikis, D

    2006-01-01

    Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the 'a trous' algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in

  19. Detection of 12.5% and 25% Salt Reduction in Bread in a Remote Indigenous Australian Community

    Science.gov (United States)

    McMahon, Emma; Clarke, Rozlynne; Jaenke, Rachael; Brimblecombe, Julie

    2016-01-01

    Food reformulation is an important strategy to reduce the excess salt intake observed in remote Indigenous Australia. We aimed to examine whether 12.5% and 25% salt reduction in bread is detectable, and, if so, whether acceptability is changed, in a sample of adults living in a remote Indigenous community in the Northern Territory of Australia. Convenience samples were recruited for testing of reduced-salt (300 and 350 mg Na/100 g) versus Standard (~400 mg Na/100 g) white and wholemeal breads (n = 62 for white; n = 72 for wholemeal). Triangle testing was used to examine whether participants could detect a difference between the breads. Liking of each bread was also measured; standard consumer acceptability questionnaires were modified to maximise cultural appropriateness and understanding. Participants were unable to detect a difference between Standard and reduced-salt breads (all p values > 0.05 when analysed using binomial probability). Further, as expected, liking of the breads was not changed with salt reduction (all p values > 0.05 when analysed using ANOVA). Reducing salt in products commonly purchased in remote Indigenous communities has potential as an equitable, cost-effective and sustainable strategy to reduce population salt intake and reduce risk of chronic disease, without the barriers associated with strategies that require individual behaviour change. PMID:26999196

  20. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    OpenAIRE

    Dal’Asta, Ana Paula; Brigatti, Newton; Amaral, Silvana; Escada, Maria Isabel Sobral; Monteiro, Antonio Miguel Vieira

    2012-01-01

    Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-...

  1. Detecting overlapping community structure of networks based on vertex–vertex correlations

    International Nuclear Information System (INIS)

    Zarei, Mina; Izadi, Dena; Samani, Keivan Aghababaei

    2009-01-01

    Using the NMF (non-negative matrix factorization) method, the structure of overlapping communities in complex networks is investigated. For the feature matrix of the NMF method we introduce a vertex–vertex correlation matrix. The method is applied to some computer-generated and real-world networks. Simulations show that this feature matrix gives more reasonable results

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

    Science.gov (United States)

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

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

  3. Real-time community detection in full social networks on a laptop

    Science.gov (United States)

    Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive

    2018-01-01

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  4. Real-time community detection in full social networks on a laptop.

    Directory of Open Access Journals (Sweden)

    Benjamin Paul Chamberlain

    Full Text Available For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph. As global social networks (e.g., Facebook and Twitter are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to

  5. Real-time community detection in full social networks on a laptop.

    Science.gov (United States)

    Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive; Deisenroth, Marc Peter

    2018-01-01

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  6. Picophytoplankton community in a tropical estuary: Detection of Prochlorococcus-like populations

    Digital Repository Service at National Institute of Oceanography (India)

    Mitbavkar, S.; Rajaneesh, K.M.; Anil, A.C.; Sundar, D.

    The influence of hydrography on the picophytoplankton (PP) abundance in estuaries was studied by sampling along a salinity gradient in an Indian estuary. Prochlorococcus-like cells were detected at salinities ranging from 0.06 to 35, which otherwise...

  7. The objective of this program is to develop innovative DNA detection technologies to achieve fast microbial community assessment. The specific approaches are (1) to develop inexpensive and reliable sequence-proof hybridization DNA detection technology (2) to develop quantitative DNA hybridization technology for microbial community assessment and (3) to study the microbes which have demonstrated the potential to have nuclear waste bioremediation

    International Nuclear Information System (INIS)

    Chen, Chung H.

    2004-01-01

    The objective of this program is to develop innovative DNA detection technologies to achieve fast microbial community assessment. The specific approaches are (1) to develop inexpensive and reliable sequence-proof hybridization DNA detection technology (2) to develop quantitative DNA hybridization technology for microbial community assessment and (3) to study the microbes which have demonstrated the potential to have nuclear waste bioremediation

  8. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities.

    Science.gov (United States)

    Gilbert, Jack A; Field, Dawn; Huang, Ying; Edwards, Rob; Li, Weizhong; Gilna, Paul; Joint, Ian

    2008-08-22

    Sequencing the expressed genetic information of an ecosystem (metatranscriptome) can provide information about the response of organisms to varying environmental conditions. Until recently, metatranscriptomics has been limited to microarray technology and random cloning methodologies. The application of high-throughput sequencing technology is now enabling access to both known and previously unknown transcripts in natural communities. We present a study of a complex marine metatranscriptome obtained from random whole-community mRNA using the GS-FLX Pyrosequencing technology. Eight samples, four DNA and four mRNA, were processed from two time points in a controlled coastal ocean mesocosm study (Bergen, Norway) involving an induced phytoplankton bloom producing a total of 323,161,989 base pairs. Our study confirms the finding of the first published metatranscriptomic studies of marine and soil environments that metatranscriptomics targets highly expressed sequences which are frequently novel. Our alternative methodology increases the range of experimental options available for conducting such studies and is characterized by an exceptional enrichment of mRNA (99.92%) versus ribosomal RNA. Analysis of corresponding metagenomes confirms much higher levels of assembly in the metatranscriptomic samples and a far higher yield of large gene families with >100 members, approximately 91% of which were novel. This study provides further evidence that metatranscriptomic studies of natural microbial communities are not only feasible, but when paired with metagenomic data sets, offer an unprecedented opportunity to explore both structure and function of microbial communities--if we can overcome the challenges of elucidating the functions of so many never-seen-before gene families.

  9. Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems.

    Science.gov (United States)

    Kim, Won Hwa; Chung, Moo K; Singh, Vikas

    2013-01-01

    The analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.

  10. Sensitive and substrate-specific detection of metabolically active microorganisms in natural microbial consortia using community isotope arrays.

    Science.gov (United States)

    Tourlousse, Dieter M; Kurisu, Futoshi; Tobino, Tomohiro; Furumai, Hiroaki

    2013-05-01

    The goal of this study was to develop and validate a novel fosmid-clone-based metagenome isotope array approach - termed the community isotope array (CIArray) - for sensitive detection and identification of microorganisms assimilating a radiolabeled substrate within complex microbial communities. More specifically, a sample-specific CIArray was used to identify anoxic phenol-degrading microorganisms in activated sludge treating synthetic coke-oven wastewater in a single-sludge predenitrification-nitrification process. Hybridization of the CIArray with DNA from the (14) C-phenol-amended sample indicated that bacteria assimilating (14) C-atoms, presumably directly from phenol, under nitrate-reducing conditions were abundant in the reactor, and taxonomic assignment of the fosmid clone end sequences suggested that they belonged to the Gammaproteobacteria. The specificity of the CIArray was validated by quantification of fosmid-clone-specific DNA in density-resolved DNA fractions from samples incubated with (13) C-phenol, which verified that all CIArray-positive probes stemmed from microorganisms that assimilated isotopically labeled carbon. This also demonstrated that the CIArray was more sensitive than DNA-SIP, as the former enabled positive detection at a phenol concentration that failed to yield a 'heavy' DNA fraction. Finally, two operational taxonomic units distantly related to marine Gammaproteobacteria were identified to account for more than half of 16S rRNA gene clones in the 'heavy' DNA library, corroborating the CIArray-based identification. © 2013 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2017-01-01

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

  12. Contribution of the microbial communities detected on an oil painting on canvas to its biodeterioration.

    Directory of Open Access Journals (Sweden)

    María Del Mar López-Miras

    Full Text Available In this study, we investigated the microbial community (bacteria and fungi colonising an oil painting on canvas, which showed visible signs of biodeterioration. A combined strategy, comprising culture-dependent and -independent techniques, was selected. The results derived from the two techniques were disparate. Most of the isolated bacterial strains belonged to related species of the phylum Firmicutes, as Bacillus sp. and Paenisporosarcina sp., whereas the majority of the non-cultivable members of the bacterial community were shown to be related to species of the phylum Proteobacteria, as Stenotrophomonas sp. Fungal communities also showed discrepancies: the isolated fungal strains belonged to different genera of the order Eurotiales, as Penicillium and Eurotium, and the non-cultivable belonged to species of the order Pleosporales and Saccharomycetales. The cultivable microorganisms, which exhibited enzymatic activities related to the deterioration processes, were selected to evaluate their biodeteriorative potential on canvas paintings; namely Arthrobacter sp. as the representative bacterium and Penicillium sp. as the representative fungus. With this aim, a sample taken from the painting studied in this work was examined to determine the stratigraphic sequence of its cross-section. From this information, "mock paintings," simulating the structure of the original painting, were prepared, inoculated with the selected bacterial and fungal strains, and subsequently examined by micro-Fourier Transform Infrared spectroscopy, in order to determine their potential susceptibility to microbial degradation. The FTIR-spectra revealed that neither Arthrobacter sp. nor Penicillium sp. alone, were able to induce chemical changes on the various materials used to prepare "mock paintings." Only when inoculated together, could a synergistic effect on the FTIR-spectra be observed, in the form of a variation in band position on the spectrum.

  13. Detection of domestic violence by community mental health teams: a multi-center, cluster randomized controlled trial.

    Science.gov (United States)

    Ruijne, Roos E; Howard, Louise M; Trevillion, Kylee; Jongejan, Femke E; Garofalo, Carlo; Bogaerts, Stefan; Mulder, Cornelis L; Kamperman, Astrid M

    2017-08-07

    Domestic Violence and Abuse (DVA) is associated with a range of psychosocial and mental health problems. Having a psychiatric illness increases likelihood of being a victim of DVA. Despite the evidence of a high risk for DVA and the serious effects of violent victimization in psychiatric patients, detection rates are low and responses are inadequate. The aim of the BRAVE (Better Reduction trough Assessment of Violence and Evaluation) study is to improve detection of and response to DVA in psychiatric patients. In this article, we present the protocol of the BRAVE study which follows the SPIRIT guidelines. The BRAVE study is a cluster randomized controlled trial. We will include 24 community mental health teams from Rotterdam and The Hague. Twelve teams will provide care as usual and 12 teams will receive the intervention. The intervention consists of 1) a knowledge and skills training for mental health professionals about DVA, 2) a knowledge and skills training of DVA professionals about mental illness, 3) provision and implementation of a referral pathway between community mental health and DVA services. The follow up period is 12 months. Our primary outcome is the rate of detected cases of recent or any history of DVA in patients per team in 12 months. Detection rates are obtained through a systematic search in electronic patient files. Our secondary aims are to obtain information about the gain and sustainability of knowledge on DVA in mental health professionals, and to obtain insight into the feasibility, sustainability and acceptability of the intervention. Data on our secondary aims will be obtained through structured in depth interviews and a questionnaire on knowledge and attitudes on DVA. This study is the first cluster randomized controlled trial to target both male and female psychiatric patients that experience DVA, using an intervention that involves training of professionals. We expect the rate of detected cases of DVA to increase in the

  14. Prevalence of type 2 diabetes among newly detected pulmonary tuberculosis patients in China: a community based cohort study.

    Directory of Open Access Journals (Sweden)

    Qiuzhen Wang

    Full Text Available BACKGROUND: Patients with type 2 diabetes (DM have a higher risk of developing pulmonary tuberculosis (PTB; moreover, DM co-morbidity in PTB is associated with poor PTB treatment outcomes. Community based prevalence data on DM and prediabetes (pre-DM among TB patients is lacking, particularly from the developing world. Therefore we conducted a prospective study to investigate the prevalence of DM and pre-DM and evaluated the risk factors for the presence of DM among newly detected PTB patients in rural areas of China. METHODS AND FINDINGS: In a prospective community based study carried out from 2010 to 2012, a representative sample of 6382 newly detected PTB patients from 7 TB clinics in Linyi were tested for DM. A population of 6674 non-TB controls from the same community was similarly tested as well. The prevalence of DM in TB patients (6.3% was higher than that in non-TB controls (4.7%, p<0.05. PTB patients had a higher odds of DM than non-TB controls (adjusted OR 3.17, 95% CI 1.14-8.84. The prevalence of DM increased with age and was significantly higher in TB patients in the age categories above 30 years (p<0.05. Among TB patients, those with normal weight (BMI 18.5-23.9 had the lowest prevalence of DM (5.8%. Increasing age, family history of DM, positive sputum smear, cavity on chest X-ray and higher yearly income (≥10000 RMB yuan were positively associated and frequent outdoor activity was negatively associated with DM in PTB patients. CONCLUSIONS: The prevalence of DM in PTB patients was higher than in non-TB controls with a 3 fold higher adjusted odds ratio of having DM. Given the increasing DM prevalence and still high burden of TB in China, this association may represent a new public health challenge concerning the prevention and treatment of both diseases.

  15. Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

    International Nuclear Information System (INIS)

    Tang, Jing; Rahmim, Arman

    2015-01-01

    A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE

  16. Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

    Science.gov (United States)

    Tang, Jing; Rahmim, Arman

    2015-01-01

    A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE

  17. Analysis of the population at high risk of stroke detected with carotid artery ultrasonography in Tianjin urban communities

    Directory of Open Access Journals (Sweden)

    Wei YUE

    2015-04-01

    Full Text Available Objective To investigate the features of carotid atherosclerosis in a population at high risk of stroke in urban communities of Tianjin, so as to provide inspiration for carotid ultrasonography to play a greater role in the prevention and control of stroke.  Methods A total of 956 residents at high risk of stroke were selected from 4 urban communities in Tianjin using cluster random sampling method. Doppler ultrasound screening was performed in bilateral common carotid artery (CCA, internal carotid artery (ICA, external carotid artery (ECA, vertebral artery (VA, subclavian artery (SCA and innominate artery of the population. The intima-media thickness (IMT, atherosclerotic plaque formation and its location and size, vascular stenosis or occlusion, and flow spectrum were detected. The results and features of carotid ultrasound screening were analyzed and compared among different gender and age groups.  Results 1 The detection rate of carotid atherosclerosis was 71.55% (684/956, and the detection rate in males was significantly higher than that in females (79.08% vs 65.87%; χ2 = 20.067, P = 0.000. 2 Among the population with carotid atherosclerosis, the most common manifestation was the formation of atherosclerotic plaques (81.58%, 558/684, secondly intima-media thickening (13.01%, 89/684, followed by moderate to severe stenosis or occlusion (5.41%, 37/684. The proportion of intima-media thickening in males was lower than that in females (7.08% vs 18.38%; χ2 = 19.269, P = 0.000. The proportion of carotid atherosclerotic plaque formation in males was higher than that in females (86.46% vs 77.16%; χ2 = 9.824, P = 0.002. The median rating of carotid atherosclerosis was 1.79, with males higher than females [1.98 (0.70, 3.26 vs 1.52 (0.20, 2.84; Z = 2.304, P = 0.042]. The site of plaque formation was most commonly located in carotid bulb (36.61%, secondly SCA (22.18%. Of the type of carotid stenosis, ICA stenosis was detected in 30 cases, VA

  18. Structure analysis and core community detection of embodied resources networks among regional industries

    Science.gov (United States)

    He, Xijun; Dong, Yanbo; Wu, Yuying; Wei, Guodan; Xing, Lizhi; Yan, Jia

    2017-08-01

    To address the double pressure of scarce resources and regional industrial isomorphism, this paper applied the concepts of exergy and embodied resources based on economic input-output (I-O) data. We constructed the embodied resources networks among the regional industries of Beijing-Tianjin-Hebei (also known as Jing-Jin-Ji) in China. We analyzed the rules of embodied resources consumption in the area's industries, identified the core community structures, and studied the characteristics of industrial homogeneity through regional comparisons. The results showed that the dependence on scarce resources of industrial operations in Beijing was less than in Jin-Ji, while the dependence on finance, technology, information, and other service resources in Beijing was higher than in Jin-Ji. The I-O efficiency of embodied resources among industries and the agglomeration of correlation relationships in industries with large embodied resources were higher than in Jin-Ji. The industrial coincidence degree in the ;bridge; industries and in the core community in Jin-Ji was higher than in Jing-Jin and Jing-Ji, which means the industrial homogeneous competition of Jin-Ji was higher, too. This study makes a significant contribution toward promoting the dislocation development of regional industries, accelerating the coordination of resources, and reducing homogeneity competition.

  19. 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.

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

    Science.gov (United States)

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

    2017-03-01

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

  1. Effectiveness of DIAGNOdent in Detecting Root Caries Without Dental Scaling Among Community-dwelling Elderly.

    Science.gov (United States)

    Zhang, Wen; McGrath, Colman; Lo, Edward C M

    The purpose of this clinical research was to analyze the effectiveness of DIAGNOdent in detecting root caries without dental scaling. The status of 750 exposed, unfilled root surfaces was assessed by visual-tactile examination and DIAGNOdent before and after root scaling. The sensitivity and specificity of different cut-off DIAGNOdent values in diagnosing root caries with reference to visual-tactile criteria were evaluated on those root surfaces without visible plaque/calculus. The DIAGNOdent values from sound and carious root surfaces were compared using the nonparametric Mann-Whitney U-test. The level of statistical significance was set at 0.05. On root surfaces without plaque/calculus, significantly different (p 0.05). Furthermore, on root surfaces with visible calculus, all DIAGNOdent readings obtained from sound root surfaces were > 50, which might be misinterpreted as carious. After scaling, the DIAGNOdent readings obtained from sound root surfaces (8.1 ± 11.3), active carious root surfaces (37.9 ± 31.9) and inactive carious root surfaces (24.9 ± 11.5) presented significant differences (p calculus before scaling, but the combined sensitivity and specificity are both around 70%. These findings suggest that on exposed, unfilled root surfaces without visible plaque/calculus, DIAGNOdent can be used as an adjunct to the visual-tactile criteria in detecting root-surface status without pre-treatment by dental scaling.

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

    Science.gov (United States)

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

    2017-11-02

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

  3. Management of computed tomography-detected pneumothorax in patients with blunt trauma: experience from a community-based hospital

    Science.gov (United States)

    Hefny, Ashraf F; Kunhivalappil, Fathima T; Matev, Nikolay; Avila, Norman A; Bashir, Masoud O; Abu-Zidan, Fikri M

    2018-01-01

    INTRODUCTION Diagnoses of pneumothorax, especially occult pneumothorax, have increased as the use of computed tomography (CT) for imaging trauma patients becomes near-routine. However, the need for chest tube insertion remains controversial. We aimed to study the management of pneumothorax detected on CT among patients with blunt trauma, including the decision for tube thoracostomy, in a community-based hospital. METHODS Chest CT scans of patients with blunt trauma treated at Al Rahba Hospital, Abu Dhabi, United Arab Emirates, from October 2010 to October 2014 were retrospectively studied. Variables studied included demography, mechanism of injury, endotracheal intubation, pneumothorax volume, chest tube insertion, Injury Severity Score, hospital length of stay and mortality. RESULTS CT was performed in 703 patients with blunt trauma. Overall, pneumothorax was detected on CT for 74 (10.5%) patients. Among the 65 patients for whom pneumothorax was detected before chest tube insertion, 25 (38.5%) needed chest tube insertion, while 40 (61.5%) did not. Backward stepwise likelihood regression showed that independent factors that significantly predicted chest tube insertion were endotracheal intubation (p = 0.01), non-United Arab Emirates nationality (p = 0.01) and pneumothorax volume (p = 0.03). The receiver operating characteristic curve showed that the best pneumothorax volume that predicted chest tube insertion was 30 mL. CONCLUSION Chest tube was inserted in less than half of the patients with blunt trauma for whom pneumothorax was detected on CT. Pneumothorax volume should be considered in decision-making regarding chest tube insertion. Conservative treatment may be sufficient for pneumothorax of volume < 30 mL. PMID:28741012

  4. Management of computed tomography-detected pneumothorax in patients with blunt trauma: experience from a community-based hospital.

    Science.gov (United States)

    Hefny, Ashraf F; Kunhivalappil, Fathima T; Matev, Nikolay; Avila, Norman A; Bashir, Masoud O; Abu-Zidan, Fikri M

    2018-03-01

    Diagnoses of pneumothorax, especially occult pneumothorax, have increased as the use of computed tomography (CT) for imaging trauma patients becomes near-routine. However, the need for chest tube insertion remains controversial. We aimed to study the management of pneumothorax detected on CT among patients with blunt trauma, including the decision for tube thoracostomy, in a community-based hospital. Chest CT scans of patients with blunt trauma treated at Al Rahba Hospital, Abu Dhabi, United Arab Emirates, from October 2010 to October 2014 were retrospectively studied. Variables studied included demography, mechanism of injury, endotracheal intubation, pneumothorax volume, chest tube insertion, Injury Severity Score, hospital length of stay and mortality. CT was performed in 703 patients with blunt trauma. Overall, pneumothorax was detected on CT for 74 (10.5%) patients. Among the 65 patients for whom pneumothorax was detected before chest tube insertion, 25 (38.5%) needed chest tube insertion, while 40 (61.5%) did not. Backward stepwise likelihood regression showed that independent factors that significantly predicted chest tube insertion were endotracheal intubation (p = 0.01), non-United Arab Emirates nationality (p = 0.01) and pneumothorax volume (p = 0.03). The receiver operating characteristic curve showed that the best pneumothorax volume that predicted chest tube insertion was 30 mL. Chest tube was inserted in less than half of the patients with blunt trauma for whom pneumothorax was detected on CT. Pneumothorax volume should be considered in decision-making regarding chest tube insertion. Conservative treatment may be sufficient for pneumothorax of volume < 30 mL. Copyright: © Singapore Medical Association.

  5. Development of monitoring protocols to detect change in rocky intertidal communities of Glacier Bay National Park and Preserve

    Science.gov (United States)

    Irvine, Gail V.

    2010-01-01

    Glacier Bay National Park and Preserve in southeastern Alaska includes extensive coastlines representing a major proportion of all coastlines held by the National Park Service. The marine plants and invertebrates that occupy intertidal shores form highly productive communities that are ecologically important to a number of vertebrate and invertebrate consumers and that are vulnerable to human disturbances. To better understand these communities and their sensitivity, it is important to obtain information on species abundances over space and time. During field studies from 1997 to 2001, I investigated probability-based rocky intertidal monitoring designs that allow inference of results to similar habitat within the bay and that reduce bias. Aerial surveys of a subset of intertidal habitat indicated that the original target habitat of bedrock-dominated sites with slope less than or equal to 30 degrees was rare. This finding illustrated the value of probability-based surveys and led to a shift in the target habitat type to more mixed rocky habitat with steeper slopes. Subsequently, I investigated different sampling methods and strategies for their relative power to detect changes in the abundances of the predominant sessile intertidal taxa: barnacles -Balanomorpha, the mussel Mytilus trossulus and the rockweed Fucus distichus subsp. evanescens. I found that lower-intensity sampling of 25 randomly selected sites (= coarse-grained sampling) provided a greater ability to detect changes in the abundances of these taxa than did more intensive sampling of 6 sites (= fine-grained sampling). Because of its greater power, the coarse-grained sampling scheme was adopted in subsequent years. This report provides detailed analyses of the 4 years of data and evaluates the relative effect of different sampling attributes and management-set parameters on the ability of the sampling to detect changes in the abundances of these taxa. The intent was to provide managers with information

  6. Variation of the Pseudomonas community structure on oak leaf lettuce during storage detected by culture-dependent and -independent methods.

    Science.gov (United States)

    Nübling, Simone; Schmidt, Herbert; Weiss, Agnes

    2016-01-04

    The genus Pseudomonas plays an important role in the lettuce leaf microbiota and certain species can induce spoilage. The aim of this study was to investigate the occurrence and diversity of Pseudomonas spp. on oak leaf lettuce and to follow their community shift during a six day cold storage with culture-dependent and culture-independent methods. In total, 21 analysed partial Pseudomonas 16S rRNA gene sequences matched closely (> 98.3%) to the different reference strain sequences, which were distributed among 13 different phylogenetic groups or subgroups within the genus Pseudomonas. It could be shown that all detected Pseudomonas species belonged to the P. fluorescens lineage. In the culture-dependent analysis, 73% of the isolates at day 0 and 79% of the isolates at day 6 belonged to the P. fluorescens subgroup. The second most frequent group, with 12% of the isolates, was the P. koreensis subgroup. This subgroup was only detected at day 0. In the culture-independent analysis the P. fluorescens subgroup and P. extremaustralis could not be differentiated by RFLP. Both groups were most abundant and amounted to approximately 46% at day 0 and 79% at day 6. The phytopathogenic species P. salmonii, P. viridiflava and P. marginalis increased during storage. Both approaches identified the P. fluorescens group as the main phylogenetic group. The results of the present study suggest that pseudomonads found by plating methods indeed represent the most abundant part of the Pseudomonas community on oak leaf lettuce. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. The psychometric properties of GHQ for detecting common mental disorder among community dwelling men in Goa, India.

