WorldWideScience

Sample records for community cancer network

  1. Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

    Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

    2010-01-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

  2. Addressing cancer disparities via community network mobilization and intersectoral partnerships: a social network analysis.

    Shoba Ramanadhan

    Full Text Available Community mobilization and collaboration among diverse partners are vital components of the effort to reduce and eliminate cancer disparities in the United States. We studied the development and impact of intersectoral connections among the members of the Massachusetts Community Network for Cancer Education, Research, and Training (MassCONECT. As one of the Community Network Program sites funded by the National Cancer Institute, this infrastructure-building initiative utilized principles of Community-based Participatory Research (CBPR to unite community coalitions, researchers, policymakers, and other important stakeholders to address cancer disparities in three Massachusetts communities: Boston, Lawrence, and Worcester. We conducted a cross-sectional, sociometric network analysis four years after the network was formed. A total of 38 of 55 members participated in the study (69% response rate. Over four years of collaboration, the number of intersectoral connections reported by members (intersectoral out-degree increased, as did the extent to which such connections were reported reciprocally (intersectoral reciprocity. We assessed relationships between these markers of intersectoral collaboration and three intermediate outcomes in the effort to reduce and eliminate cancer disparities: delivery of community activities, policy engagement, and grants/publications. We found a positive and statistically significant relationship between intersectoral out-degree and community activities and policy engagement (the relationship was borderline significant for grants/publications. We found a positive and statistically significant relationship between intersectoral reciprocity and community activities and grants/publications (the relationship was borderline significant for policy engagement. The study suggests that intersectoral connections may be important drivers of diverse intermediate outcomes in the effort to reduce and eliminate cancer disparities

  3. Bladder Cancer Advocacy Network

    ... Grants Bladder Cancer Think Tank Bladder Cancer Research Network Bladder Cancer Genomics Consortium Get Involved Ways to ... us? Who we are The Bladder Cancer Advocacy Network (BCAN) is a community of patients, caregivers, survivors, ...

  4. Fox Chase Network: Fox Chase Cancer Center's community hospital affiliation program.

    Higman, S A; McKay, F J; Engstrom, P F; O'Grady, M A; Young, R C

    2000-01-01

    Fox Chase Cancer Center developed a format for affiliation with community providers in 1986. Fox Chase Network was formed to establish hospital-based community cancer centers to increase access to patients involved in clinical research. Under this program, the Fox Chase Network now contributes 500 patients per year to prevention and clinical research studies. As relationships with community providers form, patient referrals have increased at Fox Chase Cancer Center and for each Fox Chase Network member. A dedicated staff is required to operate the central office on a day-to-day basis as well as at each affiliate. We have found this to be a critical element in each program's success. New challenges in the cancer business-increasing volumes with declining revenue-have caused us to reconfigure the services offered to affiliates, while maintaining true to our mission: to reduce the burden of human cancer.

  5. Understanding the social and community support networks of American Indian women cancer survivors.

    Burnette, Catherine E; Liddell, Jessica; Roh, Soonhee; Lee, Yeon-Shim; Lee, Hee Yun

    2018-04-02

    Cancer is the leading cause of death among American Indian and Alaska Native (AI/AN) women, and although cancer disparities among AI women are alarming, there is little research focused on the topic of social support and cancer treatment and outcomes. A community advisory board was used to develop and administer the project, and a qualitative descriptive study methodology was used. This research was conducted in partnership with two community-based hospitals in the Northern Plains. The sample included 43 AI female cancer survivors who were interviewed with a semi-structured interview guide. The data were analyzed using content analysis. Emergent themes revealed that AI cancer survivors' non-familial support systems included friends (n = 12), support groups (n = 6), churches (n = 10), co-workers (n = 5), communities (n = 4), support from health practitioners (n = 3) and additional forms of support. Results indicate that survivors' networks are diverse, and support broad prevention programs that reach out to churches, community groups, and online forums. These sources of supports can be enhanced through sustainable community-based infrastructures.

  6. Synthesizing community wisdom: A model for sharing cancer-related resources through social networking and collaborative partnerships.

    Weiss, Jacob B; Lorenzi, Nancy M; Lorenzi, Nancy

    2008-11-06

    Despite the availability of community-based support services, cancer patients and survivors are not aware of many of these resources. Without access to community programs, cancer survivors are at risk for lower quality of care and lower quality of life. At the same time, non-profit community organizations lack access to advanced consumer informatics applications to effectively promote awareness of their services. In addition to the current models of print and online resource guides, new community-driven informatics approaches are needed to achieve the goal of comprehensive care for cancer survivors. We present the formulation of a novel model for synthesizing a local communitys collective wisdom of cancer-related resources through a combination of online social networking technologies and real-world collaborative partnerships. This approach can improve awareness of essential, but underutilized community resources.

  7. Topical Network of Breast Cancer Information in a Korean American Online Community: A Semantic Network Analysis

    Park, Min Sook; Park, Hyejin

    2016-01-01

    Introduction: Health information-seeking and sharing online has become immensely intertwined with day-to-day information-seeking of US immigrants with health concerns. Despite the consistent recognition of unique health needs among different US immigrant communities, little is known about the distinctive patterns and extent of health information…

  8. Program Spotlight: National Outreach Network's Community Health Educators

    National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

  9. Decoding communities in networks

    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.

  10. Decoding communities in networks.

    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.

  11. Privacy Practices of Health Social Networking Sites: Implications for Privacy and Data Security in Online Cancer Communities.

    Charbonneau, Deborah H

    2016-08-01

    While online communities for social support continue to grow, little is known about the state of privacy practices of health social networking sites. This article reports on a structured content analysis of privacy policies and disclosure practices for 25 online ovarian cancer communities. All of the health social networking sites in the study sample provided privacy statements to users, yet privacy practices varied considerably across the sites. The majority of sites informed users that personal information was collected about participants and shared with third parties (96%, n = 24). Furthermore, more than half of the sites (56%, n = 14) stated that cookies technology was used to track user behaviors. Despite these disclosures, only 36% (n = 9) offered opt-out choices for sharing data with third parties. In addition, very few of the sites (28%, n = 7) allowed individuals to delete their personal information. Discussions about specific security measures used to protect personal information were largely missing. Implications for privacy, confidentiality, consumer choice, and data safety in online environments are discussed. Overall, nurses and other health professionals can utilize these findings to encourage individuals seeking online support and participating in social networking sites to build awareness of privacy risks to better protect their personal health information in the digital age.

  12. Prostate Cancer Biorepository Network

    2017-10-01

    AWARD NUMBER: W81XWH-14-2-0185 TITLE: Prostate Cancer Biorepository Network PRINCIPAL INVESTIGATOR: Jonathan Melamed, MD CONTRACTING ORGANIZATION...AND SUBTITLE 5a. CONTRACT NUMBER Prostate Cancer Biorepository Network 5b. GRANT NUMBER W81XWH-14-2-0185 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...infrastructure and operations of the Prostate Cancer Biorepository Network (PCBN). The aim of the PCBN is to provide prostate researchers with high-quality

  13. International Cancer Screening Network

    The International Cancer Screening Network promotes evidence-based cancer screening implementation and evaluation with cooperation from multilateral organizations around the globe. Learn more about how ICSN aims to reduce the global burden of cancer by supporting research and international collaboration.

  14. Perceived Stress in Online Prostate Cancer Community Participants: Examining Relationships with Stigmatization, Social Support Network Preference, and Social Support Seeking.

    Rising, Camella J; Bol, Nadine; Burke-Garcia, Amelia; Rains, Stephen; Wright, Kevin B

    2017-06-01

    Men with prostate cancer often need social support to help them cope with illness-related physiological and psychosocial challenges. Whether those needs are met depends on receiving support optimally matched to their needs. This study examined relationships between perceived stress, prostate cancer-related stigma, weak-tie support preference, and online community use for social support in a survey of online prostate cancer community participants (n = 149). Findings revealed a positive relationship between stigma and perceived stress. This relationship, however, was moderated by weak-tie support preference and online community use for social support. Specifically, stigma was positively related to perceived stress when weak-tie support was preferred. Analyses also showed a positive relationship between stigma and perceived stress in those who used their online community for advice or emotional support. Health communication scholars should work collaboratively with diagnosed men, clinicians, and online community administrators to develop online interventions that optimally match social support needs.

  15. Community Seismic Network (CSN)

    Clayton, R. W.; Heaton, T. H.; Kohler, M. D.; Cheng, M.; Guy, R.; Chandy, M.; Krause, A.; Bunn, J.; Olson, M.; Faulkner, M.; Liu, A.; Strand, L.

    2012-12-01

    We report on developments in sensor connectivity, architecture, and data fusion algorithms executed in Cloud computing systems in the Community Seismic Network (CSN), a network of low-cost sensors housed in homes and offices by volunteers in the Pasadena, CA area. The network has over 200 sensors continuously reporting anomalies in local acceleration through the Internet to a Cloud computing service (the Google App Engine) that continually fuses sensor data to rapidly detect shaking from earthquakes. The Cloud computing system consists of data centers geographically distributed across the continent and is likely to be resilient even during earthquakes and other local disasters. The region of Southern California is partitioned in a multi-grid style into sets of telescoping cells called geocells. Data streams from sensors within a geocell are fused to detect anomalous shaking across the geocell. Temporal spatial patterns across geocells are used to detect anomalies across regions. The challenge is to detect earthquakes rapidly with an extremely low false positive rate. We report on two data fusion algorithms, one that tessellates the surface so as to fuse data from a large region around Pasadena and the other, which uses a standard tessellation of equal-sized cells. Since September 2011, the network has successfully detected earthquakes of magnitude 2.5 or higher within 40 Km of Pasadena. In addition to the standard USB device, which connects to the host's computer, we have developed a stand-alone sensor that directly connects to the internet via Ethernet or wifi. This bypasses security concerns that some companies have with the USB-connected devices, and allows for 24/7 monitoring at sites that would otherwise shut down their computers after working hours. In buildings we use the sensors to model the behavior of the structures during weak events in order to understand how they will perform during strong events. Visualization models of instrumented buildings ranging

  16. Community detection using preference networks

    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.

  17. Addressing the Excess Breast Cancer Mortality in Filipino Women in Hawai‘i through AANCART, an NCI Community Network Program

    Muraoka, Miles; Cuaresma, Charlene; Guerrero, Reuben; Agbayani, Amy

    2010-01-01

    Filipino women are more likely to die of breast cancer than their major Asian American counterparts even though they do not have the highest incidence of that cancer. Analysis showed that they have a more advanced stage at the time of diagnosis and they have low rates of compliance to mammography guidelines, both of which factors may contribute to their high mortality rate. A broad based but targeted breast cancer awareness effort was directed to Filipino women, which included involving the media, the training of key community leaders, and the development of partnerships with health organizations with a like mission. After four years of effort, it was possible to demonstrate improvement in mammography rates in Filipino women that approached those of the general population in Hawai‘i. PMID:20680924

  18. Nuclear Community in network

    Tejedor, E.

    2014-01-01

    The internet has revolutionized the ways of communication and many companies/ organizations have adapted to the change but others have not, or have done it halfway. This presentation is a review of the main characteristics of virtual communities and their typology. The status of the Nuclear Online Community, both pro nuclear and antinuclear is analysed , and their main similarities and differences are discussed. The Pro nuclear Online Community is formed gradually. This presentation attempts to define some ways to increase the scope of the Community and encourage greater dissemination of the characteristics of nuclear energy. (Author)

  19. Networking the rural community.

    Tiongson, K H; Arneson, S I

    1993-04-01

    A branch network of affiliate hospitals has been providing home care services to rural North Dakota residents successfully for a decade. Here's how this effective system meets the special challenges that a rural environment poses for hiring, training, scheduling, and supporting home care aides.

  20. An Open Letter to the Cancer Community Regarding Community Clinical Trials

    The National Cancer Institute (NCI) is in the process of combining its two community-based research networks to create a single network that builds on the strengths of the Community Clinical Oncology Program/Minority-Based Community Clinical Oncology Prog

  1. An evolving network model with community structure

    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

  2. Searching for Communities in Bipartite Networks

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

    2008-01-01

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

  3. A method for analyzing the business case for provider participation in the National Cancer Institute's Community Clinical Oncology Program and similar federally funded, provider-based research networks.

    Reiter, Kristin L; Song, Paula H; Minasian, Lori; Good, Marjorie; Weiner, Bryan J; McAlearney, Ann Scheck

    2012-09-01

    The Community Clinical Oncology Program (CCOP) plays an essential role in the efforts of the National Cancer Institute (NCI) to increase enrollment in clinical trials. Currently, there is little practical guidance in the literature to assist provider organizations in analyzing the return on investment (ROI), or business case, for establishing and operating a provider-based research network (PBRN) such as the CCOP. In this article, the authors present a conceptual model of the business case for PBRN participation, a spreadsheet-based tool and advice for evaluating the business case for provider participation in a CCOP organization. A comparative, case-study approach was used to identify key components of the business case for hospitals attempting to support a CCOP research infrastructure. Semistructured interviews were conducted with providers and administrators. Key themes were identified and used to develop the financial analysis tool. Key components of the business case included CCOP start-up costs, direct revenue from the NCI CCOP grant, direct expenses required to maintain the CCOP research infrastructure, and incidental benefits, most notably downstream revenues from CCOP patients. The authors recognized the value of incidental benefits as an important contributor to the business case for CCOP participation; however, currently, this component is not calculated. The current results indicated that providing a method for documenting the business case for CCOP or other PBRN involvement will contribute to the long-term sustainability and expansion of these programs by improving providers' understanding of the financial implications of participation. Copyright © 2011 American Cancer Society.

  4. Introduction: Cancer Gene Networks.

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  5. Lessons Learned from the Young Breast Cancer Survivorship Network.

    Gisiger-Camata, Silvia; Nolan, Timiya S; Vo, Jacqueline B; Bail, Jennifer R; Lewis, Kayla A; Meneses, Karen

    2017-11-30

    The Young Breast Cancer Survivors Network (Network) is an academic and community-based partnership dedicated to education, support, and networking. The Network used a multi-pronged approach via monthly support and networking, annual education seminars, website networking, and individual survivor consultation. Formative and summative evaluations were conducted using group survey and individual survivor interviews for monthly gatherings, annual education meetings, and individual consultation. Google Analytics was applied to evaluate website use. The Network began with 4 initial partnerships and grew to 38 in the period from 2011 to 2017. During this 5-year period, 5 annual meetings (598 attendees), 23 support and networking meetings (373), and 115 individual survivor consultations were conducted. The Network website had nearly 12,000 individual users and more than 25,000 page views. Lessons learned include active community engagement, survivor empowerment, capacity building, social media outreach, and network sustainability. The 5-year experiences with the Network demonstrated that a regional program dedicated to the education, support, networking, and needs of young breast cancer survivors and their families can become a vital part of cancer survivorship services in a community. Strong community support, engagement, and encouragement were vital components to sustain the program.

  6. Epidemics in adaptive networks with community structure

    Shaw, Leah; Tunc, Ilker

    2010-03-01

    Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

  7. Perceived stress in online prostate cancer community participants: Examining relationships with stigmatization, social support network preference, and social support seeking

    Rising, C.J.; Bol, N.; Burke-Garcia, A.; Rains, S.; Wright, K.B.

    2017-01-01

    Men with prostate cancer often need social support to help them cope with illness-related physiological and psychosocial challenges. Whether those needs are met depends on receiving support optimally matched to their needs. This study examined relationships between perceived stress, prostate

  8. Effects of multiple spreaders in community networks

    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. Sociospatial Knowledge Networks: Appraising Community as Place.

    Skelly, Anne H.; Arcury, Thomas A.; Gesler, Wilbert M.; Cravey, Altha J.; Dougherty, Molly C.; Washburn, Sarah A.; Nash, Sally

    2002-01-01

    A new theory of geographical analysis--sociospatial knowledge networks--provides a framework for understanding the social and spatial locations of a community's health knowledge and beliefs. This theory is guiding an ethnographic study of health beliefs, knowledge, and knowledge networks in a diverse rural community at high risk for type-2…

  10. Fast unfolding of communities in large networks

    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

  11. Assessing Community Informatics: A Review of Methodological Approaches for Evaluating Community Networks and Community Technology Centers.

    O'Neil, Dara

    2002-01-01

    Analyzes the emerging community informatics evaluation literature to develop an understanding of the indicators used to gauge project impacts in community networks and community technology centers. The study finds that community networks and community technology center assessments fall into five key areas: strong democracy; social capital;…

  12. AANCART best practices: cancer awareness activities for Seattle's Combodian community.

    Seng, Paularita; Acorda, Elizabeth; Carey Jackson, J; Marchand, Ann; Thai, Hue; Tu, Shin-Ping; Taylor, Vicky

    2005-12-15

    Census data indicate that Cambodian Americans are economically disadvantaged and linguistically isolated. In addition, cancer registry data show that Southeast Asians experience several cancer-related health disparities (e.g., markedly elevated risks of cervical and liver cancer). The Seattle regional Asian American Network for Cancer, Awareness, Research, and Training (AANCART) site has implemented a community-based cancer awareness program for Cambodian immigrants in collaboration with a Cambodian community coalition. Our cancer awareness program has the following goals: to assist individuals and organizations in advocating for a healthy community, to provide information within a cultural context, and to deliver information in ways that are useful and meaningful to the community. The program was guided by a community assessment that included the use of published data as well as information from qualitative interviews, focus groups, and quantitative surveys. Examples of community awareness activities include group presentations at community-based organizations (e.g., during English as a second language classes), health fair participation (including at nontraditional venues such as a farmers' market serving Cambodians), and educational displays in neighborhood locations (e.g., at Cambodian video stores). In addition, the Seattle AANCART site has both inventoried and developed culturally appropriate Khmer language cancer education materials and disseminated materials through the ETHNO-MED website. Our approach recognizes that limited English language proficiency may preclude many Cambodians from understanding publicly disseminated information, and Cambodian immigrants are often isolated and tend to stay close to their own neighborhoods. Cancer 2005. (c) 2005 American Cancer Society.

  13. Consumer engagement in social networks brand community

    Rybakovas, Paulius

    2016-01-01

    Consumers increasingly integrate social media into their day-to-day lives. For companies consumer engagement in a brand community on social network is becoming increasingly important for developing relations with consumers. Consumer engagement in a brand community on social network creates a dynamic relationship between the community members and the brand which contributes to an increase in consumer loyalty to the brand. The literature is abundant of studies, which examines the consumer engag...

  14. Networks : Empowering Communities through Telecentre Networking

    ... Egypt, Syria, Lebanon and Morocco - have pulled together in loose networks for peer support. ... IDRC “unpacks women's empowerment” at McGill University Conference ... New funding opportunity for gender equality and climate change.

  15. Finding overlapping communities in multilayer networks.

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

    2018-01-01

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

  16. Creative Network Communities in the Translocal Space of Digital Networks

    Rasa Smite

    2013-01-01

    Full Text Available What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustainability of networks. Data comprise interviews with networking experts and founders and members of various networks. Investigating respondents’ motivations for creating online networks and communities, and interpreting those terms, allows for comparing the creative networks of the 1990s with today’s social networks and for drawing conclusions.

  17. Community Based Networks and 5G

    Williams, Idongesit

    2016-01-01

    The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  18. Immunization of networks with community structure

    Masuda, Naoki

    2009-01-01

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  19. Legacy of the Pacific Islander cancer control network.

    Hubbell, F Allan; Luce, Pat H; Afeaki, William P; Cruz, Lee Ann C; McMullin, Juliet M; Mummert, Angelina; Pouesi, June; Reyes, Maria Lourdes; Taumoepeau, Leafa Tuita; Tu'ufuli, Galeai Moali'itele; Wenzel, Lari

    2006-10-15

    The groundwork for the Pacific Islander cancer control network (PICCN) began in the early 1990s with a study of the cancer control needs of American Samoans. The necessity for similar studies among other Pacific Islander populations led to the development of PICCN. The project's principal objectives were to increase cancer awareness and to enhance cancer control research among American Samoans, Tongans, and Chamorros. PICCN was organized around a steering committee and 6 community advisory boards, 2 from each of the targeted populations. Membership included community leaders, cancer control experts, and various academic and technical organizations involved with cancer control. Through this infrastructure, the investigators developed new culturally sensitive cancer education materials and distributed them in a culturally appropriate manner. They also initiated a cancer control research training program, educated Pacific Islander students in this field, and conducted pilot research projects. PICCN conducted nearly 200 cancer awareness activities in its 6 study sites and developed cancer educational materials on prostate, colorectal, lung, breast, and cervical cancer and tobacco control in the Samoan, Tongan, and Chamorro languages. PICCN trained 9 students who conducted 7 pilot research projects designed to answer important questions regarding the cancer control needs of Pacific Islanders and to inform interventions targeting those needs. The legacy of PICCN lies in its advancement of improving cancer control among Pacific Islanders and setting the stage for interventions that will help to eliminate cancer-related health disparities. Cancer 2006. (c) 2006 American Cancer Society.

  20. Brand communities embedded in social networks.

    Zaglia, Melanie E

    2013-02-01

    Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social networks. The netnographic approach yields strong evidence of this existence; leading to a better understanding of such embedded brand communities, their peculiarities, and motivational drivers for participation; therefore the findings contribute to theory by combining two separate research streams. Due to the advantages of social networks, brand management is now able to implement brand communities with less time and financial effort; however, choosing the appropriate brand community type, cultivating consumers' interaction, and staying tuned to this social engagement are critical factors to gain anticipated brand outcomes.

  1. Social network analysis community detection and evolution

    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

  2. Network communities within and across borders

    Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

    2014-01-01

    We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by t...

  3. Brand communities embedded in social networks ?

    Zaglia, Melanie E.

    2013-01-01

    Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social ...

  4. Community Core Evolution in Mobile Social Networks

    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.

  5. Community core evolution in mobile social networks.

    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.

  6. Network Analysis in Community Psychology: Looking Back, Looking Forward

    Neal, Zachary P.; Neal, Jennifer Watling

    2017-01-01

    Highlights Network analysis is ideally suited for community psychology research because it focuses on context. Use of network analysis in community psychology is growing. Network analysis in community psychology has employed some potentially problematic practices. Recommended practices are identified to improve network analysis in community psychology.

  7. Epidemic spreading on complex networks with community structures

    Stegehuis, C.; van der Hofstad, R.W.; van Leeuwaarden, J.S.H.

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities

  8. Information transfer in community structured multiplex networks

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  9. Information transfer in community structured multiplex networks

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  10. Network communities within and across borders.

    Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

    2014-04-01

    We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

  11. Exploratory community sensing in social networks

    Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

    2010-04-01

    Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

  12. Community Structure in Online Collegiate Social Networks

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  13. Community detection in networks with unequal groups.

    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.

  14. Finding local communities in protein networks.

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-09-18

    Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

  15. Finding local communities in protein networks

    Teng Shang-Hua

    2009-09-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

  16. Network Community Detection on Metric Space

    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.

  17. Communities in Large Networks: Identification and Ranking

    Olsen, Martin

    2008-01-01

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

  18. Governance Mechanisms in Food Community Networks

    Pascucci, S.; Lombardi, A.; Cembalo, L.; Dentoni, D.

    2013-01-01

    This paper discusses the concept of the food community network (FCN) and how consumers and farmers organize credence food transactions. The FCN is based on pooling specific resources and using membership-based contracts to assign decision and property rights. It implies an organization based on a

  19. Social network fragmentation and community health.

    Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

    2017-09-05

    Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

  20. Cooperation in the prisoner's dilemma game on tunable community networks

    Liu, Penghui; Liu, Jing

    2017-04-01

    Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.

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

    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.

  2. Mapping the ecological networks of microbial communities.

    Xiao, Yandong; Angulo, Marco Tulio; Friedman, Jonathan; Waldor, Matthew K; Weiss, Scott T; Liu, Yang-Yu

    2017-12-11

    Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

  3. Facebook: Networking the Community of Society

    Tække, Jesper

    The article examines the significance of new "social media" like Facebook for the way we socialize, develop social identity, and shape society. Based on the work of Luhmann, the article proposes that community communication is fundamental to the selfregulation of our society and that this type...... but that also may pose certain risks for modern society and for the development and maintenance of social identity. The article argues that communication through and about status updates on Facebook may be categorized as network communication, and finally it discusses whether and to what extent this kind...... of communication also provides the basis for the formation and maintenance of people’s social identity, so that they and society are in harmony. In contrast to community communication, the article explores the notion of network communication, which is classified as communication that may have some positive effects...

  4. Vulnerability of R-MAT networks with communities

    Nikolay Alexandrovich Kinash

    2016-06-01

    Full Text Available A generator R-MAT for modeling networks with different laws of link constructions within and between communities has been developed. Network attack simulations have been performed and pertinent robustness of diverse network combinations has been concluded.

  5. Phase synchronization on small-world networks with community structure

    Xiao-Hua, Wang; Li-Cheng, Jiao; Jian-She, Wu

    2010-01-01

    In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)

  6. Identification of hybrid node and link communities in complex networks.

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  7. Identification of hybrid node and link communities in complex networks

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  8. Network-Based Community Brings forth Sustainable Society

    Kikuchi, Toshiko

    It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

  9. A divisive spectral method for network community detection

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

    2016-01-01

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

  10. A new hierarchical method to find community structure in networks

    Saoud, Bilal; Moussaoui, Abdelouahab

    2018-04-01

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

  11. Community Health Global Network and Sustainable Development

    Rebekah Young

    2016-01-01

    Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

  12. Research on Community Structure in Bus Transport Networks

    Yang Xuhua; Wang Bo; Sun Youxian

    2009-01-01

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

  13. Sharing cost in social community networks

    Pal, Ranjan; Elango, Divya; Wardana, Satya Ardhy

    2012-01-01

    their deployment in a residential locality. Our proposed mechanism accounts for heterogeneous user preferences towards different router features and comes up with the optimal (feature-set, user costs) router blueprint that satisfies each user in a locality, in turn motivating them to buy routers and thereby improve......Wireless social community networks (WSCNs) is an emerging technology that operate in the unlicensed spectrum and have been created as an alternative to cellular wireless networks for providing low-cost, high speed wireless data access in urban areas. WSCNs is an upcoming idea that is starting...... reflect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise a simple to implement, successful, budget-balanced, ex-post efficient, and individually rational auction-based mechanism...

  14. Towards a Community Environmental Observation Network

    Mertl, Stefan; Lettenbichler, Anton

    2014-05-01

    The Community Environmental Observation Network (CEON) is dedicated to the development of a free sensor network to collect and distribute environmental data (e.g. ground shaking, climate parameters). The data collection will be done with contributions from citizens, research institutions and public authorities like communities or schools. This will lead to a large freely available data base which can be used for public information, research, the arts,..... To start a free sensor network, the most important step is to provide easy access to free data collection and -distribution tools. The initial aims of the project CEON are dedicated to the development of these tools. A high quality data logger based on open hardware and free software is developed and a software suite of already existing free software for near-real time data communication and data distribution over the Internet will be assembled. Foremost, the development focuses on the collection of data related to the deformation of the earth (such as ground shaking, surface displacement of mass movements and glaciers) and the collection of climate data. The extent to other measurements will be considered in the design. The data logger is built using open hardware prototyping platforms like BeagleBone Black and Arduino. Main features of the data logger are: a 24Bit analog-to-digital converter; a GPS module for time reference and positioning; wireless mesh networking using Optimized Link State Routing; near real-time data transmission and communication; and near real-time differential GNSS positioning using the RTKLIB software. The project CEON is supported by the Internet Foundation Austria (IPA) within the NetIdee 2013 call.

  15. A Weighted Evolving Network with Community Size Preferential Attachment

    Zhuo Zhiwei; Shan Erfang

    2010-01-01

    Community structure is an important characteristic in real complex network. It is a network consists of groups of nodes within which links are dense but among which links are sparse. In this paper, the evolving network include node, link and community growth and we apply the community size preferential attachment and strength preferential attachment to a growing weighted network model and utilize weight assigning mechanism from BBV model. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths.

  16. Opinion Dynamics on Complex Networks with Communities

    Ru, Wang; Li-Ping, Chi

    2008-01-01

    The Ising or Potts models of ferromagnetism have been widely used to describe locally interacting social or economic systems. We consider a related model, introduced by Sznajd to describe the evolution of consensus in the scale-free networks with the tunable strength (noted by Q) of community structure. In the Sznajd model, the opinion or state of any spins can only be changed by the influence of neighbouring pairs of similar connection spins. Such pairs can polarize their neighbours. Using asynchronous updating, it is found that the smaller the community strength Q, the larger the slope of the exponential relaxation time distribution. Then the effect of the initial up- spin concentration p as a function of the final all up probability E is investigated by taking different initialization strategies, the random node-chosen initialization strategy has no difference under different community strengths, while the strategies of community node-chosen initialization and hub node-chosen initialization are different in final probability under different Q, and the latter one is more effective in reaching final state

  17. Emergence of communities and diversity in social networks

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H. Eugene

    2017-01-01

    Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities ...

  18. Community detection for networks with unipartite and bipartite structure

    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.

  19. Complex brain networks: From topological communities to clustered

    Complex brain networks: From topological communities to clustered dynamics ... Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. ... Pramana – Journal of Physics | News.

  20. Hydraulic Network Modelling of Small Community Water Distribution ...

    Prof Anyata

    ... design of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using ..... self cleansing drinking water distribution system is set at 0.4m/s, .... distribution network offers advantages over manual ...

  1. Efficient inference of overlapping communities in complex networks

    Fruergaard, Bjarne Ørum; Herlau, Tue

    2014-01-01

    We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White, Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic...

  2. Joint community and anomaly tracking in dynamic networks

    Baingana, Brian; Giannakis, Georgios B.

    2015-01-01

    Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic st...

  3. Networked Community Change: Understanding Community Systems Change through the Lens of Social Network Analysis.

    Lawlor, Jennifer A; Neal, Zachary P

    2016-06-01

    Addressing complex problems in communities has become a key area of focus in recent years (Kania & Kramer, 2013, Stanford Social Innovation Review). Building on existing approaches to understanding and addressing problems, such as action research, several new approaches have emerged that shift the way communities solve problems (e.g., Burns, 2007, Systemic Action Research; Foth, 2006, Action Research, 4, 205; Kania & Kramer, 2011, Stanford Social Innovation Review, 1, 36). Seeking to bring clarity to the emerging literature on community change strategies, this article identifies the common features of the most widespread community change strategies and explores the conditions under which such strategies have the potential to be effective. We identify and describe five common features among the approaches to change. Then, using an agent-based model, we simulate network-building behavior among stakeholders participating in community change efforts using these approaches. We find that the emergent stakeholder networks are efficient when the processes are implemented under ideal conditions. © Society for Community Research and Action 2016.

  4. Effects of multi-state links in network community detection

    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.

  5. Community Based Networks and 5G Wi-Fi

    Williams, Idongesit

    2018-01-01

    This paper argues on why Community Based Networks should be recognized as potential 5G providers using 5G Wi-Fi. The argument is hinged on findings in a research to understand why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out...... inductively using Grounded Theory. Six cases were investigated. Two Community Based Network Mobilization Models were identified. The findings indicate that 5G Wi-Fi deployment by Community Based Networks is possible if policy initiatives and the 5G Wi-Fi standards are developed to facilitate the causal...

  6. Overlapping community detection in networks with positive and negative links

    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)

  7. Network information improves cancer outcome prediction.

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  8. The National Cancer Institute's Physical Sciences - Oncology Network

    Espey, Michael Graham

    In 2009, the NCI launched the Physical Sciences - Oncology Centers (PS-OC) initiative with 12 Centers (U54) funded through 2014. The current phase of the Program includes U54 funded Centers with the added feature of soliciting new Physical Science - Oncology Projects (PS-OP) U01 grant applications through 2017; see NCI PAR-15-021. The PS-OPs, individually and along with other PS-OPs and the Physical Sciences-Oncology Centers (PS-OCs), comprise the Physical Sciences-Oncology Network (PS-ON). The foundation of the Physical Sciences-Oncology initiative is a high-risk, high-reward program that promotes a `physical sciences perspective' of cancer and fosters the convergence of physical science and cancer research by forming transdisciplinary teams of physical scientists (e.g., physicists, mathematicians, chemists, engineers, computer scientists) and cancer researchers (e.g., cancer biologists, oncologists, pathologists) who work closely together to advance our understanding of cancer. The collaborative PS-ON structure catalyzes transformative science through increased exchange of people, ideas, and approaches. PS-ON resources are leveraged to fund Trans-Network pilot projects to enable synergy and cross-testing of experimental and/or theoretical concepts. This session will include a brief PS-ON overview followed by a strategic discussion with the APS community to exchange perspectives on the progression of trans-disciplinary physical sciences in cancer research.

  9. A novel community detection method in bipartite networks

    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.

  10. Partner network communities – a resource of universities’ activities

    Romm Mark V.

    2016-01-01

    Full Text Available The network activity is not only part and parcel of the modern university, but it also demonstrates the level of its success. There appeared an urgent need for understanding the nature of universities’ network interactions and finding the most effective models of their network cooperation. The article analyzes partnership network communities with higher educational establishments (universities’ participation, which are being actively created nowadays. The conditions for successful network activities of a university in scientific, academic and professional network communities are presented.

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

    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.

  12. Navigating cancer network attractors for tumor-specific therapy

    Creixell, Pau; Schoof, Erwin; Erler, Janine Terra

    2012-01-01

    understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....

  13. Decoding network dynamics in cancer

    Linding, Rune

    2014-01-01

    Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...... and with an accuracy that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted molecular and biological studies have led to numerous pathway models of developmental and disease related processes. However, so far no global models have been derived from pathways, capable...

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

    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.

  15. Cluster synchronization in community network with hybrid coupling

    Yang, Lixin; Jiang, Jun; Liu, Xiaojun

    2016-01-01

    Highlights: • A community network model with hybrid coupling is proposed. • Control scheme is designed via combining adaptive external coupling strength and feedback control. • The influence of topology structure on synchronization of community network is discussed. - Abstract: A general model of community network with hybrid coupling is proposed in this paper. In the community network model with hybrid coupling, the inner connections are in the same type of coupling within the same community and in different types of coupling in different communities. The connections between different pair of communities are also nonidentical. Cluster synchronization of community network with hybrid coupling is investigated via adaptive couplings control scheme. Effective controllers are designed for constructing an effective control scheme and adjusting automatically the adaptive external coupling strength by taking external coupling strength as adaptive variables on a small fraction of network edges. Moreover, the impact of the topology on the synchronizability of community network is investigated. The numerical results reveal that the number of links between communities and the degree of the connector nodes have significant effects on the synchronization performance.

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

    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.

  17. Homophyly/kinship hypothesis: Natural communities, and predicting in networks

    Li, Angsheng; Li, Jiankou; Pan, Yicheng

    2015-02-01

    It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

  18. Emergence of communities and diversity in social networks.

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

    2017-03-14

    Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

  19. Prostate Cancer Biorepository Network (PCBN)

    2017-10-01

    are linked to clinical and outcome data and supported by an informatics infrastructure . In this 2nd year of operation the University of Washington...REPORT Unclassified b. ABSTRACT Unclassified c. THIS PAGE Unclassified Unclassified 19b. TELEPHONE NUMBER (include area code ) Standard Form 298...informatics infrastructure . Keywords Biorepository, prostate cancer, patient derived xenografts, rapid autopsy, biomarkers. Accomplishments The

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

    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.

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

    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.

  2. An exploration of fetish social networks and communities

    Fay, Damien; Haddadi, Hamed; Seto, Michael C.; Wang, Han; Kling, Christoph Carl

    2015-01-01

    Online Social Networks (OSNs) provide a venue for virtual interactions and relationships between individuals. In some communities, OSNs also facilitate arranging online meetings and relationships. FetLife, the worlds largest anonymous social network for the BDSM, fetish and kink communities, provides a unique example of an OSN that serves as an interaction space, community organizing tool, and sexual market. In this paper, we present a first look at the characteristics of European members of ...

  3. Network Analysis in Community Psychology: Looking Back, Looking Forward.

    Neal, Zachary P; Neal, Jennifer Watling

    2017-09-01

    Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra-individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology. Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology. © 2017 The Authors. American Journal of Community Psychology published by Wiley Periodicals, Inc. on behalf of Society for Community Research and Action.

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

    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...

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

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

    2008-01-01

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

  6. Predicting Community Evolution in Social Networks

    Stanisław Saganowski

    2015-05-01

    Full Text Available Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI and Group Evolution Discovery (GED. Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.

  7. Clustering coefficient and community structure of bipartite networks

    Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

    2008-12-01

    Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

  8. Facebook faith - social networking in a faith based community

    Lundqvist, K O; Lundqvist, Karsten Oster

    2009-01-01

    This paper views the increasing social networking as an efficient emerging ministry to the moveable generation. Through social network such as Facebook, ministry from a pastoral perspective can \\ud become more authentic and meaningful. Ministry is relational. Social Networking sites provide a strong platform to being part in other people’s life. Social networking and living online builds \\ud community beyond geographical boarders. Young adults and youths digital identity often reflects their ...

  9. Weighted Evolving Networks with Self-organized Communities

    Xie Zhou; Wang Xiaofan; Li Xiang

    2008-01-01

    In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of ν ≥ 1, γ > 2, and α > 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness

  10. Community structures and role detection in music networks

    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.

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

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

    2010-07-01

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

  12. Cluster synchronization for directed community networks via pinning partial schemes

    Hu Cheng; Jiang Haijun

    2012-01-01

    Highlights: ► Cluster synchronization for directed community networks is proposed by pinning partial schemes. ► Each community is considered as a whole. ► Several novel pinning criteria are derived based on the information of communities. ► A numerical example with simulation is provided. - Abstract: In this paper, we focus on driving a class of directed networks to achieve cluster synchronization by pinning schemes. The desired cluster synchronization states are no longer decoupled orbits but a set of un-decoupled trajectories. Each community is considered as a whole and the synchronization criteria are derived based on the information of communities. Several pinning schemes including feedback control and adaptive strategy are proposed to select controlled communities by analyzing the information of each community such as indegrees and outdegrees. In all, this paper answers several challenging problems in pinning control of directed community networks: (1) What communities should be chosen as controlled candidates? (2) How many communities are needed to be controlled? (3) How large should the control gains be used in a given community network to achieve cluster synchronization? Finally, an example with numerical simulations is given to demonstrate the effectiveness of the theoretical results.

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

    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.

  14. Postdiagnosis social networks and breast cancer mortality in the After Breast Cancer Pooling Project.

    Kroenke, Candyce H; Michael, Yvonne L; Poole, Elizabeth M; Kwan, Marilyn L; Nechuta, Sarah; Leas, Eric; Caan, Bette J; Pierce, John; Shu, Xiao-Ou; Zheng, Ying; Chen, Wendy Y

    2017-04-01

    Large social networks have been associated with better overall survival, though not consistently with breast cancer (BC)-specific outcomes. This study evaluated associations of postdiagnosis social networks and BC outcomes in a large cohort. Women from the After Breast Cancer Pooling Project (n = 9267) provided data on social networks within approximately 2 years of their diagnosis. A social network index was derived from information about the presence of a spouse/partner, religious ties, community ties, friendship ties, and numbers of living first-degree relatives. Cox models were used to evaluate associations, and a meta-analysis was used to determine whether effect estimates differed by cohort. Stratification by demographic, social, tumor, and treatment factors was performed. There were 1448 recurrences and 1521 deaths (990 due to BC). Associations were similar in 3 of 4 cohorts. After covariate adjustments, socially isolated women (small networks) had higher risks of recurrence (hazard ratio [HR], 1.43; 95% confidence interval [CI], 1.15-1.77), BC-specific mortality (HR, 1.64; 95% CI, 1.33-2.03), and total mortality (HR, 1.69; 95% CI, 1.43-1.99) than socially integrated women; associations were stronger in those with stage I/II cancer. In the fourth cohort, there were no significant associations with BC-specific outcomes. A lack of a spouse/partner (P = .02) and community ties (P = .04) predicted higher BC-specific mortality in older white women but not in other women. However, a lack of relatives (P = .02) and friendship ties (P = .01) predicted higher BC-specific mortality in nonwhite women only. In a large pooled cohort, larger social networks were associated with better BC-specific and overall survival. Clinicians should assess social network information as a marker of prognosis because critical supports may differ with sociodemographic factors. Cancer 2017;123:1228-1237. © 2016 American Cancer Society. © 2016 American Cancer Society.

  15. Information dynamics algorithm for detecting communities in networks

    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.

  16. Global and local targeted immunization in networks with community structure

    Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

    2015-01-01

    Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)

  17. Sampling from complex networks with high community structures.

    Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

    2012-06-01

    In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

  18. Epidemic spreading in time-varying community networks.

    Ren, Guangming; Wang, Xingyuan

    2014-06-01

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold qc. The epidemic will survive when q > qc and die when q epidemic spreading in complex networks with community structure.

  19. Non-coding RNA networks in cancer.

    Anastasiadou, Eleni; Jacob, Leni S; Slack, Frank J

    2018-01-01

    Thousands of unique non-coding RNA (ncRNA) sequences exist within cells. Work from the past decade has altered our perception of ncRNAs from 'junk' transcriptional products to functional regulatory molecules that mediate cellular processes including chromatin remodelling, transcription, post-transcriptional modifications and signal transduction. The networks in which ncRNAs engage can influence numerous molecular targets to drive specific cell biological responses and fates. Consequently, ncRNAs act as key regulators of physiological programmes in developmental and disease contexts. Particularly relevant in cancer, ncRNAs have been identified as oncogenic drivers and tumour suppressors in every major cancer type. Thus, a deeper understanding of the complex networks of interactions that ncRNAs coordinate would provide a unique opportunity to design better therapeutic interventions.

  20. Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

    Deng Liping; Wu Zhaoyan

    2012-01-01

    In this paper, cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated. Community networks with two kinds of topological structure are investigated. Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization. Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers. Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory, several simple and useful synchronization criteria are derived. Finally, numerical simulations are provided to verify the effectiveness of the derived results.

  1. Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

    Ting Liu

    Full Text Available The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

  2. Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

    Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

    2016-01-01

    The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

  3. A model for evolution of overlapping community networks

    Karan, Rituraj; Biswal, Bibhu

    2017-05-01

    A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

  4. Creating a "culture of research" in a community hospital: Strategies and tools from the National Cancer Institute Community Cancer Centers Program.