    Science.gov (United States)

    Endsley, Paige; Weobong, Benedict; Nadkarni, Abhijit

    2017-08-01

    There have not been many attempts to validate screening measures for common mental disorders (CMD) in low- and middle-income countries. The aim of this study was to examine the criterion validity of the General Health Questionnaire 12 (GHQ-12) in a community-based study from Goa, India. Concurrent and convergent validity of the GHQ-12 were assessed against the Mini International Neuropsychiatric Interview (MINI) and World Health Organization Disability Assessment Scale (WHODAS) for CMD and functional status through the secondary analysis of a community cohort of men from Goa, India. Criterion validity of the GHQ-12 was determined using ROC analyses with the MINI case criterion as the gold standard. Concurrent validity was assessed against the gold standard of WHODAS functional disability and number of disability days. In a sample of men (n=773), the GHQ-12 showed high internal reliability (Cronbach's alpha of 0.82) and acceptable criterion validity (Area under the receiver operating characteristic curve being 0.71). It had adequate psychometric properties for the detection of CMD (sensitivity of 68.75%; specificity of 73.14%) with the optimal cut-off score for identification of CMD being 2. In order to optimize the usefulness and validity of the GHQ-12, a low cut-off point for CMD may be beneficial in Goa, India. Further validation studies for the GHQ-12 should be conducted for continued validation of the test for use in the community. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  8. Lava cave microbial communities within mats and secondary mineral deposits: implications for life detection on other planets.

    Science.gov (United States)

    Northup, D E; Melim, L A; Spilde, M N; Hathaway, J J M; Garcia, M G; Moya, M; Stone, F D; Boston, P J; Dapkevicius, M L N E; Riquelme, C

    2011-09-01

    Lava caves contain a wealth of yellow, white, pink, tan, and gold-colored microbial mats; but in addition to these clearly biological mats, there are many secondary mineral deposits that are nonbiological in appearance. Secondary mineral deposits examined include an amorphous copper-silicate deposit (Hawai'i) that is blue-green in color and contains reticulated and fuzzy filament morphologies. In the Azores, lava tubes contain iron-oxide formations, a soft ooze-like coating, and pink hexagons on basaltic glass, while gold-colored deposits are found in lava caves in New Mexico and Hawai'i. A combination of scanning electron microscopy (SEM) and molecular techniques was used to analyze these communities. Molecular analyses of the microbial mats and secondary mineral deposits revealed a community that contains 14 phyla of bacteria across three locations: the Azores, New Mexico, and Hawai'i. Similarities exist between bacterial phyla found in microbial mats and secondary minerals, but marked differences also occur, such as the lack of Actinobacteria in two-thirds of the secondary mineral deposits. The discovery that such deposits contain abundant life can help guide our detection of life on extraterrestrial bodies.

  9. Decompositions of bubbly flow PIV velocity fields using discrete wavelets multi-resolution and multi-section image method

    International Nuclear Information System (INIS)

    Choi, Je-Eun; Takei, Masahiro; Doh, Deog-Hee; Jo, Hyo-Jae; Hassan, Yassin A.; Ortiz-Villafuerte, Javier

    2008-01-01

    Currently, wavelet transforms are widely used for the analyses of particle image velocimetry (PIV) velocity vector fields. This is because the wavelet provides not only spatial information of the velocity vectors, but also of the time and frequency domains. In this study, a discrete wavelet transform is applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform. The performances of the discrete wavelet transforms were investigated by changing the level of power of discretization. The images decomposed by wavelet multi-resolution showed conspicuous characteristics of the bubbly flows for the different levels. A high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, in which high-leveled wavelets play dominant roles in revealing the flow characteristics

  10. Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data

    Energy Technology Data Exchange (ETDEWEB)

    Chamana, Manohar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mather, Barry A [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-10-19

    A library of load variability classes is created to produce scalable synthetic data sets using historical high-speed raw data. These data are collected from distribution monitoring units connected at the secondary side of a distribution transformer. Because of the irregular patterns and large volume of historical high-speed data sets, the utilization of current load characterization and modeling techniques are challenging. Multi-resolution analysis techniques are applied to extract the necessary components and eliminate the unnecessary components from the historical high-speed raw data to create the library of classes, which are then utilized to create new synthetic load data sets. A validation is performed to ensure that the synthesized data sets contain the same variability characteristics as the training data sets. The synthesized data sets are intended to be utilized in quasi-static time-series studies for distribution system planning studies on a granular scale, such as detailed PV interconnection studies.

  11. A multiresolution spatial parametrization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions.

    Energy Technology Data Exchange (ETDEWEB)

    Ray, Jaideep; Lee, Jina; Lefantzi, Sophia; Yadav, Vineet [Carnegie Institution for Science, Stanford, CA; Michalak, Anna M. [Carnegie Institution for Science, Stanford, CA; van Bloemen Waanders, Bart Gustaaf [Sandia National Laboratories, Albuquerque, NM; McKenna, Sean Andrew [IBM Research, Mulhuddart, Dublin 15, Ireland

    2013-04-01

    The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. To that end, we construct a multiresolution spatial parametrization for fossil-fuel CO2 emissions (ffCO2), to be used in atmospheric inversions. Such a parametrization does not currently exist. The parametrization uses wavelets to accurately capture the multiscale, nonstationary nature of ffCO2 emissions and employs proxies of human habitation, e.g., images of lights at night and maps of built-up areas to reduce the dimensionality of the multiresolution parametrization. The parametrization is used in a synthetic data inversion to test its suitability for use in atmospheric inverse problem. This linear inverse problem is predicated on observations of ffCO2 concentrations collected at measurement towers. We adapt a convex optimization technique, commonly used in the reconstruction of compressively sensed images, to perform sparse reconstruction of the time-variant ffCO2 emission field. We also borrow concepts from compressive sensing to impose boundary conditions i.e., to limit ffCO2 emissions within an irregularly shaped region (the United States, in our case). We find that the optimization algorithm performs a data-driven sparsification of the spatial parametrization and retains only of those wavelets whose weights could be estimated from the observations. Further, our method for the imposition of boundary conditions leads to a 10computational saving over conventional means of doing so. We conclude with a discussion of the accuracy of the estimated emissions and the suitability of the spatial parametrization for use in inverse problems with a significant degree of regularization.

  12. 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...

  13. Multi-resolution anisotropy studies of ultrahigh-energy cosmic rays detected at the Pierre Auger Observatory

    Czech Academy of Sciences Publication Activity Database

    Aab, A.; Abreu, P.; Aglietta, M.; Blažek, Jiří; Boháčová, Martina; Chudoba, Jiří; Ebr, Jan; Mandát, Dušan; Palatka, Miroslav; Pech, Miroslav; Prouza, Michael; Řídký, Jan; Schovánek, Petr; Trávníček, Petr; Vícha, Jakub

    2017-01-01

    Roč. 2017, č. 6 (2017), 1-26, č. článku 026. ISSN 1475-7516 R&D Projects: GA MŠk LM2015038; GA MŠk LG15014; GA ČR(CZ) GA14-17501S Institutional support: RVO:68378271 Keywords : cosmic ray experiments * ultra high energy cosmic rays Subject RIV: BF - Elementary Particles and High Energy Physics OBOR OECD: Particles and field physics Impact factor: 4.734, year: 2016

  14. Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system

    Science.gov (United States)

    Orr, Shlomo; Meystel, Alexander M.

    2005-03-01

    Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world problems in hydrogeology have been quite limited. The main reason is the ultimate reliance on first-principle models that lead to complex, distributed-parameter partial differential equations (PDE) on a given scale. While the addition of uncertainty, and hence, stochasticity or randomness has increased insight and highlighted important relationships between uncertainty, reliability, risk, and their effect on the cost function, it has also (a) introduced additional complexity that results in prohibitive computer power even for just a single uncertain/random parameter; and (b) led to the recognition in our inability to assess the full uncertainty even when including all uncertain parameters. A paradigm shift is introduced: an adaptation of new methods of intelligent control that will relax the dependency on rigid, computer-intensive, stochastic PDE, and will shift the emphasis to a goal-oriented, flexible, adaptive, multiresolutional decision support system (MRDS) with strong unsupervised learning (oriented towards anticipation rather than prediction) and highly efficient optimization capability, which could provide the needed solutions of real-world aquifer management problems. The article highlights the links between past developments and future optimization/planning/control of hydrogeologic systems. Malgré de remarquables nouveaux développements en hydrologie stochastique ainsi que de remarquables adaptations de méthodes avancées pour les opérations de recherche, le contrôle stochastique, et l'intelligence artificielle, solutions pour les problèmes complexes en hydrogéologie sont restées assez limitées. La principale raison est l'ultime confiance en les modèles qui conduisent à des équations partielles complexes aux paramètres distribués (PDE) à une

  15. SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

    KAUST Repository

    Fan, Ming

    2012-06-28

    With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

  16. SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

    KAUST Repository

    Fan, Ming; Wong, Ka-Chun; Ryu, Tae Woo; Ravasi, Timothy; Gao, Xin

    2012-01-01

    With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

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

    Science.gov (United States)

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

  18. Accuracy of "Modified Checklist for Autism in Toddlers" ("M-CHAT") in Detecting Autism and Other Developmental Disorders in Community Clinics

    Science.gov (United States)

    Toh, Teck-Hock; Tan, Vivian Wee-Yen; Lau, Peter Sie-Teck; Kiyu, Andrew

    2018-01-01

    This study determined the accuracy of "Modified Checklist for Autism in Toddlers" ("M-CHAT") in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15-36 months) who had "M-CHAT" performed in…

  19. ROBUST MOTION SEGMENTATION FOR HIGH DEFINITION VIDEO SEQUENCES USING A FAST MULTI-RESOLUTION MOTION ESTIMATION BASED ON SPATIO-TEMPORAL TUBES

    OpenAIRE

    Brouard , Olivier; Delannay , Fabrice; Ricordel , Vincent; Barba , Dominique

    2007-01-01

    4 pages; International audience; Motion segmentation methods are effective for tracking video objects. However, objects segmentation methods based on motion need to know the global motion of the video in order to back-compensate it before computing the segmentation. In this paper, we propose a method which estimates the global motion of a High Definition (HD) video shot and then segments it using the remaining motion information. First, we develop a fast method for multi-resolution motion est...

  20. Detection of Babesia caballi and Theileria equi in Blood from Equines from Four Indigenous Communities in Costa Rica

    Directory of Open Access Journals (Sweden)

    María Fernanda Posada-Guzmán

    2015-01-01

    Full Text Available A cross-sectional study was carried out in four indigenous communities of Costa Rica to detect presence and prevalence of Babesia caballi and Theileria equi and to investigate factors associated with presence of these hemoparasites. General condition of horses (n=285 was evaluated, and hematocrits and hemoglobin were determined from blood samples of 130 horses, which were also analyzed using blood smears, nested polymerase chain reaction (PCR, and immunosorbent assay (c-ELISA. The general condition of the horses (n=285 in terms of their body and coat was between regular and poor, and hematocrit and hemoglobin average values were low (19% and 10.65 g/dL, resp.. Erythrocyte inclusions were observed in 32 (24.6% of the samples. Twenty-six samples (20.0% gave positive results for B. caballi and 60 (46.2% for T. equi; 10 horses (7.7% showed mixed infection, when analyzed by PCR. Using c-ELISA, it was found that 90 (69.2% horses had antibodies against B. caballi and 115 (88.5% against T. equi, while 81 (62.3% showed mixed reactions. There were no factors associated with the presence of B. caballi and T. equi. These results contrast with results previously obtained in equines in the Central Valley of Costa Rica.

  1. Detection of Babesia caballi and Theileria equi in Blood from Equines from Four Indigenous Communities in Costa Rica.

    Science.gov (United States)

    Posada-Guzmán, María Fernanda; Dolz, Gaby; Romero-Zúñiga, Juan José; Jiménez-Rocha, Ana Eugenia

    2015-01-01

    A cross-sectional study was carried out in four indigenous communities of Costa Rica to detect presence and prevalence of Babesia caballi and Theileria equi and to investigate factors associated with presence of these hemoparasites. General condition of horses (n = 285) was evaluated, and hematocrits and hemoglobin were determined from blood samples of 130 horses, which were also analyzed using blood smears, nested polymerase chain reaction (PCR), and immunosorbent assay (c-ELISA). The general condition of the horses (n = 285) in terms of their body and coat was between regular and poor, and hematocrit and hemoglobin average values were low (19% and 10.65 g/dL, resp.). Erythrocyte inclusions were observed in 32 (24.6%) of the samples. Twenty-six samples (20.0%) gave positive results for B. caballi and 60 (46.2%) for T. equi; 10 horses (7.7%) showed mixed infection, when analyzed by PCR. Using c-ELISA, it was found that 90 (69.2%) horses had antibodies against B. caballi and 115 (88.5%) against T. equi, while 81 (62.3%) showed mixed reactions. There were no factors associated with the presence of B. caballi and T. equi. These results contrast with results previously obtained in equines in the Central Valley of Costa Rica.

  2. Detection of IgM and IgG antibodies to Chlamydophila pneumoniae in pediatric community-acquired lower respiratory tract infections

    Directory of Open Access Journals (Sweden)

    Surinder Kumar

    2011-01-01

    Full Text Available Context: Chlamydophila pneumoniae (C. pneumoniae is an emerging infectious agent with a spectrum of clinical manifestations including lower and upper respiratory tract infections. Aims: To investigate the role of C. pneumoniae in community-acquired lower respiratory tract infections (LRTIs in children using serological tests. Settings and Design: Two hundred children, age 2 months to 12 years, hospitalized for community-acquired LRTIs were investigated for C. pneumoniae etiology. Materials and Methods: We investigated 200 children hospitalized for community-acquired LRTIs, using ELISA for detecting anti-C. pneumoniae IgM and IgG antibodies. The demographic, clinical and radiological findings for C. pneumoniae antibody positive and C. pneumoniae antibody negative cases were compared. Statistical Analysis Used: Data analysis was performed by Chi-square test and Fisher′s exact tests using Epi Info (2002. Results: Clinical and radiological findings in both the groups were comparable. Serological evidence of C. pneumoniae infection was observed in 12 (6% patients; specific IgM antibodies were detected in 11 (91.67%; specific IgG antibodies in 1 (8.33% patients, while 4-fold rise in C. pneumoniae IgG antibody titers were noted in none of the patients. Conclusions: C. pneumoniae has a role in community-acquired LRTIs, even in children aged < 5 years. Serological detection using ELISA would enable pediatricians in better management of C. pneumoniae infections.

  3. The use of the temporal scan statistic to detect methicillin-resistant Staphylococcus aureus clusters in a community hospital.

    Science.gov (United States)

    Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

    2014-07-08

    In healthcare facilities, conventional surveillance techniques using rule-based guidelines may result in under- or over-reporting of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks, as these guidelines are generally unvalidated. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting MRSA clusters, validate clusters using molecular techniques and hospital records, and determine significant differences in the rate of MRSA cases using regression models. Patients admitted to a community hospital between August 2006 and February 2011, and identified with MRSA>48 hours following hospital admission, were included in this study. Between March 2010 and February 2011, MRSA specimens were obtained for spa typing. MRSA clusters were investigated using a retrospective temporal scan statistic. Tests were conducted on a monthly scale and significant clusters were compared to MRSA outbreaks identified by hospital personnel. Associations between the rate of MRSA cases and the variables year, month, and season were investigated using a negative binomial regression model. During the study period, 735 MRSA cases were identified and 167 MRSA isolates were spa typed. Nine different spa types were identified with spa type 2/t002 (88.6%) the most prevalent. The temporal scan statistic identified significant MRSA clusters at the hospital (n=2), service (n=16), and ward (n=10) levels (P ≤ 0.05). Seven clusters were concordant with nine MRSA outbreaks identified by hospital staff. For the remaining clusters, seven events may have been equivalent to true outbreaks and six clusters demonstrated possible transmission events. The regression analysis indicated years 2009-2011, compared to 2006, and months March and April, compared to January, were associated with an increase in the rate of MRSA cases (P ≤ 0.05). The application of the temporal scan statistic identified several MRSA clusters that were not detected by hospital

  4. Packaging Waste and Hitting Home Runs: How Education and Lightning Strike Detection Technology Supports Company and Community Activities

    International Nuclear Information System (INIS)

    Deecke, T.A.; Hyde, J.V.; Hylko, J.M.

    2006-01-01

    The weather is the most significant and unmanageable variable when performing environmental remediation activities. This variable can contribute to the failure of a project in two ways: 1) severe injury to an employee or employees following a cloud-to-ground lightning strike without prior visual or audible warnings; and 2) excessive 'down time' associated with mobilization and demobilization activities after a false alarm (e.g., lightning was seen in the distance but was actually moving away from the site). Therefore, in order for a project to be successful from both safety and financial viewpoints, the uncertainties associated with inclement weather, specifically lightning, need to be understood to eliminate the element of surprise. This paper discusses educational information related to the history and research of lightning, how lightning storms develop, types of lightning, the mechanisms of lightning injuries and fatalities, and follow-up medical treatment. Fortunately, lightning storm monitoring does not have to be either costly or elaborate. WESKEM, LLC selected the Boltek StormTracker Lightning Detection System with the Aninoquisi Lightning 2000 TM software. This fixed system, used in combination with online weather web pages, monitors and alarms WESKEM, LLC field personnel in the event of an approaching lightning storm. This application was expanded to justify the purchase of the hand-held Sky Scan Lightning/Storm Detector Model P5 used by the Heath Youth Athletic Association (HYAA) which is a non-profit, charitable organization offering sports programs for the youth and young adults in the local community. Fortunately, a lightning injury or fatality has never occurred on a WESKEM Paducah project or an HYAA-sponsored event. Using these fixed and hand-held systems will continue to prevent such injuries from occurring in the foreseeable future. (authors)

  5. Molecular detection of Candidatus Scalindua pacifica and environmental responses of sediment anammox bacterial community in the Bohai Sea, China.

    Directory of Open Access Journals (Sweden)

    Hongyue Dang

    Full Text Available The Bohai Sea is a large semi-enclosed shallow water basin, which receives extensive river discharges of various terrestrial and anthropogenic materials such as sediments, nutrients and contaminants. How these terrigenous inputs may influence the diversity, community structure, biogeographical distribution, abundance and ecophysiology of the sediment anaerobic ammonium oxidation (anammox bacteria was unknown. To answer this question, an investigation employing both 16S rRNA and hzo gene biomarkers was carried out. Ca. Scalindua bacteria were predominant in the surface sediments of the Bohai Sea, while non-Scalindua anammox bacteria were also detected in the Yellow River estuary and inner part of Liaodong Bay that received strong riverine and anthropogenic impacts. A novel 16S rRNA gene sequence clade was identified, putatively representing an anammox bacterial new candidate species tentatively named "Ca. Scalindua pacifica". Several groups of environmental factors, usually with distinct physicochemical or biogeochemical natures, including general marine and estuarine physicochemical properties, availability of anammox substrates (inorganic N compounds, alternative reductants and oxidants, environmental variations caused by river discharges and associated contaminants such as heavy metals, were identified to likely play important roles in influencing the ecology and biogeochemical functioning of the sediment anammox bacteria. In addition to inorganic N compounds that might play a key role in shaping the anammox microbiota, organic carbon, organic nitrogen, sulfate, sulfide and metals all showed the potentials to participate in the anammox process, releasing the strict dependence of the anammox bacteria upon the direct availability of inorganic N nutrients that might be limiting in certain areas of the Bohai Sea. The importance of inorganic N nutrients and certain other environmental factors to the sediment anammox microbiota suggests that these

  6. Evaluation of a PCR Assay for Detection of Streptococcus pneumoniae in Respiratory and Nonrespiratory Samples from Adults with Community-Acquired Pneumonia

    OpenAIRE

    Murdoch, David R.; Anderson, Trevor P.; Beynon, Kirsten A.; Chua, Alvin; Fleming, Angela M.; Laing, Richard T. R.; Town, G. Ian; Mills, Graham D.; Chambers, Stephen T.; Jennings, Lance C.

    2003-01-01

    Streptococcus pneumoniae is the most common cause of community-acquired pneumonia, but it is undoubtedly underdiagnosed. We used a nested PCR assay (targeting the pneumolysin gene) to detect S. pneumoniae DNA in multiple sample types from 474 adults with community-acquired pneumonia and 183 control patients who did not have pneumonia. Plasma or buffy coat samples were PCR positive in only 6 of the 21 patients with positive blood cultures for S. pneumoniae and in 12 other patients (4 of whom h...

  7. The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data

    Science.gov (United States)

    Wickham, James D.; Homer, Collin G.; Vogelmann, James E.; McKerrow, Alexa; Mueller, Rick; Herold, Nate; Coluston, John

    2014-01-01

    The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

  8. Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

    Science.gov (United States)

    Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

    2018-02-01

    In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

  9. A multi-resolution approach for an automated fusion of different low-cost 3D sensors.

    Science.gov (United States)

    Dupuis, Jan; Paulus, Stefan; Behmann, Jan; Plümer, Lutz; Kuhlmann, Heiner

    2014-04-24

    The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

  10. Compressed modes for variational problems in mathematical physics and compactly supported multiresolution basis for the Laplace operator

    Science.gov (United States)

    Ozolins, Vidvuds; Lai, Rongjie; Caflisch, Russel; Osher, Stanley

    2014-03-01

    We will describe a general formalism for obtaining spatially localized (``sparse'') solutions to a class of problems in mathematical physics, which can be recast as variational optimization problems, such as the important case of Schrödinger's equation in quantum mechanics. Sparsity is achieved by adding an L1 regularization term to the variational principle, which is shown to yield solutions with compact support (``compressed modes''). Linear combinations of these modes approximate the eigenvalue spectrum and eigenfunctions in a systematically improvable manner, and the localization properties of compressed modes make them an attractive choice for use with efficient numerical algorithms that scale linearly with the problem size. In addition, we introduce an L1 regularized variational framework for developing a spatially localized basis, compressed plane waves (CPWs), that spans the eigenspace of a differential operator, for instance, the Laplace operator. Our approach generalizes the concept of plane waves to an orthogonal real-space basis with multiresolution capabilities. Supported by NSF Award DMR-1106024 (VO), DOE Contract No. DE-FG02-05ER25710 (RC) and ONR Grant No. N00014-11-1-719 (SO).

  11. Computerized mappings of the cerebral cortex: a multiresolution flattening method and a surface-based coordinate system

    Science.gov (United States)

    Drury, H. A.; Van Essen, D. C.; Anderson, C. H.; Lee, C. W.; Coogan, T. A.; Lewis, J. W.

    1996-01-01

    We present a new method for generating two-dimensional maps of the cerebral cortex. Our computerized, two-stage flattening method takes as its input any well-defined representation of a surface within the three-dimensional cortex. The first stage rapidly converts this surface to a topologically correct two-dimensional map, without regard for the amount of distortion introduced. The second stage reduces distortions using a multiresolution strategy that makes gross shape changes on a coarsely sampled map and further shape refinements on progressively finer resolution maps. We demonstrate the utility of this approach by creating flat maps of the entire cerebral cortex in the macaque monkey and by displaying various types of experimental data on such maps. We also introduce a surface-based coordinate system that has advantages over conventional stereotaxic coordinates and is relevant to studies of cortical organization in humans as well as non-human primates. Together, these methods provide an improved basis for quantitative studies of individual variability in cortical organization.

  12. Suitability of an MRMCE (multi-resolution minimum cross entropy) algorithm for online monitoring of a two-phase flow

    International Nuclear Information System (INIS)

    Wang, Qi; Wang, Huaxiang; Xin, Shan

    2011-01-01

    The flow regimes are important characteristics to describe two-phase flows, and measurement of two-phase flow parameters is becoming increasingly important in many industrial processes. Computerized tomography (CT) has been applied to two-phase/multi-phase flow measurement in recent years. Image reconstruction of CT often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive, especially for the case of online flow regime identification. In this paper, minimum cross entropy reconstruction based on multi-resolution processing (MRMCE) is presented for oil–gas two-phase flow regime identification. A regularized MCE solution is obtained using the simultaneous multiplicative algebraic reconstruction technique (SMART) at a coarse resolution level, where important information on the reconstructed image is contained. Then, the solution in the finest resolution is obtained by inverse fast wavelet transformation. Both computer simulation and static/dynamic experiments were carried out for typical flow regimes. Results obtained indicate that the proposed method can dramatically reduce the computational time and improve the quality of the reconstructed image with suitable decomposition levels compared with the single-resolution maximum likelihood expectation maximization (MLEM), alternating minimization (AM), Landweber, iterative least square technique (ILST) and minimum cross entropy (MCE) methods. Therefore, the MRMCE method is suitable for identification of dynamic two-phase flow regimes

  13. Molecular community analysis of arbuscular mycorrhizal fungi-Contributions of PCR primer and host plant selectivity to the detected community profiles

    Czech Academy of Sciences Publication Activity Database

    Řezáčová, Veronika; Gryndler, Milan; Bukovská, Petra; Šmilauer, P.; Jansa, Jan

    2016-01-01

    Roč. 15, č. 4 (2016), s. 179-187 ISSN 0031-4056 R&D Projects: GA MŠk(CZ) LK11224; GA ČR GAP504/12/1665 Keywords : Glomeromycota * Trap cultures * Community analysis Subject RIV: EE - Microbiology, Virology Impact factor: 2.000, year: 2016

  14. Factors associated with routine screening for the early detection of breast cancer in cultural-ethnic and faith-based communities.