    Dimond, Eileen P; St Germain, Diane; Nacpil, Lianne M; Zaren, Howard A; Swanson, Sandra M; Minnick, Christopher; Carrigan, Angela; Denicoff, Andrea M; Igo, Kathleen E; Acoba, Jared D; Gonzalez, Maria M; McCaskill-Stevens, Worta

    2015-06-01

    identify and collect. The model requires a strong emphasis on outreach that challenges hospitals to improve and expand their reach, particularly into underrepresented populations and catchment areas. These efforts build on trust and a referral pipeline within the community which take time and significant commitment to establish. The National Cancer Institute Community Cancer Centers Program experience provides a relevant model to broadly address creating a culture of research in community hospitals that are increasingly networked via systems and consortiums. The strategies used align well with the National Cancer Institute-American Society of Clinical Oncology Accrual Symposium recommendations for patient-/community-, physician-/provider-, and site-/organizational-level approaches to clinical trials; they helped sites achieve organizational culture shifts that enhanced their cancer research programs. The National Cancer Institute Community Cancer Centers Program hospitals reported that the strategies were challenging to implement yet proved valuable as they provided useful metrics for programmatic assessment, planning, reporting, and growth. While focused on oncology trials, these concepts may be useful within other disease-focused research as well. © The Author(s) 2015.

  5. Extending a configuration model to find communities in complex networks

    Jin, Di; Hu, Qinghua; He, Dongxiao; Yang, Bo; Baquero, Carlos

    2013-01-01

    Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities. (paper)

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

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

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

  7. Hydraulic Network Modelling of Small Community Water Distribution ...

    Prof Anyata

    community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using. WaterCAD ..... Table 1: Criteria Relating Population to Water Demand (NWSP, 2000) ..... timely manner ... Department, Middle East Technical.

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

    Jin Qi

    2015-01-01

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

  9. Evolution properties of the community members for dynamic networks

    Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

    2017-03-01

    The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

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

    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.

  11. Exponential random graph models for networks with community structure.

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  12. Dynamical community structure of populations evolving on genotype networks

    Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

    2015-01-01

    Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

  13. Social networks and performance in distributed learning communities

    Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

  14. Taking Our Seat at the Table: Community Cancer Survivorship.

    Polo, Katie M; Smith, Caitlin

    Cancer survivors are at risk for occupational performance issues related to activities of daily living, instrumental activities of daily living, work, and social and community participation. Occupational therapy practitioners can address these performance issues by offering services within existing community cancer survivorship programs that focus on adaptive and compensatory strategies to facilitate meaningful lifestyles and optimize health and well-being. Occupational therapy services do not currently exist at these community sites, nor are occupational therapy practitioners recognized as providers in existing community cancer survivorship programs. Recognition of practitioners' distinct value in cancer survivorship, advocacy for occupational therapy services in the community, development of supporting documentation for occupational therapy's role in community survivorship, and research on the efficacy of interventions in community cancer survivorship are needed to expand occupational therapy's role with this growing population. Copyright © 2017 by the American Occupational Therapy Association, Inc.

  15. Improving clinical research and cancer care delivery in community settings: evaluating the NCI community cancer centers program

    Fennell Mary L

    2009-09-01

    Full Text Available Abstract Background In this article, we describe the National Cancer Institute (NCI Community Cancer Centers Program (NCCCP pilot and the evaluation designed to assess its role, function, and relevance to the NCI's research mission. In doing so, we describe the evolution of and rationale for the NCCCP concept, participating sites' characteristics, its multi-faceted aims to enhance clinical research and quality of care in community settings, and the role of strategic partnerships, both within and outside of the NCCCP network, in achieving program objectives. Discussion The evaluation of the NCCCP is conceptualized as a mixed method multi-layered assessment of organizational innovation and performance which includes mapping the evolution of site development as a means of understanding the inter- and intra-organizational change in the pilot, and the application of specific evaluation metrics for assessing the implementation, operations, and performance of the NCCCP pilot. The assessment of the cost of the pilot as an additional means of informing the longer-term feasibility and sustainability of the program is also discussed. Summary The NCCCP is a major systems-level set of organizational innovations to enhance clinical research and care delivery in diverse communities across the United States. Assessment of the extent to which the program achieves its aims will depend on a full understanding of how individual, organizational, and environmental factors align (or fail to align to achieve these improvements, and at what cost.

  16. Improving clinical research and cancer care delivery in community settings: evaluating the NCI community cancer centers program.

    Clauser, Steven B; Johnson, Maureen R; O'Brien, Donna M; Beveridge, Joy M; Fennell, Mary L; Kaluzny, Arnold D

    2009-09-26

    In this article, we describe the National Cancer Institute (NCI) Community Cancer Centers Program (NCCCP) pilot and the evaluation designed to assess its role, function, and relevance to the NCI's research mission. In doing so, we describe the evolution of and rationale for the NCCCP concept, participating sites' characteristics, its multi-faceted aims to enhance clinical research and quality of care in community settings, and the role of strategic partnerships, both within and outside of the NCCCP network, in achieving program objectives. The evaluation of the NCCCP is conceptualized as a mixed method multi-layered assessment of organizational innovation and performance which includes mapping the evolution of site development as a means of understanding the inter- and intra-organizational change in the pilot, and the application of specific evaluation metrics for assessing the implementation, operations, and performance of the NCCCP pilot. The assessment of the cost of the pilot as an additional means of informing the longer-term feasibility and sustainability of the program is also discussed. The NCCCP is a major systems-level set of organizational innovations to enhance clinical research and care delivery in diverse communities across the United States. Assessment of the extent to which the program achieves its aims will depend on a full understanding of how individual, organizational, and environmental factors align (or fail to align) to achieve these improvements, and at what cost.

  17. Supporting Communities in Programmable Grid Networks: gTBN

    Christea, M.L; Strijkers, R.J.; Marchal, D.; Gommans, L.; Laat, C. de; Meijer, R.J.

    2009-01-01

    Abstract—This paper presents the generalised Token Based Networking (gTBN) architecture, which enables dynamic binding of communities and their applications to specialised network services. gTBN uses protocol independent tokens to provide decoupling of authorisation from time of usage as well as

  18. Social Networks: Gated Communities or Free Cantons?

    CERN. Geneva

    2017-01-01

    Online social networks and other cloud-based services have concentrated the control of the web in the hands of a few corporations. Our personal data has been commodified, often without our knowledge or consent. Is there a way to retain all the benefits of social networking without giving up control of our data?

  19. Community Size Effects on Epidemic Spreading in Multiplex Social Networks

    Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

    2016-01-01

    The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explo...

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

    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.

  1. Home-School Links: Networking the Learning Community.

    1996

    The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

  2. New Ideas for Communities of Practice: Networks of Networks

    WimHugo

    mobile phones, and smart devices. We have used .... Google. ICSU WDS. GEO. Figure 2: Comparing Initiatives using Dimensions. Practical ... Practical implementation will require networks that are just complex enough to serve the purpose:.

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

    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.

  4. Reducing cancer risk in rural communities through supermarket interventions.

    McCool, Barent N; Lyford, Conrad P; Hensarling, Natalie; Pence, Barbara; McCool, Audrey C; Thapa, Janani; Belasco, Eric; Carter, Tyra M

    2013-09-01

    Cancer risk is high, and prevention efforts are often minimal in rural communities. Feasible means of encouraging lifestyles that will reduce cancer risk for residents of rural communities are needed. This project developed and tested a model that could be feasibly adopted by rural communities to reduce cancer risk. This model focuses on incorporating multi-faceted cancer risk education in the local supermarket. As the supermarket functions both as the primary food source and an information source in small rural communities, the supermarket focus encourages the development of a community environment supportive of lifestyles that should reduce residents' risk for cancer. The actions taken to implement the model and the challenges that communities would have in implementing the model are identified.

  5. Communities in Large Networks: Identification and Ranking

    Olsen, Martin

    2008-01-01

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

  6. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural...

  7. Multi-Relational Characterization of Dynamic Social Network Communities

    Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

    The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

  8. Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

    Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

    2013-01-01

    This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

  9. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

    Lan Liu

    2017-01-01

    Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qnetwork malware and provide a theoretical basis to reduce and prevent network security incidents.

  10. Stylized facts in social networks: Community-based static modeling

    Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

    2018-06-01

    The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

  11. Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

    Konnerup, Ulla; Dirckinck-Holmfeld, Lone

    2010-01-01

    This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...

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

    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)

  13. Social networking for web-based communities

    Issa, T.; Kommers, Petrus A.M.

    2013-01-01

    In the 21st century, a new technology was introduced to facilitate communication, collaboration, and interaction between individuals and businesses. This technology is called social networking; this technology is now part of Internet commodities like email, browsing and blogging. From the 20th

  14. Comparison and validation of community structures in complex networks

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  15. Epidemic spreading in time-varying community networks

    Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510665 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China)

    2014-06-15

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

  16. Epidemic spreading in time-varying community networks

    Ren, Guangming; Wang, Xingyuan

    2014-01-01

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q c . The epidemic will survive when q > q c and die when q  c . These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure

  17. Network Exposure and Homicide Victimization in an African American Community

    Wildeman, Christopher

    2014-01-01

    Objectives. We estimated the association of an individual’s exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Methods. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Results. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood’s population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one’s odds of being a homicide victim by 57%. Conclusions. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities. PMID:24228655

  18. Online Social Networks - Opportunities for Empowering Cancer Patients.

    Mohammadzadeh, Zeinab; Davoodi, Somayeh; Ghazisaeidi, Marjan

    2016-01-01

    Online social network technologies have become important to health and apply in most health care areas. Particularly in cancer care, because it is a disease which involves many social aspects, online social networks can be very useful. Use of online social networks provides a suitable platform for cancer patients and families to present and share information about their medical conditions, address their educational needs, support decision making, and help to coping with their disease and improve their own outcomes. Like any other new technologies, online social networks, along with many benefits, have some negative effects such as violation of privacy and publication of incorrect information. However, if these effects are managed properly, they can empower patients to manage cancer through changing behavioral patterns and enhancing the quality of cancer patients lives This paper explains some application of online social networks in the cancer patient care process. It also covers advantages and disadvantages of related technologies.

  19. Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Outcomes at a Community College

    Evans, Eliza D.; McFarland, Daniel A.; Rios-Aguilar, Cecilia; Deil-Amen, Regina

    2016-01-01

    Objective: This study explores the relationship between online social network involvement and academic outcomes among community college students. Prior theory hypothesizes that socio-academic moments are especially important for the integration of students into community colleges and that integration is related to academic outcomes. Online social…

  20. Collaboration: the Key to Establishing Community Networks in Regional Australia

    Wal Taylor

    2002-01-01

    Full Text Available Despite the promise of community involvement, cohesion and empowerment offered by local community networks (CN using Internet Technologies, few communities in regional Australia have been able to demonstrate sustainable and vibrant CN which demonstrate increased social, cultural or self-reliance capital. The Faculty of Informatics and Communication at Central Queensland University (CQU and a local council have established a formal alliance to establish the COIN (Community Informatics projects to research issues around this topic. This paper presents the initial findings from this work and draws conclusions for possible comparison with other international experience. The research focuses attention on community understanding and cohesion, local government priorities in a community with relatively low diffusion of the Internet and the competing demands in a regional university between traditional service provision in an increasingly competitive market and the needs of establishing outreach research for altruistic, industry establishment and commercial rationale.

  1. Virality Prediction and Community Structure in Social Networks

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-08-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  2. Identifying influential user communities on the social network

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

    2015-10-01

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

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

    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.

  4. Optimal Pricing Strategy for Wireless Social Community Networks

    Mazloumian, Amin; Manshaei, Mohammad Hossein; Felegyhazi, Mark; Hubaux, Jean-Pierre

    2008-01-01

    The increasing number of mobile applications fuels the demand for affordable and ubiquitous wireless access. The traditional wireless network technologies such as EV-DO or WiMAX provide this service but require a huge upfront investment in infrastructure and spectrum. On the contrary, as they do not have to face such an investment, social community operators rely on subscribers who constitute a community of users. The pricing strategy of the provided wireless access is an open problem for thi...

  5. Detecting Network Communities: An Application to Phylogenetic Analysis

    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

  6. Modeling information diffusion in time-varying community networks

    Cui, Xuelian; Zhao, Narisa

    2017-12-01

    Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

  7. Social network characteristics and cervical cancer screening among Quechua women in Andean Peru.

    Luque, John S; Opoku, Samuel; Ferris, Daron G; Guevara Condorhuaman, Wendy S

    2016-02-24

    Peru has high cervical cancer incidence and mortality rates compared to other Andean countries. Therefore, partnerships between governmental and international organizations have targeted rural areas of Peru to receive cervical cancer screening via outreach campaigns. Previous studies have found a relationship between a person's social networks and cancer screening behaviors. Screening outreach campaigns conducted by the nonprofit organization CerviCusco created an opportunity for a social network study to examine cervical cancer screening history and social network characteristics in a rural indigenous community that participated in these campaigns in 2012 and 2013. The aim of this study was to explore social network characteristics in this community related to receipt of cervical cancer screening following the campaigns. An egocentric social network questionnaire was used to collect cross-sectional network data on community participants. Each survey participant (ego) was asked to name six other women they knew (alters) and identify the nature of their relationship or tie (family, friend, neighbor, other), residential closeness (within 5 km), length of time known, frequency of communication, topics of conversation, and whether they lent money to the person, provided childcare or helped with transportation. In addition, each participant was asked to report the nature of the relationship between all alters identified (e.g., friend, family, or neighbor). Bivariate and multivariate analyses were used to explore the relationship between Pap test receipt at the CerviCusco outreach screening campaigns and social network characteristics. Bivariate results found significant differences in percentage of alter composition for neighbors and family, and for mean number of years known, mean density, and mean degree centrality between women who had received a Pap test (n = 19) compared to those who had not (n = 50) (p's < 0.05). The final logistic regression model was

  8. Connectivity and Nestedness in Bipartite Networks from Community Ecology

    Corso, Gilberto; De Araujo, A I Levartoski; De Almeida, Adriana M

    2011-01-01

    Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 - τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.

  9. Connectivity and Nestedness in Bipartite Networks from Community Ecology

    Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil); De Araujo, A I Levartoski [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara Av. Treze de Maio, 2081 - Benfica CEP 60040-531 - Fortaleza, CE (Brazil); De Almeida, Adriana M, E-mail: corso@cb.ufrn.br [Departamento de Botanica, Ecologia e Zoologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil)

    2011-03-01

    Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index {nu}. The index {nu} is found using the relation {nu} = 1 - {tau} where {tau} is the temperature of the adjacency matrix of the bipartite network. By its turn {tau} is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index {nu} is a function of the connectivities of the bipartite network. In addition we find a concise way to find {nu} which avoid cumbersome algorithm manupulation of the adjacency matrix.

  10. Fragmentation alters stream fish community structure in dendritic ecological networks.

    Perkin, Joshuah S; Gido, Keith B

    2012-12-01

    Effects of fragmentation on the ecology of organisms occupying dendritic ecological networks (DENs) have recently been described through both conceptual and mathematical models, but few hypotheses have been tested in complex, real-world ecosystems. Stream fishes provide a model system for assessing effects of fragmentation on the structure of communities occurring within DENs, including how fragmentation alters metacommunity dynamics and biodiversity. A recently developed habitat-availability measure, the "dendritic connectivity index" (DCI), allows for assigning quantitative measures of connectivity in DENs regardless of network extent or complexity, and might be used to predict fish community response to fragmentation. We characterized stream fish community structure in 12 DENs in the Great Plains, USA, during periods of dynamic (summer) and muted (fall) discharge regimes to test the DCI as a predictive model of fish community response to fragmentation imposed by road crossings. Results indicated that fish communities in stream segments isolated by road crossings had reduced species richness (alpha diversity) relative to communities that maintained connectivity with the surrounding DEN during summer and fall. Furthermore, isolated communities had greater dissimilarity (beta diversity) to downstream sites notisolated by road crossings during summer and fall. Finally, dissimilarity among communities within DENs decreased as a function of increased habitat connectivity (measured using the DCI) for summer and fall, suggesting that communities within highly connected DENs tend to be more homogeneous. Our results indicate that the DCI is sensitive to community effects of fragmentation in riverscapes and might be used by managers to predict ecological responses to changes in habitat connectivity. Moreover, our findings illustrate that relating structural connectivity of riverscapes to functional connectivity among communities might aid in maintaining metacommunity

  11. Model of community emergence in weighted social networks

    Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

    2009-04-01

    Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

  12. The Healthy Aging Research Network: Modeling Collaboration for Community Impact.

    Belza, Basia; Altpeter, Mary; Smith, Matthew Lee; Ory, Marcia G

    2017-03-01

    As the first Centers for Disease Control and Prevention (CDC) Prevention Research Centers Program thematic network, the Healthy Aging Research Network was established to better understand the determinants of healthy aging within older adult populations, identify interventions that promote healthy aging, and assist in translating research into sustainable community-based programs throughout the nation. To achieve these goals requires concerted efforts of a collaborative network of academic, community, and public health organizational partnerships. For the 2001-2014 Prevention Research Center funding cycles, the Healthy Aging Research Network conducted prevention research and promoted the wide use of practices known to foster optimal health. Organized around components necessary for successful collaborations (i.e., governance and infrastructure, shaping focus, community involvement, and evaluation and improvement), this commentary highlights exemplars that demonstrate the Healthy Aging Research Network's unique contributions to the field. The Healthy Aging Research Network's collaboration provided a means to collectively build capacity for practice and policy, reduce fragmentation and duplication in health promotion and aging research efforts, maximize the efficient use of existing resources and generate additional resources, and ultimately, create synergies for advancing the healthy aging agenda. This collaborative model was built upon a backbone organization (coordinating center); setting of common agendas and mutually reinforcing activities; and continuous communications. Given its successes, the Healthy Aging Research Network model could be used to create new and evaluate existing thematic networks to guide the translation of research into policy and practice. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

  13. Finding and testing network communities by lumped Markov chains.

    Piccardi, Carlo

    2011-01-01

    Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

  14. Finding and testing network communities by lumped Markov chains.

    Carlo Piccardi

    Full Text Available Identifying communities (or clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

  15. Temporal prediction of epidemic patterns in community networks

    Peng, Xiao-Long; Xu, Xin-Jian; Fu, Xinchu; Small, Michael

    2013-01-01

    Most previous studies of epidemic dynamics on complex networks suppose that the disease will eventually stabilize at either a disease-free state or an endemic one. In reality, however, some epidemics always exhibit sporadic and recurrent behaviour in one region because of the invasion from an endemic population elsewhere. In this paper we address this issue and study a susceptible–infected–susceptible epidemiological model on a network consisting of two communities, where the disease is endemic in one community but alternates between outbreaks and extinctions in the other. We provide a detailed characterization of the temporal dynamics of epidemic patterns in the latter community. In particular, we investigate the time duration of both outbreak and extinction, and the time interval between two consecutive inter-community infections, as well as their frequency distributions. Based on the mean-field theory, we theoretically analyse these three timescales and their dependence on the average node degree of each community, the transmission parameters and the number of inter-community links, which are in good agreement with simulations, except when the probability of overlaps between successive outbreaks is too large. These findings aid us in better understanding the bursty nature of disease spreading in a local community, and thereby suggesting effective time-dependent control strategies. (paper)

  16. Audit Trail Management System in Community Health Care Information Network.

    Nakamura, Naoki; Nakayama, Masaharu; Nakaya, Jun; Tominaga, Teiji; Suganuma, Takuo; Shiratori, Norio

    2015-01-01

    After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system.

  17. Water Distribution Network Modelling of a Small Community using ...

    ... of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using WaterCAD simulator. The analysis included a review of pressures, velocities and head loss gradients under steady state average day demand, maximum day demand conditions, and fire flow under maximum day ...

  18. Social Networks and Performance in Distributed Learning Communities

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

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

    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.

  20. Vietnamese American women’s beliefs and perceptions on cervical cancer, cervical cancer screening, and cancer prevention vaccines: A community-based participatory study

    Connie Kim Yen Nguyen-Truong

    2017-12-01

    Full Text Available Cervical cancer remains commonly diagnosed in Vietnamese American women. Despite efforts to increase cervical cancer screening among Vietnamese American women, participation rates are persistently lower than the national goal. The objective of this study is to explore beliefs of Vietnamese American women about cervical cancer, cervical cancer screening, and cancer prevention vaccines. A qualitative descriptive investigation captured group perceptions about meaning and beliefs of cervical cancer, screening, and cancer prevention vaccines, and participants’ stories using a community-based participatory research approach. Forty Vietnamese American women were recruited from the Portland, Oregon metropolitan area into four focus groups. Using a process of directed content analysis, focus group transcripts were coded for themes. We found that cervical cancer continues to be a difficult topic to discuss, and Vietnamese American women may not bring the topic up themselves to their health care providers. Some women experienced intense emotions of fear or shame of having their cervix examined. Women delayed seeking cervical cancer screening and needed to have early warning signs, which guided them as to when to seek health care. Women focused on cleanliness through vaginal and/or perineal washing as primary prevention for cervical cancer. There were limited awareness and knowledge about cancer prevention vaccines, specifically the human papillomavirus. Some women relied heavily on their informal social networks of family, friends, or community for health knowledge. Fear and misunderstanding dominated the beliefs of Vietnamese American women about cervical cancer screening and prevention. These findings underscored the importance of having culturally-specific findings, which will inform a multicomponent intervention to promote cervical cancer screening and cancer prevention vaccine uptake within this population.

  1. Anonymity versus privacy: Selective information sharing in online cancer communities

    Frost, J.H.; Vermeulen, I.E.; Beekers, N.

    2014-01-01

    Background: Active sharing in online cancer communities benefits patients. However, many patients refrain from sharing health information online due to privacy concerns. Existing research on privacy emphasizes data security and confidentiality, largely focusing on electronic medical records. Patient

  2. National Native American Breast Cancer Survivor's Network

    Burhansstipanov, Linda

    2002-01-01

    .... The purpose of this project is to improve the survival from breast cancer and quality of life after being diagnosed with breast cancer for both the patient and loved ones of the cancer patient...

  3. National Native American Breast Cancer Survivor's Network

    Burhansstipanov, Linda

    2003-01-01

    .... The purpose of this project is to improve the survival from breast cancer and quality of life after being diagnosed with breast cancer for both the patient and loved ones of the cancer patient...

  4. Hyperbolic mapping of complex networks based on community information

    Wang, Zuxi; Li, Qingguang; Jin, Fengdong; Xiong, Wei; Wu, Yao

    2016-08-01

    To improve the hyperbolic mapping methods both in terms of accuracy and running time, a novel mapping method called Community and Hyperbolic Mapping (CHM) is proposed based on community information in this paper. Firstly, an index called Community Intimacy (CI) is presented to measure the adjacency relationship between the communities, based on which a community ordering algorithm is introduced. According to the proposed Community-Sector hypothesis, which supposes that most nodes of one community gather in a same sector in hyperbolic space, CHM maps the ordered communities into hyperbolic space, and then the angular coordinates of nodes are randomly initialized within the sector that they belong to. Therefore, all the network nodes are so far mapped to hyperbolic space, and then the initialized angular coordinates can be optimized by employing the information of all nodes, which can greatly improve the algorithm precision. By applying the proposed dual-layer angle sampling method in the optimization procedure, CHM reduces the time complexity to O(n2) . The experiments show that our algorithm outperforms the state-of-the-art methods.

  5. Human cancer protein-protein interaction network: a structural perspective.

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  6. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

    Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

    2018-01-06

    Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

  7. On a new concept of community: social networks, personal communities and collective intelligence

    Rogério da Costa

    2006-01-01

    Full Text Available This text essentially deals with the transmutation of the concept of "community" into "social networks". This change is due largely to the boom of virtual communities in cyberspace, a fact that has generated a number of studies not only on this new way of weaving a society, but also on the dynamic structure of communication networks. At the core of this transformation, concepts such as social capital, trust and partial sympathy are called upon, to enable us to think about the new forms of association that regulate human activity in our time.

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

    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.

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

    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.

  10. A Community Network of 100 Black Carbon Sensors

    Preble, C.; Kirchstetter, T.; Caubel, J.; Cados, T.; Keeling, C.; Chang, S.

    2017-12-01

    We developed a low-cost black carbon sensor, field tested its performance, and then built and deployed a network of 100 sensors in West Oakland, California. We operated the network for 100 days beginning mid-May 2017 to measure spatially resolved black carbon concentrations throughout the community. West Oakland is a San Francisco Bay Area mixed residential and industrial community that is adjacent to regional port and rail yard facilities and surrounded by major freeways. As such, the community is affected by diesel particulate matter emissions from heavy-duty diesel trucks, locomotives, and ships associated with freight movement. In partnership with Environmental Defense Fund, the Bay Area Air Quality Management District, and the West Oakland Environmental Indicators Project, we deployed the black carbon monitoring network outside of residences and business, along truck routes and arterial streets, and at upwind locations. The sensor employs the filter-based light transmission method to measure black carbon and has good precision and correspondence with current commercial black carbon instruments. Throughout the 100-day period, each of the 100 sensors transmitted data via a cellular network. A MySQL database was built to receive and manage the data in real-time. The database included diagnostic features to monitor each sensor's operational status and facilitate the maintenance of the network. Spatial and temporal patterns in black carbon concentrations will be presented, including patterns around industrial facilities, freeways, and truck routes, as well as the relationship between neighborhood concentrations and the BAAQMD's monitoring site. Lessons learned during this first of its kind black carbon monitoring network will also be shared.

  11. Guifi.net: Security analysis of a heterogeneous community network

    Ramos García, Patricia

    2018-01-01

    Guifi.net is a heterogeneous community network that brings Internet to rural areas or vulnerable groups. This opens the door to many advances, but encompasses some risks as well. The aim of this project is to assess the general security of Guifi.net from tests performed on a key network element: the router. In particular, MikroTik and Ubiquiti are the most used makes in Guifi.net and hence, the target of this project. Basic, yet important, security settings are tested. On the plus side, the ...

  12. Who Networks? The Social Psychology of Virtual Communities

    2004-06-01

    Massively-Multiplayer Online Role-Playing Game ( MMORPG ). One of the strongest attractions of EverQuest is the opportunity to engage in fantasy identity...another but,you can catch me mostly in 30’s but if i am not there you can’t catch me anywhere. I am addicted to Pogo too :oP (sic) However, community...their arrest. 1. Network Capital In order to feed their addiction using the Internet, pedophiles display all of the elements of network capital

  13. Interest communities and flow roles in directed networks: the Twitter network of the UK riots.

    Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N; Barahona, Mauricio

    2014-12-06

    Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.

  14. A community of practice: librarians in a biomedical research network.

    De Jager-Loftus, Danielle P; Midyette, J David; Harvey, Barbara

    2014-01-01

    Providing library and reference services within a biomedical research community presents special challenges for librarians, especially those in historically lower-funded states. These challenges can include understanding needs, defining and communicating the library's role, building relationships, and developing and maintaining general and subject specific knowledge. This article describes a biomedical research network and the work of health sciences librarians at the lead intensive research institution with librarians from primarily undergraduate institutions and tribal colleges. Applying the concept of a community of practice to a collaborative effort suggests how librarians can work together to provide effective reference services to researchers in biomedicine.

  15. Efficient community-based control strategies in adaptive networks

    Yang Hui; Tang Ming; Zhang Haifeng

    2012-01-01

    Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans. (paper)

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

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

    2018-02-01

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

  17. Trends in intensity modulated radiation therapy use for locally advanced rectal cancer at National Comprehensive Cancer Network centers

    Marsha Reyngold, MD, PhD; Joyce Niland, PhD; Anna ter Veer, MS; Tanios Bekaii-Saab, MD; Lily Lai, MD; Joshua E. Meyer, MD; Steven J. Nurkin, MD, MS; Deborah Schrag, MD, MPH; John M. Skibber, MD, FACS; Al B. Benson, MD; Martin R. Weiser, MD; Christopher H. Crane, MD; Karyn A. Goodman, MD, MS

    2018-01-01

    Purpose: Intensity modulated radiation therapy (IMRT) has been rapidly incorporated into clinical practice because of its technological advantages over 3-dimensional conformal radiation therapy (CRT). We characterized trends in IMRT utilization in trimodality treatment of locally advanced rectal cancer at National Comprehensive Cancer Network cancer centers between 2005 and 2011. Methods and materials: Using the prospective National Comprehensive Cancer Network Colorectal Cancer Database, ...

  18. Cancer signaling networks and their implications for personalized medicine

    Creixell, Pau

    Amongst the unique features of cancer cells perhaps the most crucial one is the change in the cellular decision-making process. While both non-cancer and cancer cells are constantly integrating different external cues that reach them and computing cellular decisions (e.g. proliferation or apoptosis......) based on the integration of these cues; this integration and consequently the cellular decisions taken by cancer cells are arguably very distinct from the decisions that would be expected from non-cancer cells. Since cellular signaling networks and its different states are the computational circuits...

  19. Community, Collective or Movement? Evaluating Theoretical Perspectives on Network Building

    Spitzer, W.

    2015-12-01

    Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. What is the most useful theoretical model for conceptualizing the work of the NNOCCI community? This presentation will examine the pros and cons of three perspectives -- community of practice, collective impact, and social movements. The community of practice approach emphasizes use of common tools, support for practice, social learning, and organic development of leadership. A collective impact model focuses on defining common outcomes, aligning activities toward a common goal, structured collaboration. A social movement emphasizes building group identity and creating a sense of group efficacy. This presentation will address how these models compare in terms of their utility in program planning and evaluation, their fit with the unique characteristics of the NNOCCI community, and their relevance to our program goals.

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

    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.

  1. Mass media influence spreading in social networks with community structure

    Candia, Julián; Mazzitello, Karina I.

    2008-07-01

    We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

  2. Cancer awareness among community pharmacist: a systematic review.

    Mensah, Kofi Boamah; Oosthuizen, Frasia; Bonsu, Adwoa Bemah

    2018-03-16

    The WHO recognises that community pharmacists are the most accessible healthcare professionals to the general public. Most patients regularly visit community pharmacies for health information and also seek advice from pharmacists with respect to signs and symptoms of cancer. As readily accessible health care professionals, community pharmacists are also in the best position to include cancer-screening initiatives into their practice. Pharmacists are therefore in a good position to raise awareness when they counsel people who buy over-the-counter medication for the control of possible cancer-related symptoms. The aim of this review was to critically appraise evidence gathered from studies that; (1) explore or assess knowledge of community pharmacist on signs and symptoms of cancer, (2) explore or assess knowledge of community pharmacist on cancer screening. EMBASE (ovid), CINAHL (EBSCOhost) and MEDLINE (EBSCOhost) were systematically searched for studies conducted between 2005 to July 2017. Studies that focused on knowledge of community pharmacist in cancer screening, signs and symptoms were included. A total of 1538 articles were identified from the search, of which 4 out of the 28 potentially relevant abstracts were included in the review. Findings of the selected studies revealed lack of sufficient knowledge on breast cancer screening, signs and symptoms. Both studies attributed knowledge limitation as the cause of reason for the key findings of their studies. The selected studies focused largely on breast cancer, which hinder the generalizability and transferability of the findings. Hence there is a need for more studies to be conducted in this area to draw a better conclusion.

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

    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.

  4. Community-Based Multidisciplinary Computed Tomography Screening Program Improves Lung Cancer Survival.

    Miller, Daniel L; Mayfield, William R; Luu, Theresa D; Helms, Gerald A; Muster, Alan R; Beckler, Vickie J; Cann, Aaron

    2016-05-01

    Lung cancer is the most common cause of cancer deaths in the United States. Overall survival is less than 20%, with the majority of patients presenting with advanced disease. The National Lung Screening Trial, performed mainly in academic medical centers, showed that cancer mortality can be reduced with computed tomography (CT) screening compared with chest radiography in high-risk patients. To determine whether this survival advantage can be duplicated in a community-based multidisciplinary thoracic oncology program, we initiated a CT scan screening program for lung cancer within an established health care system. In 2008, we launched a lung cancer CT screening program within the WellStar Health System (WHS) consisting of five hospitals, three health parks, 140 outpatient medical offices, and 12 imaging centers that provide care in a five-county area of approximately 1.4 million people in Metro-Atlanta. Screening criteria incorporated were the International Early Lung Cancer Action Program (2008 to 2010) and National Comprehensive Cancer Network guidelines (2011 to 2013) for moderate- and high-risk patients. A total of 1,267 persons underwent CT lung cancer screening in WHS from 2008 through 2013; 53% were men, 87% were 50 years of age or older, and 83% were current or former smokers. Noncalcified indeterminate pulmonary nodules were found in 518 patients (41%). Thirty-six patients (2.8%) underwent a diagnostic procedure for positive findings on their CT scan; 30 proved to have cancer, 28 (2.2%) primary lung cancer and 2 metastatic cancer, and 6 had benign disease. Fourteen patients (50%) had their lung cancer discovered on their initial CT scan, 11 on subsequent scans associated with indeterminate pulmonary nodules growth and 3 patients who had a new indeterminate pulmonary nodules. Only 15 (54%) of these 28 patients would have qualified as a National Lung Screening Trial high-risk patient; 75% had stage I or II disease. Overall 5-year survival was 64% and 5-year

  5. The role of organizational affiliations and research networks in the diffusion of breast cancer treatment innovation.

    Carpenter, William R; Reeder-Hayes, Katherine; Bainbridge, John; Meyer, Anne-Marie; Amos, Keith D; Weiner, Bryan J; Godley, Paul A

    2011-02-01

    The National Institutes of Health (NIH) sees provider-based research networks and other organizational linkages between academic researchers and community practitioners as promising vehicles for accelerating the translation of research into practice. This study examines whether organizational research affiliations and teaching affiliations are associated with accelerated diffusion of sentinel lymph node biopsy (SLNB), an innovation in the treatment of early-stage breast cancer. Surveillance Epidemiology and End Results-Medicare data were used to examine the diffusion of SLNB for treatment of early-stage breast cancer among women aged 65 years and older diagnosed between 2000 and 2002, shortly after Medicare approved and began reimbursing for the procedure. In this population, patients treated at an organization affiliated with a research network--the American College of Surgeons Oncology Group (ACOSOG) or other National Cancer Institute (NCI) cooperative groups--were more likely to receive the innovative treatment (SLNB) than patients treated at unaffiliated organizations (odds ratio: 2.70, 95% confidence interval: 1.77-4.12; odds ratio: 1.84, 95% confidence interval: 1.26-2.69, respectively). Neither hospital teaching status nor surgical volume was significantly associated with differences in SLNB use. Patients who receive cancer treatment at organizations affiliated with cancer research networks have an enhanced probability of receiving SLNB, an innovative procedure that offers the promise of improved patient outcomes. Study findings support the NIH Roadmap and programs such as the NCI's Community Clinical Oncology Program, as they seek to accelerate the translation of research into practice by simultaneously accelerating and broadening cancer research in the community.

  6. American Indian Women Cancer Survivor's Needs and Preferences: Community Support for Cancer Experiences.

    Burnette, Catherine E; Roh, Soonhee; Liddell, Jessica; Lee, Yeon-Shim

    2018-03-15

    Cancer (the focus of this inquiry) is the leading cause of death among American Indian and Alaska Native women. The purpose of this study was to identify American Indian women cancer survivors' needs and preferences related to community supports for their cancer experience. This qualitative study examined female American Indian cancer survivors' needs and preferences about community support. The sample included 43 American Indian women cancer survivors (the types of cancer survivors included cervical cancer: n = 14; breast cancer: n = 14; and colon and other types: n = 15) residing in the Northern Plains region, in the state of South Dakota. Data were analyzed using qualitative content analysis and were collected between June of 2014 and February of 2015. When asked about their needs and preferences, 82% of participants (n = 35) of female American Indian cancer survivors reported at least one of the following most commonly reported themes: cancer support groups (n = 31, 72%), infrastructure for community support (n = 17, 40%), and cancer education (n = 11, 26%). In addition to the aforementioned themes, 33% of participants (n = 14) indicated the need for an improved healthcare system, with 11% (n = 5) of participants expressly desiring the integration of spirituality and holistic healing options. The majority of American Indian women cancer survivor participants of this study identified a need for more community-based support systems and infrastructures to aid with the cancer survivor experience. Results warrant a community approach to raise awareness, education, and support for American Indian cancer survivors.

  7. Vortex network community based reduced-order force model

    Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya; Taira, Kunihiko

    2017-11-01

    We characterize the vortical wake interactions by utilizing network theory and cluster-based approaches, and develop a data-inspired unsteady force model. In the present work, the vortical interaction network is defined by nodes representing vortical elements and the edges quantified by induced velocity measures amongst the vortices. The full vorticity field is reduced to a finite number of vortical clusters based on network community detection algorithm, which serves as a basis for a skeleton network that captures the essence of the wake dynamics. We use this reduced representation of the wake to develop a data-inspired reduced-order force model that can predict unsteady fluid forces on the body. The overall formulation is demonstrated for laminar flows around canonical bluff body wake and stalled flow over an airfoil. We also show the robustness of the present network-based model against noisy data, which motivates applications towards turbulent flows and experimental measurements. Supported by the National Science Foundation (Grant 1632003).

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

    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.

  9. Wisconsin’s Environmental Public Health Tracking Network: Information Systems Design for Childhood Cancer Surveillance

    Hanrahan, Lawrence P.; Anderson, Henry A.; Busby, Brian; Bekkedal, Marni; Sieger, Thomas; Stephenson, Laura; Knobeloch, Lynda; Werner, Mark; Imm, Pamela; Olson, Joseph

    2004-01-01

    In this article we describe the development of an information system for environmental childhood cancer surveillance. The Wisconsin Cancer Registry annually receives more than 25,000 incident case reports. Approximately 269 cases per year involve children. Over time, there has been considerable community interest in understanding the role the environment plays as a cause of these cancer cases. Wisconsin’s Public Health Information Network (WI-PHIN) is a robust web portal integrating both Health Alert Network and National Electronic Disease Surveillance System components. WI-PHIN is the information technology platform for all public health surveillance programs. Functions include the secure, automated exchange of cancer case data between public health–based and hospital-based cancer registrars; web-based supplemental data entry for environmental exposure confirmation and hypothesis testing; automated data analysis, visualization, and exposure–outcome record linkage; directories of public health and clinical personnel for role-based access control of sensitive surveillance information; public health information dissemination and alerting; and information technology security and critical infrastructure protection. For hypothesis generation, cancer case data are sent electronically to WI-PHIN and populate the integrated data repository. Environmental data are linked and the exposure–disease relationships are explored using statistical tools for ecologic exposure risk assessment. For hypothesis testing, case–control interviews collect exposure histories, including parental employment and residential histories. This information technology approach can thus serve as the basis for building a comprehensive system to assess environmental cancer etiology. PMID:15471739

  10. Development of a virtual multidisciplinary lung cancer tumor board in a community setting.

    Stevenson, Marvaretta M; Irwin, Tonia; Lowry, Terry; Ahmed, Maleka Z; Walden, Thomas L; Watson, Melanie; Sutton, Linda

    2013-05-01

    Creating an effective platform for multidisciplinary tumor conferences can be challenging in the rural community setting. The Duke Cancer Network created an Internet-based platform for a multidisciplinary conference to enhance the care of patients with lung cancer. This conference incorporates providers from different physical locations within a rural community and affiliated providers from a university-based cancer center 2 hours away. An electronic Web conferencing tool connects providers aurally and visually. Conferences were set up using a commercially available Web conferencing platform. The video platform provides a secure Web site coupled with a secure teleconference platform to ensure patient confidentiality. Multiple disciplines are invited to participate, including radiology, radiation oncology, thoracic surgery, pathology, and medical oncology. Participants only need telephone access and Internet connection to participate. Patient histories and physicals are presented, and the Web conferencing platform allows radiologic and histologic images to be reviewed. Treatment plans for patients are discussed, allowing providers to coordinate care among the different subspecialties. Patients who need referral to the affiliated university-based cancer center for specialized services are identified. Pertinent treatment guidelines and journal articles are reviewed. On average, there are 10 participants with one to two cases presented per session. The use of a Web conferencing platform allows subspecialty providers throughout the community and hours away to discuss lung cancer patient cases. This platform increases convenience for providers, eliminating travel to a central location. Coordination of care for patients requiring multidisciplinary care is facilitated, shortening evaluation time before definitive treatment plan.

  11. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  12. The role of community-based and philanthropic organizations in meeting cancer patient and caregiver needs.

    Shelby, Rebecca A; Taylor, Kathryn L; Kerner, Jon F; Coleman, Ellen; Blum, Diane

    2002-01-01

    We examined information from community-based and philanthropic organizations to document the cancer-related services that are currently available, establish which services are still needed, and determine who utilizes these formal support networks. In Phase I, 32 of 41 eligible organizations participated in a survey conducted from December 1999 to March 2000. The most common mission focus among participating organizations was information/referral-centered. The most common services provided were referrals to information resources and provision of cancer-related information. Only two of the organizations in Phase I provided client demographic information and both indicated that client populations were predominantly white, female, and over age 40. Phase II of the study involved analyzing patient data from Cancer Care, Inc., a national service organizations for cancer patients. Between 1983 and 1997, there were 2,714 prostate cancer patients and 9,451 breast cancer patients included in the Cancer Care database. Their most commonly reported problems were related to personal adjustment to illness, financial, home care, and transportation needs. There were significant differences in problems reported depending upon age and disease status. In addition, the results of this study support the idea that those at highest risk for developing and dying of cancer are the least likely to utilize formal support networks. Further, a gap in service provision for assistance with practical needs (e.g., transportation, home care, child care, psychosocial support) was identified. Due to the increasing use of outpatient care for cancer patients, a greater demand for practical assistance can be expected in the future. The availability of practical services will need to be increased in order to effectively meet cancer patient needs.

  13. Multilabel user classification using the community structure of online networks.

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  14. Multilabel user classification using the community structure of online networks.

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  15. Time, trust, and transparency: Lessons learned from collecting blood biospecimens for cancer research from the Asian American community.