    Science.gov (United States)

    Freund, Anat; Cohen, Miri; Azaiza, Faisal

    2017-07-04

    Studies have shown a lower adherence to health behaviors among women in cultural-ethnic minorities and faith-based communities, especially lower screening attendance for the early detection of breast cancer. This study compares factors related to cancer screening adherence in two distinct cultural-ethnic minorities in Israel: Arab women as a cultural-ethnic minority and Jewish ultra-Orthodox women as a cultural-ethnic faith-based minority. During the year 2014, a total of 398 Jewish ultra-Orthodox women and 401 Arab women between the ages of 40-60, were randomly selected using population-based registries. These women answered questionnaires regarding adherence to mammography and clinical breast examination (CBE), health beliefs and cultural barriers. Arab women adhered more than ultra-Orthodox women to mammography (p faith-based communities. In order to increase adherence, health care professionals and policymakers should direct their attention to the specific nature of each community.

  15. Collaborative regression-based anatomical landmark detection

    International Nuclear Information System (INIS)

    Gao, Yaozong; Shen, Dinggang

    2015-01-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)

  16. Detecting and staging podoconiosis cases in North West Cameroon: positive predictive value of clinical screening of patients by community health workers and researchers

    Directory of Open Access Journals (Sweden)

    Samuel Wanji

    2016-09-01

    Full Text Available Abstract Background The suitability of using clinical assessment to identify patients with podoconiosis in endemic communities has previously been demonstrated. In this study, we explored the feasibility and accuracy of using Community Health Implementers (CHIs for the large scale clinical screening of the population for podoconiosis in North-west Cameroon. Methods Before a regional podoconiosis mapping, 193 CHIs and 50 health personnel selected from 6 health districts were trained in the clinical diagnosis of the disease. After training, CHIs undertook community screening for podoconiosis patients under health personnel supervision. Identified cases were later re-examined by a research team with experience in the clinical identification of podoconiosis. Results Cases were identified by CHIs with an overall positive predictive value (PPV of 48.5% [34.1–70%]. They were more accurate in detecting advanced stages of the disease compared to early stages; OR 2.07, 95% CI = 1.15–3.73, p = 0.015 for all advanced stages. Accuracy of detecting cases showed statistically significant differences among health districts (χ2 = 25.30, p = 0.0001. Conclusion Podoconiosis being a stigmatized disease, the use of CHIs who are familiar to the community appears appropriate for identifying cases through clinical diagnosis. However, to improve their effectiveness and accuracy, more training, supervision and support are required. More emphasis must be given in identifying early clinical stages and in health districts with relatively lower PPVs.

  17. Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

    NARCIS (Netherlands)

    Schmidt, M.; Lucas, R.; Bunting, P.; Verbesselt, J.; Armston, J.

    2015-01-01

    High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30 m spatial resolution data was generated by the use of

  18. Community detection based on joint matrix factorization in networks with no de attributes%基于联合矩阵分解的节点多属性网络社团检测∗

    Institute of Scientific and Technical Information of China (English)

    常振超; 陈鸿昶; 刘阳; 于洪涛; 黄瑞阳

    2015-01-01

    An important problem in the area of social networking is the community detection. In the problem of community detection, the goal is to partition the network into dense regions of the graph. Such dense regions typically correspond to entities which are closely related with each other, and can hence be said to belong to a community. Detecting communities is of great importance in computing biology and sociology networks. There have been lots of methods to detect community. When detecting communities in social media networks, there are two possible sources of information one can use: the network link structure, and the features and attributes of nodes. Nodes in social media networks have plenty of attributes information, which presents unprecedented opportunities and flexibility for the community detection process. Some community detection algorithms only use the links between the nodes in order to determine the dense regions in the graph. Such methods are typically based purely on the linkage structure of the underlying social media network. Some other community detection algorithms may utilize the nodes’ attributes to cluster the nodes, i.e. which nodes with the same attributes would be put into the same cluster. While traditional methods only use one of the two sources or simple linearly combine the results of community detection based on different sources, they cannot detect community with node attributes effectively. In recent years, matrix factorization (MF) has received considerable interest from the data mining and information retrieval fields. MF has been successfully applied in document clustering, image representation, and other domains. In this paper, we use nodes attributes as a better supervision to the community detection process, and propose an algorithm based on joint matrix factorization (CDJMF). Our method is based on the assumption that the two different information sources of linkage and node attributes can get an identical nodes’ a

  19. The cultivation bias: different communities of arbuscular mycorrhizal fungi detected in roots from the field, from bait plants transplanted to the field, and from a greenhouse trap experiment.

    Science.gov (United States)

    Sýkorová, Zuzana; Ineichen, Kurt; Wiemken, Andres; Redecker, Dirk

    2007-12-01

    The community composition of arbuscular mycorrhizal fungi (AMF) was investigated in roots of four different plant species (Inula salicina, Medicago sativa, Origanum vulgare, and Bromus erectus) sampled in (1) a plant species-rich calcareous grassland, (2) a bait plant bioassay conducted directly in that grassland, and (3) a greenhouse trap experiment using soil and a transplanted whole plant from that grassland as inoculum. Roots were analyzed by AMF-specific nested polymerase chain reaction, restriction fragment length polymorphism screening, and sequence analyses of rDNA small subunit and internal transcribed spacer regions. The AMF sequences were analyzed phylogenetically and used to define monophyletic phylotypes. Overall, 16 phylotypes from several lineages of AMF were detected. The community composition was strongly influenced by the experimental approach, with additional influence of cultivation duration, substrate, and host plant species in some experiments. Some fungal phylotypes, e.g., GLOM-A3 (Glomus mosseae) and several members of Glomus group B, appeared predominantly in the greenhouse experiment or in bait plants. Thus, these phylotypes can be considered r strategists, rapidly colonizing uncolonized ruderal habitats in early successional stages of the fungal community. In the greenhouse experiment, for instance, G. mosseae was abundant after 3 months, but could not be detected anymore after 10 months. In contrast, other phylotypes as GLOM-A17 (G. badium) and GLOM-A16 were detected almost exclusively in roots sampled from plants naturally growing in the grassland or from bait plants exposed in the field, indicating that they preferentially occur in late successional stages of fungal communities and thus represent the K strategy. The only phylotype found with high frequency in all three experimental approaches was GLOM A-1 (G. intraradices), which is known to be a generalist. These results indicate that, in greenhouse trap experiments, it is difficult

  20. Digital Correlation based on Wavelet Transform for Image Detection

    International Nuclear Information System (INIS)

    Barba, L; Vargas, L; Torres, C; Mattos, L

    2011-01-01

    In this work is presented a method for the optimization of digital correlators to improve the characteristic detection on images using wavelet transform as well as subband filtering. It is proposed an approach of wavelet-based image contrast enhancement in order to increase the performance of digital correlators. The multiresolution representation is employed to improve the high frequency content of images taken into account the input contrast measured for the original image. The energy of correlation peaks and discrimination level of several objects are improved with this technique. To demonstrate the potentiality in extracting characteristics using the wavelet transform, small objects inside reference images are detected successfully.

  1. A multi-resolution analysis of lidar-DTMs to identify geomorphic processes from characteristic topographic length scales

    Science.gov (United States)

    Sangireddy, H.; Passalacqua, P.; Stark, C. P.

    2013-12-01

    Characteristic length scales are often present in topography, and they reflect the driving geomorphic processes. The wide availability of high resolution lidar Digital Terrain Models (DTMs) allows us to measure such characteristic scales, but new methods of topographic analysis are needed in order to do so. Here, we explore how transitions in probability distributions (pdfs) of topographic variables such as (log(area/slope)), defined as topoindex by Beven and Kirkby[1979], can be measured by Multi-Resolution Analysis (MRA) of lidar DTMs [Stark and Stark, 2001; Sangireddy et al.,2012] and used to infer dominant geomorphic processes such as non-linear diffusion and critical shear. We show this correlation between dominant geomorphic processes to characteristic length scales by comparing results from a landscape evolution model to natural landscapes. The landscape evolution model MARSSIM Howard[1994] includes components for modeling rock weathering, mass wasting by non-linear creep, detachment-limited channel erosion, and bedload sediment transport. We use MARSSIM to simulate steady state landscapes for a range of hillslope diffusivity and critical shear stresses. Using the MRA approach, we estimate modal values and inter-quartile ranges of slope, curvature, and topoindex as a function of resolution. We also construct pdfs at each resolution and identify and extract characteristic scale breaks. Following the approach of Tucker et al.,[2001], we measure the average length to channel from ridges, within the GeoNet framework developed by Passalacqua et al.,[2010] and compute pdfs for hillslope lengths at each scale defined in the MRA. We compare the hillslope diffusivity used in MARSSIM against inter-quartile ranges of topoindex and hillslope length scales, and observe power law relationships between the compared variables for simulated landscapes at steady state. We plot similar measures for natural landscapes and are able to qualitatively infer the dominant geomorphic

  2. A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

    Science.gov (United States)

    Wang, Qiang; Wekerle, Claudia; Danilov, Sergey; Wang, Xuezhu; Jung, Thomas

    2018-04-01

    In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high- to midlatitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution Finite Element Sea ice-Ocean Model (FESOM). To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 km vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer, in terms of both Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse-resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high-resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences the release of water masses through the other gateways but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. By highlighting the issues that are independent of model resolution, we address that other efforts including the improvement of parameterizations are still required.

  3. A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

    Directory of Open Access Journals (Sweden)

    J. Ray

    2014-09-01

    Full Text Available The characterization of fossil-fuel CO2 (ffCO2 emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a~parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP, is used to identify the wavelet coefficients. We find that (i the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

  4. a Web-Based Interactive Tool for Multi-Resolution 3d Models of a Maya Archaeological Site

    Science.gov (United States)

    Agugiaro, G.; Remondino, F.; Girardi, G.; von Schwerin, J.; Richards-Rissetto, H.; De Amicis, R.

    2011-09-01

    Continuous technological advances in surveying, computing and digital-content delivery are strongly contributing to a change in the way Cultural Heritage is "perceived": new tools and methodologies for documentation, reconstruction and research are being created to assist not only scholars, but also to reach more potential users (e.g. students and tourists) willing to access more detailed information about art history and archaeology. 3D computer-simulated models, sometimes set in virtual landscapes, offer for example the chance to explore possible hypothetical reconstructions, while on-line GIS resources can help interactive analyses of relationships and change over space and time. While for some research purposes a traditional 2D approach may suffice, this is not the case for more complex analyses concerning spatial and temporal features of architecture, like for example the relationship of architecture and landscape, visibility studies etc. The project aims therefore at creating a tool, called "QueryArch3D" tool, which enables the web-based visualisation and queries of an interactive, multi-resolution 3D model in the framework of Cultural Heritage. More specifically, a complete Maya archaeological site, located in Copan (Honduras), has been chosen as case study to test and demonstrate the platform's capabilities. Much of the site has been surveyed and modelled at different levels of detail (LoD) and the geometric model has been semantically segmented and integrated with attribute data gathered from several external data sources. The paper describes the characteristics of the research work, along with its implementation issues and the initial results of the developed prototype.

  5. PCR detection of oxytetracycline resistance genes from diverse habitats in total community DNA and in streptomycete isolates.

    NARCIS (Netherlands)

    Nikolakopoulou, T.L.; Egan, S.; Overbeek, van L.S.; Guillaume, G.; Heuer, H.; Wellington, E.M.H.; Elsas, van J.D.; Collard, J.M.; Smalla, K.; Karagouni, A.D.

    2005-01-01

    A range of European habitats was screened by PCR for detection of the oxytetracycline resistance genes otr(A) and otr(B), found in the oxytetracycline-producing strain Streptomyces rimosus. Primers were developed to detect these otr genes in tetracycline-resistant (TcR) streptomycete isolates from

  6. Method for Car in Dangerous Action Detection by Means of Wavelet Multi Resolution Analysis Based on Appropriate Support Length of Base Function

    OpenAIRE

    Kohei Arai; Tomoko Nishikawa

    2013-01-01

    Multi-Resolution Analysis: MRA based on the mother wavelet function with which support length differs from the image of the automobile rear under run is performed, and the run characteristic of a car is searched for. Speed, deflection, etc. are analyzed and the method of detecting vehicles with high accident danger is proposed. The experimental results show that vehicles in a dangerous action can be detected by the proposed method.

  7. Early Detection of Plant Equipment Failures: A Case Study in Just-in-Time Maintenance

    International Nuclear Information System (INIS)

    Parlos, Alexander G.; Kim, Kyusung; Bharadwaj, Raj M.

    2001-01-01

    The development and testing of a model-based fault detection system for electric motors is briefly presented. The fault detection system was developed using only motor nameplate information. The fault detection results presented utilize only motor voltage and current sensor information, minimizing the need for expensive or intrusive sensors. Dynamic recurrent neural networks are used to predict the input-output response of a three-phase induction motor while using an estimate of the motor speed signal. Multiresolution (or wavelet) signal-processing techniques are used in combination with more traditional methods to estimate fault features for use in winding insulation and motor mechanical and electromechanical failure detection

  8. Early Detection of Plant Equipment Failures: A Case Study in Just-in-Time Maintenance

    Energy Technology Data Exchange (ETDEWEB)

    Parlos, Alexander G.; Kim, Kyusung; Bharadwaj, Raj M.

    2001-06-17

    The development and testing of a model-based fault detection system for electric motors is briefly presented. The fault detection system was developed using only motor nameplate information. The fault detection results presented utilize only motor voltage and current sensor information, minimizing the need for expensive or intrusive sensors. Dynamic recurrent neural networks are used to predict the input-output response of a three-phase induction motor while using an estimate of the motor speed signal. Multiresolution (or wavelet) signal-processing techniques are used in combination with more traditional methods to estimate fault features for use in winding insulation and motor mechanical and electromechanical failure detection.

  9. Benefits of an ultra large and multiresolution ensemble for estimating available wind power

    Science.gov (United States)

    Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

    2016-04-01

    In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

  10. Rural health centres, communities and malaria case detection in Zambia using mobile telephones: a means to detect potential reservoirs of infection in unstable transmission conditions.

    Science.gov (United States)

    Kamanga, Aniset; Moono, Petros; Stresman, Gillian; Mharakurwa, Sungano; Shiff, Clive

    2010-04-15

    Effective malaria control depends on timely acquisition of information on new cases, their location and their frequency so as to deploy supplies, plan interventions or focus attention on specific locations appropriately to intervene and prevent an upsurge in transmission. The process is known as active case detection, but because the information is time sensitive, it is difficult to carry out. In Zambia, the rural health services are operating effectively and for the most part are provided with adequate supplies of rapid diagnostic tests (RDT) as well as effective drugs for the diagnosis and treatment of malaria. The tests are administered to all prior to treatment and appropriate records are kept. Data are obtained in a timely manner and distribution of this information is important for the effective management of malaria control operations. The work reported here involves combining the process of positive diagnoses in rural health centres (passive case detection) to help detect potential outbreaks of malaria and target interventions to foci where parasite reservoirs are likely to occur. Twelve rural health centres in the Choma and Namwala Districts were recruited to send weekly information of rapid malaria tests used and number of positive diagnoses to the Malaria Institute at Macha using mobile telephone SMS. Data were entered in excel, expressed as number of cases per rural health centre and distributed weekly to interested parties. These data from each of the health centres which were mapped using geographical positioning system (GPS) coordinates were used in a time sensitive manner to plot the patterns of malaria case detection in the vicinity of each location. The data were passed on to the appropriate authorities. The seasonal pattern of malaria transmission associated with local ecological conditions can be seen in the distribution of cases diagnosed. Adequate supplies of RDT are essential in health centres and the system can be expanded throughout the

  11. EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques

    Science.gov (United States)

    Ivanov, Anton; Muller, Jan-Peter; Tao, Yu; Kim, Jung-Rack; Gwinner, Klaus; Van Gasselt, Stephan; Morley, Jeremy; Houghton, Robert; Bamford, Steven; Sidiropoulos, Panagiotis; Fanara, Lida; Waenlish, Marita; Walter, Sebastian; Steinkert, Ralf; Schreiner, Bjorn; Cantini, Federico; Wardlaw, Jessica; Sprinks, James; Giordano, Michele; Marsh, Stuart

    2016-07-01

    Understanding planetary atmosphere-surface and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to be able to overlay different epochs back in time to the mid 1970s, to examine time-varying changes, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, UCL have developed a fully automated multi-resolution DTM processing chain, called the Co-registration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP), which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed and is being applied to level-1 EDR images taken by the 4 NASA orbital cameras since 1976 using the HRSC map products (both mosaics and orbital strips) as a map-base. The project has also included Mars Radar profiles from Mars Express and Mars Reconnaissance Orbiter missions. A webGIS has been developed for displaying this time sequence of imagery and a demonstration will be shown applied to one of the map-sheets. Automated quality control techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. These data mining techniques are then being employed within a citizen science project within the Zooniverse family

  12. Which randomizations detect convergence and divergence in trait-based community assembly? A test of commonly used null models

    Czech Academy of Sciences Publication Activity Database

    Götzenberger, Lars; Botta-Dukát, Z.; Lepš, Jan; Pärtel, M.; Zobel, M.; de Bello, Francesco

    2016-01-01

    Roč. 27, č. 6 (2016), s. 1275-1287 ISSN 1100-9233 R&D Projects: GA ČR GAP505/12/1296 Institutional support: RVO:67985939 ; RVO:60077344 Keywords : Community ecology * Competition * Null model Subject RIV: EH - Ecology, Behaviour Impact factor: 2.924, year: 2016

  13. Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

    Directory of Open Access Journals (Sweden)

    Gianni Cristian Iannelli

    2014-09-01

    Full Text Available Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR data prove the usefulness and flexibility of the framework.

  14. Feasibility of Invasive Grass Detection in a Desertscrub Community Using Hyperspectral Field Measurements and Landsat TM Imagery

    OpenAIRE

    Olsson, Aaryn D.; Leeuwen, Willem J.D. van; Marsh, Stuart E.

    2011-01-01

    Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming ecosystems on three continents and a number of neotropical islands by introducing a grass-fire cycle. However, previous attempts at discriminating P. c...

  15. Rural health centres, communities and malaria case detection in Zambia using mobile telephones: a means to detect potential reservoirs of infection in unstable transmission conditions

    Directory of Open Access Journals (Sweden)

    Kamanga Aniset

    2010-04-01

    Full Text Available Abstract Background Effective malaria control depends on timely acquisition of information on new cases, their location and their frequency so as to deploy supplies, plan interventions or focus attention on specific locations appropriately to intervene and prevent an upsurge in transmission. The process is known as active case detection, but because the information is time sensitive, it is difficult to carry out. In Zambia, the rural health services are operating effectively and for the most part are provided with adequate supplies of rapid diagnostic tests (RDT as well as effective drugs for the diagnosis and treatment of malaria. The tests are administered to all prior to treatment and appropriate records are kept. Data are obtained in a timely manner and distribution of this information is important for the effective management of malaria control operations. The work reported here involves combining the process of positive diagnoses in rural health centres (passive case detection to help detect potential outbreaks of malaria and target interventions to foci where parasite reservoirs are likely to occur. Methods Twelve rural health centres in the Choma and Namwala Districts were recruited to send weekly information of rapid malaria tests used and number of positive diagnoses to the Malaria Institute at Macha using mobile telephone SMS. Data were entered in excel, expressed as number of cases per rural health centre and distributed weekly to interested parties. Results These data from each of the health centres which were mapped using geographical positioning system (GPS coordinates were used in a time sensitive manner to plot the patterns of malaria case detection in the vicinity of each location. The data were passed on to the appropriate authorities. The seasonal pattern of malaria transmission associated with local ecological conditions can be seen in the distribution of cases diagnosed. Conclusions Adequate supplies of RDT are essential in

  16. Analyzing tree cores to detect petroleum hydrocarbon-contaminated groundwater at a former landfill site in the community of Happy Valley-Goose Bay, eastern Canadian subarctic

    DEFF Research Database (Denmark)

    Fonkwe, Merline L D; Trapp, Stefan

    2016-01-01

    -gas chromatography-mass spectrometry. BTEX compounds were detected in tree cores, corroborating known groundwater contamination. A zone of anomalously high concentrations of total BTEX constituents was identified and recommended for monitoring by groundwater wells. Tree cores collected outside the landfill site......This research examines the feasibility of analyzing tree cores to detect benzene, toluene, ethylbenzene, and m, p, o-xylene (BTEX) compounds and methyl tertiary-butyl ether (MTBE) in groundwater in eastern Canada subarctic environments, using a former landfill site in the remote community of Happy...... Valley-Goose Bay, Labrador. Petroleum hydrocarbon contamination at the landfill site is the result of environmentally unsound pre-1990s disposal of households and industrial solid wastes. Tree cores were taken from trembling aspen, black spruce, and white birch and analyzed by headspace...

  17. Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach

    Science.gov (United States)

    Gauvin, Laetitia; Panisson, André; Cattuto, Ciro

    2014-01-01

    The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule. PMID:24497935

  18. EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques: A Mid-term Report

    Science.gov (United States)

    Muller, J.-P.; Yershov, V.; Sidiropoulos, P.; Gwinner, K.; Willner, K.; Fanara, L.; Waelisch, M.; van Gasselt, S.; Walter, S.; Ivanov, A.; Cantini, F.; Morley, J. G.; Sprinks, J.; Giordano, M.; Wardlaw, J.; Kim, J.-R.; Chen, W.-T.; Houghton, R.; Bamford, S.

    2015-10-01

    Understanding the role of different solid surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 8 years, especially in 3D imaging of surface shape (down to resolutions of 10s of cms) and subsequent terrain correction of imagery from orbiting spacecraft. This has led to the potential to be able to overlay different epochs back to the mid-1970s. Within iMars, a processing system has been developed to generate 3D Digital Terrain Models (DTMs) and corresponding OrthoRectified Images (ORIs) fully automatically from NASA MRO HiRISE and CTX stereo-pairs which are coregistered to corresponding HRSC ORI/DTMs. In parallel, iMars has developed a fully automated processing chain for co-registering level-1 (EDR) images from all previous NASA orbital missions to these HRSC ORIs and in the case of HiRISE these are further co-registered to previously co-registered CTX-to-HRSC ORIs. Examples will be shown of these multi-resolution ORIs and the application of different data mining algorithms to change detection using these co-registered images. iMars has recently launched a citizen science experiment to evaluate best practices for future citizen scientist validation of such data mining processed results. An example of the iMars website will be shown along with an embedded Version 0 prototype of a webGIS based on OGC standards.

  19. Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers

    Science.gov (United States)

    Lakshmi Madhavan, Bomidi; Deneke, Hartwig; Witthuhn, Jonas; Macke, Andreas

    2017-03-01

    The time series of global radiation observed by a dense network of 99 autonomous pyranometers during the HOPE campaign around Jülich, Germany, are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken-cloud conditions compared to clear, cirrus, or overcast skies. The spatial autocorrelation function including its frequency dependence is determined to quantify the degree of similarity of two time series measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies above 1/3 min-1 and points separated by more than 1 km, variations in transmittance become completely uncorrelated. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness; on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid box of 10 km × 10 km and averaging periods of 1.5-3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 (clear), 1.8 (cirrus), 1.5 (overcast), and 4.2 % (broken

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

    Science.gov (United States)

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

    2014-01-01

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

  1. Southwest U.S. Seismo-Acoustic Network: An Autonomous Data Aggregation, Detection, Localization and Ground-Truth Bulletin for the Infrasound Community

    Science.gov (United States)

    Jones, K. R.; Arrowsmith, S.

    2013-12-01

    The Southwest U.S. Seismo-Acoustic Network (SUSSAN) is a collaborative project designed to produce infrasound event detection bulletins for the infrasound community for research purposes. We are aggregating a large, unique, near real-time data set with available ground truth information from seismo-acoustic arrays across New Mexico, Utah, Nevada, California, Texas and Hawaii. The data are processed in near real-time (~ every 20 minutes) with detections being made on individual arrays and locations determined for networks of arrays. The detection and location data are then combined with any available ground truth information and compiled into a bulletin that will be released to the general public directly and eventually through the IRIS infrasound event bulletin. We use the open source Earthworm seismic data aggregation software to acquire waveform data either directly from the station operator or via the Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC), if available. The data are processed using InfraMonitor, a powerful infrasound event detection and localization software program developed by Stephen Arrowsmith at Los Alamos National Laboratory (LANL). Our goal with this program is to provide the infrasound community with an event database that can be used collaboratively to study various natural and man-made sources. We encourage participation in this program directly or by making infrasound array data available through the IRIS DMC or other means. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. R&A 5317326

  2. Nonphysician Care Providers Can Help to Increase Detection of Cognitive Impairment and Encourage Diagnostic Evaluation for Dementia in Community and Residential Care Settings.

    Science.gov (United States)

    Maslow, Katie; Fortinsky, Richard H

    2018-01-18

    In the United States, at least half of older adults living with dementia do not have a diagnosis. Their cognitive impairment may not have been detected, and some older adults whose physician recommends that they obtain a diagnostic evaluation do not follow through on the recommendation. Initiatives to increase detection of cognitive impairment and diagnosis of dementia have focused primarily on physician practices and public information programs to raise awareness about the importance of detection and diagnosis. Nonphysician care providers who work with older adults in community and residential care settings, such as aging network agencies, public health agencies, senior housing, assisted living, and nursing homes, interact frequently with older adults who have cognitive impairment but have not had a diagnostic evaluation. These care providers may be aware of signs of cognitive impairment and older adults' concerns about their cognition that have not been expressed to their physician. Within their scope of practice and training, nonphysician care providers can help to increase detection of cognitive impairment and encourage older adults with cognitive impairment to obtain a diagnostic evaluation to determine the cause of the condition. This article provides seven practice recommendations intended to increase involvement of nonphysician care providers in detecting cognitive impairment and encouraging older adults to obtain a diagnostic evaluation. The Kickstart-Assess-Evaluate-Refer (KAER) framework for physician practice in detection and diagnosis of dementia is used to identify ways to coordinate physician and nonphysician efforts and thereby increase the proportion of older adults living with dementia who have a diagnosis. © The Author(s) 2018. 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.