    Dang, Julie H T; Chen, Moon S

    2018-04-01

    Biospecimens from racially diverse groups are needed to advance cancer research. The Asian American Cancer Education Study was developed to increase the number and proportion of blood biospecimen donations from Asian Americans for cancer research. The authors' targeted approach included 2 types of community engagement, in-reach (within institution to Asian American patients with cancer) and outreach (external to institution to the general Asian American community). Participants received in-language biospecimen education followed by the opportunity to donate blood biospecimens. Outreach participants donated through our community biospecimen blood drives, and in-reach participants consented to donating an extra tube of blood during their routine blood draws as a patient. Donated blood biospecimens were spun down to serum and plasma to be stored in a biorepository or were sent to the laboratory to test for cancer-related risk factors. Three hundred eighty-eight Asian Americans donated 1127 blood biospecimens for cancer research. Four hundred twenty tubes of plasma and serum are currently being stored at the cancer center's biorepository, 39 tubes have been used for cancer genomic research, and 668 tubes were used to characterize cancer-related risk factors. Building upon the past decade of the National Cancer Institute-funded Asian American Network for Cancer Awareness, Research, and Training's foundation of trust and service among Asian Americans, researchers were able to leverage relationships not only to introduce the idea of biospecimen contribution to the community but to also exceed expectations with regard to the quantity of blood biospecimens collected from Asian Americans. Cancer 2018;124:1614-21. © 2018 American Cancer Society. © 2018 American Cancer Society.

  16. The Internet and Community Networks: Case Studies of Five U.S. Cities

    Horrigan, John B.

    2001-01-01

    This paper looks at five U.S. cities (Austin, Cleveland, Nashville, Portland, and Washington, DC) and explores strategies being employed by community activists and local governments to create and sustain community networking projects. In some cities, community networking initiatives are relatively mature, while in others they are in early or intermediate stages. The paper looks at several factors that help explain the evolution of community networks in cities: 1) Local government support; 2) ...

  17. Epidemic spreading on complex networks with overlapping and non-overlapping community structure

    Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

    2015-02-01

    Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities' effect interacts with the average degree's effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

  18. Analysis of metastasis associated signal regulatory network in colorectal cancer.

    Qi, Lu; Ding, Yanqing

    2018-06-18

    Metastasis is a key factor that affects the survival and prognosis of colorectal cancer patients. To elucidate molecular mechanism associated with the metastasis of colorectal cancer, genes related to the metastasis time of colorectal cancer were screened. Then, a network was constructed with this genes. Data was obtained from colorectal cancer expression profile. Molecular mechanism elucidated the time of tumor metastasis and the expression of genes related to colorectal cancer. We found that metastasis-promoting and metastasis-inhibiting networks included protein hubs of high connectivity. These protein hubs were components of organelles. Some ribosomal proteins promoted the metastasis of colorectal cancer. In some components of organelles, such as proteasomes, mitochondrial ribosome, ATP synthase, and splicing factors, the metastasis of colorectal cancer was inhibited by some sections of these organelles. After performing survival analysis of proteins in organelles, joint survival curve of proteins was constructed in ribosomal network. This joint survival curve showed metastasis was promoted in patients with colorectal cancer (P = 0.0022939). Joint survival curve of proteins was plotted against proteasomes (P = 7 e-07), mitochondrial ribosome (P = 0.0001157), ATP synthase (P = 0.0001936), and splicing factors (P = 1.35e-05). These curves indicate that metastasis of colorectal cancer can be inhibited. After analyzing proteins that bind with organelle components, we also found that some proteins were associated with the time of colorectal cancer metastasis. Hence, different cellular components play different roles in the metastasis of colorectal cancer. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Increasing colon cancer testing in rural Colorado: evaluation of the exposure to a community-based awareness campaign

    Norman Ned

    2009-08-01

    Full Text Available Abstract Background Despite effective prevention and early detection screening methods, colorectal cancer is the second leading cause of cancer death in the United States. Colorectal cancer screening community-based interventions are rare, and the literature lacks information about community-based intervention processes. Using participatory research methods, the High Plains Research Network developed a community-based awareness and educational intervention to increase colorectal cancer screening rates in rural northeastern Colorado. This study describes the program components and implementation and explores whether the target population was exposed to the intervention, the reach of the individual intervention components, and the effect on screening intentions. Methods A random digit dial survey was conducted of residents age 40 and older in the first 3 communities to receive the intervention to estimate exposure to the intervention and its effect on colorectal cancer screening intentions. Results Exposure to at least intervention component was reported by 68% of respondents (n = 460. As the level of exposure increased, intentions to talk to a doctor about colorectal cancer screening increased significantly more in respondents who had not been tested in the past 5 years than those who had (p = .025. Intentions to get tested increased significantly in both groups at the same rate as level of exposure increased (p Conclusion Using local community members led to the successful implementation of the intervention. Program materials and messages reached a high percentage of the target population and increased colorectal cancer screening intentions.

  20. Proceedings of the Neural Network Workshop for the Hanford Community

    Keller, P.E.

    1994-01-01

    These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

  1. Community strategies to address cancer disparities in Appalachian Kentucky.

    Schoenberg, Nancy E; Howell, Britteny M; Fields, Nell

    2012-01-01

    Central Appalachian residents suffer disproportionate health disparities, including an all-cancer mortality rate 17% higher than the general population. During 10 focus groups and 19 key informant interviews, 91 Appalachian residents identified cancer screening challenges and strategies. Challenges included (1) inadequate awareness of screening need, (2) insufficient access to screening, and (3) lack of privacy. Strategies included (1) witnessing/storytelling, (2) capitalizing on family history, (3) improving publicity about screening resources, (4) relying on lay health advisors, and (5) bundling preventive services. These insights shaped our community-based participatory research intervention and offered strategies to others working in Appalachia, rural locales, and other traditionally underserved communities.

  2. Community and Social Network Sites as Technology Enhanced Learning Environments

    Ryberg, Thomas; Christiansen, Ellen

    2008-01-01

    This paper examines the affordance of the Danish social networking site Mingler.dk for peer-to-peer learning and development. With inspiration from different theoretical frameworks, the authors argue how learning and development in such social online systems can be conceptualised and analysed....... Theoretically the paper defines development in accordance with Vygotsky's concept of the zone of proximal development, and learning in accordance with Wenger's concept of communities of practice. The authors suggest analysing the learning and development taking place on Mingler.dk by using these concepts...... supplemented by the notion of horizontal learning adopted from Engestrm and Wenger. Their analysis shows how horizontal learning happens by crossing boundaries between several sites of engagement, and how the actors' multiple membership enables the community members to draw on a vast amount of resources from...

  3. Analysis of the communities of an urban mobile phone network.

    Botta, Federico; Del Genio, Charo I

    2017-01-01

    Being able to characterise the patterns of communications between individuals across different time scales is of great importance in understanding people's social interactions. Here, we present a detailed analysis of the community structure of the network of mobile phone calls in the metropolitan area of Milan revealing temporal patterns of communications between people. We show that circadian and weekly patterns can be found in the evolution of communities, presenting evidence that these cycles arise not only at the individual level but also at that of social groups. Our findings suggest that these trends are present across a range of time scales, from hours to days and weeks, and can be used to detect socially relevant events.

  4. Development of community plans to enhance survivorship from colorectal cancer: community-based participatory research in rural communities.

    Lengerich, Eugene J; Kluhsman, Brenda C; Bencivenga, Marcyann; Allen, Regina; Miele, Mary Beth; Farace, Elana

    2007-09-01

    In 2002, 10.4% of the 10 million persons alive who have ever been diagnosed with cancer had colorectal cancer (CRC). Barriers, such as distance, terrain, access to care and cultural differences, to CRC survivorship may be especially relevant in rural communities. We tested the hypothesis that teams from rural cancer coalitions and hospitals would develop a Community Plan (CP) to enhance CRC survivorship. We used community-based participatory research and the PRECEDE-PROCEED model to train teams from rural cancer coalitions and hospitals in Pennsylvania and New York. We measured knowledge at three points in time and tested the change with McNemar's test, corrected for multiple comparisons (p < 0.0167). We also conducted a qualitative review of the CP contents. Fourteen (93.3%) of the 15 coalitions or hospitals initially recruited to the study completed a CP. Knowledge in public health, sponsorship of A National Action Plan for Cancer Survivorship, and CRC survivorship and treatment increased. Teams identified perceived barriers and community assets. All teams planned to increase awareness of community assets and almost all planned to enhance treatment-related care and psychosocial care for the CRC survivor; 50% planned to enhance primary care and CRC screening. The study demonstrated the interest and ability of rural organizations to plan to enhance CRC survivorship, including linkage of CRC survivorship to primary care. Rural cancer coalitions and hospitals may be a vehicle to develop local action for A National Action Plan. Access to more comprehensive care for CRC cancer survivors in rural communities appears to be facilitated by the community-based initiative described and investigated in this study. Efforts such as these could be replicated in other rural communities and may impact the care and quality of life of survivors with many types of cancers. While access to health services may be increased through community-based initiatives, we still need to measure

  5. Pathway and network analysis of cancer genomes

    Creixell, Pau; Reimand, Jueri; Haider, Syed

    2015-01-01

    Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been...

  6. Anonymity versus privacy: Selective information sharing in online cancer communities

    Frost, J.H.; Vermeulen, I.E.; Beekers, N.

    2014-01-01

    Background Active sharing in online cancer communities benefits patients. However, many patients refrain from sharing health information online due to privacy concerns. Existing research on privacy emphasizes data security and confidentiality, largely focusing on electronic medical records. Patient preferences around information sharing in online communities remain poorly understood. Consistent with the privacy calculus perspective adopted from e-commerce research, we suggest that patients ap...

  7. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-01-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  8. Gender perspective on the social networks of household heads and community leaders after involuntary resettlement

    Quetulio-Navarra, Melissa; Znidarsic, Anja; Niehof, Anke

    2017-01-01

    The study of social network analysis in Indonesia and the Philippines reveals that after a certain period in a new community and living among involuntarily resettled strangers, household heads and community leaders will eventually replace their disrupted previous networks with new network ties. The

  9. Prostate Cancer Disparities in an Incarcerated Community

    2013-09-01

    diseases, Liver diseases, Cerebrovascular accident , non-Illnesses, and finally AIDS. 30% of inmates incarcerated in the WDOC die from Cancer while it is...are higher among WDOC inmates than the general U.S. population; while Cerebrovascular Accidents (CVA) are lower among WDOC inmates than the general

  10. Talking about cancer with confidence: evaluation of cancer awareness training for community-based health workers.

    Grimmett, Chloe; Macherianakis, Alexis; Rendell, Helen; George, Helen; Kaplan, Gwen; Kilgour, Gillian; Power, Emily

    2014-09-01

    To examine the impact of cancer awareness training for community-based health workers on confidence to talk about cancer, and knowledge of cancer risk factors and signs and symptoms. Community-based health workers from Sandwell, Birmingham and Solihull were invited to take part in one of 14 one-day training workshops. Trainees completed questionnaires at the beginning of the workshop and were followed up one month later. Confidence in talking about cancer was examined. Knowledge of cancer risk factors and signs and symptoms was assessed. Trainees were asked to rate the usefulness of the workshop, whether they would recommend it to others and whether they had put what they had learnt into practice. A total of 187 community-based health workers took part in the workshops, and 167 (89%) completed the one-month follow-up. Considerable improvements were observed in confidence to discuss cancer. For example, the proportion of participants reporting feeling 'very confident'/'fairly confident' in discussing signs and symptoms of cancer increased from 32% to 96% (p cancer at one month compared with 21% before training (p cancer signs and symptoms also increased from 2.3 (± 1.6) to 2.7 (± 1.5), (p = .02). Most trainees (83%) rated the workshop as 'very useful', and 89% said they would 'definitely' recommend the workshop. The cancer awareness training was reviewed positively by community-based health workers and led to improvements in confidence to talk about cancer, and knowledge of risk factors and warning signs of cancer. It is hoped that raising awareness among this group will help them to communicate and drive behaviour change in the at-risk populations with whom they work. © Royal Society for Public Health 2014.

  11. The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

    Frank eEmmert-Streib

    2014-02-01

    Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

  12. Attributions of cancer 'alarm' symptoms in a community sample.

    Katriina L Whitaker

    Full Text Available Attribution of early cancer symptoms to a non-serious cause may lead to longer diagnostic intervals. We investigated attributions of potential cancer 'alarm' and non-alarm symptoms experienced in everyday life in a community sample of adults, without mention of a cancer context.A questionnaire was mailed to 4858 adults (≥50 years old, no cancer diagnosis through primary care, asking about symptom experiences in the past 3 months. The word cancer was not mentioned. Target 'alarm' symptoms, publicised by Cancer Research UK, were embedded in a longer symptom list. For each symptom experienced, respondents were asked for their attribution ('what do you think caused it', concern about seriousness ('not at all' to 'extremely', and help-seeking ('did you contact a doctor about it': Yes/No.The response rate was 35% (n = 1724. Over half the respondents (915/1724; 53% had experienced an 'alarm' symptom, and 20 (2% cited cancer as a possible cause. Cancer attributions were highest for 'unexplained lump'; 7% (6/87. Cancer attributions were lowest for 'unexplained weight loss' (0/47. A higher proportion (375/1638; 23% were concerned their symptom might be 'serious', ranging from 12% (13/112 for change in a mole to 41% (100/247 for unexplained pain. Just over half had contacted their doctor about their symptom (59%, although this varied by symptom. Alarm symptoms were appraised as more serious than non-alarm symptoms, and were more likely to trigger help-seeking.Consistent with retrospective reports from cancer patients, 'alarm' symptoms experienced in daily life were rarely attributed to cancer. These results have implications for understanding how people appraise and act on symptoms that could be early warning signs of cancer.

  13. Associations between successful palliative cancer pathways and community nurse involvement

    Neergaard, Mette Asbjoern; Vedsted, Peter; Olesen, Frede

    2009-01-01

    ABSTRACT: BACKGROUND: Most terminally ill cancer patients and their relatives wish that the patient dies at home. Community nurses (CNs) are often frontline workers in the patients' homes and CN involvement may be important in attaining successful palliative pathways at home.The aim of the present...

  14. Road networks predict human influence on Amazonian bird communities

    Ahmed, Sadia E.; Lees, Alexander C.; Moura, Nárgila G.; Gardner, Toby A.; Barlow, Jos; Ferreira, Joice; Ewers, Robert M.

    2014-01-01

    Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santarém and Belterra in Pará state, eastern Brazilian Amazon, with a road network metric called ‘roadless volume (RV)’ at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation. PMID:25274363

  15. Perceived Risk of Developing Cancer in a Suburban Community

    Pasi, Hafizah; Md Isa, Zaleha; Azhar Shah, Shamsul

    2018-02-26

    Objective: To determine the perceived risk of developing cancer in a suburban community in Malaysia. Methods: A cross sectional study using a simple random sampling was conducted among residents aged 18 years old and above (n=520) who had never been diagnosed with cancer, in selected households in a suburban area of Pahang state in Malaysia. The study instruments were a validated questionnaire on risk perception, an automatic blood pressure monitor, a weighing scale and a measuring tape. Data were analyzed using IBM SPSS Statistics for Windows, Version 20.0 and the p value was taken at p history of cancer (adjusted OR of 4.80; 95% CI = 1.45-15.82) (p=0.010) among this population. Conclusion: The perceived risk of developing cancer in the selected suburban community of Pahang state in Malaysia is low as compared to that for other non-communicable diseases in this country, whilst the perceived severity was higher than for other diseases. Thus, considering the importance of correct perceptions for behavioral changes, more health education and promotion is needed to make the community better aware of the actual threat of cancer. Creative Commons Attribution License

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

    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.

  17. Communication networks of men facing a diagnosis of prostate cancer.

    Brown, Dot; Oetzel, John; Henderson, Alison

    2016-11-01

    This study seeks to identify the factors that shape the communication networks of men who face a potential diagnosis of prostate cancer, and how these factors relate to their disclosure about their changing health status. Men facing a potential diagnosis of prostate cancer are in a challenging situation; the support benefits of disclosing their changing health status to others in their communication networks is set against a backdrop of the potential stigma and uncertainty of the diagnosis. All men on a prostate biopsy waiting list were eligible for inclusion in an exploratory and interpretive study. Semi-structured interviews with 40 men explored their network structures and disclosure of health information. Thematic analysis highlighted the factors which contributed to their network structures and their disclosure about their health status. Four network factors shaped men's perspectives about disclosing their health status: (1) tie strength, comprising both strong and weak ties; (2) knowledgeable others, with a focus on medical professionals in the family; (3) homophily, which included other individuals with a similar medical condition; and (4) geographical proximity, with a preference for face-to-face communication. Communication networks influence men's disclosure of their health status and in particular weak ties with medical knowledge have an important role. Men who use the potential for support in their networks may experience improved psychosocial outcomes. Using these four network factors-tie strength, knowledgeable others, homophily or geographical proximity-to forecast men's willingness to disclose helps identify men who lack potential support and so are at risk of poor psychosocial health. Those with few strong ties or knowledgeable others in their networks may be in the at-risk cohort. The support provided in communication networks complements formal medical care from nurses and other health professionals, and encouraging patients to use their

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

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

    2014-01-01

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

  19. The Asian American Network for Cancer Awareness, Research, and Training’s Role in Cancer Awareness, Research, and Training

    Chen, Moon S.

    2006-01-01

    Purpose The purpose of this paper is to describe the content for the Asian American Network for Cancer Awareness Research and Training (AANCART) with respect to Asian American demographic characteristics and their cancer burden, highlights of accomplishments in various AANCART regions, aspirations for AANCART, and an interim assessment of AANCART’s activities to date. Methods The author compiled literature and other data references to describe the context for Asian American demographic characteristics and their cancer burden. As the AANCART Principal Investigator, he collected data from internal AANCART reports to depict highlights of accomplishments in various AANCART regions and offer evidence that AANCART’s first two specific aims have been attained. Principal Findings With respect to our first specific aim, we have built an infrastructure for cancer awareness, research and training operationally at a Network-wide basis through program directors for biostatistics, community, clinical, and research and in our four original AANCART regions: New York, Seattle, San Francisco, and Los Angeles. With respect to our second specific aim, we have established partnerships as exemplified by working collaboratively with New York’s Charles B. Wang Community Health Center in securing external funding with them for a tobacco control initiative and nationally with the American Cancer Society. With respect to our third specific aim, we have been fortunate to assist at least eight junior investigators in receiving NCI-funded pilot studies. The most notable change was the transfer of AANCART’s national headquarters from Columbus, Ohio to Sacramento, California along with potentially an increased diversification of Asian American ethnic groups as well as an expansion to Hawaii and Houston. Conclusion As of the end of year 2 of AANCART, AANCART’s two specific aims have been achieved. We are focusing on our third specific aim. PMID:15352772

  20. A Network Pharmacology Approach to Uncover the Multiple Mechanisms of Hedyotis diffusa Willd. on Colorectal Cancer

    Xinkui Liu

    2018-01-01

    Full Text Available Background. As one of the most frequently diagnosed cancer diseases globally, colorectal cancer (CRC remains an important cause of cancer-related death. Although the traditional Chinese herb Hedyotis diffusa Willd. (HDW has been proven to be effective for treating CRC in clinical practice, its definite mechanisms have not been completely deciphered. Objective. The aim of our research is to systematically explore the multiple mechanisms of HDW on CRC. Methods. This study adopted the network pharmacology approach, which was mainly composed of active component gathering, target prediction, CRC gene collection, network analysis, and gene enrichment analysis. Results. The network analysis showed that 10 targets might be the therapeutic targets of HDW on CRC, namely, HRAS, PIK3CA, KRAS, TP53, APC, BRAF, GSK3B, CDK2, AKT1, and RAF1. The gene enrichment analysis implied that HDW probably benefits patients with CRC by modulating pathways related to cancers, infectious diseases, endocrine system, immune system, nervous system, signal transduction, cellular community, and cell motility. Conclusions. This study partially verified and predicted the pharmacological and molecular mechanism of HDW against CRC from a holistic perspective, which will also lay a foundation for the further experimental research and clinical rational application of HDW.