  3. Evaluation of a PCR Assay for Detection of Streptococcus pneumoniae in Respiratory and Nonrespiratory Samples from Adults with Community-Acquired Pneumonia

    Science.gov (United States)

    Murdoch, David R.; Anderson, Trevor P.; Beynon, Kirsten A.; Chua, Alvin; Fleming, Angela M.; Laing, Richard T. R.; Town, G. Ian; Mills, Graham D.; Chambers, Stephen T.; Jennings, Lance C.

    2003-01-01

    Streptococcus pneumoniae is the most common cause of community-acquired pneumonia, but it is undoubtedly underdiagnosed. We used a nested PCR assay (targeting the pneumolysin gene) to detect S. pneumoniae DNA in multiple sample types from 474 adults with community-acquired pneumonia and 183 control patients who did not have pneumonia. Plasma or buffy coat samples were PCR positive in only 6 of the 21 patients with positive blood cultures for S. pneumoniae and in 12 other patients (4 of whom had no other laboratory evidence of S. pneumoniae infection). Buffy coat samples from two control patients (neither having evidence of S. pneumoniae infection), but no control plasma samples, were PCR positive. Although pneumococcal antigen was detected in the urine from 120 of 420 (29%) patients, only 4 of 227 (2%) urine samples tested were PCR positive. Overall, 256 of 318 (81%) patients had PCR-positive sputum samples, including 58 of 59 samples from which S. pneumoniae was cultured. Throat swab samples from 229 of 417 (55%) patients were PCR positive and, in those who produced sputum, 96% also had positive PCR results from sputum. Throat swabs from 73 of 126 (58%) control patients were also PCR positive. We conclude that the pneumolysin PCR assay adds little to existing diagnostic tests for S. pneumoniae and is unable to distinguish colonization from infection when respiratory samples are tested. PMID:12517826

  4. Detection of immunoglobulin M and immunoglobulin G antibodies to Mycoplasma pneumoniae in children with community-acquired lower respiratory tract infections

    Directory of Open Access Journals (Sweden)

    Surinder Kumar

    2018-01-01

    Full Text Available Context: Mycoplasma pneumoniae (M. pneumoniae causes up to 40% of community-acquired pneumonia in children. It is impossible to identify M. pneumoniae infection on the basis of clinical signs, symptoms, and radiological features. Therefore, correct etiological diagnosis strongly depends on laboratory diagnosis. Aims: This study aims to investigate the role of M. pneumonia e in pediatric lower respiratory tract infections (LRTIs employing enzyme-linked immunosorbent assays (ELISA and particle agglutination (PA test. Settings and Design: Two hundred and eighty children, age 6 months to 12 years with community-acquired LRTIs were investigated for M. pneumoniae etiology. Materials and Methods: We investigated 280 children hospitalized for community-acquired LRTIs, using ELISA and PA test for detecting M. pneumoniae immunoglobulin M (IgM and immunoglobulin G antibodies. Statistical Analysis Used: The difference of proportion between the qualitative variables was tested using the Chi-square test and Fischer exact test. P ≤ 0.05 was considered as statistically significant. Kappa value was used to assess agreement between ELISA and PA test. Results: M. pneumoniae was positive in 51 (23.2% 5 years of age.

  5. Community Sewage Sensors towards Evaluation of Drug Use Trends: Detection of Cocaine in Wastewater with DNA-Directed Immobilization Aptamer Sensors

    Science.gov (United States)

    Yang, Zhugen; Castrignanò, Erika; Estrela, Pedro; Frost, Christopher G.; Kasprzyk-Hordern, Barbara

    2016-02-01

    Illicit drug use has a global concern and effective monitoring and interventions are highly required to combat drug abuse. Wastewater-based epidemiology (WBE) is an innovative and cost-effective approach to evaluate community-wide drug use trends, compared to traditional population surveys. Here we report for the first time, a novel quantitative community sewage sensor (namely DNA-directed immobilization of aptamer sensors, DDIAS) for rapid and cost-effective estimation of cocaine use trends via WBE. Thiolated single-stranded DNA (ssDNA) probe was hybridized with aptamer ssDNA in solution, followed by co-immobilization with 6-mercapto-hexane onto the gold electrodes to control the surface density to effectively bind with cocaine. DDIAS was optimized to detect cocaine at as low as 10 nM with a dynamic range from 10 nM to 5 μM, which were further employed for the quantification of cocaine in wastewater samples collected from a wastewater treatment plant in seven consecutive days. The concentration pattern of the sampling week is comparable with that from mass spectrometry. Our results demonstrate that the developed DDIAS can be used as community sewage sensors for rapid and cost-effective evaluation of drug use trends, and potentially implemented as a powerful tool for on-site and real-time monitoring of wastewater by un-skilled personnel.

  6. Detection of a Reproducible, Single-Member Shift in Soil Bacterial Communities Exposed to Low Levels of Hydrogen▿

    Science.gov (United States)

    Osborne, Catherine A.; Peoples, Mark B.; Janssen, Peter H.

    2010-01-01

    Soil is exposed to hydrogen when symbiotic rhizobia in legume root nodules cannot recycle the hydrogen that is generated during nitrogen fixation. The hydrogen emitted is most likely taken up by free-living soil bacteria that use hydrogen as an energy source, though the bacteria that do this in situ remain unclear. In this study, we investigated the effect of hydrogen exposure on the bacteria of two different soils in a microcosm setup designed to simulate hydrogen-emitting root nodules. Although the size and overall composition of the soil bacterial community did not significantly alter after hydrogen exposure, one ribotype increased in relative abundance within each soil. This single-ribotype shift was identified by generating multiple terminal restriction fragment length polymorphism (T-RFLP) profiles of 16S rRNA genes from each soil sample, with gene sequence confirmation to identify terminal restriction fragments. The increased abundance of a single ribotype after hydrogen exposure, within an otherwise similar community, was found in replicate samples taken from each microcosm and was reproducible across replicate experiments. Similarly, only one member of the soil bacterial community increased in abundance in response to hydrogen exposure in soil surrounding the root nodules of field-grown soybean (Glycine max). The ribotypes that increased after hydrogen exposure in each soil system tested were all from known hydrogen-oxidizing lineages within the order Actinomycetales. We suggest that soil actinomycetes are important utilizers of hydrogen at relevant concentrations in soil and could be key contributors to soil's function as a sink in the global hydrogen cycle. PMID:20061453

  7. Home care assistants’ perspectives on detecting mental health problems and promoting mental health among community-dwelling seniors with multimorbidity

    Science.gov (United States)

    Grundberg, Åke; Hansson, Anna; Religa, Dorota; Hillerås, Pernilla

    2016-01-01

    Introduction Elderly people with multiple chronic conditions, or multimorbidity, are at risk of developing poor mental health. These seniors often remain in their homes with support from home care assistants (HCAs). Mental health promotion by HCAs needs to be studied further because they may be among the first to observe changes in clients’ mental health status. Aim To describe HCAs’ perspectives on detecting mental health problems and promoting mental health among homebound seniors with multimorbidity. Methods We applied a descriptive qualitative study design using semi-structured interviews. Content analyses were performed on five focus group interviews conducted in 2014 with 26 HCAs. Results Most HCAs stated that they were experienced in caring for clients with mental health problems such as anxiety, depression, sleep problems, and high alcohol consumption. The HCAs mentioned as causes, or risk factors, multiple chronic conditions, feelings of loneliness, and social isolation. The findings reveal that continuity of care and seniors’ own thoughts and perceptions were essential to detecting mental health problems. Observation, collaboration, and social support emerged as important means of detecting mental health problems and promoting mental health. Conclusion The HCAs had knowledge of risk factors, but they seemed insecure about which health professionals had the primary responsibility for mental health. They also seemed to have detected early signs of mental health problems, even though good personal knowledge of the client and continuity in home visits were crucial to do so. When it came to mental health promotion, the suggestions related to the aim of ending social isolation, decreasing feelings of loneliness, and increasing physical activity. The results indicate that the HCAs seemed dependent on supervision by district nurses and on care managers’ decisions to support the needed care, to schedule assignments related to the detection of mental health

  8. In Situ Field Sequencing and Life Detection in Remote (79°26′N Canadian High Arctic Permafrost Ice Wedge Microbial Communities

    Directory of Open Access Journals (Sweden)

    J. Goordial

    2017-12-01

    Full Text Available Significant progress is being made in the development of the next generation of low cost life detection instrumentation with much smaller size, mass and energy requirements. Here, we describe in situ life detection and sequencing in the field in soils over laying ice wedges in polygonal permafrost terrain on Axel Heiberg Island, located in the Canadian high Arctic (79°26′N, an analog to the polygonal permafrost terrain observed on Mars. The life detection methods used here include (1 the cryo-iPlate for culturing microorganisms using diffusion of in situ nutrients into semi-solid media (2 a Microbial Activity Microassay (MAM plate (BIOLOG Ecoplate for detecting viable extant microorganisms through a colourimetric assay, and (3 the Oxford Nanopore MinION for nucleic acid detection and sequencing of environmental samples and the products of MAM plate and cryo-iPlate. We obtained 39 microbial isolates using the cryo-iPlate, which included several putatively novel strains based on the 16S rRNA gene, including a Pedobacter sp. (96% closest similarity in GenBank which we partially genome sequenced using the MinION. The MAM plate successfully identified an active community capable of L-serine metabolism, which was used for metagenomic sequencing with the MinION to identify the active and enriched community. A metagenome on environmental ice wedge soil samples was completed, with base calling and uplink/downlink carried out via satellite internet. Validation of MinION sequencing using the Illumina MiSeq platform was consistent with the results obtained with the MinION. The instrumentation and technology utilized here is pre-existing, low cost, low mass, low volume, and offers the prospect of equipping micro-rovers and micro-penetrators with aggressive astrobiological capabilities. Since potentially habitable astrobiology targets have been identified (RSLs on Mars, near subsurface water ice on Mars, the plumes and oceans of Europa and Enceladus

  9. Methicillin-Resistant Staphylococcus aureus (MRSA Detection from the Hands of Jatinangor Community Health Center’s Health Care Providers

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    Jeevanisha Patmanathan

    2015-06-01

    Full Text Available Background: Methicillin-resistant Staphylococcus aureus is a bacterium that is resistance towards β-lactam antibiotics, and it seems to be one of the leading causes of nosocomial infections. Hands of the health care workers are said to be the main source for the nosocomial transmission. Thus, the study aims to determine methicillin-resistant S. aureus from the hands of Jatinangor Community Health Center’s health care workers. Methods: Samples were taken from the hands of 30 Jatinangor Community Health Center’s staffs, including medical and paramedical; from October 2012 to November 2012. Then, these samples underwent further laboratory examinations, starting from culture, identification and susceptibility test towards cefoxitin, in identifying methicillin-resistant S. aureus. Results: Out of the 30 samples taken, 6 samples (20% were positive for S. aureus isolates. In which, 4 (13.33% of the samples were positive for methicillin-resistant S. aureus. Conclusions: Since, health care workers are the main people in contact with patients and maintaining proper hand hygiene makes a huge difference; hand hygiene should be given adequate attention for the benefit of all.

  10. Detection of Sialic Acid-Utilising Bacteria in a Caecal Community Batch Culture Using RNA-Based Stable Isotope Probing

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    Wayne Young

    2015-03-01

    Full Text Available Sialic acids are monosaccharides typically found on cell surfaces and attached to soluble proteins, or as essential components of ganglioside structures that play a critical role in brain development and neural transmission. Human milk also contains sialic acid conjugated to oligosaccharides, glycolipids, and glycoproteins. These nutrients can reach the large bowel where they may be metabolised by the microbiota. However, little is known about the members of the microbiota involved in this function. To identify intestinal bacteria that utilise sialic acid within a complex intestinal community, we cultured the caecal microbiota from piglets in the presence of 13C-labelled sialic acid. Using RNA-based stable isotope probing, we identified bacteria that consumed 13C-sialic acid by fractionating total RNA in isopycnic buoyant density gradients followed by 16S rRNA gene analysis. Addition of sialic acid caused significant microbial community changes. A relative rise in Prevotella and Lactobacillus species was accompanied by a corresponding reduction in the genera Escherichia/Shigella, Ruminococcus and Eubacterium. Inspection of isotopically labelled RNA sequences suggests that the labelled sialic acid was consumed by a wide range of bacteria. However, species affiliated with the genus Prevotella were clearly identified as the most prolific users, as solely their RNA showed significantly higher relative shares among the most labelled RNA species. Given the relevance of sialic acid in nutrition, this study contributes to a better understanding of their microbial transformation in the intestinal tract with potential implications for human health.

  11. Feasibility of Invasive Grass Detection in a Desertscrub Community Using Hyperspectral Field Measurements and Landsat TM Imagery

    Directory of Open Access Journals (Sweden)

    Stuart E. Marsh

    2011-10-01

    Full Text Available Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming ecosystems on three continents and a number of neotropical islands by introducing a grass-fire cycle. However, previous attempts at discriminating P. ciliare in North America using multi-spectral imagery have been unsuccessful. In this paper, we integrate field measurements of hyperspectral plant species signatures and canopy cover with multi-temporal spectral analysis to identify opportunities for detection using moderate-resolution multi-spectral imagery. By applying these results to Landsat TM imagery, we show that multi-spectral discrimination of P. ciliare in heterogeneous mixed desert scrub is feasible, but only at high abundance levels that may have limited value to land managers seeking to control invasion. Much higher discriminability is possible with hyperspectral shortwave infrared imagery because of differences in non-photosynthetic vegetation in uninvaded and invaded landscapes during dormant seasons but these spectra are unavailable in multispectral sensors. Therefore, we recommend hyperspectral imagery for distinguishing invasive grass-dominated landscapes from uninvaded desert scrub.

  12. Home care assistants’ perspectives on detecting mental health problems and promoting mental health among community-dwelling seniors with multimorbidity

    Directory of Open Access Journals (Sweden)

    Grundberg Å

    2016-02-01

    Full Text Available Åke Grundberg,1,2 Anna Hansson,2 Dorota Religa,1 Pernilla Hillerås1,2 1Division of Neurogeriatrics, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Huddinge, 2Sophiahemmet University, Stockholm, Sweden Introduction: Elderly people with multiple chronic conditions, or multimorbidity, are at risk of developing poor mental health. These seniors often remain in their homes with support from home care assistants (HCAs. Mental health promotion by HCAs needs to be studied further because they may be among the first to observe changes in clients’ mental health status. Aim: To describe HCAs’ perspectives on detecting mental health problems and promoting mental health among homebound seniors with multimorbidity. Methods: We applied a descriptive qualitative study design using semi-structured interviews. Content analyses were performed on five focus group interviews conducted in 2014 with 26 HCAs. Results: Most HCAs stated that they were experienced in caring for clients with mental health problems such as anxiety, depression, sleep problems, and high alcohol consumption. The HCAs mentioned as causes, or risk factors, multiple chronic conditions, feelings of loneliness, and social isolation. The findings reveal that continuity of care and seniors’ own thoughts and perceptions were essential to detecting mental health problems. Observation, collaboration, and social support emerged as important means of detecting mental health problems and promoting mental health. Conclusion: The HCAs had knowledge of risk factors, but they seemed insecure about which health professionals had the primary responsibility for mental health. They also seemed to have detected early signs of mental health problems, even though good personal knowledge of the client and continuity in home visits were crucial to do so. When it came to mental health promotion, the suggestions related to the aim of ending social isolation, decreasing feelings of

  13. [Primary care follow-up of newborns with sickle cell disease detected in neonatal screening in the Community of Madrid].

    Science.gov (United States)

    Rodríguez-Moldes, B; Carbajo, A J; Sánchez, B; Fernández, M; Garí, M; Fernández, M C; Álvarez, J; García, A; Cela, E

    2015-04-01

    The main aim of the study was to assess the effects of the recommended preventive program in the population affected with Sickle Cell Disease in Primary Care. The program included, antibiotic prophylaxis, immunizations and health education, following the introduction of universal neonatal screening program for Sickle Cell Disease in the Community of Madrid. A cross-sectional observational study was performed with retrospective data collected from a cohort of newborns with Sickle Cell Disease diagnosed by neonatal screening test in the Community of Madrid. From the data obtained from a sample of 20 patients, it was found that 95% had been diagnosed by the newborn screening test performed between 5 and 13 days of life. The mean age was 39 months when the study was conducted. During follow-up, from Primary Care Paediatric clinic, it was observed that the compliance for antibiotic prophylaxis was 90%, and the coverage for the official vaccination schedule was 85%. Specific vaccine coverage as a risk population was highly variable (85% for pneumococcal 23V, 50% for influenza, and 15% for hepatitis A). Health education only reached one in every four families. Acceptable compliance with antibiotic prophylaxis was observed during the follow-up of patients with sickle cell disease in Primary Care, but a low coverage of routine immunization, as well as specific immunizations. Coverage of health education was very low. Improving these parameters would require greater coordination and involvement of Primary Care Professionals so that these patients were followed up appropriately, and could be translated into a reduction of disease complications and an improvement in the quality of life of these patients. Copyright © 2013 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

  14. Analyzing tree cores to detect petroleum hydrocarbon-contaminated groundwater at a former landfill site in the community of Happy Valley-Goose Bay, eastern Canadian subarctic.

    Science.gov (United States)

    Fonkwe, Merline L D; Trapp, Stefan

    2016-08-01

    This research examines the feasibility of analyzing tree cores to detect benzene, toluene, ethylbenzene, and m, p, o-xylene (BTEX) compounds and methyl tertiary-butyl ether (MTBE) in groundwater in eastern Canada subarctic environments, using a former landfill site in the remote community of Happy Valley-Goose Bay, Labrador. Petroleum hydrocarbon contamination at the landfill site is the result of environmentally unsound pre-1990s disposal of households and industrial solid wastes. Tree cores were taken from trembling aspen, black spruce, and white birch and analyzed by headspace-gas chromatography-mass spectrometry. BTEX compounds were detected in tree cores, corroborating known groundwater contamination. A zone of anomalously high concentrations of total BTEX constituents was identified and recommended for monitoring by groundwater wells. Tree cores collected outside the landfill site at a local control area suggest the migration of contaminants off-site. Tree species exhibit different concentrations of BTEX constituents, indicating selective uptake and accumulation. Toluene in wood exhibited the highest concentrations, which may also be due to endogenous production. Meanwhile, MTBE was not found in the tree cores and is considered to be absent in the groundwater. The results demonstrate that tree-core analysis can be useful for detecting anomalous concentrations of petroleum hydrocarbons, such as BTEX compounds, in subarctic sites with shallow unconfined aquifers and permeable soils. This method can therefore aid in the proper management of contamination during landfill operations and after site closures.

  15. [Detection and differentiation of Entamoeba histolytica and Entamoeba dispar by polymerase chain reaction in a community in Zulia State, Venezuela].

    Science.gov (United States)

    Rivero, Zulbey; Bracho, Angela; Calchi, Marinella; Díaz, Iris; Acurero, Ellen; Maldonado, Adriana; Chourio, Glenis; Arráiz, Nailet; Corzo, Gilbert

    2009-01-01

    Differential identification of Entamoeba histolytica and Entamoeba dispar is essential for both appropriate patient treatment and epidemiological purposes. To determine the prevalence of these amoeba infections in Santa Rosa de Agua (Maracaibo, Zulia State, Venezuela), a PCR assay using specific primers for each species was standardized and applied. 204 stool samples were analyzed through direct microscopic examination with SSF (0.85%) and lugol, formol-ether concentration, and PCR. Under direct microscopy, 42 individuals (20.58%) presented the E. histolytica/E. dispar complex. Meanwhile PCR showed 47 positive cases for these amoebas: 22 E. histolytica (10.78%), 16 E. dispar (7.84%), and 9 (4.41%) mixed infections. There was no significant difference in the presence of E. histolytica and/or E. dispar according to either gender or age. There were no cases of these amoebas in children under 2 years of age. Observed frequency of E. histolytica (31/204) shows the endemic nature of amoeba infection in this community.

  16. Using health and demographic surveillance for the early detection of cholera outbreaks: analysis of community- and hospital-based data from Matlab, Bangladesh.

    Science.gov (United States)

    Saulnier, Dell D; Persson, Lars-Åke; Streatfield, Peter Kim; Faruque, A S G; Rahman, Anisur

    2016-01-01

    Cholera outbreaks are a continuing problem in Bangladesh, and the timely detection of an outbreak is important for reducing morbidity and mortality. In Matlab, the ongoing Health and Demographic Surveillance System (HDSS) data records symptoms of diarrhea in children under the age of 5 years at the community level. Cholera surveillance in Matlab currently uses hospital-based data. The objective of this study is to determine whether increases in cholera in Matlab can be detected earlier by using HDSS diarrhea symptom data in a syndromic surveillance analysis, when compared to hospital admissions for cholera. HDSS diarrhea symptom data and hospital admissions for cholera in children under 5 years of age over a 2-year period were analyzed with the syndromic surveillance statistical program EARS (Early Aberration Reporting System). Dates when significant increases in either symptoms or cholera cases occurred were compared to one another. The analysis revealed that there were 43 days over 16 months when the cholera cases or diarrhea symptoms increased significantly. There were 8 months when both data sets detected days with significant increases. In 5 of the 8 months, increases in diarrheal symptoms occurred before increases of cholera cases. The increases in symptoms occurred between 1 and 15 days before the increases in cholera cases. The results suggest that the HDSS survey data may be able to detect an increase in cholera before an increase in hospital admissions is seen. However, there was no direct link between diarrheal symptom increases and cholera cases, and this, as well as other methodological weaknesses, should be taken into consideration.

  17. BNLs Synchrotron-radiation Research Hub for Characterizing Detection Materials and Devices for the NA-22 Community

    Energy Technology Data Exchange (ETDEWEB)

    Camarda, G. S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Bolotnikov, A. E. [Brookhaven National Lab. (BNL), Upton, NY (United States); Cui, Y. [Brookhaven National Lab. (BNL), Upton, NY (United States); Hossain, A. [Brookhaven National Lab. (BNL), Upton, NY (United States); Roy, U. [Brookhaven National Lab. (BNL), Upton, NY (United States); Yang, G. [Brookhaven National Lab. (BNL), Upton, NY (United States); Vanier, P. [Brookhaven National Lab. (BNL), Upton, NY (United States); McDowell, Alastair [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rosen, Chris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Labrum, Joseph [Univ. of California, Berkeley, CA (United States)

    2017-03-01

    The goal of this project is to obtain and characterize scintillators, emerging- and commercial-compoundsemiconductor radiation- detection materials and devices provided by vendors and research organizations. The focus of our proposed research is to clarify the role of the deleterious defects and impurities responsible for the detectors' non-uniformity in scintillating crystals, commercial semiconductor radiation-detector materials, and in emerging R&D ones. Some benefits of this project addresses the need for fabricating high-performance scintillators and compound-semiconductor radiation-detectors with the proven potential for large-scale manufacturing. The findings help researchers to resolve the problems of non-uniformities in scintillating crystals, commercial semiconductor radiation-detector materials, and in emerging R&D ones.

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

    Science.gov (United States)

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

    2012-04-01

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

  19. Electrocardiogram as an important tool in Preventive & Community Medicine - A rare case report of asymptomatic non paroxysmal accelerated junctional rhythm detected on routine ECG

    Directory of Open Access Journals (Sweden)

    Raghavendra Deolalikar

    2015-03-01

    Full Text Available Fifty four year old asymptomatic employee was detected to have Inverted P waves with normal QRS complex on Electrocardiogram [ECG] during his Annual Medical Examination. The ECG reverted to normal after few days. Inverted P is suggestive of retrograde conduct of impulse from A-V Node. Case of Non Paroxysmal Accelerated Junctional Rhythm. Causes are inferior wall myocardial infarction, myocarditis or recent open heart surgery. Troponin T Test was negative, Treadmill test was negative, and 2D Echo showed 55 % ejection fraction with no regional wall motion abnormalities. It needs no treatment if underlying causes are ruled out. Case would have gone un-noticed as patient was asymptomatic, thus emphasizing the importance of ECG in preventive and community medicine.

  20. Cancer early detection program based on awareness and clinical breast examination: Interim results from an urban community in Mumbai, India.

    Science.gov (United States)

    Gadgil, Anita; Sauvaget, Catherine; Roy, Nobhojit; Muwonge, Richard; Kantharia, Surita; Chakrabarty, Anuradha; Bantwal, Kanchan; Haldar, Indrani; Sankaranarayanan, Rengaswamy

    2017-02-01

    Indian women with breast cancer are usually diagnosed in advanced stages leading to poor survival. Improving breast awareness and increasing access to early diagnosis and adequate treatment has been advocated for breast cancer control. We implemented a program to increase awareness on breast cancer and access to its early detection in an occupational health care scheme in Mumbai, India. Breast awareness brochures were mailed annually between June 2013 and June 2016 to a cohort of 22,500 eligible women aged 30-69 years old receiving universal health care from an occupational health care scheme comprising of primary health centres and a referral secondary care hospital in Mumbai. Women with suspected breast cancers were provided with diagnostic investigations and treatment. Socio-demographic information and tumour characteristics were compared between the breast awareness pre-intervention period (Jan 2005-May 2013) and the breast awareness intervention period after four rounds of mailers (June 2013-June 2016). The proportion of women with early tumours and axillary lymph node negative cancers increased from 74% to 81% and 46% to 53% respectively, between the two periods. While the proportion of patients receiving breast conserving surgery increased from 39% to 51%, the proportion receiving chemotherapy decreased from 84% to 56%. Interim results following efforts to improve breast awareness and access to care in a cohort of women in an occupational health care scheme indicate early detection and more conservative treatment of breast cancers. Creating awareness and improving access to care may result in cancer down-staging. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. A Community-Based, Technology-Supported Health Service for Detecting and Preventing Frailty among Older Adults: A Participatory Design Development Process.

    Science.gov (United States)

    van Velsen, Lex; Illario, Maddalena; Jansen-Kosterink, Stephanie; Crola, Catherine; Di Somma, Carolina; Colao, Annamaria; Vollenbroek-Hutten, Miriam

    2015-01-01

    Frailty is a multifaceted condition that affects many older adults and marks decline on areas such as cognition, physical condition, and nutritional status. Frail individuals are at increased risk for the development of disability, dementia, and falls. There are hardly any health services that enable the identification of prefrail individuals and that focus on prevention of further functional decline. In this paper, we discuss the development of a community-based, technology-supported health service for detecting prefrailty and preventing frailty and further functional decline via participatory design with a wide range of stakeholders. The result is an innovative service model in which an online platform supports the integration of traditional services with novel, Information Communication Technology supported tools. This service is capable of supporting the different phases of screening and offers training services, by also integrating them with community-based services. The service model can be used as a basis for developing similar services within a wide range of healthcare systems. We present the service model, the general functioning of the technology platform, and the different ways in which screening for and prevention of frailty has been localized. Finally, we reflect on the added value of participatory design for creating such health services.

  2. Detecting the impact of bank and channel modification on invertebrate communities in Mediterranean temporary streams (Sardinia, SW Italy).

    Science.gov (United States)

    Buffagni, Andrea; Tenchini, Roberta; Cazzola, Marcello; Erba, Stefania; Balestrini, Raffaella; Belfiore, Carlo; Pagnotta, Romano

    2016-09-15

    We hypothesized that reach-scale, bank and channel modification would impact benthic communities in temporary rivers of Sardinia, when pollution and water abstraction are not relevant. A range of variables were considered, which include both artificial structures/alterations and natural features observed in a stream reach. Multivariate regression trees (MRT) were used to assess the effects of the explanatory variables on invertebrate assemblages and five groups, characterized by different habitat modification and/or features, were recognized. Four node variables determined the splits in the MRT analysis: channel reinforcement, tree-related bank and channel habitats, channel modification and bank modification. Continuity of trees in the river corridor diverged among MRT groups and significant differences among groups include presence of alders, extent of channel shading and substrate diversity. Also, the percentage of in-stream organic substrates, in particular CPOM/Xylal, showed highly significant differences among groups. For practical applications, thresholds for the extent of channel reinforcement (40%) and modification (10%) and for bank alteration (≈30%) were provided, that can be used to guide the implementation of restoration measures. In moderately altered river reaches, a significant extent of tree-related habitats (≈5%) can noticeably mitigate the effects of morphological alteration on aquatic invertebrates. The outcomes highlight the importance of riparian zone management as an opportune, achievable prospect in the restoration of Mediterranean temporary streams. The impact of bank and channel modification on ecological status (sensu WFD) was investigated and the tested benthic metrics, especially those based on abundance data, showed legible differences among MRT groups. Finally, bank and channel modification appears to be a potential threat for the conservation of a few Sardo-Corsican endemic species. The introduction of management criteria that

  3. Community health workers and smartphones for the detection of surgical site infections in rural Haiti: a pilot study.

    Science.gov (United States)

    Matousek, Alexi; Paik, Ken; Winkler, Eric; Denike, Jennifer; Addington, Stephen R; Exe, Chauvet; Louis, Rodolphe R E Jean; Riviello, Robert

    2015-04-27

    Absence of outcome data is a barrier to quality improvement in resource poor settings. To address this challenge, we set out to determine whether follow up for surgical site infections (SSIs) using community health workers (CHWs) and smartphones is feasible in rural Haiti. In this pilot study, all patients from a specific mountain region who received an operation between March 10, and July 1, 2014, at Hôpital Albert Schweitzer in rural Haiti were eligible for inclusion. Patients or guardians of minors were approached for consent. We designed a smartphone application to enable CHWs to screen for SSIs during home visits by administering a questionnaire, obtaining GPS data, and submitting a photograph of an incision. We selected and trained CHWs to use the smartphone application and compensated them based on performance. CHWs completed home visits for 30 days after an operation for all participants. Surgeons examined all participants within 24 h after the second CHW home visit. Primary outcomes included the number of participants completing 30-day follow-up and home visits made on time. Secondary outcomes included the quality of the photographs and the agreement between surgeons and CHWs on the diagnosis of SSI. The Partners Healthcare institutional review board and the Ethics Committee at Hôpital Albert Schweitzer approved the study protocol. Five CHWs completed 30-day follow up for 37 of 39 participants (94·9%) and completed 107 of 117 home visits on time (91·5%). High quality photographs were submitted for 101 of 117 visits (86·3%). Surgeons and CHWs agreed on the diagnosis of SSI in 28 of 33 cases (84·8%). Outpatient follow up for SSIs with CHWs and smartphones is feasible in rural Haiti. Further validation of the programme needs to be done before widespread adoption or advocating for task shifting post-operative follow up to CHWs. Partners Healthcare, Children's Hospital Boston, and Swiss Bündner Partnerschaft Hôpital Albert Schweitzer Haiti. Copyright

  4. Detection of CTX-M-15 beta-lactamases in Enterobacteriaceae causing hospital- and community-acquired urinary tract infections as early as 2004, in Dar es Salaam, Tanzania.

    Science.gov (United States)

    Manyahi, Joel; Moyo, Sabrina J; Tellevik, Marit Gjerde; Ndugulile, Faustine; Urassa, Willy; Blomberg, Bjørn; Langeland, Nina

    2017-04-17

    The spread of Extended Spectrum β-lactamases (ESBLs) among Enterobacteriaceae and other Gram-Negative pathogens in the community and hospitals represents a major challenge to combat infections. We conducted a study to assess the prevalence and genetic makeup of ESBL-type resistance in bacterial isolates causing community- and hospital-acquired urinary tract infections. A total of 172 isolates of Enterobacteriaceae were collected in Dar es Salaam, Tanzania, from patients who met criteria of community and hospital-acquired urinary tract infections. We used E-test ESBL strips to test for ESBL-phenotype and PCR and sequencing for detection of ESBL genes. Overall 23.8% (41/172) of all isolates were ESBL-producers. ESBL-producers were more frequently isolated from hospital-acquired infections (32%, 27/84 than from community-acquired infections (16%, 14/88, p Enterobacteriaceae causing both hospital- and community-acquired infections in Tanzania.

  5. Interrater and Test-Retest Reliability and Minimal Detectable Change of the Balance Evaluation Systems Test (BESTest) and Subsystems With Community-Dwelling Older Adults.

    Science.gov (United States)

    Wang-Hsu, Elizabeth; Smith, Susan S

    2017-01-10

    Falls are a common cause of injuries and hospital admissions in older adults. Balance limitation is a potentially modifiable factor contributing to falls. The Balance Evaluation Systems Test (BESTest), a clinical balance measure, categorizes balance into 6 underlying subsystems. Each of the subsystems is scored individually and summed to obtain a total score. The reliability of the BESTest and its individual subsystems has been reported in patients with various neurological disorders and cancer survivors. However, the reliability and minimal detectable change (MDC) of the BESTest with community-dwelling older adults have not been reported. The purposes of our study were to (1) determine the interrater and test-retest reliability of the BESTest total and subsystem scores; and (2) estimate the MDC of the BESTest and its individual subsystem scores with community-dwelling older adults. We used a prospective cohort methodological design. Community-dwelling older adults (N = 70; aged 70-94 years; mean = 85.0 [5.5] years) were recruited from a senior independent living community. Trained testers (N = 3) administered the BESTest. All participants were tested with the BESTest by the same tester initially and then retested 7 to 14 days later. With 32 of the participants, a second tester concurrently scored the retest for interrater reliability. Testers were blinded to each other's scores. Intraclass correlation coefficients [ICC(2,1)] were used to determine the interrater and test-retest reliability. Test-retest reliability was also analyzed using method error and the associated coefficients of variation (CVME). MDC was calculated using standard error of measurement. Interrater reliability (N = 32) of the BESTest total score was ICC(2, 1) = 0.97 (95% confidence interval [CI], 0.94-0.99). The ICCs for the individual subsystem scores ranged from 0.85 to 0.94. Test-retest reliability (N = 70) of the BESTest total score was ICC(2,1) = 0.93 (95% CI, 0.89-0.96). ICCs for the

  6. Urinary Biomarkers KIM-1 and NGAL for Detection of Chronic Kidney Disease of Uncertain Etiology (CKDu) among Agricultural Communities in Sri Lanka.

    Science.gov (United States)

    De Silva, Pallagae Mangala C S; Mohammed Abdul, Khaja Shameem; Eakanayake, Eakanayake M D V; Jayasinghe, Sudheera Sammanthi; Jayasumana, Channa; Asanthi, Hewa Bandulage; Perera, Hettiarachigae S D; Chaminda, Gamage G Tushara; Chandana, Ediriweera P S; Siribaddana, Sisira H

    2016-09-01

    Chronic Kidney Disease of uncertain etiology (CKDu) is an emerging epidemic among farming communities in rural Sri Lanka. Victims do not exhibit common causative factors, however, histopathological studies revealed that CKDu is a tubulointerstitial disease. Urine albumin or albumin-creatinine ratio is still being used as a traditional diagnostic tool to identify CKDu, but accuracy and prevalence data generated are questionable. Urinary biomarkers have been used in similar nephropathy and are widely recognised for their sensitivity, specificity and accuracy in determining CKDu and early renal injury. However, these biomarkers have never been used in diagnosing CKDu in Sri Lanka. Male farmers (n = 1734) were recruited from 4 regions in Sri Lanka i.e. Matara and Nuwara Eliya (farming locations with no CKDu prevalence) and two CKDu emerging locations from Hambantota District in Southern Sri Lanka; Angunakolapelessa (EL1) and Bandagiriya (EL2). Albuminuria (ACR ≥ 30mg/g); serum creatinine based estimation of glomerular filtration rate (eGFR); creatinine normalized urinary kidney injury molecule (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) were measured. Fourteen new CKDu cases (18%) from EL1 and nine CKDu cases (9%) from EL2 were recognized for the first time from EL1, EL2 locations, which were previously considered as non-endemic of the disease and associated with persistent albuminuria (ACR ≥ 30mg/g Cr). No CKDu cases were identified in non-endemic study locations in Matara (CM) and Nuwara Eliya (CN). Analysis of urinary biomarkers showed urinary KIM-1 and NGAL were significantly higher in new CKDu cases in EL1 and EL2. However, we also reported significantly higher KIM-1 and NGAL in apparently healthy farmers in EL 1 and EL 2 with comparison to both control groups. These observations may indicate possible early renal damage in absence of persistent albuminuria and potential capabilities of urinary KIM-1 and NGAL in early detection of renal injury

  7. A self-reported screening tool for detecting community-dwelling older persons with frailty syndrome in the absence of mobility disability: the FiND questionnaire.

    Science.gov (United States)

    Cesari, Matteo; Demougeot, Laurent; Boccalon, Henri; Guyonnet, Sophie; Abellan Van Kan, Gabor; Vellas, Bruno; Andrieu, Sandrine

    2014-01-01

    The "frailty syndrome" (a geriatric multidimensional condition characterized by decreased reserve and diminished resistance to stressors) represents a promising target of preventive interventions against disability in elders. Available screening tools for the identification of frailty in the absence of disability present major limitations. In particular, they have to be administered by a trained assessor, require special equipment, and/or do not discriminate between frail and disabled individuals. Aim of this study is to verify the agreement of a novel self-reported questionnaire (the "Frail Non-Disabled" [FiND] instrument) designed for detecting non-mobility disabled frail older persons with results from reference tools. Data are from 45 community-dwelling individuals aged ≥60 years. Participants were asked to complete the FiND questionnaire separately exploring the frailty and disability domains. Then, a blinded assessor objectively measured the frailty status (using the phenotype proposed by Fried and colleagues) and mobility disability (using the 400-meter walk test). Cohen's kappa coefficients were calculated to determine the agreement between the FiND questionnaire with the reference instruments. Mean age of participants (women 62.2%) was 72.5 (standard deviation 8.2) years. Seven (15.6%) participants presented mobility disability as being unable to complete the 400-meter walk test. According to the frailty phenotype criteria, 25 (55.6%) participants were pre-frail or frail, and 13 (28.9%) were robust. Overall, a substantial agreement of the instrument with the reference tools (kappa = 0.748, quadratic weighted kappa = 0.836, both p valuesFiND disability domain and the 400-meter walk test was excellent (kappa = 0.920, pFiND questionnaire presents a very good capacity to correctly identify frail older persons without mobility disability living in the community. This screening tool may represent an opportunity for diffusing awareness about frailty

  8. Detecting prediabetes among Hispanics/Latinos from diverse heritage groups: Does the test matter? Findings from the Hispanic Community Health Study/Study of Latinos.

    Science.gov (United States)

    Avilés-Santa, M Larissa; Pérez, Cynthia M; Schneiderman, Neil; Savage, Peter J; Kaplan, Robert C; Teng, Yanping; Suárez, Erick L; Cai, Jianwen; Giachello, Aida L; Talavera, Gregory A; Cowie, Catherine C

    2017-02-01

    The objectives of this analysis were to compare the ability of fasting plasma glucose (FPG), post oral load plasma glucose (2hPG), and hemoglobin A 1c (HbA 1c ) to identify U.S. Hispanic/Latino individuals with prediabetes, and to assess its cardiovascular risk factor correlates. This is a cross-sectional analysis of baseline data from 15,507 adults without self-reported diabetes mellitus from six Hispanic/Latino heritage groups, enrolled in the Hispanic Community Health Study/Study of Latinos, which takes place in four U.S. communities. The prevalence of prediabetes was determined according to individual or combinations of ADA-defined cut points: FPG=5.6-7.0mmol/L, 2hPG=7.8-11.1mmol/L, and HbA 1c =5.7%-6.4% (39-46mmol/mol). The sensitivity of these criteria to detect prediabetes was estimated. The prevalence ratios (PRs) for selected cardiovascular risk factors were compared among alternative categories of prediabetes versus normoglycemia [FPGprediabetes criteria. Using 2hPG as the gold standard, the sensitivity of FPG was 40.1%, HbA 1c was 45.6%, and that of HbA 1c +FPG was 62.2%. The number of significant PRs for cardiovascular risk factors was higher among individuals with isolated 2hPG=7.8-11.1mmol/L, FPG=5.6-7.0mmol/L+HbA 1c =5.7%-6.4%, or those who met the three prediabetes criteria. Assessing FPG, HbA 1c , and cardiovascular risk factors in Hispanics/Latinos at risk might enhance the early prevention of diabetes mellitus and cardiovascular complications in this young and growing population, independent of their heritage group. Published by Elsevier Inc.

  9. Molecular analysis of bacterial communities and detection of potential pathogens in a recirculating aquaculture system for Scophthalmus maximus and Solea senegalensis.

    Science.gov (United States)

    Martins, Patrícia; Cleary, Daniel F R; Pires, Ana C C; Rodrigues, Ana Maria; Quintino, Victor; Calado, Ricardo; Gomes, Newton C M

    2013-01-01

    The present study combined a DGGE and barcoded 16S rRNA pyrosequencing approach to assess bacterial composition in the water of a recirculating aquaculture system (RAS) with a shallow raceway system (SRS) for turbot (Scophthalmus maximus) and sole (Solea senegalensis). Barcoded pyrosequencing results were also used to determine the potential pathogen load in the RAS studied. Samples were collected from the water supply pipeline (Sup), fish production tanks (Pro), sedimentation filter (Sed), biofilter tank (Bio), and protein skimmer (Ozo; also used as an ozone reaction chamber) of twin RAS operating in parallel (one for each fish species). Our results revealed pronounced differences in bacterial community composition between turbot and sole RAS, suggesting that in the systems studied there is a strong species-specific effect on water bacterial communities. Proteobacteria was the most abundant phylum in the water supply and all RAS compartments. Other important taxonomic groups included the phylum Bacteriodetes. The saltwater supplied displayed a markedly lower richness and appeared to have very little influence on bacterial composition. The following potentially pathogenic species were detected: Photobacterium damselae in turbot (all compartments), Tenacibaculum discolor in turbot and sole (all compartments), Tenacibaculum soleae in turbot (all compartments) and sole (Pro, Sed and Bio), and Serratia marcescens in turbot (Sup, Sed, Bio and Ozo) and sole (only Sed) RAS. Despite the presence of these pathogens, no symptomatic fish were observed. Although we were able to identify potential pathogens, this approach should be employed with caution when monitoring aquaculture systems, as the required phylogenetic resolution for reliable identification of pathogens may not always be possible to achieve when employing 16S rRNA gene fragments.

  10. Molecular analysis of bacterial communities and detection of potential pathogens in a recirculating aquaculture system for Scophthalmus maximus and Solea senegalensis.

    Directory of Open Access Journals (Sweden)

    Patrícia Martins

    Full Text Available The present study combined a DGGE and barcoded 16S rRNA pyrosequencing approach to assess bacterial composition in the water of a recirculating aquaculture system (RAS with a shallow raceway system (SRS for turbot (Scophthalmus maximus and sole (Solea senegalensis. Barcoded pyrosequencing results were also used to determine the potential pathogen load in the RAS studied. Samples were collected from the water supply pipeline (Sup, fish production tanks (Pro, sedimentation filter (Sed, biofilter tank (Bio, and protein skimmer (Ozo; also used as an ozone reaction chamber of twin RAS operating in parallel (one for each fish species. Our results revealed pronounced differences in bacterial community composition between turbot and sole RAS, suggesting that in the systems studied there is a strong species-specific effect on water bacterial communities. Proteobacteria was the most abundant phylum in the water supply and all RAS compartments. Other important taxonomic groups included the phylum Bacteriodetes. The saltwater supplied displayed a markedly lower richness and appeared to have very little influence on bacterial composition. The following potentially pathogenic species were detected: Photobacterium damselae in turbot (all compartments, Tenacibaculum discolor in turbot and sole (all compartments, Tenacibaculum soleae in turbot (all compartments and sole (Pro, Sed and Bio, and Serratia marcescens in turbot (Sup, Sed, Bio and Ozo and sole (only Sed RAS. Despite the presence of these pathogens, no symptomatic fish were observed. Although we were able to identify potential pathogens, this approach should be employed with caution when monitoring aquaculture systems, as the required phylogenetic resolution for reliable identification of pathogens may not always be possible to achieve when employing 16S rRNA gene fragments.

  11. Urinary Biomarkers KIM-1 and NGAL for Detection of Chronic Kidney Disease of Uncertain Etiology (CKDu) among Agricultural Communities in Sri Lanka

    Science.gov (United States)

    Mohammed Abdul, Khaja Shameem; Eakanayake, Eakanayake M. D. V.; Jayasinghe, Sudheera Sammanthi; Jayasumana, Channa; Asanthi, Hewa Bandulage; Perera, Hettiarachigae S. D.; Chaminda, Gamage G. Tushara; Chandana, Ediriweera P. S.; Siribaddana, Sisira H.

    2016-01-01

    Chronic Kidney Disease of uncertain etiology (CKDu) is an emerging epidemic among farming communities in rural Sri Lanka. Victims do not exhibit common causative factors, however, histopathological studies revealed that CKDu is a tubulointerstitial disease. Urine albumin or albumin-creatinine ratio is still being used as a traditional diagnostic tool to identify CKDu, but accuracy and prevalence data generated are questionable. Urinary biomarkers have been used in similar nephropathy and are widely recognised for their sensitivity, specificity and accuracy in determining CKDu and early renal injury. However, these biomarkers have never been used in diagnosing CKDu in Sri Lanka. Male farmers (n = 1734) were recruited from 4 regions in Sri Lanka i.e. Matara and Nuwara Eliya (farming locations with no CKDu prevalence) and two CKDu emerging locations from Hambantota District in Southern Sri Lanka; Angunakolapelessa (EL1) and Bandagiriya (EL2). Albuminuria (ACR ≥ 30mg/g); serum creatinine based estimation of glomerular filtration rate (eGFR); creatinine normalized urinary kidney injury molecule (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) were measured. Fourteen new CKDu cases (18%) from EL1 and nine CKDu cases (9%) from EL2 were recognized for the first time from EL1, EL2 locations, which were previously considered as non-endemic of the disease and associated with persistent albuminuria (ACR ≥ 30mg/g Cr). No CKDu cases were identified in non-endemic study locations in Matara (CM) and Nuwara Eliya (CN). Analysis of urinary biomarkers showed urinary KIM-1 and NGAL were significantly higher in new CKDu cases in EL1 and EL2. However, we also reported significantly higher KIM-1 and NGAL in apparently healthy farmers in EL 1 and EL 2 with comparison to both control groups. These observations may indicate possible early renal damage in absence of persistent albuminuria and potential capabilities of urinary KIM-1 and NGAL in early detection of renal injury

  12. Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006-2011.

    Science.gov (United States)

    Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

    2014-05-12

    In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. Bacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases. Overall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007-2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023). Application of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital

  13. EU-FP7-iMARS: Analysis of Mars Multi-Resolution Images Using Auto-Coregistration Data Mining and Crowd Source Techniques: Processed Results - a First Look

    Science.gov (United States)

    Muller, Jan-Peter; Tao, Yu; Sidiropoulos, Panagiotis; Gwinner, Klaus; Willner, Konrad; Fanara, Lida; Waehlisch, Marita; van Gasselt, Stephan; Walter, Sebastian; Steikert, Ralf; Schreiner, Bjoern; Ivanov, Anton; Cantini, Federico; Wardlaw, Jessica; Morley, Jeremy; Sprinks, James; Giordano, Michele; Marsh, Stuart; Kim, Jungrack; Houghton, Robert; Bamford, Steven

    2016-06-01

    Understanding planetary atmosphere-surface exchange and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to overlay image data and derived information from different epochs, back in time to the mid 1970s, to examine changes through time, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, we have developed a fully automated multi-resolution DTM processing chain, called the Coregistration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP) [Tao et al., this conference], which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR [Gwinner et al., 2015] have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed by [Sidiropoulos & Muller, this conference]. Using the HRSC map products (both mosaics and orbital strips) as a map-base it is being applied to many of the 400,000 level-1 EDR images taken by the 4 NASA orbital cameras. In particular, the NASA Viking Orbiter camera (VO), Mars Orbiter Camera (MOC), Context Camera (CTX) as well as the High Resolution Imaging Science Experiment (HiRISE) back to 1976. A webGIS has been developed [van Gasselt et al., this conference] for displaying this time sequence of imagery and will be demonstrated showing an example from one of the HRSC quadrangle map-sheets. Automated quality control [Sidiropoulos & Muller, 2015] techniques are applied to screen for

  14. Influenza vaccine effectiveness for hospital and community patients using control groups with and without non-influenza respiratory viruses detected, Auckland, New Zealand 2014.

    Science.gov (United States)

    Pierse, Nevil; Kelly, Heath; Thompson, Mark G; Bissielo, Ange; Radke, Sarah; Huang, Q Sue; Baker, Michael G; Turner, Nikki

    2016-01-20

    We aimed to estimate the protection afforded by inactivated influenza vaccine, in both community and hospital settings, in a well characterised urban population in Auckland during 2014. We used two different comparison groups, all patients who tested negative for influenza and only those patients who tested negative for influenza and had a non-influenza respiratory virus detected, to calculate the vaccine effectiveness in a test negative study design. Estimates were made separately for general practice outpatient consultations and hospitalised patients, stratified by age group and by influenza type and subtype. Vaccine status was confirmed by electronic record for general practice patients and all respiratory viruses were detected by real time polymerase chain reaction. 1039 hospitalised and 1154 general practice outpatient consultations met all the study inclusion criteria and had a respiratory sample tested for influenza and other respiratory viruses. Compared to general practice patients, hospitalised patients were more likely to be very young or very old, to be Māori or Pacific Islander, to have a low income and to suffer from chronic disease. Vaccine effectiveness (VE) adjusted for age and other participant characteristics using all influenza negative controls was 42% (95% CI: 16 to 60%) for hospitalised and 56% (95% CI: 35 to 70%) for general practice patients. The vaccine appeared to be most effective against the influenza A(H1N1)pdm09 strain with an adjusted VE of 62% (95% CI:38 to 77%) for hospitalised and 59% (95% CI:36 to 74%) for general practice patients, using influenza virus negative controls. Similar results found when patients testing positive for a non-influenza respiratory virus were used as the control group. This study contributes to validation of the test negative design and confirms that inactivated influenza vaccines continue to provide modest but significant protection against laboratory-confirmed influenza. Copyright © 2015 Elsevier Ltd

  15. Absenteeism in schools during the 2009 influenza A(H1N1) pandemic: a useful tool for early detection of influenza activity in the community?