  1. Social network characteristics and cervical cancer screening among Quechua women in Andean Peru

    John S. Luque

    2016-02-01

    Full Text Available Abstract Background Peru has high cervical cancer incidence and mortality rates compared to other Andean countries. Therefore, partnerships between governmental and international organizations have targeted rural areas of Peru to receive cervical cancer screening via outreach campaigns. Previous studies have found a relationship between a person’s social networks and cancer screening behaviors. Screening outreach campaigns conducted by the nonprofit organization CerviCusco created an opportunity for a social network study to examine cervical cancer screening history and social network characteristics in a rural indigenous community that participated in these campaigns in 2012 and 2013. The aim of this study was to explore social network characteristics in this community related to receipt of cervical cancer screening following the campaigns. Methods An egocentric social network questionnaire was used to collect cross-sectional network data on community participants. Each survey participant (ego was asked to name six other women they knew (alters and identify the nature of their relationship or tie (family, friend, neighbor, other, residential closeness (within 5 km, length of time known, frequency of communication, topics of conversation, and whether they lent money to the person, provided childcare or helped with transportation. In addition, each participant was asked to report the nature of the relationship between all alters identified (e.g., friend, family, or neighbor. Bivariate and multivariate analyses were used to explore the relationship between Pap test receipt at the CerviCusco outreach screening campaigns and social network characteristics. Results Bivariate results found significant differences in percentage of alter composition for neighbors and family, and for mean number of years known, mean density, and mean degree centrality between women who had received a Pap test (n = 19 compared to those who had not (n = 50 (p

  2. Network communities as a new form of social organization in conditions of postmodern

    N. V. Burmaha

    2016-03-01

    Full Text Available This article deals with the approach to interpretation of essence of the network community concept in which we propose to consider it as a new form of social organization that is substantiated by the specificity of how our society is functioning in conditions of Postmodern. There were explored two main approaches to network communities studying: the first approach considers social networks in a classic, traditional interpretation of modernity as a special kind of social structure, and the second one represents social networks as a specific virtual formation, a social structure of virtual Internet reality. There were revealed some common features of a social organization and a network community: presence of permanent communication between members of the group, united by certain common interests and goals, as well as presence of the certain hierarchy among all members of the community, and the rules of conduct, implementation of communication. Distinctive features: network community is more informal, offers its members considerable leeway in the implementation of their own goals and satisfying the needs, full virtualization of communication absence of direct interaction during communication, under conditions where the main resource for the interchange in network communities is information. It was shown that in the process of emergence, development and distribution of network communities, the fundamental role is played by modern communications - namely, unification them in a stable set of interconnected networks and, in particular network communities.

  3. Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

    Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

    2015-01-01

    Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

  4. Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

    Kaida Song

    Full Text Available Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

  5. Extracting weights from edge directions to find communities in directed networks

    Lai, Darong; Lu, Hongtao; Nardini, Christine

    2010-01-01

    Community structures are found to exist ubiquitously in real-world complex networks. We address here the problem of community detection in directed networks. Most of the previous literature ignores edge directions and applies methods designed for community detection in undirected networks, which discards valuable information and often fails when different communities are defined on the basis of incoming and outgoing edges. We suggest extracting information about edge directions using a PageRank random walk and translating such information into edge weights. After extraction we obtain a new weighted directed network in which edge directions can then be safely ignored. We thus transform community detection in directed networks into community detection in reweighted undirected networks. Such an approach can benefit directly from the large volume of algorithms for the detection of communities in undirected networks already developed, since it is not obvious how to extend these algorithms to account for directed networks and the procedure is often difficult. Validations on synthetic and real-world networks demonstrate that the proposed framework can effectively detect communities in directed networks

  6. Efficient Cancer Detection Using Multiple Neural Networks.

    Shell, John; Gregory, William D

    2017-01-01

    The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

  7. Rural Embedded Assistants for Community Health (REACH) network: first-person accounts in a community-university partnership.

    Brown, Louis D; Alter, Theodore R; Brown, Leigh Gordon; Corbin, Marilyn A; Flaherty-Craig, Claire; McPhail, Lindsay G; Nevel, Pauline; Shoop, Kimbra; Sterner, Glenn; Terndrup, Thomas E; Weaver, M Ellen

    2013-03-01

    Community research and action projects undertaken by community-university partnerships can lead to contextually appropriate and sustainable community improvements in rural and urban localities. However, effective implementation is challenging and prone to failure when poorly executed. The current paper seeks to inform rural community-university partnership practice through consideration of first-person accounts from five stakeholders in the Rural Embedded Assistants for Community Health (REACH) Network. The REACH Network is a unique community-university partnership aimed at improving rural health services by identifying, implementing, and evaluating innovative health interventions delivered by local caregivers. The first-person accounts provide an insider's perspective on the nature of collaboration. The unique perspectives identify three critical challenges facing the REACH Network: trust, coordination, and sustainability. Through consideration of the challenges, we identified several strategies for success. We hope readers can learn their own lessons when considering the details of our partnership's efforts to improve the delivery infrastructure for rural healthcare.

  8. Network perturbation by recurrent regulatory variants in cancer.

    Kiwon Jang

    2017-03-01

    Full Text Available Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

  9. Uncovering the community structure associated with the diffusion dynamics on networks

    Cheng, Xue-Qi; Shen, Hua-Wei

    2010-01-01

    As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of the network, which measures how easily the diffusion among different communities occurs. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods in identifying the community structure of networks. Applications to real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the community structure obtained can naturally reflect the diffusion capability of the underlying network

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

    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.

  11. Reconstructing the Prostate Cancer Transcriptional Regulatory Network

    2010-09-01

    and disease prognosis. J Clin Oncol 2006;24:3763–70. 13. Klein CA, Schmidt- Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmuller G. Comparative...Cancer Gene Discovery Jessica Kao1., Keyan Salari1,2., Melanie Bocanegra1, Yoon-La Choi1,3, Luc Girard4, Jeet Gandhi4, Kevin A. Kwei1, Tina Hernandez...JM, Klein RC, Oka M, Cowan KH (1995) Posttranscriptional regulation of the c-myb proto-oncogene in estrogen receptor-positive breast cancer cells

  12. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    Neha Sharma

    2015-01-01

    Full Text Available In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  13. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer.

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

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

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

    2012-01-01

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

  15. Implementing academic detailing for breast cancer screening in underserved communities

    Ashford Alfred R

    2007-12-01

    Full Text Available Abstract Background African American and Hispanic women, such as those living in the northern Manhattan and the South Bronx neighborhoods of New York City, are generally underserved with regard to breast cancer prevention and screening practices, even though they are more likely to die of breast cancer than are other women. Primary care physicians (PCPs are critical for the recommendation of breast cancer screening to their patients. Academic detailing is a promising strategy for improving PCP performance in recommending breast cancer screening, yet little is known about the effects of academic detailing on breast cancer screening among physicians who practice in medically underserved areas. We assessed the effectiveness of an enhanced, multi-component academic detailing intervention in increasing recommendations for breast cancer screening within a sample of community-based urban physicians. Methods Two medically underserved communities were matched and randomized to intervention and control arms. Ninety-four primary care community (i.e., not hospital based physicians in northern Manhattan were compared to 74 physicians in the South Bronx neighborhoods of the New York City metropolitan area. Intervention participants received enhanced physician-directed academic detailing, using the American Cancer Society guidelines for the early detection of breast cancer. Control group physicians received no intervention. We conducted interviews to measure primary care physicians' self-reported recommendation of mammography and Clinical Breast Examination (CBE, and whether PCPs taught women how to perform breast self examination (BSE. Results Using multivariate analyses, we found a statistically significant intervention effect on the recommendation of CBE to women patients age 40 and over; mammography and breast self examination reports increased across both arms from baseline to follow-up, according to physician self-report. At post-test, physician

  16. At the edge? HIV stigma and centrality in a community's social network in Namibia.

    Smith, Rachel A; Baker, Michelle

    2012-04-01

    Social network analysis was used to examine the relationship between HIV/AIDS stigmatization, perceived risk, and centrality in the community network (via participation in community groups). The findings from respondents in Keetmanshoop, Namibia (N = 375) showed an interaction between stigma and risk perceptions\\hose who perceived higher HIV risk and stronger HIV stigma participated in fewer community groups and participated in groups with members who participated less widely across the network. In contrast, those who perceived higher HIV risk and weaker HIV stigma participated more, and were in community groups that are located on a greater share of the paths between entities in the network. Taboo, secrecy, resistance, knowing a person living with HIV/AIDS, and desire for diagnosis secrecy were also related to centrality. Findings suggest that the interaction of perceived HIV risk and HIV stigma are related to structural-level features of community networks based on participation in community groups.

  17. Design The Cervical Cancer Detector Use The Artificial Neural Network

    Af'idah, Dwi Intan; Widianto, Eko Didik; Setyawan, Budi

    2013-01-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  18. Road networks predict human influence on Amazonian bird communities.

    Ahmed, Sadia E; Lees, Alexander C; Moura, Nárgila G; Gardner, Toby A; Barlow, Jos; Ferreira, Joice; Ewers, Robert M

    2014-11-22

    Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santarém and Belterra in Pará state, eastern Brazilian Amazon, with a road network metric called 'roadless volume (RV)' at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  19. Building a Network of Internships for a Diverse Geoscience Community

    Sloan, V.; Haacker-Santos, R.; Pandya, R.

    2011-12-01

    Individual undergraduate internship programs, however effective, are not sufficient to address the lack of diversity in the geoscience workforce. Rather than competing with each other for a small pool of students from historically under-represented groups, REU and internship programs might share recruiting efforts and application processes. For example, in 2011, the RESESS program at UNAVCO and the SOARS program at UCAR shared recruiting websites and advertising. This contributed to a substantial increase in the number of applicants to the RESESS program, the majority of which were from historically under-represented groups. RESESS and SOARS shared qualified applications with other REU/internship programs and helped several additional minority students secure summer internships. RESESS and SOARS also leveraged their geographic proximity to pool resources for community building activities, a two-day science field trip, a weekly writing workshop, and our final poster session. This provided our interns with an expanded network of peers and gave our staff opportunities to work together on planning. Recently we have reached out to include other programs and agencies in activities for our interns, such as mentoring high-school students, leading outreach to elementary school students, and exposing our interns to geoscience careers options and graduate schools. Informal feedback from students suggests that they value these interactions and appreciate learning with interns from partner programs. Through this work, we are building a network of program managers who support one another professionally and share effective strategies. We would like to expand that network, and future plans include a workshop with university partners and an expanded list of REU programs to explore further collaborations.

  20. Network analysis of cancer PSB 2014 Poster

    McDermott, Jason

    2014-01-01

    Cancer is, in general, a complex disease; it operates on pathways and systems, not solely on the individual components of those systems (genes or proteins). We have gathered global proteomic and phosphoproteomic data from a set of tumors with existing genomic data (sequencing, methylation, miRNA and mRNA expression) associated with a range of survival phenotypes. We report on our recent progress in analyzing proteomic, phosphoproteomic, and accompanying genomic data in terms of pathways that ...

  1. The Community Seismic Network: Enabling Observations Through Citizen Science Participation

    Kohler, M. D.; Clayton, R. W.; Heaton, T. H.; Bunn, J.; Guy, R.; Massari, A.; Chandy, K. M.

    2017-12-01

    The Community Seismic Network is a dense accelerometer array deployed in the greater Los Angeles area and represents the future of densely instrumented urban cities where localized vibration measurements are collected continuously throughout the free-field and built environment. The hardware takes advantage of developments in the semiconductor industry in the form of inexpensive MEMS accelerometers that are each coupled with a single board computer. The data processing and archival architecture borrows from developments in cloud computing and network connectedness. The ability to deploy densely in the free field and in upper stories of mid/high-rise buildings is enabled by community hosts for sensor locations. To this end, CSN has partnered with the Los Angeles Unified School District (LAUSD), the NASA-Jet Propulsion Laboratory (JPL), and commercial and civic building owners to host sensors. At these sites, site amplification estimates from RMS noise measurements illustrate the lateral variation in amplification over length scales of 100 m or less, that correlate with gradients in the local geology such as sedimentary basins that abut crystalline rock foothills. This is complemented by high-resolution, shallow seismic velocity models obtained using an H/V method. In addition, noise statistics are used to determine the reliability of sites for ShakeMap and earthquake early warning data. The LAUSD and JPL deployments are examples of how situational awareness and centralized warning products such as ShakeMap and ShakeCast are enabled by citizen science participation. Several buildings have been instrumented with at least one triaxial accelerometer per floor, providing measurements for real-time structural health monitoring through local, customized displays. For real-time and post-event evaluation, the free-field and built environment CSN data and products illustrate the feasibility of order-of-magnitude higher spatial resolution mapping compared to what is currently

  2. Analysis of Content Shared in Online Cancer Communities: Systematic Review.

    van Eenbergen, Mies C; van de Poll-Franse, Lonneke V; Krahmer, Emiel; Verberne, Suzan; Mols, Floortje

    2018-04-03

    The content that cancer patients and their relatives (ie, posters) share in online cancer communities has been researched in various ways. In the past decade, researchers have used automated analysis methods in addition to manual coding methods. Patients, providers, researchers, and health care professionals can learn from experienced patients, provided that their experience is findable. The aim of this study was to systematically review all relevant literature that analyzes user-generated content shared within online cancer communities. We reviewed the quality of available research and the kind of content that posters share with each other on the internet. A computerized literature search was performed via PubMed (MEDLINE), PsycINFO (5 and 4 stars), Cochrane Central Register of Controlled Trials, and ScienceDirect. The last search was conducted in July 2017. Papers were selected if they included the following terms: (cancer patient) and (support group or health communities) and (online or internet). We selected 27 papers and then subjected them to a 14-item quality checklist independently scored by 2 investigators. The methodological quality of the selected studies varied: 16 were of high quality and 11 were of adequate quality. Of those 27 studies, 15 were manually coded, 7 automated, and 5 used a combination of methods. The best results can be seen in the papers that combined both analytical methods. The number of analyzed posts ranged from 200 to 1,500,000; the number of analyzed posters ranged from 75 to 90,000. The studies analyzing large numbers of posts mainly related to breast cancer, whereas those analyzing small numbers were related to other types of cancers. A total of 12 studies involved some or entirely automatic analysis of the user-generated content. All the authors referred to two main content categories: informational support and emotional support. In all, 15 studies reported only on the content, 6 studies explicitly reported on content and social

  3. Network analysis as a tool for community capacity measurement and assessing partnerships between community-based organizations in Korea.

    Jung, Minsoo

    2012-01-01

    The community partnership is a foundation laid by the local community that has been historically and geographically formed to develop itself. This article, an exploratory community network survey for capacity building, assessed collaborations among community-based organizations (CBOs) in the S-district, Republic of Korea, and evaluated methods for the reconstruction of a resident-governing healthy network. Using CBOs' evaluation questionnaire, the author surveyed 83 CBOs that were collected by snowball sampling. The CBOs in the S-district had formed community networks based on vocational associations established in the 1980s and the 1990s. The entire network evidenced a cooperative partnership, in which women's organizations and civic groups carried out essential functions. In the capacity-building process through CBOs, community collaboration can be naturally cultivated, and health promotion programs to improve the residents' health will tend to be more systematic than the current approach and yield higher compliance and practice rates. Thus, it will be necessary to construct an effective partnership of community networks by reorganizing existing exclusive relations.

  4. Dynamic robustness of knowledge collaboration network of open source product development community

    Zhou, Hong-Li; Zhang, Xiao-Dong

    2018-01-01

    As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

  5. The overlapping community structure of structural brain network in young healthy individuals.

    Kai Wu

    2011-05-01

    Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

  6. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion.

    Baggio, Jacopo A; BurnSilver, Shauna B; Arenas, Alex; Magdanz, James S; Kofinas, Gary P; De Domenico, Manlio

    2016-11-29

    Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social-ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.

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

    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.

  8. Large-Scale Analysis of Network Bistability for Human Cancers

    Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki

    2010-01-01

    Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618

  9. The meaning and validation of social support networks for close family of persons with advanced cancer

    Sjolander Catarina

    2012-09-01

    Full Text Available Abstract Background To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study’s empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Methods Seventeen family members with a relative who 8–14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. Results The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. Conclusions The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could

  10. The meaning and validation of social support networks for close family of persons with advanced cancer.

    Sjolander, Catarina; Ahlstrom, Gerd

    2012-09-17

    To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study's empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Seventeen family members with a relative who 8-14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could encourage nurses and other health-care professionals to focus on family members

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

    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.

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

    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.

  13. Deregulation of an imprinted gene network in prostate cancer.

    Ribarska, Teodora; Goering, Wolfgang; Droop, Johanna; Bastian, Klaus-Marius; Ingenwerth, Marc; Schulz, Wolfgang A

    2014-05-01

    Multiple epigenetic alterations contribute to prostate cancer progression by deregulating gene expression. Epigenetic mechanisms, especially differential DNA methylation at imprinting control regions (termed DMRs), normally ensure the exclusive expression of imprinted genes from one specific parental allele. We therefore wondered to which extent imprinted genes become deregulated in prostate cancer and, if so, whether deregulation is due to altered DNA methylation at DMRs. Therefore, we selected presumptive deregulated imprinted genes from a previously conducted in silico analysis and from the literature and analyzed their expression in prostate cancer tissues by qRT-PCR. We found significantly diminished expression of PLAGL1/ZAC1, MEG3, NDN, CDKN1C, IGF2, and H19, while LIT1 was significantly overexpressed. The PPP1R9A gene, which is imprinted in selected tissues only, was strongly overexpressed, but was expressed biallelically in benign and cancerous prostatic tissues. Expression of many of these genes was strongly correlated, suggesting co-regulation, as in an imprinted gene network (IGN) reported in mice. Deregulation of the network genes also correlated with EZH2 and HOXC6 overexpression. Pyrosequencing analysis of all relevant DMRs revealed generally stable DNA methylation between benign and cancerous prostatic tissues, but frequent hypo- and hyper-methylation was observed at the H19 DMR in both benign and cancerous tissues. Re-expression of the ZAC1 transcription factor induced H19, CDKN1C and IGF2, supporting its function as a nodal regulator of the IGN. Our results indicate that a group of imprinted genes are coordinately deregulated in prostate cancers, independently of DNA methylation changes.

  14. Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

    Dart, Richard A; Egan, Brent M

    2014-06-01

    Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

  15. Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks

    Thebault, E.M.C.; Fontaine, C.

    2010-01-01

    Research on the relationship between the architecture of ecological networks and community stability has mainly focused on one type of interaction at a time, making difficult any comparison between different network types. We used a theoretical approach to show that the network architecture favoring

  16. A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

    Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

    2016-01-01

    Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

  17. Place-based attributes predict community membership in a mobile phone communication network.

    T Trevor Caughlin

    Full Text Available Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97 between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

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

    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.

  19. Place-based attributes predict community membership in a mobile phone communication network.

    Caughlin, T Trevor; Ruktanonchai, Nick; Acevedo, Miguel A; Lopiano, Kenneth K; Prosper, Olivia; Eagle, Nathan; Tatem, Andrew J

    2013-01-01

    Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

  20. The cancer cell map initiative: defining the hallmark networks of cancer.

    Krogan, Nevan J; Lippman, Scott; Agard, David A; Ashworth, Alan; Ideker, Trey

    2015-05-21

    Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Choosing your network: social preferences in an online health community.

    Centola, Damon; van de Rijt, Arnout

    2015-01-01

    A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts

  2. The National LGBT Cancer Action Plan: A White Paper of the 2014 National Summit on Cancer in the LGBT Communities

    Margolies, Liz; Sigurdsson, Hrafn Oli; Walland, Jonathan; Radix, Asa; Rice, David; Buchting, Francisco O.; Sanchez, Nelson F.; Bare, Michael G.; Boehmer, Ulrike; Cahill, Sean; Griebling, Tomas L.; Bruessow, Diane; Maingi, Shail

    2016-01-01

    Abstract Despite growing social acceptance of lesbians, gay men, bisexuals, and transgender (LGBT) persons and the extension of marriage rights for same-sex couples, LGBT persons experience stigma and discrimination, including within the healthcare system. Each population within the LGBT umbrella term is likely at elevated risk for cancer due to prevalent, significant cancer risk factors, such as tobacco use and human immunodeficiency virus infection; however, cancer incidence and mortality data among LGBT persons are lacking. This absence of cancer incidence data impedes research and policy development, LGBT communities' awareness and activation, and interventions to address cancer disparities. In this context, in 2014, a 2-day National Summit on Cancer in the LGBT Communities was convened by a planning committee for the purpose of accelerating progress in identifying and addressing the LGBT communities' concerns and needs in the spheres of cancer research, clinical cancer care, healthcare policy, and advocacy for cancer survivorship and LGBT health equity. Summit participants were 56 invited persons from the United States, United Kingdom, and Canada, representatives of diverse identities, experiences, and knowledge about LGBT communities and cancer. Participants shared lessons learned and identified gaps and remedies regarding LGBT cancer concerns across the cancer care continuum from prevention to survivorship. This white paper presents background on each of the Summit themes and 16 recommendations covering the following: sexual orientation and gender identity data collection in national and state health surveys and research on LGBT communities and cancer, the clinical care of LGBT persons, and the education and training of healthcare providers.

  3. The Rare Cancer Network: ongoing studies and future strategy

    Mahmut Ozsahin

    2014-08-01

    Full Text Available The Rare Cancer Network (RCN was formed in the early 1990’s to create a global network that could pool knowledge and resources in the studies of rare malignancies whose infrequency prevented both their study with prospective clinical trials. To date, the RCN has initiated 74 studies resulting in 46 peer reviewed publications. The First International Symposium of the Rare Cancer Network took place in Nice in March of 2014. Status updates and proposals for new studies were heard for fifteen topics. Ongoing studies continue for cardiac sarcomas, thyroid cancers, glomus tumors, and adult medulloblastomas. New proposals were presented at the symposium for primary hepatic lymphoma, solitary fibrous tumors, Rosai-Dorfman disease, tumors of the ampulla of Vater, salivary gland tumors, anorectal melanoma, midline nuclear protein in testes carcinoma, pulmonary lymphoepithelioma-like carcinoma, adenoid cystic carcinoma of the trachea, osteosarcomas of the mandible, and extra-cranial hemangiopericytoma. This manuscript presents the abstracts of those proposals and updates on ongoing studies, as well a brief summary of the vision and future of the RCN.

  4. Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

    Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

    Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

  5. Assessing Breast Cancer Risk with an Artificial Neural Network

    Sepandi, Mojtaba; Taghdir, Maryam; Rezaianzadeh, Abbas; Rahimikazerooni, Salar

    2018-04-25

    Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imaging methods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neural network (ANN) technique was used on a retrospectively collected dataset including mammographic results, risk factors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: The network incorporating the selected features performed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90. In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANN has potential applications as a decision-support tool to help underperforming practitioners to improve the positive predictive value of biopsy recommendations. Creative Commons Attribution License

  6. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

    Cannistraci, C.V.; Alanis-Lobato, G.; Ravasi, Timothy

    2013-01-01

    for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems

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

    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.

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

    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.

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

    István A Kovács

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

  10. Utilization of an interorganizational network analysis to evaluate the development of community capacity among a community-academic partnership.

    Clark, Heather R; Ramirez, Albert; Drake, Kelly N; Beaudoin, Christopher E; Garney, Whitney R; Wendel, Monica L; Outley, Corliss; Burdine, James N; Player, Harold D

    2014-01-01

    Following a community health assessment the Brazos Valley Health Partnership (BVHP) organized to address fragmentation of services and local health needs. This regional partnership employs the fundamental principles of community-based participatory research, fostering an equitable partnership with the aim of building community capacity to address local health issues. This article describes changes in relationships as a result of capacity building efforts in a community-academic partnership. Growth in network structure among organizations is hypothesized to be indicative of less fragmentation of services for residents and increased capacity of the BVHP to collectively address local health issues. Each of the participant organizations responded to a series of questions regarding its relationships with other organizations. Each organization was asked about information sharing, joint planning, resource sharing, and formal agreements with other organizations. The network survey has been administered 3 times between 2004 and 2009. Network density increased for sharing information and jointly planning events. Growth in the complexity of relationships was reported for sharing tangible resources and formal agreements. The average number of ties between organizations as well as the strength of relationships increased. This study provides evidence that the community capacity building efforts within these communities have contributed to beneficial changes in interorganizational relationships. Results from this analysis are useful for understanding how a community partnership's efforts to address access to care can strengthen a community's capacity for future action. Increased collaboration also leads to new assets, resources, and the transfer of knowledge and skills.

  11. Community Oncologists' Decision-Making for Treatment of Older Patients With Cancer.

    Mohile, Supriya G; Magnuson, Allison; Pandya, Chintan; Velarde, Carla; Duberstein, Paul; Hurria, Arti; Loh, Kah Poh; Wells, Megan; Plumb, Sandy; Gilmore, Nikesha; Flannery, Marie; Wittink, Marsha; Epstein, Ronald; Heckler, Charles E; Janelsins, Michelle; Mustian, Karen; Hopkins, Judith O; Liu, Jane; Peri, Srihari; Dale, William

    2018-03-01

    Background: This study's objectives were to describe community oncologists' beliefs about and confidence with geriatric care and to determine whether geriatric-relevant information influences cancer treatment decisions. Methods: Community oncologists were recruited to participate in 2 multisite geriatric oncology trials. Participants shared their beliefs about and confidence in caring for older adults. They were also asked to make a first-line chemotherapy recommendation (combination vs single-agent vs no chemotherapy) for a hypothetical vignette of an older patient with advanced pancreatic cancer. Each oncologist received one randomly chosen vignette that varied on 3 variables: age (72/84 years), impaired function (yes/no), and cognitive impairment (yes/no). Other patient characteristics were held constant. Logistic regression models were used to identify associations between oncologist/vignette-patient characteristics and treatment decisions. Results: Oncologist response rate was 61% (n=305/498). Most oncologists agreed that "the care of older adults with cancer needs to be improved" (89%) and that "geriatrics training is essential" (72%). However, decision to treat: younger age (adjusted odds ratio [aOR], 5.01; 95% CI, 2.73-9.20), normal cognition (aOR, 5.42; 95% CI, 3.01-9.76), and being functionally intact (aOR, 3.85; 95% CI, 2.12-7.00). Accounting for all vignettes across all scenarios, 161 oncologists (52%) said they would offer chemotherapy. All variables were independently associated with prescribing single-agent over combination chemotherapy (older age: aOR, 3.22; 95% CI 1.43-7.25, impaired cognition: aOR, 3.13; 95% CI, 1.36-7.20, impaired function: aOR, 2.48; 95% CI, 1.12-5.72). Oncologists' characteristics were not associated with decisions about providing chemotherapy. Conclusion: Geriatric-relevant information, when available, strongly influences community oncologists' treatment decisions. Copyright © 2018 by the National Comprehensive Cancer Network.

  12. Dedicated researcher brings cancer care to rural communities

    Sharan Bhuller

    2016-10-01

    Full Text Available As an ardent cancer researcher, Dr. Smita Asthana has a vision to create wider awareness on cancer and its prevention, and aims to work on translational research to benefit the general public through the implementation of evidence-based research. “I have been associated with the National Institute of Cancer Prevention and Research (NICPR and Institute of Cytology and Preventive Oncology (ICPO since November 2004 and have progressed over a period of time from being a staff scientist to the current role of a senior scientist,” says Dr. Asthana, who is presently with NICPR’s Biostatistics and Epidemiology division.“I have been working in various positions that deal with the design, execution, and evaluation of medical projects. Recently, we have concluded two major cervical cancer screening projects and conducted a screening of 10,000 women in rural areas,” she tells AMOR. One project, funded by the Indian Council of Medical Research, was carried out 100 km west of New Delhi in the rural town of Dadri “as part of an operational research to see the implementation of VIA (visual inspection with acetic acid and VILI (visual inspection with Lugol's iodine screenings with the help of existing healthcare infrastructure,” she explains.As a leading researcher in cervical cancer screening, she completed an Indo-US collaborative project on the clinical performance of a human papillomavirus (HPV test, used as a strategy for screening cervical cancer in rural communities, with funding from the Bill and Melinda Gates Foundation via the international non-profit global health organization PATH. “The primary objective of the project was to observe the performance of careHPV, a new diagnostic kit, in a rural setup,” she says.CareHPV is a highly sensitive DNA test, which detects 14 different types of the human papillomavirus that cause cervical cancer, providing results more rapidly than other DNA tests and is designed especially for use in clinics

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

    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. Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

    Ang, Chee Siang; Zaphiris, Panayiotis

    We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

  15. Rural Health Networks: How Network Analysis Can Inform Patient Care and Organizational Collaboration in a Rural Breast Cancer Screening Network.

    Prusaczyk, Beth; Maki, Julia; Luke, Douglas A; Lobb, Rebecca

    2018-04-15

    Rural health networks have the potential to improve health care quality and access. Despite this, the use of network analysis to study rural health networks is limited. The purpose of this study was to use network analysis to understand how a network of rural breast cancer care providers deliver services and to demonstrate the value of this methodology in this research area. Leaders at 47 Federally Qualified Health Centers and Rural Health Clinics across 10 adjacent rural counties were asked where they refer patients for mammograms or breast biopsies. These clinics and the 22 referral providers that respondents named comprised the network. The network was analyzed graphically and statistically with exponential random graph modeling. Most (96%, n = 45) of the clinics and referral sites (95%, n = 21) are connected to each other. Two clinics of the same type were 62% less likely to refer patients to the same providers as 2 clinics of different types (OR = 0.38, 95% CI = 0.29-0.50). Clinics in the same county have approximately 8 times higher odds of referring patients to the same providers compared to clinics in different counties (OR = 7.80, CI = 4.57-13.31). This study found that geographic location of resources is an important factor in rural health care providers' referral decisions and demonstrated the usefulness of network analysis for understanding rural health networks. These results can be used to guide delivery of patient care and strengthen the network by building resources that take location into account. © 2018 National Rural Health Association.

  16. Topic Modeling of Smoking- and Cessation-Related Posts to the American Cancer Society's Cancer Survivor Network (CSN): Implications for Cessation Treatment for Cancer Survivors Who Smoke.

    Westmaas, J Lee; McDonald, Bennett R; Portier, Kenneth M

    2017-08-01

    Smoking is a risk factor in at least 18 cancers, and approximately two-thirds of cancer survivors continue smoking following diagnosis. Text mining of survivors' online posts related to smoking and quitting could inform strategies to reduce smoking in this vulnerable population. We identified posts containing smoking/cessation-related keywords from the Cancer Survivors Network (CSN), an online cancer survivor community of 166 000 members and over 468 000 posts since inception. Unsupervised topic model analysis of posts since 2000 using Latent Dirichlet Allocation extracted 70 latent topics which two subject experts inspected for themes based on representative terms. Posterior analysis assessed the distribution of topics within posts, and the range of themes discussed across posts. Less than 1% of posts (n = 3998) contained smoking/cessation-related terms, and covered topics related to cancer diagnoses, treatments, and coping. The most frequent smoking-related topics were quit smoking methods (5.4% of posts), and the environment for quitters (2.9% of posts), such as the stigma associated with being a smoker diagnosed with cancer and lack of empathy experienced compared to nonsmokers. Smoking as a risk factor for one's diagnosis was a primary topic in only 1.7% of smoking/cessation-related posts. The low frequency of smoking/cessation-related posts may be due to expected criticism/stigma for smoking but may also suggests a need for health care providers to address smoking and assist with quitting in the diagnostic and treatment process. Topic model analysis revealed potential barriers that should be addressed in devising clinical or population-level interventions for cancer survivors who smoke. Although smoking is a major risk factor for cancer, little is known about cancer patients' or survivors' views or concerns about smoking and quitting. This study used text mining of posts to an online community of cancer patients and survivors to investigate contexts in which

  17. A Markov random walk under constraint for discovering overlapping communities in complex networks

    Jin, Di; Yang, Bo; Liu, Dayou; He, Dongxiao; Liu, Jie; Baquero, Carlos

    2011-01-01

    The detection of overlapping communities in complex networks has motivated recent research in relevant fields. Aiming to address this problem, we propose a Markov-dynamics-based algorithm, called UEOC, which means 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge of the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and has been compared with a set of competing algorithms. The experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities

  18. Complex brain networks: From topological communities to clustered ...

    functional connectivity of the human brain has shown that both types of brain networks share .... the areas and also of the whole network, the Pearson correlation coefficient r and ..... Several areas important for intercommunity communication.

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

    Kathleen T Durant

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

  20. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  1. Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

    2016-04-26

    Systems, Statistics & Management Science, University of Alabama, USA. 1 DISTRIBUTION A: Distribution approved for public release. Contents 1 Summary 5...13 5 Application to Real Networks 18 5.1 2012 FBS Football Schedule Network... football schedule network. . . . . . . . . . . . . . . . . . . . . . 21 14 Stem plot of degree-ordered vertices versus the degree for college football

  2. The redox biology network in cancer pathophysiology and therapeutics

    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  3. Making big communities small: using network science to understand the ecological and behavioral requirements for community social capital.

    Neal, Zachary

    2015-06-01

    The concept of social capital is becoming increasingly common in community psychology and elsewhere. However, the multiple conceptual and operational definitions of social capital challenge its utility as a theoretical tool. The goals of this paper are to clarify two forms of social capital (bridging and bonding), explicitly link them to the structural characteristics of small world networks, and explore the behavioral and ecological prerequisites of its formation. First, I use the tools of network science and specifically the concept of small-world networks to clarify what patterns of social relationships are likely to facilitate social capital formation. Second, I use an agent-based model to explore how different ecological characteristics (diversity and segregation) and behavioral tendencies (homophily and proximity) impact communities' potential for developing social capital. The results suggest diverse communities have the greatest potential to develop community social capital, and that segregation moderates the effects that the behavioral tendencies of homophily and proximity have on community social capital. The discussion highlights how these findings provide community-based researchers with both a deeper understanding of the contextual constraints with which they must contend, and a useful tool for targeting their efforts in communities with the greatest need or greatest potential.

  4. An approach of community evolution based on gravitational relationship refactoring in dynamic networks

    Yin, Guisheng; Chi, Kuo; Dong, Yuxin; Dong, Hongbin

    2017-01-01

    In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks. - Highlights: • The proposed approach considers both the static community detection and dynamic community evolution. • The approach of community evolution can identify the whole 6 common evolution events. • The proposed approach can judge the evolutionary events according to the variations of the core nodes chains.

  5. Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

    Romero, David; Molina, Arturo

    Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

  6. Ecological Networks and Community Attachment and Support Among Recently Resettled Refugees.

    Soller, Brian; Goodkind, Jessica R; Greene, R Neil; Browning, Christopher R; Shantzek, Cece

    2018-03-25

    Interventions aimed at enhancing mental health are increasingly centered around promoting community attachment and support. However, few have examined and tested the specific ecological factors that give rise to these key community processes. Drawing from insights from the ecological network perspective, we tested whether spatial and social overlap in routine activity settings (e.g., work, school, childcare) with fellow ethnic community members is associated with individuals' attachment to their ethnic communities and access to social resources embedded in their communities. Data on routine activity locations drawn from the Refugee Well-Being Project (based in a city in the Southwestern United States) were used to reconstruct the ecological networks of recently resettled refugee communities, which were two-mode networks that comprise individuals and their routine activity locations. Results indicated that respondents' community attachment and support increased with their ecological network extensity-which taps the extent to which respondents share routine activity locations with other community members. Our study highlights a key ecological process that potentially enhances individuals' ethnic community attachment that extends beyond residential neighborhoods. © Society for Community Research and Action 2018.

  7. Provider-based research networks and diffusion of surgical technologies among patients with early-stage kidney cancer.

    Tan, Hung-Jui; Meyer, Anne-Marie; Kuo, Tzy-Mey; Smith, Angela B; Wheeler, Stephanie B; Carpenter, William R; Nielsen, Matthew E

    2015-03-15

    Provider-based research networks such as the National Cancer Institute's Community Clinical Oncology Program (CCOP) have been shown to facilitate the translation of evidence-based cancer care into clinical practice. This study compared the utilization of laparoscopy and partial nephrectomy among patients with early-stage kidney cancer according to their exposure to CCOP-affiliated providers. With linked Surveillance, Epidemiology, and End Results-Medicare data, patients with T1aN0M0 kidney cancer who had been treated with nephrectomy from 2000 to 2007 were identified. For each patient, the receipt of care from a CCOP physician or hospital and treatment with laparoscopy or partial nephrectomy were determined. Adjusted for patient characteristics (eg, age, sex, and marital status) and other organizational features (eg, community hospital and National Cancer Institute-designated cancer center), multivariate logistic regression was used to estimate the association between each surgical innovation and CCOP affiliation. During the study interval, 1578 patients (26.8%) were treated by a provider with a CCOP affiliation. Trends in the utilization of laparoscopy and partial nephrectomy remained similar between affiliated and nonaffiliated providers (P ≥ .05). With adjustments for patient characteristics, organizational features, and clustering, no association was noted between CCOP affiliation and the use of laparoscopy (odds ratio [OR], 1.11; 95% confidence interval [CI], 0.81-1.53) or partial nephrectomy (OR, 1.04; 95% CI, 0.82-1.32) despite the more frequent receipt of these treatments in academic settings (P kidney cancer, indicating perhaps a more limited scope to provider-based research networks as they pertain to translational efforts in cancer care. © 2014 American Cancer Society.

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

    Ma, Xiaoke; Gao, Lin; Yong, Xuerong

    2010-01-01

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

  9. Classification of breast cancer histology images using Convolutional Neural Networks.

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  10. Bayesian networks for clinical decision support in lung cancer care.

    M Berkan Sesen

    Full Text Available Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs, which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA, we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03, whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03. Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included.

  11. A prospective audit of early stoma complications in colorectal cancer treatment throughout the Greater Manchester and Cheshire colorectal cancer network.

    Parmar, K L; Zammit, M; Smith, A; Kenyon, D; Lees, N P

    2011-08-01

    The study aimed to identify the incidence of early stoma problems after surgery for colorectal cancer to identify predisposing factors and to assess the effect on discharge from hospital and the greater need for community stoma care. A prospective study of 192 patients was carried out over a six-month period in the 13 units of the Greater Manchester and Cheshire Cancer Network. Stoma problems were categorized into fistula, leakage, pancaking, necrosis, retraction, separation, stenosis, skin problems, parastomal hernia, suboptimal stoma site and need for resiting or refashioning. Differences in incidence between units (anonymized) were analysed, and the effect of stoma complications on length of hospital stay and the need for additional community stoma care was determined. One hundred and ninety-two patients with stomas were included, of which 52 (27.1%) were identified as being problematic (range 0-66.7% between units). Significant risk factors included stoma type (colostomy) (P stoma length (P = 0.006), higher BMI (P = 0.043), emergency surgery (P = 0.002) and lack of preoperative site marking (P stomas were associated with longer hospital stay (P care (P Stoma type, stoma length, body mass index, emergency surgery and lack of preoperative marking were significant risk factors. Overall complication rates compare favourably with other studies. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

  12. Anonymity versus privacy: selective information sharing in online cancer communities.

    Frost, Jeana; Vermeulen, Ivar E; Beekers, Nienke

    2014-05-14

    Active sharing in online cancer communities benefits patients. However, many patients refrain from sharing health information online due to privacy concerns. Existing research on privacy emphasizes data security and confidentiality, largely focusing on electronic medical records. Patient preferences around information sharing in online communities remain poorly understood. Consistent with the privacy calculus perspective adopted from e-commerce research, we suggest that patients approach online information sharing instrumentally, weighing privacy costs against participation benefits when deciding whether to share certain information. Consequently, we argue that patients prefer sharing clinical information over daily life and identity information that potentially compromises anonymity. Furthermore, we explore whether patients' prior experiences, age, health, and gender affect perceived privacy costs and thus willingness to share information. The goal of the present study is to document patient preferences for sharing information within online health platforms. A total of 115 cancer patients reported sharing intentions for 15 different types of information, demographics, health status, prior privacy experiences, expected community utility, and privacy concerns. Factor analysis on the 15 information types revealed 3 factors coinciding with 3 proposed information categories: clinical, daily life, and identity information. A within-subject ANOVA showed a strong preference for sharing clinical information compared to daily life and identity information (F1,114=135.59, P=.001, η(2)=.93). Also, adverse online privacy experiences, age, and health status negatively affected information-sharing intentions. Female patients shared information less willingly. Respondents' information-sharing intentions depend on dispositional and situational factors. Patients share medical details more willingly than daily life or identity information. The results suggest the need to focus on

  13. Anonymity Versus Privacy: Selective Information Sharing in Online Cancer Communities

    Vermeulen, Ivar E; Beekers, Nienke

    2014-01-01

    Background Active sharing in online cancer communities benefits patients. However, many patients refrain from sharing health information online due to privacy concerns. Existing research on privacy emphasizes data security and confidentiality, largely focusing on electronic medical records. Patient preferences around information sharing in online communities remain poorly understood. Consistent with the privacy calculus perspective adopted from e-commerce research, we suggest that patients approach online information sharing instrumentally, weighing privacy costs against participation benefits when deciding whether to share certain information. Consequently, we argue that patients prefer sharing clinical information over daily life and identity information that potentially compromises anonymity. Furthermore, we explore whether patients’ prior experiences, age, health, and gender affect perceived privacy costs and thus willingness to share information. Objective The goal of the present study is to document patient preferences for sharing information within online health platforms. Methods A total of 115 cancer patients reported sharing intentions for 15 different types of information, demographics, health status, prior privacy experiences, expected community utility, and privacy concerns. Results Factor analysis on the 15 information types revealed 3 factors coinciding with 3 proposed information categories: clinical, daily life, and identity information. A within-subject ANOVA showed a strong preference for sharing clinical information compared to daily life and identity information (F 1,114=135.59, P=.001, η2=.93). Also, adverse online privacy experiences, age, and health status negatively affected information-sharing intentions. Female patients shared information less willingly. Conclusions Respondents’ information-sharing intentions depend on dispositional and situational factors. Patients share medical details more willingly than daily life or identity

  14. MicroRNA functional network in pancreatic cancer: From biology to ...

    [Wang J and Sen S 2011 MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease. ... growth factor type I receptor; INSR, insulin receptor; IPA, Ingenuity Pathway Analysis; IPMN, ..... Prostate cancer signalling.

  15. Community Health Status Indicators (CHSI) to Combat Obesity, Heart Disease and Cancer

    U.S. Department of Health & Human Services — Community Health Status Indicators (CHSI) to combat obesity, heart disease, and cancer are major components of the Community Health Data Initiative. This dataset...

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

    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.

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

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

    2016-01-01

    Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression, for instance. Thus, monitoring the mood of the patients is an important part of their treatment. Many cancer patients are users of online social networks and many of them take part in cancer virtual communities where they exchange messages commenting about their treatment or giving support to other patients in the community. Most of these communities are of public access and thus are useful sources of information about the mood of patients. Based on that, Sentiment Analysis methods can be useful to automatically detect positive or negative mood of cancer patients by analyzing their messages in these online communities. The objective of this work is to present a Sentiment Analysis tool, named SentiHealth-Cancer (SHC-pt), that improves the detection of emotional state of patients in Brazilian online cancer communities, by inspecting their posts written in Portuguese language. The SHC-pt is a sentiment analysis tool which is tailored specifically to detect positive, negative or neutral messages of patients in online communities of cancer patients. We conducted a comparative study of the proposed method with a set of general-purpose sentiment analysis tools adapted to this context. Different collections of posts were obtained from two cancer communities in Facebook. Additionally, the posts were analyzed by sentiment analysis tools that support the Portuguese language (Semantria and SentiStrength) and by the tool SHC-pt, developed based on the method proposed in this paper called SentiHealth. Moreover, as a second alternative to analyze the texts in Portuguese, the collected texts were automatically translated

  18. Cancer-related marketing centrality motifs acting as pivot units in the human signaling network and mediating cross-talk between biological pathways.