    Science.gov (United States)

    Kara, E O; Elliot, A J; Bagnall, H; Foord, D G F; Pnaiser, R; Osman, H; Smith, G E; Olowokure, B

    2012-07-01

    Certain influenza outbreaks, including the 2009 influenza A(H1N1) pandemic, can predominantly affect school-age children. Therefore the use of school absenteeism data has been considered as a potential tool for providing early warning of increasing influenza activity in the community. This study retrospectively evaluates the usefulness of these data by comparing them with existing syndromic surveillance systems and laboratory data. Weekly mean percentages of absenteeism in 373 state schools (children aged 4-18 years) in Birmingham, UK, from September 2006 to September 2009, were compared with established syndromic surveillance systems including a telephone health helpline, a general practitioner sentinel network and laboratory data for influenza. Correlation coefficients were used to examine the relationship between each syndromic system. In June 2009, school absenteeism generally peaked concomitantly with the existing influenza surveillance systems in England. Weekly school absenteeism surveillance would not have detected pandemic influenza A(H1N1) earlier but daily absenteeism data and the development of baselines could improve the timeliness of the system.

  16. Community-Based Intrusion Detection

    OpenAIRE

    Weigert, Stefan

    2017-01-01

    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the advers...

  17. 4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

    Science.gov (United States)

    Min, Yugang; Neylon, John; Shah, Amish; Meeks, Sanford; Lee, Percy; Kupelian, Patrick; Santhanam, Anand P

    2014-09-01

    The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

  18. Shoreline change after 12 years of tsunami in Banda Aceh, Indonesia: a multi-resolution, multi-temporal satellite data and GIS approach

    Science.gov (United States)

    Sugianto, S.; Heriansyah; Darusman; Rusdi, M.; Karim, A.

    2018-04-01

    The Indian Ocean Tsunami event on the 26 December 2004 has caused severe damage of some shorelines in Banda Aceh City, Indonesia. Tracing back the impact can be seen using remote sensing data combined with GIS. The approach is incorporated with image processing to analyze the extent of shoreline changes with multi-temporal data after 12 years of tsunami. This study demonstrates multi-resolution and multi-temporal satellite images of QuickBird and IKONOS to demarcate the shoreline of Banda Aceh shoreline from before and after tsunami. The research has demonstrated a significant change to the shoreline in the form of abrasion between 2004 and 2005 from few meters to hundred meters’ change. The change between 2004 and 2011 has not returned to the previous stage of shoreline before the tsunami, considered post tsunami impact. The abrasion occurs between 18.3 to 194.93 meters. Further, the change in 2009-2011 shows slowly change of shoreline of Banda Aceh, considered without impact of tsunami e.g. abrasion caused by ocean waves that erode the coast and on specific areas accretion occurs caused by sediment carried by the river flow into the sea near the shoreline of the study area.

  19. A Multi-Resolution Mode CMOS Image Sensor with a Novel Two-Step Single-Slope ADC for Intelligent Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Daehyeok Kim

    2017-06-01

    Full Text Available In this paper, we present a multi-resolution mode CMOS image sensor (CIS for intelligent surveillance system (ISS applications. A low column fixed-pattern noise (CFPN comparator is proposed in 8-bit two-step single-slope analog-to-digital converter (TSSS ADC for the CIS that supports normal, 1/2, 1/4, 1/8, 1/16, 1/32, and 1/64 mode of pixel resolution. We show that the scaled-resolution images enable CIS to reduce total power consumption while images hold steady without events. A prototype sensor of 176 × 144 pixels has been fabricated with a 0.18 μm 1-poly 4-metal CMOS process. The area of 4-shared 4T-active pixel sensor (APS is 4.4 μm × 4.4 μm and the total chip size is 2.35 mm × 2.35 mm. The maximum power consumption is 10 mW (with full resolution with supply voltages of 3.3 V (analog and 1.8 V (digital and 14 frame/s of frame rates.

  20. New Resolution Strategy for Multi-scale Reaction Waves using Time Operator Splitting and Space Adaptive Multiresolution: Application to Human Ischemic Stroke*

    Directory of Open Access Journals (Sweden)

    Louvet Violaine

    2011-12-01

    Full Text Available We tackle the numerical simulation of reaction-diffusion equations modeling multi-scale reaction waves. This type of problems induces peculiar difficulties and potentially large stiffness which stem from the broad spectrum of temporal scales in the nonlinear chemical source term as well as from the presence of large spatial gradients in the reactive fronts, spatially very localized. A new resolution strategy was recently introduced ? that combines a performing time operator splitting with high oder dedicated time integration methods and space adaptive multiresolution. Based on recent theoretical studies of numerical analysis, such a strategy leads to a splitting time step which is not restricted neither by the fastest scales in the source term nor by stability limits related to the diffusion problem, but only by the physics of the phenomenon. In this paper, the efficiency of the method is evaluated through 2D and 3D numerical simulations of a human ischemic stroke model, conducted on a simplified brain geometry, for which a simple parallelization strategy for shared memory architectures was implemented, in order to reduce computing costs related to “detailed chemistry” features of the model.

  1. A Multi-Resolution Mode CMOS Image Sensor with a Novel Two-Step Single-Slope ADC for Intelligent Surveillance Systems.

    Science.gov (United States)

    Kim, Daehyeok; Song, Minkyu; Choe, Byeongseong; Kim, Soo Youn

    2017-06-25

    In this paper, we present a multi-resolution mode CMOS image sensor (CIS) for intelligent surveillance system (ISS) applications. A low column fixed-pattern noise (CFPN) comparator is proposed in 8-bit two-step single-slope analog-to-digital converter (TSSS ADC) for the CIS that supports normal, 1/2, 1/4, 1/8, 1/16, 1/32, and 1/64 mode of pixel resolution. We show that the scaled-resolution images enable CIS to reduce total power consumption while images hold steady without events. A prototype sensor of 176 × 144 pixels has been fabricated with a 0.18 μm 1-poly 4-metal CMOS process. The area of 4-shared 4T-active pixel sensor (APS) is 4.4 μm × 4.4 μm and the total chip size is 2.35 mm × 2.35 mm. The maximum power consumption is 10 mW (with full resolution) with supply voltages of 3.3 V (analog) and 1.8 V (digital) and 14 frame/s of frame rates.

  2. Urinary Biomarkers KIM-1 and NGAL for Detection of Chronic Kidney Disease of Uncertain Etiology (CKDu among Agricultural Communities in Sri Lanka.

    Directory of Open Access Journals (Sweden)

    Pallagae Mangala C S De Silva

    2016-09-01

    Full Text Available Chronic Kidney Disease of uncertain etiology (CKDu is an emerging epidemic among farming communities in rural Sri Lanka. Victims do not exhibit common causative factors, however, histopathological studies revealed that CKDu is a tubulointerstitial disease. Urine albumin or albumin-creatinine ratio is still being used as a traditional diagnostic tool to identify CKDu, but accuracy and prevalence data generated are questionable. Urinary biomarkers have been used in similar nephropathy and are widely recognised for their sensitivity, specificity and accuracy in determining CKDu and early renal injury. However, these biomarkers have never been used in diagnosing CKDu in Sri Lanka. Male farmers (n = 1734 were recruited from 4 regions in Sri Lanka i.e. Matara and Nuwara Eliya (farming locations with no CKDu prevalence and two CKDu emerging locations from Hambantota District in Southern Sri Lanka; Angunakolapelessa (EL1 and Bandagiriya (EL2. Albuminuria (ACR ≥ 30mg/g; serum creatinine based estimation of glomerular filtration rate (eGFR; creatinine normalized urinary kidney injury molecule (KIM-1 and neutrophil gelatinase-associated lipocalin (NGAL were measured. Fourteen new CKDu cases (18% from EL1 and nine CKDu cases (9% from EL2 were recognized for the first time from EL1, EL2 locations, which were previously considered as non-endemic of the disease and associated with persistent albuminuria (ACR ≥ 30mg/g Cr. No CKDu cases were identified in non-endemic study locations in Matara (CM and Nuwara Eliya (CN. Analysis of urinary biomarkers showed urinary KIM-1 and NGAL were significantly higher in new CKDu cases in EL1 and EL2. However, we also reported significantly higher KIM-1 and NGAL in apparently healthy farmers in EL 1 and EL 2 with comparison to both control groups. These observations may indicate possible early renal damage in absence of persistent albuminuria and potential capabilities of urinary KIM-1 and NGAL in early detection of renal

  3. A Community-Oriented Approach to Breast Cancer in a Low-Resource Setting: Improving Awareness, Early Detection and Treatment of Breast Cancer in Tajikistan.

    Science.gov (United States)

    Talib, Zohray; Shukurbekova, Irina; Sadonshoeva, Guldarbogh; Alibekov, Alibek; Jamshedov, Nekruz; Moloo, Zahir; Welji, Almas; Amersi, Farin; Muhammad, Aliya Amin; Jiwani, Aliya; Rais, Sheliza; Nazrishoeva, Akoyat; Ilnazarova, Surayo; Nuridinova, Shifo; Ukani, Hafiza; Alwani, Shireen; Saleh, Mansoor

    2016-05-01

    Breast cancer is one of the most common cancers and causes of death in females in Tajikistan; yet less than half of the adult women in Tajikistan have heard of breast cancer. Limited access to health care contributes to late stage presentation. We developed a public-private partnership to implement a breast cancer awareness intervention in a low-resource community in Khorog, Tajikistan. We trained local health professionals in clinical breast care and conducted a breast cancer screening and treatment program. The partnership involved visiting USA-based health professionals working alongside local health care providers (HCP) in the continuum of breast care-from education to the diagnostic evaluation and management of detected breast abnormalities. Patient data were collected using a web-based program (VirtualDoc). Twenty-four HCP received didactic and clinical breast examination training. 441 women underwent clinical breast evaluation. 74 (17%) had abnormal exams and underwent additional diagnostic procedures. We identified six (1.4%) cases of breast cancer (all locally advanced) and two women had benign fibroadenomas. All women with cancer underwent modified radical mastectomy, while the fibroadenomas were treated by cosmetically appropriate lumpectomy. Five of six subjects with cancer were previously aware of their breast lump and three had recently seen a family medicine (FM) doctor. Health systems assessment revealed availability of diagnostic equipment but lack of well-trained operators and clinician interpreters. We were successful in integrating clinical breast exams into the routine care of female patients by local FM doctors and in the process, achieved a better understanding of existing risk factors and barriers to breast cancer care. This public-private partnership, leveraging the technical expertise of visiting health professionals, demonstrates how a focused onsite training and awareness program can provide sustained improvements in breast care in a low

  4. 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.

  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. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT. Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU.

  7. Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps

    Directory of Open Access Journals (Sweden)

    Xinchuan Fu

    2018-04-01

    Full Text Available The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales. In this pipeline, feature pyramid construction is time consuming and becomes the bottleneck for fast detection. Recently, a method called multiresolution filtered channels (MRFC was proposed which only used single scale feature maps to achieve fast detection. However, there are two shortcomings in MRFC which limit its accuracy. One is that the receptive field correspondence in different scales is weak. Another is that the features used are not scale invariance. In this paper, two solutions are proposed to tackle with the two shortcomings respectively. Specifically, scale-aware pooling is proposed to make a better receptive field correspondence, and soft decision tree is proposed to relive scale variance problem. When coupled with efficient sliding window classification strategy, our detector achieves fast detecting speed at the same time with state-of-the-art accuracy.

  8. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Science.gov (United States)

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  9. Clustering of France Monthly Precipitation, Temperature and Discharge Based on their Multiresolution Links with 500mb Geopotential Height from 1968 to 2008

    Science.gov (United States)

    Massei, N.; Fossa, M.; Dieppois, B.; Vidal, J. P.; Fournier, M.; Laignel, B.

    2017-12-01

    In the context of climate change and ever growing use of water resources, identifying how the climate and watershed signature in discharge variability changes with the geographic location is of prime importance. This study aims at establishing how 1968-2008 multiresolution links between 3 local hydrometerological variables (precipitation, temperature and discharge) and 500 mb geopotential height are structured over France. First, a methodology that allows to encode the 3D geopotential height data into its 1D conformal modulus time series is introduced. Then, for each local variable, their covariations with the geopotential height are computed with cross wavelet analysis. Finally, a clustering analysis of each variable cross spectra is done using bootstrap clustering.We compare the clustering results for each local variable in order to untangle the watershed from the climate drivers in France's rivers discharge. Additionally, we identify the areas in the geopotential height field that are responsible for the spatial structure of each local variable.Main results from this study show that for precipitation and discharge, clear spatial zones emerge. Each cluster is characterized either by different different amplitudes and/or time scales of covariations with geopotential height. Precipitation and discharge clustering differ with the later being simpler which indicates a strong low frequency modulation by the watersheds all over France. Temperature on the other hand shows less clearer spatial zones. For precipitation and discharge, we show that the main action path starts at the northern tropical zone then moves up the to central North Atlantic zone which seems to indicates an interaction between the convective cells variability and the reinforcement of the westerlies jets as one of the main control of the precipitation and discharge over France. Temperature shows a main zone of action directly over France hinting at local temperature/pressure interactions.

  10. Extending 'Contact Tracing' into the Community within a 50-Metre Radius of an Index Tuberculosis Patient Using Xpert MTB/RIF in Urban, Pakistan: Did It Increase Case Detection?

    Directory of Open Access Journals (Sweden)

    Razia Fatima

    Full Text Available Currently, only 62% of incident tuberculosis (TB cases are reported to the national programme in Pakistan. Several innovative interventions are being recommended to detect the remaining 'missed' TB cases. One such intervention involved expanding contact investigation to the community using the Xpert MTB/RIF test.This was a before and after intervention study involving retrospective record review. Passive case finding and household contact investigation was routinely done in the pre-intervention period July 2011-June 2013. Four districts with a high concentration of slums were selected as intervention areas; Lahore, Rawalpindi, Faisalabad and Islamabad. Here, in the intervention period, July 2013-June 2015, contact investigation beyond household was conducted: all people staying within a radius of 50 metres (using Geographical Information System from the household of smear positive TB patients were screened for tuberculosis. Those with presumptive TB were investigated using smear microscopy and the Xpert MTB/RIF test was performed on smear negative patients. All the diagnosed TB patients were linked to TB treatment and care.A total of 783043 contacts were screened for tuberculosis: 23741(3.0% presumptive TB patients were identified of whom, 4710 (19.8% all forms and 4084(17.2% bacteriologically confirmed TB patients were detected. The contribution of Xpert MTB/RIF to bacteriologically confirmed TB patients was 7.6%. The yield among investigated presumptive child TB patients was 5.1%. The overall yield of all forms TB patients among investigated was 22.3% among household and 19.1% in close community. The intervention contributed an increase of case detection of bacteriologically confirmed tuberculosis by 6.8% and all forms TB patients by 7.9%.Community contact investigation beyond household not only detected additional TB patients but also increased TB case detection. However, further long term assessments and cost-effectiveness studies are

  11. Unique honey bee (Apis mellifera hive component-based communities as detected by a hybrid of phospholipid fatty-acid and fatty-acid methyl ester analyses.

    Directory of Open Access Journals (Sweden)

    Kirk J Grubbs

    Full Text Available Microbial communities (microbiomes are associated with almost all metazoans, including the honey bee Apis mellifera. Honey bees are social insects, maintaining complex hive systems composed of a variety of integral components including bees, comb, propolis, honey, and stored pollen. Given that the different components within hives can be physically separated and are nutritionally variable, we hypothesize that unique microbial communities may occur within the different microenvironments of honey bee colonies. To explore this hypothesis and to provide further insights into the microbiome of honey bees, we use a hybrid of fatty acid methyl ester (FAME and phospholipid-derived fatty acid (PLFA analysis to produce broad, lipid-based microbial community profiles of stored pollen, adults, pupae, honey, empty comb, and propolis for 11 honey bee hives. Averaging component lipid profiles by hive, we show that, in decreasing order, lipid markers representing fungi, Gram-negative bacteria, and Gram-positive bacteria have the highest relative abundances within honey bee colonies. Our lipid profiles reveal the presence of viable microbial communities in each of the six hive components sampled, with overall microbial community richness varying from lowest to highest in honey, comb, pupae, pollen, adults and propolis, respectively. Finally, microbial community lipid profiles were more similar when compared by component than by hive, location, or sampling year. Specifically, we found that individual hive components typically exhibited several dominant lipids and that these dominant lipids differ between components. Principal component and two-way clustering analyses both support significant grouping of lipids by hive component. Our findings indicate that in addition to the microbial communities present in individual workers, honey bee hives have resident microbial communities associated with different colony components.

  12. Unique honey bee (Apis mellifera) hive component-based communities as detected by a hybrid of phospholipid fatty-acid and fatty-acid methyl ester analyses.

    Science.gov (United States)

    Grubbs, Kirk J; Scott, Jarrod J; Budsberg, Kevin J; Read, Harry; Balser, Teri C; Currie, Cameron R

    2015-01-01

    Microbial communities (microbiomes) are associated with almost all metazoans, including the honey bee Apis mellifera. Honey bees are social insects, maintaining complex hive systems composed of a variety of integral components including bees, comb, propolis, honey, and stored pollen. Given that the different components within hives can be physically separated and are nutritionally variable, we hypothesize that unique microbial communities may occur within the different microenvironments of honey bee colonies. To explore this hypothesis and to provide further insights into the microbiome of honey bees, we use a hybrid of fatty acid methyl ester (FAME) and phospholipid-derived fatty acid (PLFA) analysis to produce broad, lipid-based microbial community profiles of stored pollen, adults, pupae, honey, empty comb, and propolis for 11 honey bee hives. Averaging component lipid profiles by hive, we show that, in decreasing order, lipid markers representing fungi, Gram-negative bacteria, and Gram-positive bacteria have the highest relative abundances within honey bee colonies. Our lipid profiles reveal the presence of viable microbial communities in each of the six hive components sampled, with overall microbial community richness varying from lowest to highest in honey, comb, pupae, pollen, adults and propolis, respectively. Finally, microbial community lipid profiles were more similar when compared by component than by hive, location, or sampling year. Specifically, we found that individual hive components typically exhibited several dominant lipids and that these dominant lipids differ between components. Principal component and two-way clustering analyses both support significant grouping of lipids by hive component. Our findings indicate that in addition to the microbial communities present in individual workers, honey bee hives have resident microbial communities associated with different colony components.

  13. Feasibility of a molecular screening method for detection of Salmonella enterica and Campylobacter jejuni in a routine community-based clinical microbiology laboratory

    NARCIS (Netherlands)

    Schuurman, T.; de Boer, R. F.; van Zanten, E.; van Slochteren, K. R.; Scheper, H. R.; Dijk-Alberts, B. G.; Moller, A. V. M.; Kooistra-Smid, A. M. D.

    Conventional diagnostic methods for the detection of Salmonella enterica and Campylobacter jejuni are laborious and time-consuming procedures, resulting in final results, for the majority of specimens, only after 3 to 4 days. Molecular detection can improve the time to reporting of the final results

  14. Evaluation of the leucine incorporation technique for detection of pollution-induced community tolerance to copper in a long-term agricultural field trial with urban waste fertilizers

    DEFF Research Database (Denmark)

    Lekfeldt, Jonas Duus Stevens; Magid, Jakob; Holm, Peter Engelund

    2014-01-01

    increased bacterial community tolerance to Cu was observed for soils amended with organic waste fertilizers and was positively correlated with total soil Cu. However, metal speciation and whole-cell bacterial biosensor analysis demonstrated that the observed PICT responses could be explained entirely by Cu......Copper (Cu) is known to accumulate in agricultural soils receiving urban waste products as fertilizers. We here report the use of the leucine incorporation technique to determine pollution-induced community tolerance (Leu-PICT) to Cu in a long-term agricultural field trial. A significantly...

  15. Automated detection of geomagnetic storms with heightened risk of GIC

    Science.gov (United States)

    Bailey, Rachel L.; Leonhardt, Roman

    2016-06-01

    Automated detection of geomagnetic storms is of growing importance to operators of technical infrastructure (e.g., power grids, satellites), which is susceptible to damage caused by the consequences of geomagnetic storms. In this study, we compare three methods for automated geomagnetic storm detection: a method analyzing the first derivative of the geomagnetic variations, another looking at the Akaike information criterion, and a third using multi-resolution analysis of the maximal overlap discrete wavelet transform of the variations. These detection methods are used in combination with an algorithm for the detection of coronal mass ejection shock fronts in ACE solar wind data prior to the storm arrival on Earth as an additional constraint for possible storm detection. The maximal overlap discrete wavelet transform is found to be the most accurate of the detection methods. The final storm detection software, implementing analysis of both satellite solar wind and geomagnetic ground data, detects 14 of 15 more powerful geomagnetic storms over a period of 2 years.

  16. Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations

    OpenAIRE

    Creavin, Sam T; Wisniewski, Susanna; Noel-Storr, Anna H; Trevelyan, Clare M; Hampton, Thomas; Rayment, Dane; Thom, Victoria M; Nash, Kirsty J E; Elhamoui, Hosam; Milligan, Rowena; Patel, Anish S; Tsivos, Demitra V; Wing, Tracey; Phillips, Emma; Kellman, Sophie M

    2016-01-01

    BACKGROUND: The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia.OBJECTIVES: To determine the diagnostic accuracy of the Mini-Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia.SEARCH METHODS: We searched the specialised register of the Cochrane Dementia and Cognitive Improvement G...

  17. Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

    Science.gov (United States)

    Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

    2009-02-01

    Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

  18. [Opinion and Participation in the Regional Early Breast Cancer Detection Program in 2007 on the part of family physicians from a health district in the Autonomous Community of Madrid, Spain].

    Science.gov (United States)

    Garrido Elustondo, Sofía; Sánchez Padilla, Elisabeth; Ramírez Alesón, Victoria; González Hernández, Ma José; González Navarro, Andrés; López Gómez, Carlos

    2008-01-01

    Mammogram screening is the most effective method for the early detection of breast cancer. The objective of this study is to evaluate the degree of knowledge, the opinion and the participation in the early breast cancer detection program on the part of the family physicians of the Autonomous Community of Madrid. The population studied was comprised of family physicians from Madrid Health District Seven. An anonymous, self-administered questionnaire comprised of 30 questions grouped into physicians characteristics and opinion concerning the early breast cancer detection programs. A total of 46% of the physicians replied. A total of 94% of the physicians believed that it is their duty to inform their patients concerning preventive activities, including breast cancer screening, and 95% believed their advice to be useful for convincing women to have a mammogram. A total of 72% believed information to be lacking on this program. During the time when mammograms are being taken at their centres, 24% of the physicians surveyed always or almost always ask the women if they have any doubts or would like further information, 43% having set up appointments for them and 95% advising them to have a mammogram taken. The family physicians have a good opinion of the early breast cancer detection program and feel their advice to be effective for improving the participation in the program. They report lack of information and inform women about the program to only a small degree.

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

    Science.gov (United States)

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

    2016-11-01

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

  20. Detective fiction and the

    African Journals Online (AJOL)

    Tienie01

    2006-06-15

    Jun 15, 2006 ... tionalist agenda, then the third wave concerns reformulating healthy community ... In real life, no doubt, the best detectives are the professional police, but .... Marple – the ever-observant, intelligent and intuitive elderly, amateur detec- ..... tive, promoting alternative social models for the community at large.

  1. The Community Balance and Mobility Scale: A Pilot Study Detecting Impairments in Military Service Members With Comorbid Mild TBI and Psychological Health Conditions.

    Science.gov (United States)

    Pape, Marcy M; Williams, Kathy; Kodosky, Paula N; Dretsch, Michael

    2016-01-01

    To compare the capacity of the Community Balance and Mobility Scale (CB&M) to identify balance and mobility deficits in Service Members (SMs) with mild traumatic brain injury and comorbid psychological health conditions (mTBI/PH) to other commonly used balance assessments. A clinical research institute that provides a 4-week, outpatient, interdisciplinary program for active-duty SMs with mTBI/PH. A nonrandomized, cross-sectional design that compared multiple measures between 2 groups-active duty SMs with (n = 8) and without (n = 8) the dual diagnosis of mTBI/PH. Gait speed, Activities-specific Balance Confidence scale (ABC), Functional Gait Assessment (FGA), and CB&M to assess functional balance among the community-dwelling, TBI population. Across all measures, the mTBI/PH group performed significantly worse (P ≤ .01) with the exception of the FGA. The abilities of all objective measures to distinguish participants with mTBI/PH from healthy controls ranged from fair to excellent (area under the curve [AUC] = 0.66-0.94). However, the CB&M showed the largest group differences in effect size (d = 2.6) and had the highest discriminate ability (AUC = 0.98; sensitivity 100%; specificity 88%). The CB&M appears to have higher sensitivity and specificity than other measures of balance in SMs with mTBI/PH. A higher cut score for the CB&M is needed for this population.

  2. EU-FP7-iMars: Analysis of Mars Multi-Resolution Images using Auto-Coregistration, Data Mining and Crowd Source Techniques

    Science.gov (United States)

    Ivanov, Anton; Oberst, Jürgen; Yershov, Vladimir; Muller, Jan-Peter; Kim, Jung-Rack; Gwinner, Klaus; Van Gasselt, Stephan; Morley, Jeremy; Houghton, Robert; Bamford, Steven; Sidiropoulos, Panagiotis

    Understanding the role of different planetary surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to be able to overlay different epochs back to the mid-1970s, examine time-varying changes (such as the recent discovery of boulder movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Consequently we are seeing a dramatic improvement in our understanding of surface formation processes. Since January 2004, the ESA Mars Express has been acquiring global data, especially HRSC stereo (12.5-25 m nadir images) with 87% coverage with more than 65% useful for stereo mapping. NASA began imaging the surface of Mars, initially from flybys in the 1960s and then from the first orbiter with image resolution less than 100 m in the late 1970s from Viking Orbiter. The most recent orbiter, NASA MRO, has acquired surface imagery of around 1% of the Martian surface from HiRISE (at ≈20 cm) and ≈5% from CTX (≈6 m) in stereo. Within the iMars project (http://i-Mars.eu), a fully automated large-scale processing (“Big Data”) solution is being developed to generate the best possible multi-resolution DTM of Mars. In addition, HRSC OrthoRectified Images (ORI) will be used as a georeference basis so that all higher resolution ORIs will be co-registered to the HRSC DTMs (50-100m grid) products generated at DLR and, from CTX (6-20 m grid) and HiRISE (1-3 m grids) on a large-scale Linux cluster based at MSSL. The HRSC products will be employed to provide a geographic reference for all current, future and historical NASA products using automated co-registration based on feature points and initial results will be shown here. In 2015, many of the entire NASA and ESA orbital images will be co-registered and the updated georeferencing

  3. Increase in mammography detected breast cancer over time at a community based regional cancer center: a longitudinal cohort study 1990–2005

    International Nuclear Information System (INIS)

    Malmgren, Judith A; Atwood, Mary K; Kaplan, Henry G

    2008-01-01

    Coincident with the advent of mammography screening, breast carcinoma in situ has increased in the US population. We conducted a prospective cohort study of all women presenting with primary breast cancer, aged 21–94, and biopsy confirmed Stage 0-IV from 1990–2005 identified and tracked by our registry. Clinical presentation characteristics including age, race, TNM stage, family and pregnancy history, histologic type and method of detection by patient (PtD), physician (PhysD) or mammography (MgD) were chart abstracted at time of diagnosis. Cases with unknown or other method of detection (n = 84), or unusual cell types (n = 26) were removed (n = 6074). From 1990 to 1998 the percentage of PtD and MgD cases was roughly equivalent. In 1999 the percentage of MgD cases increased to 56% and PtD dropped to 37%, a significant 20% differential, constant to 2005 (Pearson chi square = 120.99, p < .001). Overall, percent TNM stage 0 (breast carcinoma in situ) cases increased after 1990, percent stage I and III cases declined, and stage II and IV cases remained constant (Pearson chi square = 218.36, p < .001). Increase in MgD over time differed by age group with an 8.5% increase among women age 40–49 and 12% increase among women age 50–95. Women age 21–39 rarely had MgD BC. In forward stepwise logistic regression modeling, significant predictors of MgD BC by order of entry were TNM stage, age at diagnosis, diagnosis year, and race (chi square = 1867.56, p < .001). In our cohort the relative proportion of mammography detected breast cancer increased over time with a higher increase among women age 50+ and an increase of breast carcinoma in situ exclusively among MgD cases. The increase among women currently targeted by mammography screening programs (age ≥ 50) combined with an increase of breast carcinoma in situ most often detected by mammography screening indicates a possible incidence shift to lower stage breast cancer as a result of mammographic detection

  4. Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations.

    Science.gov (United States)

    Creavin, Sam T; Wisniewski, Susanna; Noel-Storr, Anna H; Trevelyan, Clare M; Hampton, Thomas; Rayment, Dane; Thom, Victoria M; Nash, Kirsty J E; Elhamoui, Hosam; Milligan, Rowena; Patel, Anish S; Tsivos, Demitra V; Wing, Tracey; Phillips, Emma; Kellman, Sophie M; Shackleton, Hannah L; Singleton, Georgina F; Neale, Bethany E; Watton, Martha E; Cullum, Sarah

    2016-01-13

    The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia. To determine the diagnostic accuracy of the Mini-Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia. We searched the specialised register of the Cochrane Dementia and Cognitive Improvement Group, MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), LILACS (BIREME), ALOIS, BIOSIS previews (Thomson Reuters Web of Science), and Web of Science Core Collection, including the Science Citation Index and the Conference Proceedings Citation Index (Thomson Reuters Web of Science). We also searched specialised sources of diagnostic test accuracy studies and reviews: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). We attempted to locate possibly relevant but unpublished data by contacting researchers in this field. We first performed the searches in November 2012 and then fully updated them in May 2014. We did not apply any language or date restrictions to the electronic searches, and we did not use any methodological filters as a method to restrict the search overall. We included studies that compared the 11-item (maximum score 30) MMSE test (at any cut point) in people who had not undergone prior testing versus a commonly accepted clinical reference standard for all-cause dementia and subtypes (Alzheimer disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia). Clinical diagnosis included all-cause (unspecified) dementia, as defined by any version of the Diagnostic and Statistical Manual of Mental Disorders (DSM); International Classification of

  5. Validation of the "early detection Primary Care Checklist" in an Italian community help-seeking sample: The "checklist per la Valutazione dell'Esordio Psicotico".

    Science.gov (United States)

    Pelizza, Lorenzo; Raballo, Andrea; Semrov, Enrico; Chiri, Luigi Rocco; Azzali, Silvia; Scazza, Ilaria; Garlassi, Sara; Paterlini, Federica; Fontana, Francesca; Favazzo, Rosanna; Pensieri, Luana; Fabiani, Michela; Cioncolini, Leonardo; Pupo, Simona

    2017-07-26

    To establish the concordant validity of the "Checklist per la Valutazione dell'Esordio Psicotico" (CVEP) in an Italian help-seeking population. The CVEP is the Italian adaptation of the "early detection Primary Care Checklist," a 20-item tool specifically designed to assist primary care practitioners in identifying young people in the early stages of psychosis. The checklist was completed by the referring practitioners of 168 young people referred to the "Reggio Emilia At Risk Mental States" Project, an early detection infrastructure developed under the aegis of the Regional Project on Early Detection of Psychosis in the Reggio Emilia Department of Mental Health. The concordant validity of the CVEP was established by comparing screen results with the outcome of the "Comprehensive Assessment of At Risk Mental States" (CAARMS), a gold standard assessment for identifying young people who may be at risk of developing psychosis. The simple checklist as originally conceived had excellent sensitivity (98%), but lower specificity (58%). Using only a CVEP total score of 20 or above as cut-off, the tool showed a slightly lower sensitivity (93%) with a substantial improvement in specificity (87%). Simple cross-tabulations of the individual CVEP item scores against CAARMS outcome to identify the more discriminant item in terms of sensitivity and specificity were carried out. In comparison to other, much longer, screening tools, the CVEP performed well to identify young people in the early stages of psychosis. Therefore, the CVEP is well suited to optimize appropriate referrals to specialist services, building on the skills and knowledge already available in primary care settings. © 2017 John Wiley & Sons Australia, Ltd.

  6. From early detection to rehabilitation in the community: reading beyond the blog testimonies of survivors' quality of life and prostate cancer representation.

    Science.gov (United States)

    Zanchetta, Margareth Santos; Cognet, Marguerite; Lam-Kin-Teng, Mary Rachel; Dumitriu, Marie Elisabeth; Renaud, Lise; Rhéaume, Jacques

    2016-12-16

    Survivors' testimonies can reveal much about men's experiences of prostate cancer and impacts on their quality of life (QOL) during the clinical trajectory of the disease. These survivors' shared thoughts and views were hypothesized to reflect salient features of their lived social representation of prostate cancer. We explored the content of testimonies posted by men to a public blog hosted by a French national prostate cancer patients' association. The study question, "What do French bloggers' testimonies reveal about their lived experiences with prostate cancer, especially regarding their quality of life in community settings, that underpin their social representation of prostate cancer?" guided the exploration and analysis of the textual data. The aims were to better understand men's experiences and predominant thoughts and views, to elucidate patients' behaviours, and to enlighten medical policy and practice. Explore issues of QOL as reported by French prostate cancer survivors in a public blog by: (a) identifying the salient aspects and issues of the experience of living with prostate cancer from the perspective of survivors; and (b) analyzing the content in the posted testimonies regarding perceived and lived impacts of prostate cancer on QOL. A critical ethnographic study guided the selection of textual data from 196 male bloggers' testimonies about prostate cancer posted in the period from 2008 to 2013. Media content analysis method was undertaken on blog testimonies, framed by a multidimensional conceptual framework of QOL. Testimonies focused mainly on medical care and rehabilitation, recovery, health education and self-care, as well as on a global vision of prostate cancer and its impacts on personal views of manhood and masculinity. The language used indicated that political, educative and compassionate discourses were intertwined to create a complex representation of the experience and effects of prostate cancer; this multi-faceted representation can

  7. Human resource constraints and the prospect of task-sharing among community health workers for the detection of early signs of pre-eclampsia in Ogun State, Nigeria.

    Science.gov (United States)

    Akeju, David O; Vidler, Marianne; Sotunsa, J O; Osiberu, M O; Orenuga, E O; Oladapo, Olufemi T; Adepoju, A A; Qureshi, Rahat; Sawchuck, Diane; Adetoro, Olalekan O; von Dadelszen, Peter; Dada, Olukayode A

    2016-09-30

    The dearth of health personnel in low income countries has attracted global attention. Ways as to how health care services can be delivered in a more efficient and effective way using available health personnel are being explored. Task-sharing expands the responsibilities of low-cadre health workers and allows them to share these responsibilities with highly qualified health care providers in an effort to best utilize available human resources. This is appropriate in a country like Nigeria where there is a shortage of qualified health professionals and a huge burden of maternal mortality resulting from obstetric complications like pre-eclampsia. This study examines the prospect for task-sharing among Community Health Extension Workers (CHEW) for the detection of early signs of pre-eclampsia, in Ogun State, Nigeria. This study is part of a larger community-based trial evaluating the acceptability of community treatment for severe pre-eclampsia in Ogun State, Nigeria. Data was collected between 2011 and 2012 using focus group discussions; seven with CHEWs (n = 71), three with male decision-makers (n = 35), six with community leaders (n = 68), and one with member of the Society of Obstetricians and Gynaecologists of Nigeria (n = 9). In addition, interviews were conducted with the heads of the local government administration (n = 4), directors of planning (n = 4), medical officers (n = 4), and Chief Nursing Officers (n = 4). Qualitative data were analysed using NVivo version 10.0 3 computer software. The non-availability of health personnel is a major challenge, and has resulted in a high proportion of facility-based care performed by CHEWs. As a result, CHEWs often take on roles that are designated for senior health workers. This role expansion has exposed CHEWs to the basics of obstetric care, and has resulted in informal task-sharing among the health workers. The knowledge and ability of CHEWs to perform basic clinical assessments

  8. Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

    Science.gov (United States)

    Moody, Daniela Irina

    2018-04-17

    An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

  9. Performance of cognitive tests, individually and combined, for the detection of cognitive disorders amongst community-dwelling elderly people with memory complaints: the EVATEM study.

    Science.gov (United States)

    Vannier-Nitenberg, C; Dauphinot, V; Bongue, B; Sass, C; Bathsavanis, A; Rouch, I; Deville, N; Beauchet, O; Krolak-Salmon, P; Fantino, B

    2016-03-01

    Dementia is a leading cause of dependence amongst the aged population. Early identification of cognitive impairment could help to delay advanced stages of dependence. This study aimed at assessing the performance of three neuropsychological tests to detect cognitive disorders in elderly subjects with memory complaints. The EVATEM study is a prospective multicentre cohort with a 1-year follow-up. Subjects with memory complaints were selected during preventive health examinations, and three neuropsychological tests (five-word, cognitive disorders examination, verbal fluency) were administered. Two groups were identified in memory clinics: (i) cognitively healthy individuals (CHI) and (ii) mild cognitive impairment or demented individuals (MCI-DI). Cross-sectional analyses were performed on data at inclusion. The relationship between the diagnosis of MCI-DI/CHI and the neuropsychological tests was assessed using logistic regressions. The performance of the neuropsychological tests, individually and combined, to detect cognitive disorders was calculated. Of 585 subjects, 31.11% had cognitive disorders (MCI, 176 subjects; DI, six subjects). Amongst the three tests studied, the odds ratio for MCI-DI was higher for the five-word test <10 [odds ratio 3.2 (1.81; 5.63)]. The best performance was observed when the three tests were combined: specificity 90.5% and sensitivity 42.4% compared to respectively 89.2% and 28.3% for the five-word test. Despite the poor sensitivity of the five-word test, it seems to be the most adapted for the diagnosis of MCI-DI in older adults with a memory complaint, in prevention centres, taking into account its high specificity and its rapid administration compared to the other tests. © 2015 EAN.

  10. Enhancement of optic cup detection through an improved vessel kink detection framework

    Science.gov (United States)

    Wong, Damon W. K.; Liu, Jiang; Tan, Ngan Meng; Zhang, Zhuo; Lu, Shijian; Lim, Joo Hwee; Li, Huiqi; Wong, Tien Yin

    2010-03-01

    Glaucoma is a leading cause of blindness. The presence and extent of progression of glaucoma can be determined if the optic cup can be accurately segmented from retinal images. In this paper, we present a framework which improves the detection of the optic cup. First, a region of interest is obtained from the retinal fundus image, and a pallor-based preliminary cup contour estimate is determined. Patches are then extracted from the ROI along this contour. To improve the usability of the patches, adaptive methods are introduced to ensure the patches are within the optic disc and to minimize redundant information. The patches are then analyzed for vessels by an edge transform which generates pixel segments of likely vessel candidates. Wavelet, color and gradient information are used as input features for a SVM model to classify the candidates as vessel or non-vessel. Subsequently, a rigourous non-parametric method is adopted in which a bi-stage multi-resolution approach is used to probe and localize the location of kinks along the vessels. Finally, contenxtual information is used to fuse pallor and kink information to obtain an enhanced optic cup segmentation. Using a batch of 21 images obtained from the Singapore Eye Research Institute, the new method results in a 12.64% reduction in the average overlap error against a pallor only cup, indicating viable improvements in the segmentation and supporting the use of kinks for optic cup detection.

  11. Community Economics

    OpenAIRE

    武藤, 宣道; Nobumichi, MUTOH

    2000-01-01

    This paper examines the new field of community economics with respect to Japan. A number of studies in community economics have already been produced in OECD countries including the United States. Although these are of great interest, each country has its own historical, socioeconomic context and must therefore develop its own approach to community economics. Community-oriented economics is neither macro-nor micro-economics in the standard economics textbook sense. Most community economics st...

  12. Medium-to-high prevalence of screening-detected parkinsonism in the urban area of Tehran, Iran: data from a community-based door-to-door study.

    Science.gov (United States)

    Fereshtehnejad, Seyed-Mohammad; Shafieesabet, Mahdiyeh; Rahmani, Arash; Delbari, Ahmad; Lökk, Johan

    2015-01-01

    Parkinsonism occurs in all ethnic groups worldwide; however, there are wide variations in the prevalence rates reported from different countries, even for neighboring regions. The huge socioeconomic burden of parkinsonism necessitates the need for prevalence studies in each country. So far, there is neither data registry nor prevalence information on parkinsonism in the Iranian population. The aim of our study was to estimate the prevalence rate of probable parkinsonism in a huge urban area in Iran, Tehran using a community-based door-to-door survey. We used a random multistage sampling of the households within the network of health centers consisting of 374 subunits in all 22 districts throughout the entire urban area of Tehran. Overall, 20,621 individuals answered the baseline checklist and screening questionnaire and data from 19,500 persons aged ≥30 years were entered in the final analysis. Health care professionals used a new six-item screening questionnaire for parkinsonism, which has been previously shown to have a high validity and diagnostic value in the same population. A total of 157 cases were screened for parkinsonism using the validated six-item questionnaire. After age and sex adjustment based on the Tehran population, the prevalence of parkinsonism was calculated as 222.9 per 100,000. Using the World Health Organization's World Standard Population, the standardized prevalence rate of parkinsonism was 285 per 100,000 (95% confidence interval 240-329). The male:female ratio of probable parkinsonism was calculated as 1.62, and there was a significant increase in the screening rate by advancing age. The calculated rates for the prevalence of parkinsonism in our study are closer to reports from some European and Middle Eastern countries, higher than reports from Eastern Asian and African populations, and lower than Australia. The prevalence rate of >200 in 100,000 for parkinsonism in Tehran, Iran could be considered a medium-to-high rate.

  13. The lost micro-deserts of the Patuxent River using landscape history, insect and plant specimens, and field work to detect and define a unique community

    Science.gov (United States)

    Droege, S.; Davis, C.A.; Steiner, W.E.; =Mawdsley, J.

    2009-01-01

    Historical and recent records of both plants and insects are synthesized for uplands along the eastern edge of Maryland?s Patuxent River from the edge of the Piedmont south to Jug Bay. This strip is characterized by deep sandy soils found in the Evesboro and Galestown sandy loams soil series. Within this narrow strip there exists a unique flora and fauna adapted to open dry sandy soils and occurring in small remnant patches associated with old sand mining operations and scattered protected areas. We illustrate the uniqueness of these sites using four groups, vascular plants, tenebrionid beetles (Coleoptera: Tenebrionidae), tiger beetles (Coleoptera: Cicindelidae), and bees (Hymenoptera: Apoidea: Anthophila). Within each of these groups, rare species were detected whose populations were locally restricted to this soil type and whose nearest known populations were often hundreds of kilometers away. In addition to documenting the direct conservation importance of these small sandy openings along the Patuxent, we contrast the lack of any indication from vertebrate inventories that this region is unique. The combination of plant and insect inventories appears to be a better means of clarifying a site?s importance than does any survey of a single taxonomic group.

  14. 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)

  15. Medium-to-high prevalence of screening-detected parkinsonism in the urban area of Tehran, Iran: data from a community-based door-to-door study

    Directory of Open Access Journals (Sweden)

    Fereshtehnejad SM

    2015-02-01

    Full Text Available Seyed-Mohammad Fereshtehnejad,1,2 Mahdiyeh Shafieesabet,3 Arash Rahmani,4 Ahmad Delbari,1,5 Johan Lökk1,6 1Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences, and Society (NVS, Karolinska Institutet, Stockholm, Sweden; 2Firoozgar Clinical Research Development Center (FCRDC, Firoozgar Hospital, 3Medical Student Research Committee (MSRC, Faculty of Medicine, 4Mental Health Research Center, Tehran Institute of Psychiatry, School of Behavioral Sciences and Mental Health, Iran University of Medical Sciences, Tehran, Iran; 5Iranian Research Center on Aging, University of Social Welfare and Rehabilitation, Tehran, Iran; 6Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden Introduction: Parkinsonism occurs in all ethnic groups worldwide; however, there are wide variations in the prevalence rates reported from different countries, even for neighboring regions. The huge socioeconomic burden of parkinsonism necessitates the need for prevalence studies in each country. So far, there is neither data registry nor prevalence information on parkinsonism in the Iranian population. The aim of our study was to estimate the prevalence rate of probable parkinsonism in a huge urban area in Iran, Tehran using a community-based door-to-door survey.Materials and methods: We used a random multistage sampling of the households within the network of health centers consisting of 374 subunits in all 22 districts throughout the entire urban area of Tehran. Overall, 20,621 individuals answered the baseline checklist and screening questionnaire and data from 19,500 persons aged ≥30 years were entered in the final analysis. Health care professionals used a new six-item screening questionnaire for parkinsonism, which has been previously shown to have a high validity and diagnostic value in the same population.Results: A total of 157 cases were screened for parkinsonism using the validated six-item questionnaire. After

  16. AUTOMATIC ROAD GAP DETECTION USING FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    S. Hashemi

    2012-09-01

    Full Text Available Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1 Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2 Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3 Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4 Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  17. Automatic Road Gap Detection Using Fuzzy Inference System

    Science.gov (United States)

    Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

    2011-09-01

    Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  18. Signal-based Gas Leakage Detection for Fluid Power Accumulators in Wind Turbines

    DEFF Research Database (Denmark)

    Liniger, Jesper; Sepehri, Nariman; N. Soltani, Mohsen

    2017-01-01

    This paper describes the development and application of a signal-based fault detection method for identifying gas leakage in hydraulic accumulators used in wind turbines. The method uses Multiresolution Signal Decomposition (MSD) based on wavelets for feature extraction from a~single fluid pressure...... measurement located close to the accumulator. Gas leakage is shown to create increased variations in this pressure signal. The Root Mean Square (RMS) of the detail coefficient Level 9 from the MSD is found as the most sensitive and robust fault indicator of gas leakage. The method is verified...... on an experimental setup allowing for the replication of the conditions for accumulators in wind turbines. Robustness is tested in a multi-fault environment where gas and external fluid leakage occurs simultaneously. In total, 24 experiments are performed, which show that the method is sensitive to gas leakage...

  19. Fast Detection of Material Deformation through Structural Dissimilarity

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela; Perciano, Talita; Parkinson, Dilworth

    2015-10-29

    Designing materials that are resistant to extreme temperatures and brittleness relies on assessing structural dynamics of samples. Algorithms are critically important to characterize material deformation under stress conditions. Here, we report on our design of coarse-grain parallel algorithms for image quality assessment based on structural information and on crack detection of gigabyte-scale experimental datasets. We show how key steps can be decomposed into distinct processing flows, one based on structural similarity (SSIM) quality measure, and another on spectral content. These algorithms act upon image blocks that fit into memory, and can execute independently. We discuss the scientific relevance of the problem, key developments, and decomposition of complementary tasks into separate executions. We show how to apply SSIM to detect material degradation, and illustrate how this metric can be allied to spectral analysis for structure probing, while using tiled multi-resolution pyramids stored in HDF5 chunked multi-dimensional arrays. Results show that the proposed experimental data representation supports an average compression rate of 10X, and data compression scales linearly with the data size. We also illustrate how to correlate SSIM to crack formation, and how to use our numerical schemes to enable fast detection of deformation from 3D datasets evolving in time.

  20. Pharmacy study of natural health product adverse reactions (SONAR): a cross-sectional study using active surveillance in community pharmacies to detect adverse events associated with natural health products and assess causality.

    Science.gov (United States)

    Necyk, Candace; Tsuyuki, Ross T; Boon, Heather; Foster, Brian C; Legatt, Don; Cembrowski, George; Murty, Mano; Barnes, Joanne; Charrois, Theresa L; Arnason, John T; Ware, Mark A; Rosychuk, Rhonda J; Vohra, Sunita

    2014-03-28

    To investigate the rates and causality of adverse event(s) (AE) associated with natural health product (NHP) use, prescription drug use and concurrent NHP-drug use through active surveillance in community pharmacies. Cross-sectional study of screened patients. 10 community pharmacies across Alberta and British Columbia, Canada from 14 January to 30 July 2011. The participating pharmacy staff screened consecutive patients, or agents of patients, who were dropping or picking up prescription medications. Patients were screened to determine the proportions of them using prescription drugs and/or NHPs, as well as their respective AE rates. All AEs reported by the screened patients who took a NHP, consented to, and were available for, a detailed telephone interview (14%) were adjudicated fully to assess for causality. Over a total of 105 pharmacy weeks and 1118 patients screened, 410 patients reported taking prescription drugs only (36.7%; 95% CI 33.9% to 39.5%), 37 reported taking NHPs only (3.3%; 95% CI 2.4% to 4.5%) and 657 reported taking prescription drugs and NHPs concurrently (58.8%; 95% CI 55.9% to 61.6%). In total, 54 patients reported an AE, representing 1.2% (95% CI 0.51% to 2.9%), 2.7% (95% CI 0.4% to 16.9%) and 7.3% (95% CI 5.6% to 9.6%) of each population, respectively. Compared with patients who reported using prescription drugs, the patients who reported using prescription drugs and NHPs concurrently were 6.4 times more likely to experience an AE (OR; 95% CI 2.52 to 16.17; ppharmacies take NHPs and prescription drugs concurrently, and of those, 7.4% (95% CI 6.3% to 8.8%) report an AE. A substantial proportion of community pharmacy patients use prescription drugs and NHPs concurrently; these patients are at a greater risk of experiencing an AE. Active surveillance provides a means of detecting such AEs and collecting high-quality data on which causality assessment can be based.

  1. Community relations

    International Nuclear Information System (INIS)

    O'Neil, C.

    1997-01-01

    The interaction of the oil and gas companies with the Northern communities regarding drilling activities was an important aspect of oil and gas operations conducted in the Beaufort Sea. During the 1960s the industry and aboriginal people basically ignored each other. Later, the industry put more emphasis on community consultation until finally two-way communication was established. Respect for the land and the environment were very important to aboriginal people who depended on the land and its resources for their traditional way of life. Community relations policies by the various companies involved in the area, and the impact they have had on their respective communities were recounted. Not all efforts were successful, however, the companies and the communities learned from their experiences, and by the time operations ceased, the communities seemed to be more appreciative of the ways they were being treated by the oil companies. 22 figs

  2. Claiming Community

    DEFF Research Database (Denmark)

    Jensen, Steffen Bo

    As its point of departure this working paper takes the multitude of different uses and meanings of the concept of community in local politics in Cape Town. Instead of attempting to define it in substantive terms, the paper takes a social constructivist approach to the study of community...... is termed community work. First, the paper explores how community has become a governmental strategy, employed by the apartheid regime as well, although in different ways, as post-apartheid local government. Secondly, the paper explores the ways in which community becomes the means in which local residents...... lay claim on the state, as well as how it enters into local power struggles between different political groups within the township. In the third part, the paper explores how the meanings of community and the struggles to realise it have changed as South Africa, nationally and locally, has become...

  3. [Community Nutrition].

    Science.gov (United States)

    Aranceta, Javier

    2004-06-01

    In the last 20 years, Public Health Nutrition focused mainly on the qualitative aspects which may influence the onset of chronic diseases, quality of life, physical and mental performance and life expectancy. This applied knowledge organised as part of preventive and health promotion programs led to the development of Community Nutrition. The aim of Community Nutrition actions is to adequate lifestyles related to food consumption patterns in order to improve the quality of life and contribute to health promotion of the population in the community where programs and services are delivered. Key functions to develop in a Community Nutrition Unit consist in the identification and assessment of nutrition problems in the community as well as the design, implementation and evaluation of intervention programs by means of appropriate strategies. These should aim at different populations groups and settings, such as work places, schools, high risk groups or the general public. Nowadays, Community Nutrition work efforts should focus on three main aspects: nutrition education in schools and in the community; food safety and food security and the development and reinforcement of food preparation skills across all age groups. Social catering services, either in schools, the work place or at the community level, need to ensure adequate nutritional supply, provide foods contributing to healthy eating practices as well as to enhance culinary traditions and social learning. Food safety and food security have become a top priority in Public Health. The concepts referes to the availability of food safe and adequate as well as in sufficient amount in order to satisfy nutrition requirements of all individuals in the community. Social changes along new scientific developments will introduce new demands in Community Nutrition work and individual dietary counselling will become a key strategy. In order to face new challenges, community nutrition pactitioners require a high quality

  4. Community noise

    Science.gov (United States)

    Bragdon, C. R.

    Airport and community land use planning as they relate to airport noise reduction are discussed. Legislation, community relations, and the physiological effect of airport noise are considered. Noise at the Logan, Los Angeles, and Minneapolis/St. Paul airports is discussed.

  5. Community Drive

    DEFF Research Database (Denmark)

    Magnussen, Rikke

    2018-01-01

    Schools and educational institutions are challenged by not adequately educating students for independent knowledge collaboration and solving of complex societal challenges (Bundsgaard & Hansen, 2016; Slot et al., 2017). As an alternative strategy to formal learning has Community-driven research...... opportunity to break boundaries between research institutions and surrounding communities through the involvement of new types of actors, knowledge forms and institutions (OECD, 2011). This paper presents the project Community Drive a three year cross disciplinary community-driven game– and data-based project....... In the paper we present how the project Community Drive initiated in May 2018 is based on results from pilot projects conducted from 2014 – 2017. Overall these studies showed that it is a strong motivational factor for students to be given the task to change their living conditions through redesign...

  6. Community Ecology

    CERN Document Server

    1988-01-01

    This book presents the proceedings of a workshop on community ecology organized at Davis, in April, 1986, sponsored by the Sloan Foundation. There have been several recent symposia on community ecology (Strong et. al., 1984, Diamond and Case, 1987) which have covered a wide range of topics. The goal of the workshop at Davis was more narrow: to explore the role of scale in developing a theoretical approach to understanding communities. There are a number of aspects of scale that enter into attempts to understand ecological communities. One of the most basic is organizational scale. Should community ecology proceed by building up from population biology? This question and its ramifications are stressed throughout the book and explored in the first chapter by Simon Levin. Notions of scale have long been important in understanding physical systems. Thus, in understanding the interactions of organisms with their physical environment, questions of scale become paramount. These more physical questions illustrate the...

  7. European Community.

    Science.gov (United States)

    1987-05-01

    The European Community was established in 1951 to reconcile France and Germany after World War II and to make possible the eventual federation of Europe. By 1986, there were 12 member countries: France, Italy, Belgium, the Federal Republic of Germany, Luxembourg, the Netherlands, Denmark, Ireland, the United Kingdom, Greece, Spain, and Portugal. Principal areas of concern are internal and external trade, agriculture, monetary coordination, fisheries, common industrial and commercial policies, assistance, science and research, and common social and regional policies. The European Community has a budget of US$34.035 billion/year, funded by customs duties and 1.4% of each member's value-added tax. The treaties establishing the European Community call for members to form a common market, a common customs tariff, and common agricultural, transport, economic, and nuclear policies. Major European Community institutions include the Commission, Council of Ministers, European Parliament, Court of Justice, and Economic and Social Committee. The Community is the world's largest trading unit, accounting for 15% of world trade. The 2 main goals of the Community's industrial policy are to create an open internal market and to promote technological innovation in order to improve international competitiveness. The European Community aims to contribute to the economic and social development of Third World countries as well.

  8. Measurement Error, Reliability, and Minimum Detectable Change in the Mini-Mental State Examination, Montreal Cognitive Assessment, and Color Trails Test among Community Living Middle-Aged and Older Adults.

    Science.gov (United States)

    Feeney, Joanne; Savva, George M; O'Regan, Claire; King-Kallimanis, Bellinda; Cronin, Hilary; Kenny, Rose Anne

    2016-05-31

    Knowing the reliability of cognitive tests, particularly those commonly used in clinical practice, is important in order to interpret the clinical significance of a change in performance or a low score on a single test. To report the intra-class correlation (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) for the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Color Trails Test (CTT) among community dwelling older adults. 130 participants aged 55 and older without severe cognitive impairment underwent two cognitive assessments between two and four months apart. Half the group changed rater between assessments and half changed time of day. Mean (standard deviation) MMSE was 28.1 (2.1) at baseline and 28.4 (2.1) at repeat. Mean (SD) MoCA increased from 24.8 (3.6) to 25.2 (3.6). There was a rater effect on CTT, but not on the MMSE or MoCA. The SEM of the MMSE was 1.0, leading to an MDC (based on a 95% confidence interval) of 3 points. The SEM of the MoCA was 1.5, implying an MDC95 of 4 points. MoCA (ICC = 0.81) was more reliable than MMSE (ICC = 0.75), but all tests examined showed substantial within-patient variation. An individual's score would have to change by greater than or equal to 3 points on the MMSE and 4 points on the MoCA for the rater to be confident that the change was not due to measurement error. This has important implications for epidemiologists and clinicians in dementia screening and diagnosis.

  9. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  10. Natural Communities

    Data.gov (United States)

    Kansas Data Access and Support Center — This dataset shows the locations of known tracts of high quality natural communities in Kansas, generalized to the PLSS section. It is not a compehensive dataset of...

  11. COMMUNITY OPHTHALMOLOGY

    African Journals Online (AJOL)

    2016-11-04

    Nov 4, 2016 ... The Role of Research in Social Marketing: The Community Eye. Outreach Clinic .... Serious thought was given to the possibility of establishing static eye care .... Information obtained included vital statistics, work history, job.

  12. Community concepts.

    Science.gov (United States)

    Yeung, Thomas; Bates, Tony

    2004-03-01

    Since the publication of "Sustainable Communities--building for the future", Government attention has focused largely on high-density affordable housing in the four "growth areas": Thames Gateway; Ashford; Milton Keynes--South Midlands, and London--Stansted--Cambridge. In this article, Thomas Yeung and Tony Bates suggest that a greater and more sustainable impact would be achieved if architects, planners, and developers considered the potential for community-based water and waste management and on-site energy generation and distribution right from the start of the project. In particular, they consider that the communal nature of hospitals, universities, and public/community housing provides a great opportunity for on-site renewable CHP and/or distributed heating, which could combine global environmental benefits with improved local amenities. They describe a simple model for prioritising energy management in the built environment, and draw on lessons learnt at ETRCL in Dagenham and BedZED in Surrey to offer a few recommendations for Government and developers. Tony Bates is the business development manager for Scott Wilson in the South East and is responsible for the promotion of sustainable communities through relationships with architects, developers, land owners and local authorities. Thomas Yeung leads the Energy Infrastructure Technologies group in Scott Wilson. This team offers an integrated approach to clean community-based energy generation, energy management, waste and water management, sustainable transport, and sustainable buildings/communities.

  13. Community expectations

    International Nuclear Information System (INIS)

    Kraemer, L.

    2004-01-01

    Historically, the relationship between the nuclear generator and the local community has been one of stability and co-operation. However in more recent times (2000-2003) the nuclear landscape has had several major issues that directly effect the local nuclear host communities. - The associations mandate is to be supportive of the nuclear industry through ongoing dialogue, mutual cooperation and education, - To strengthen community representation with the nuclear industry and politically through networking with other nuclear host communities. As a result of these issues, the Mayors of a number of communities started having informal meetings to discuss the issues at hand and how they effect their constituents. These meetings led to the official formation of the CANHC with representation from: In Canada it is almost impossible to discuss decommissioning and dismantling of Nuclear Facilities without also discussing Nuclear Waste disposal for reasons that I will soon make clear. Also I would like to briefly touch on how and why expectation of communities may differ by geography and circumstance. (author)

  14. A method for real-time memory efficient implementation of blob detection in large images

    Directory of Open Access Journals (Sweden)

    Petrović Vladimir L.

    2017-01-01

    Full Text Available In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many applications such as medical imaging, text recognition, as well as video surveillance or wide area motion imagery (WAMI. We explored the possibilities of usage of detected blobs in the feature-based image alignment as well. When large images are processed, our approach is 10 to over 20 times more memory efficient than the state of the art hardware implementation of the MSER.

  15. 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

  16. Analysis and visualization of social user communities

    Directory of Open Access Journals (Sweden)

    Daniel LÓPEZ SÁNCHEZ

    2016-06-01

    Full Text Available In this paper, a novel framework for social user clustering is proposed. Given a current controversial political topic, the Louvain Modularity algorithm is used to detect communities of users sharing the same political preferences. The political alignment of a set of users is labeled manually by a human expert and then the quality of the community detection is evaluated against this gold standard. In the last section, we propose a novel force-directed graph algorithm to generate a visual representation of the detected communities.   

  17. A novel featureless approach to mass detection in digital mammograms based on support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    Campanini, Renato [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Dongiovanni, Danilo [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Iampieri, Emiro [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Lanconelli, Nico [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Masotti, Matteo [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Palermo, Giuseppe [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Riccardi, Alessandro [Department of Physics, University of Bologna, and INFN, Bologna (Italy); Roffilli, Matteo [Department of Computer Science, University of Bologna, Bologna (Italy)

    2004-03-21

    In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the appearance of masses is the main obstacle to building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; in contrast, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first support vector machine (SVM) classifier. The detection task is considered here as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.

  18. Designed communities?

    DEFF Research Database (Denmark)

    Stender, Marie

    2013-01-01

    In current residential spaces there seem to be an increasing emphasis on small-scale communities. A number of new, high profiled residential complexes thus seek to promote new ways of social living by rethinking architectural design, typologies and concepts. In this paper I explore the emergence ...

  19. Walkable Communities

    Centers for Disease Control (CDC) Podcasts

    2008-04-18

    This podcast is for a general audience and discusses the benefits of walkable communities, as they relate to health, the environment, and social interaction.  Created: 4/18/2008 by National Center for Environmental Health (NCEH), ATSDR.   Date Released: 5/8/2008.

  20. Interfirm communities

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten

    2012-01-01

    . These results yield a paradox which the present paper aims to address. Based on an in-depth case study of how a high-tech small firm organizes its interfirm activity, I show how a hybrid social relation, that is neither weak nor strong, is a useful conception for interfirm communities. Hereby, the study also...

  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. EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques: A Status Report

    Science.gov (United States)

    Muller, Jan-Peter; Sidiropoulos, Panagiotis; Tao, Yu; Gwinner, Klaus; Willner, Konrad; Fanara, Lida; Waehlisch, Marita; Walter, Sebastian; Schreiner, Bjoern; Steikert, Ralf; Ivanov, Anton B.; Cantini, Federico; Wardlaw, Jessica; Sprinks, James Christopher; Giordano, Michele; Kim, Jungrack; Houghton, Robert; Bamford, Steven

    2016-10-01

    There has been a revolution in 3D surface imaging of Mars over the last 12 years with systematic stereoscopy from HRSC and the production for almost 50% of the Martian surface of DTMs and ORIs. The iMars project has been exploiting this unique set of 3D products as a basemap to co-register NASA imagery going back to the 1970s. DLR have produced 3D HRSC mosaic products for large regions with c. 100 individual strips/region (MC-11E/W). UCL have developed an automated processing chain for CTX and HiRISE 3D processing to densify this global HRSC dataset with DTMs down to 18m and 75cm respectively [1]. A fully Automated Co-Registration and Orthorectification (ACRO) system has been developed at UCL and applied to the production of around 8,000 images co-registered to a HRSC pixel (typically 12.5m) and orthorectified to HRSC DTMs of 50-150m spacing [2]. These images are viewable through an OGC-compliant webGIS developed at FUB including tools for viewing sequences over the same area [3]. Corresponding MARSIS and SHARAD data can be viewed through the QGIS plugin available [4]. An automated data mining system is being developed at UCL [5] for change detection to search and classify features in images going back to Viking Orbiter of IFoV ≤100m. In parallel, a citizen science project at Nottingham University [6] is defining training samples for classification of change features and eventually for verification of change [7]. Scientific applications include change mapping over the SPRC [8], mass movements near the North Pole [9]; dark streaks [10] CRISM mapping of mineralogy of dust in the SPRC "Swiss cheese" layers [11] and mapping of dune movement [12]. [1] Tao, Y. & J.-P. Muller LPSC16-2074; [2] Gwinner, K. et al. EPSC15-672; [3] van Gasselt, S. et al. EPSC14-693; [4] Ivanov, A. & Cantini, F. EPSC16; [5] Sidiropoulos, P. & J.-P. Muller EPSC16; [6] Sprinks et al. EPSC16; [7] Wardlaw et al. EPSC16; [8] Putri et al., EPSC16;[9] Fanara, L. et al. EPSC 16; [10] Schreiner, B. et

  3. Community Evolution

    OpenAIRE

    Saganowski, Stanisław; Bródka, Piotr; Kazienko, Przemysław

    2016-01-01

    The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire social network, extracted social groups and single individuals as well. One of the most interesting research topic is the network dynamics and dynamics of social groups in particular, it means analysis of group evolution over time. It is the natural step forward after social community extraction. Havi...

  4. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Bin Hou

    2016-08-01

    Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  5. An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

    Science.gov (United States)

    Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

    2017-01-01

    R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

  6. Online Communities

    DEFF Research Database (Denmark)

    Sørensen, Henrik; Lassen, Astrid Heidemann; Gorm Hansen, Katrine

    ”Online Communities” er et medie for brugere og fagfolk, hvor de kan mødes digitalt for at dele erfaringer, og dette kan anvendes som inspiration indenfor Brugerdreven Innovation. Via ”desk research” kan virksomheder opnå adgang til varierende mængder af brugere på en forholdsvist enkelt måde. I...... denne rapport beskrives eksperimentets opbygning, resultater og mulige værdi. Vi håber hermed på at kunne give praktisk indsigt i, hvorledes virksomheder fra byggematerialeindustrien kan agere i online communities....

  7. True Detective

    OpenAIRE

    Gajhede, Andreas; Westmark, [No Value; Bantoulidis, Georgios

    2016-01-01

    This paper examines the HBO’s television-series True Detective, a 2014 crime drama, attempting to point out the central elements of detective-fiction, to be found in its narrative. True Detective narrative is a heavy text with a number of references from various well-known, non-adjacent texts within the field of weird fiction, horror fiction and detective fiction. Therefore, the case study of this research will focus on narratological and literary analysis, in order to discover the predominan...

  8. Effects of multiple spreaders in community networks

    Science.gov (United States)

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

    2014-12-01

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

  9. Characterizing Variability and Multi-Resolution Predictions

    Data.gov (United States)

    National Aeronautics and Space Administration — In previous papers, we introduced the idea of a Virtual Sensor, which is a mathematical model trained to learn the potentially nonlinear relationships between...

  10. Multiresolution signal decomposition transforms, subbands, and wavelets

    CERN Document Server

    Akansu, Ali N; Haddad, Paul R

    2001-01-01

    The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course

  11. On the feasibility of using satellite gravity observations for detecting large-scale solid mass transfer events

    Science.gov (United States)

    Peidou, Athina C.; Fotopoulos, Georgia; Pagiatakis, Spiros

    2017-10-01

    The main focus of this paper is to assess the feasibility of utilizing dedicated satellite gravity missions in order to detect large-scale solid mass transfer events (e.g. landslides). Specifically, a sensitivity analysis of Gravity Recovery and Climate Experiment (GRACE) gravity field solutions in conjunction with simulated case studies is employed to predict gravity changes due to past subaerial and submarine mass transfer events, namely the Agulhas slump in southeastern Africa and the Heart Mountain Landslide in northwestern Wyoming. The detectability of these events is evaluated by taking into account the expected noise level in the GRACE gravity field solutions and simulating their impact on the gravity field through forward modelling of the mass transfer. The spectral content of the estimated gravity changes induced by a simulated large-scale landslide event is estimated for the known spatial resolution of the GRACE observations using wavelet multiresolution analysis. The results indicate that both the Agulhas slump and the Heart Mountain Landslide could have been detected by GRACE, resulting in {\\vert }0.4{\\vert } and {\\vert }0.18{\\vert } mGal change on GRACE solutions, respectively. The suggested methodology is further extended to the case studies of the submarine landslide in Tohoku, Japan, and the Grand Banks landslide in Newfoundland, Canada. The detectability of these events using GRACE solutions is assessed through their impact on the gravity field.

  12. Detecting the Killer Toxin (Harmful Algal Blooms)

    International Nuclear Information System (INIS)

    Quevenco, Rodolfo

    2011-01-01

    IAEA is stepping up efforts to help countries understand the phenomenon and use more reliable methods for early detection and monitoring so as to limit harmful algal blooms (HABs) adverse effects on coastal communities everywhere.

  13. Statistical methods for change-point detection in surface temperature records

    Science.gov (United States)

    Pintar, A. L.; Possolo, A.; Zhang, N. F.

    2013-09-01

    We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.

  14. Implementing wavelet packet transform for valve failure detection using vibration and acoustic emission signals

    International Nuclear Information System (INIS)

    Sim, H Y; Ramli, R; Abdullah, M A K

    2012-01-01

    The efficiency of reciprocating compressors relies heavily on the health condition of its moving components, most importantly its valves. Previous studies showed good correlation between the dynamic response and the physical condition of the valves. These can be achieved by employing vibration technique which is capable of monitoring the response of the valve, and acoustic emission technique which is capable of detecting the valves' material deformation. However, the relationship/comparison between the two techniques is rarely investigated. In this paper, the two techniques were examined using time-frequency analysis. Wavelet packet transform (WPT) was chosen as the multi-resolution analysis technique over continuous wavelet transform (CWT), and discrete wavelet transform (DWT). This is because WPT could overcome the high computational time and high redundancy problem in CWT and could provide detailed analysis of the high frequency components compared to DWT. The features of both signals can be extracted by evaluating the normalised WPT coefficients for different time window under different valve conditions. By comparing the normalised coefficients over a certain time frame and frequency range, the feature vectors revealing the condition of valves can be constructed. One way analysis of variance was employed on these feature vectors to test the significance of data under different valve conditions. It is believed that AE signals can give a better representation of the valve condition as it can detect both the fluid motion and material deformation of valves as compared to the vibration signals.

  15. Mountain Plant Community Sentinels: AWOL

    Science.gov (United States)

    Malanson, G. P.

    2017-12-01

    Mountain plant communities are thought to be sensitive to climate change. Because climatic gradients are steep on mountain slopes, the spatial response of plant communities to climate change should be compressed and easier to detect. These expectations have led to identifying mountain plant communities as sentinels for climate change. This idea has, however, been criticized. Two critiques, for alpine treeline and alpine tundra, are rehearsed and supplemented. The critique of alpine treeline as sentinel is bolstered with new model results on the confounding role of dispersal mechanisms and sensitivity to climatic volatility. In alpine tundra, for which background turnover rates have yet to be established, community composition may reflect environmental gradients only for extremes where effects of climate are most indirect. Both plant communities, while primarily determined by energy at broad scales, may respond to water as a proximate driver at local scales. These plant communities may not be in equilibrium with climate, and differently scaled time lags may mean that ongoing vegetation change may not signal ongoing climate change (or lack thereof). In both cases a double-whammy is created by scale dependence for time lags and for drivers leading to confusion, but these cases present opportunities for insights into basic ecology.

  16. Developing Learning Communities: Using Communities of Practice within Community Psychology

    Science.gov (United States)

    Lawthom, Rebecca

    2011-01-01

    The idea that communities need to be inclusive is almost axiomatic. The process, whereby, community members engage in inclusive practices is far less understood. Similarly, UK universities are being encouraged to include the wider community and extent campus boundaries. Here, I suggest a particular theoretical lens which sheds light on engagement…

  17. Additional measures do not improve the diagnostic accuracy of the Hospital Admission Risk Profile for detecting downstream quality of life in community-dwelling older people presenting to a hospital emergency department.

    Science.gov (United States)

    Grimmer, K; Milanese, S; Beaton, K; Atlas, A

    2014-01-01

    The Hospital Admission Risk Profile (HARP) instrument is commonly used to assess risk of functional decline when older people are admitted to hospital. HARP has moderate diagnostic accuracy (65%) for downstream decreased scores in activities of daily living. This paper reports the diagnostic accuracy of HARP for downstream quality of life. It also tests whether adding other measures to HARP improves its diagnostic accuracy. One hundred and forty-eight independent community dwelling individuals aged 65 years or older were recruited in the emergency department of one large Australian hospital with a medical problem for which they were discharged without a hospital ward admission. Data, including age, sex, primary language, highest level of education, postcode, living status, requiring care for daily activities, using a gait aid, receiving formal community supports, instrumental activities of daily living in the last week, hospitalization and falls in the last 12 months, and mental state were collected at recruitment. HARP scores were derived from a formula that summed scores assigned to age, activities of daily living, and mental state categories. Physical and mental component scores of a quality of life measure were captured by telephone interview at 1 and 3 months after recruitment. HARP scores are moderately accurate at predicting downstream decline in physical quality of life, but did not predict downstream decline in mental quality of life. The addition of other variables to HARP did not improve its diagnostic accuracy for either measure of quality of life. HARP is a poor predictor of quality of life.

  18. Acclimation of subsurface microbial communities to mercury

    DEFF Research Database (Denmark)

    de Lipthay, Julia R; Rasmussen, Lasse D; Øregaard, Gunnar

    2008-01-01

    of mercury tolerance and functional versatility of bacterial communities in contaminated soils initially were higher for surface soil, compared with the deeper soils. However, following new mercury exposure, no differences between bacterial communities were observed, which indicates a high adaptive potential......We studied the acclimation to mercury of bacterial communities of different depths from contaminated and noncontaminated floodplain soils. The level of mercury tolerance of the bacterial communities from the contaminated site was higher than those of the reference site. Furthermore, the level...... of the subsurface communities, possibly due to differences in the availability of mercury. IncP-1 trfA genes were detected in extracted community DNA from all soil depths of the contaminated site, and this finding was correlated to the isolation of four different mercury-resistance plasmids, all belonging...

  19. The IASI detection chain

    Science.gov (United States)

    Nicol, Patrick; Fleury, Joel; Le Naour, Claire; Bernard, Frédéric

    2017-11-01

    IASI (Infrared Atmospheric Sounding Interferometer) is an infrared atmospheric sounder. It will provide meteorologist and scientific community with atmospheric spectra. The instrument is composed of a Fourier transform spectrometer and an associated infrared imager. The presentation will describe the spectrometer detection chain architecture, composed by three different detectors cooled in a passive cryo-cooler (so called CBS : Cold Box Subsystem) and associated analog electronics up to digital conversion. It will mainly focus on design choices with regards to environment constraints, implemented technologies, and associated performances. CNES is leading the IASI program in collaboration with EUMETSAT. The instrument Prime is ALCATEL SPACE responsible, notably, of the detection chain architecture. SAGEM SA provides the detector package (so called CAU : Cold Acquisition Unit).

  20. Community Core Evolution in Mobile Social Networks

    Directory of Open Access Journals (Sweden)

    Hao Xu

    2013-01-01

    Full Text Available Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

  1. Community core evolution in mobile social networks.

    Science.gov (United States)

    Xu, Hao; Xiao, Weidong; Tang, Daquan; Tang, Jiuyang; Wang, Zhenwen

    2013-01-01

    Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

  2. Community Hospital Telehealth Consortium

    National Research Council Canada - National Science Library

    Williams, Elton

    2004-01-01

    The Community Hospital Telehealth Consortium is a unique, forward-thinking, community-based healthcare service project organized around 5 not-for-profit community hospitals located throughout Louisiana and Mississippi...

  3. 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.

  4. Community Hospital Telehealth Consortium

    National Research Council Canada - National Science Library

    Williams, Elton

    2003-01-01

    The Community Hospital Telehealth Consortium is a unique, forward-thinking, community-based healthcare service project organized around 5 not-for-profit com