    Li, Wan; Chen, Lina; Li, Xia; Jia, Xu; Feng, Chenchen; Zhang, Liangcai; He, Weiming; Lv, Junjie; He, Yuehan; Li, Weiguo; Qu, Xiaoli; Zhou, Yanyan; Shi, Yuchen

    2013-12-01

    Network motifs in central positions are considered to not only have more in-coming and out-going connections but are also localized in an area where more paths reach the networks. These central motifs have been extensively investigated to determine their consistent functions or associations with specific function categories. However, their functional potentials in the maintenance of cross-talk between different functional communities are unclear. In this paper, we constructed an integrated human signaling network from the Pathway Interaction Database. We identified 39 essential cancer-related motifs in central roles, which we called cancer-related marketing centrality motifs, using combined centrality indices on the system level. Our results demonstrated that these cancer-related marketing centrality motifs were pivotal units in the signaling network, and could mediate cross-talk between 61 biological pathways (25 could be mediated by one motif on average), most of which were cancer-related pathways. Further analysis showed that molecules of most marketing centrality motifs were in the same or adjacent subcellular localizations, such as the motif containing PI3K, PDK1 and AKT1 in the plasma membrane, to mediate signal transduction between 32 cancer-related pathways. Finally, we analyzed the pivotal roles of cancer genes in these marketing centrality motifs in the pathogenesis of cancers, and found that non-cancer genes were potential cancer-related genes.

  19. The Utrecht Pharmacy Practice network for Education and Research: a network of community and hospital pharmacies in the Netherlands.

    Koster, Ellen S; Blom, Lyda; Philbert, Daphne; Rump, Willem; Bouvy, Marcel L

    2014-08-01

    Practice-based networks can serve as effective mechanisms for the development of the profession of pharmacists, on the one hand by supporting student internships and on the other hand by collection of research data and implementation of research outcomes among public health practice settings. This paper presents the characteristics and benefits of the Utrecht Pharmacy Practice network for Education and Research, a practice based research network affiliated with the Department of Pharmaceutical Sciences of Utrecht University. Yearly, this network is used to realize approximately 600 student internships (in hospital and community pharmacies) and 20 research projects. To date, most research has been performed in community pharmacy and research questions frequently concerned prescribing behavior or adherence and subjects related to uptake of regulations in the pharmacy setting. Researchers gain access to different types of data from daily practice, pharmacists receive feedback on the functioning of their own pharmacy and students get in depth insight into pharmacy practice.

  20. Toward standardizing and reporting colorectal cancer screening indicators on an international level: The International Colorectal Cancer Screening Network

    Benson, Victoria S.; Atkin, Wendy S.; Green, Jane; Nadel, Marion R.; Patnick, Julietta; Smith, Robert A.; Villain, Patricia; Patnick, J.; Atkin, W. S.; Altenhofen, L.; Ancelle-Park, R.; Benson, V. S.; Green, J.; Levin, T. R.; Moss, S. M.; Nadel, M.; Ransohoff, D.; Segnan, N.; Smith, R. A.; Villain, P.; Weller, D.; Koukari, A.; Young, G.; López-Kostner, F.; Antoljak, N.; Suchánek, S.; Zavoral, M.; Holten, I.; Malila, N.; Salines, E.; Brenner, G.; Herszényi, L.; Tulassay, Z.; Rennert, G.; Senore, C.; Zappa, M.; Zorzi, M.; Saito, H.; Leja, M.; Dekker, E.; Jansen, J.; Hol, L.; Kuipers, E.; Kaminski, M. F.; Regula, J.; Sfarti, C.; Trifan, A.; Tang, C.-L.; Hrcka, R.; Binefa, G.; Espinàs, J. A.; Peris, M.; Chen, T. H.; Steele, R.; Pou, G.; Bisges, D.; Dwyer, D.; Groves, C.; Courteau, S.; Kramer, R.; Siegenthaler, K.; Lane, D.; Herrera, C.; Rogers, J.; Rojewski, M.; Wolf, Holly; Sung, J. J.; Ling, K.; Bryant, H.; Rabeneck, L.; Dale, J.; Sware, L.; Yang, H.; Viguier, J.; Von Karsa, L.; Kupcinskas, L.; Deutekom, M.; Törnberg, S.; Austoker, J.; Beral, V.; Monk, C.; Valori, R.; Watson, J.; Kobrin, S.; Pignone, M.; Taplin, S.

    2012-01-01

    The International Colorectal Cancer Screening Network was established in 2003 to promote best practice in the delivery of organized colorectal cancer screening programs. To facilitate evaluation of such programs, we defined a set of universally applicable colorectal cancer screening measures and

  1. The function of communities in protein interaction networks at multiple scales

    Jones Nick S

    2010-07-01

    Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

  2. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

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

    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

  4. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    Schoof, Erwin; Erler, Janine

    understanding of molecular processes which are fundamental to tumorigenesis. In Article 1, we propose a novel framework for how cancer mutations can be studied by taking into account their effect at the protein network level. In Article 2, we demonstrate how global, quantitative data on phosphorylation dynamics...... can be generated using MS, and how this can be modeled using a computational framework for deciphering kinase-substrate dynamics. This framework is described in depth in Article 3, and covers the design of KinomeXplorer, which allows the prediction of kinases responsible for modulating observed...... phosphorylation dynamics in a given biological sample. In Chapter III, we move into Integrative Network Biology, where, by combining two fundamental technologies (MS & NGS), we can obtain more in-depth insights into the links between cellular phenotype and genotype. Article 4 describes the proof...

  5. Partial Information Community Detection in a Multilayer Network

    2016-06-01

    26 3 Methodology 33 3.1 Topology of the Noordin Top Terrorist Network . . . . . . . . . . . . 33 3.2 Partial Information... Topology of Synthetic Network. . . . . . . . . . . . . . . . . . . 69 4.4 Four Discovery Algorithms Discovering Red Vertices in a Synthetic Network 72 4.5...without their expertise and analysis. I have been lucky enough to have learned from the wonderful faculty of Applied Mathe - matics Department at the Naval

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

    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.

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

    Yuquan Guo

    2017-01-01

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

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

    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.

  9. The development of a network for community-based obesity prevention: the CO-OPS Collaboration

    2011-01-01

    Background Community-based interventions are a promising approach and an important component of a comprehensive response to obesity. In this paper we describe the Collaboration of COmmunity-based Obesity Prevention Sites (CO-OPS Collaboration) in Australia as an example of a collaborative network to enhance the quality and quantity of obesity prevention action at the community level. The core aims of the CO-OPS Collaboration are to: identify and analyse the lessons learned from a range of community-based initiatives aimed at tackling obesity, and; to identify the elements that make community-based obesity prevention initiatives successful and share the knowledge gained with other communities. Methods Key activities of the collaboration to date have included the development of a set of Best Practice Principles and knowledge translation and exchange activities to promote the application (or use) of evidence, evaluation and analysis in practice. Results The establishment of the CO-OPS Collaboration is a significant step toward strengthening action in this area, by bringing together research, practice and policy expertise to promote best practice, high quality evaluation and knowledge translation and exchange. Future development of the network should include facilitation of further evidence generation and translation drawing from process, impact and outcome evaluation of existing community-based interventions. Conclusions The lessons presented in this paper may help other networks like CO-OPS as they emerge around the globe. It is important that networks integrate with each other and share the experience of creating these networks. PMID:21349185

  10. Sparks in the Fog: Social and Economic Mechanisms as Enablers for Community Network Clouds

    Muhammad Amin KHAN

    2014-10-01

    Full Text Available Internet and communication technologies have lowered the costs of enabling individuals and communities to collaborate together. This collaboration has provided new services like user-generated content and social computing, as evident from success stories like Wikipedia. Through collaboration, collectively built infrastructures like community wireless mesh networks where users provide the communication network, have also emerged. Community networks have demonstrated successful bandwidth sharing, but have not been able to extend their collective effort to other computing resources like storage and processing. The success of cloud computing has been enabled by economies of scale and the need for elastic, flexible and on-demand provisioning of computing services. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. We explore in this paper how social and economic mechanisms can play a role in overcoming the barriers of voluntary resource provisioning in such community clouds, by analysing the costs involved in building these services and how they give value to the participants. We indicate socio-economic policies and how they can be implemented in community networks, to ease the uptake and ensure the sustainability of community clouds.

  11. Navigating cancer using online communities: a grounded theory of survivor and family experiences.

    Harkin, Lydia Jo; Beaver, Kinta; Dey, Paola; Choong, Kartina

    2017-12-01

    People affected by cancer often have unmet emotional and social support needs. Online cancer communities are a convenient channel for connecting cancer survivors, allowing them to support one another. However, it is unclear whether online community use makes a meaningful contribution to cancer survivorship, as little previous research has examined the experience of using contemporary cancer communities. We aimed to explore the experiences of visitors to online cancer communities. Twenty-three in-depth interviews were conducted with online cancer community visitors, including cancer survivors (n = 18), family members (n = 2), and individuals who were both a survivor and family member (n = 3). Interviews were analysed using a grounded theory approach. A theory developed explaining how individuals 'navigated' the experience of cancer using online cancer communities. Online advice and information led participants on a 'journey to become informed'. Online friendships normalised survivorship and cast participants on a 'journey to recreate identity'. Participants navigated a 'journey through different worlds' as they discovered relevant and hidden communities. This theory highlights virtual paths people affected by cancer can take to self-manage their experience of the disease. Online community experiences can be improved by promoting online evaluation skills and signposting visitors to bereavement support. Cancer survivors can benefit through both lurking and posting in online communities. However, individuals risk becoming distressed when they befriend individuals who may soon die. Additionally, people affected by rarer cancers can struggle to find shared experiences online and may need to look elsewhere for support.

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

    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

  13. Community-Based Research Networks: Development and Lessons Learned in an Emerging Field.

    Stoecker, Randy; Ambler, Susan H.; Cutforth, Nick; Donohue, Patrick; Dougherty, Dan; Marullo, Sam; Nelson, Kris S.; Stutts, Nancy B.

    2003-01-01

    Compares seven multi-institutional community-based research networks in Appalachia; Colorado; District of Columbia; Minneapolis-St. Paul; Philadelphia; Richmond, Virginia; and Trenton, New Jersey. After reviewing the histories of the networks, conducts a comparative SWOT analysis, showing their common and unique strengths, weaknesses,…

  14. The Illinois Community College Sustainability Network--A Successful Model

    Jacobson, Bert

    2010-01-01

    In 2008, the Illinois Department of Commerce and Economic Opportunity (DCEO), Bureau of Energy and Recycling, funded a pilot project creating a network of Sustainability Centers. The pilot project demonstrated that networked campus sustainability centers are an efficient mechanism to reach consumers, business, and industry. All 48 community…

  15. Friendship Concept and Community Network Structure among Elementary School and University Students.

    Hernández-Hernández, Ana María; Viga-de Alva, Dolores; Huerta-Quintanilla, Rodrigo; Canto-Lugo, Efrain; Laviada-Molina, Hugo; Molina-Segui, Fernanda

    2016-01-01

    We use complex network theory to study the differences between the friendship concepts in elementary school and university students. Four friendship networks were identified from surveys. Three of these networks are from elementary schools; two are located in the rural area of Yucatán and the other is in the urban area of Mérida, Yucatán. We analyzed the structure and the communities of these friendship networks and found significant differences among those at the elementary schools compared with those at the university. In elementary schools, the students make friends mainly in the same classroom, but there are also links among different classrooms because of the presence of siblings and relatives in the schools. These kinds of links (sibling-friend or relative-friend) are called, in this work, "mixed links". The classification of the communities is based on their similarity with the classroom composition. If the community is composed principally of students in different classrooms, the community is classified as heterogeneous. These kinds of communities appear in the elementary school friendship networks mainly because of the presence of relatives and siblings. Once the links between siblings and relatives are removed, the communities resembled the classroom composition. On the other hand, the university students are more selective in choosing friends and therefore, even when they have friends in the same classroom, those communities are quite different to the classroom composition. Also, in the university network, we found heterogeneous communities even when the presence of sibling and relatives is negligible. These differences made up a topological structure quite different at different academic levels. We also found differences in the network characteristics. Once these differences are understood, the topological structure of the friendship network and the communities shaped in an elementary school could be predicted if we know the total number of students

  16. Designing a community-based lay health advisor training curriculum to address cancer health disparities.

    Gwede, Clement K; Ashley, Atalie A; McGinnis, Kara; Montiel-Ishino, F Alejandro; Standifer, Maisha; Baldwin, Julie; Williams, Coni; Sneed, Kevin B; Wathington, Deanna; Dash-Pitts, Lolita; Green, B Lee

    2013-05-01

    Racial and ethnic minorities have disproportionately higher cancer incidence and mortality than their White counterparts. In response to this inequity in cancer prevention and care, community-based lay health advisors (LHAs) may be suited to deliver effective, culturally relevant, quality cancer education, prevention/screening, and early detection services for underserved populations. APPROACH AND STRATEGIES: Consistent with key tenets of community-based participatory research (CBPR), this project engaged community partners to develop and implement a unique LHA training curriculum to address cancer health disparities among medically underserved communities in a tricounty area. Seven phases of curriculum development went into designing a final seven-module LHA curriculum. In keeping with principles of CBPR and community engagement, academic-community partners and LHAs themselves were involved at all phases to ensure the needs of academic and community partners were mutually addressed in development and implementation of the LHA program. Community-based LHA programs for outreach, education, and promotion of cancer screening and early detection, are ideal for addressing cancer health disparities in access and quality care. When community-based LHAs are appropriately recruited, trained, and located in communities, they provide unique opportunities to link, bridge, and facilitate quality cancer education, services, and research.

  17. Community partnerships in healthy eating and lifestyle promotion: A network analysis

    Ruopeng An

    2017-06-01

    Full Text Available Promoting healthy eating and lifestyles among populations with limited resources is a complex undertaking that often requires strong partnerships between various agencies. In local communities, these agencies are typically located in different areas, serve diverse subgroups, and operate distinct programs, limiting their communication and interactions with each other. This study assessed the network of agencies in local communities that promote healthy eating and lifestyles among populations with limited resources. Network surveys were administered in 2016 among 89 agencies located in 4 rural counties in Michigan that served limited-resource audiences. The agencies were categorized into 8 types: K-12 schools, early childhood centers, emergency food providers, health-related agencies, social resource centers, low-income/subsidized housing complexes, continuing education organizations, and others. Network analysis was conducted to examine 4 network structures—communication, funding, cooperation, and collaboration networks between agencies within each county. Agencies had a moderate level of cooperation, but were only loosely connected in the other 3 networks, indicated by low network density. Agencies in a network were decentralized rather than centralized around a few influential agencies, indicated by low centralization. There was evidence regarding homophily in a network, indicated by some significant correlations within agencies of the same type. Agencies connected in any one network were considerably more likely to be connected in all the other networks as well. In conclusion, promoting healthy eating and lifestyles among populations with limited resources warrants strong partnership between agencies in communities. Network analysis serves as a useful tool to evaluate community partnerships and facilitate coalition building.

  18. Nuclear Community in network; La comunidad nuclear en la Red

    Tejedor, E.

    2014-02-01

    The internet has revolutionized the ways of communication and many companies/ organizations have adapted to the change but others have not, or have done it halfway. This presentation is a review of the main characteristics of virtual communities and their typology. The status of the Nuclear Online Community, both pro nuclear and antinuclear is analysed , and their main similarities and differences are discussed. The Pro nuclear Online Community is formed gradually. This presentation attempts to define some ways to increase the scope of the Community and encourage greater dissemination of the characteristics of nuclear energy. (Author)

  19. HBCUs inform students and the community about cervical cancer.

    Green, Julian S; Williams, Deloris G; Scott, Dolores B; Madison, Shirley B; Comer, Kimberly D; Haynes, Joseph A

    2009-12-01

    In summary, HBCUs can no longer remain reactive, but must spearhead efforts to increase both the health of the student body, as well as the community at large. HBCUs should collaboratively initiate a "Call to Action", whereby policies and programs could be created to aid in the prevention of HPV and other STIs. To support this action, HBCUs could more actively pursue funding sources that support both universities and the communities in which they exist. Student orientation could be redefined to include short courses in STI awareness and prevention, and be communicated in a manner that is professional, yet engaging to students. Moreover, university departments which have an interest in the health of communities should supervise these efforts. The knowledge of university faculty members within departments of Nursing, Social Work, Public Health, Rehabilitation Counseling and Physical Education should extend beyond the classroom and into the community. Clark commented, "Perhaps course content across departments could be revised to encompass an increased focus on practice skills which support awareness and prevention efforts". Through employment, volunteerism and student internships, each of these disciplines have established relationships with the surrounding community and understand the associated critical needs. Such relationships provide the best environment for both the creation and implementation of services, and provide students with a model of how to "give back" to the community by utilizing their education. Campus health centers should be more prevention-driven beyond the distribution of condoms and pamphlets, to collaborate with local area high schools and community-based organizations to create an information network accessible to students and community residents. Additionally, health centers should promote the availability of HPV vaccination, which depending on state of residence and age, may be free or available at a discounted cost. According to Bynum

  20. Links between real and virtual networks: a comparative study of online communities in Japan and Korea.

    Ishii, Kenichi; Ogasahara, Morihiro

    2007-04-01

    The present study explores how online communities affect real-world personal relations based on a cross-cultural survey conducted in Japan and Korea. Findings indicate that the gratifications of online communities moderate the effects of online communities on social participation. Online communities are categorized into a real-group-based community and a virtual-network-based community. The membership of real-group-based online community is positively correlated with social bonding gratification and negatively correlated with information- seeking gratification. Japanese users prefer more virtual-network-based online communities, while their Korean counterparts prefer real-group-based online communities. Korean users are more active in online communities and seek a higher level of socializing gratifications, such as social bonding and making new friends, when compared with their Japanese counterparts. These results indicate that in Korea, personal relations via the online community are closely associated with the real-world personal relations, but this is not the case in Japan. This study suggests that the effects of the Internet are culture-specific and that the online community can serve a different function in different cultural environments.

  1. Network Analysis of a Virtual Community of Learning of Economics Educators

    Fontainha, Elsa; Martins, Jorge Tiago; Vasconcelos, Ana Cristina

    2015-01-01

    Introduction: This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It…

  2. Automated Library Networking in American Public Community College Learning Resources Centers.

    Miah, Adbul J.

    1994-01-01

    Discusses the need for community colleges to assess their participation in automated library networking systems (ALNs). Presents results of questionnaires sent to 253 community college learning resource center directors to determine their use of ALNs. Reviews benefits of automation and ALN activities, planning and communications, institution size,…

  3. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

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

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

    2017-11-01

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

  5. Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach

    Vollenbroek, Wouter Bernardus; de Vries, Sjoerd A.; Fred, Ana; Dietz, Jan; Aveiro, David; Liu, Kecheng; Bernardino, Jorge; Filipe, Joaquim

    2016-01-01

    The importance of web-based knowledge communities (WKCs) in the 'network society' is growing. This trend is seen in many disciplines, like education, government, finance and other profit- and non-profit organisations. There is a need for understanding the development of these online communities in

  6. Magnets and Seekers: A Network Perspective on Academic Integration inside Two Residential Communities

    Smith, Rachel A.

    2015-01-01

    Residential learning communities aim to foster increased academic and social integration, ideally leading to greater student success. However, the concept of academic integration is often conceptualized and measured at the individual level, rather than the theoretically more consistent community level. Network analysis provides a paradigm and…

  7. Writing Programs as Distributed Networks: A Materialist Approach to University-Community Digital Media Literacy

    Comstock, Michelle

    2006-01-01

    This article addresses how community-university digital media literacy projects are redefining literacy, literate practices, and institutions. Using Actor-Network Theory (ANT), which emphasizes the organizing process itself, I analyze the shifting definitions of literacy within one particular university-community collaboration. My analysis…

  8. Applying information network analysis to fire-prone landscapes: implications for community resilience

    Derric B. Jacobs

    2017-03-01

    Full Text Available Resilient communities promote trust, have well-developed networks, and can adapt to change. For rural communities in fire-prone landscapes, current resilience strategies may prove insufficient in light of increasing wildfire risks due to climate change. It is argued that, given the complexity of climate change, adaptations are best addressed at local levels where specific social, cultural, political, and economic conditions are matched with local risks and opportunities. Despite the importance of social networks as key attributes of community resilience, research using social network analysis on coupled human and natural systems is scarce. Furthermore, the extent to which local communities in fire-prone areas understand climate change risks, accept the likelihood of potential changes, and have the capacity to develop collaborative mitigation strategies is underexamined, yet these factors are imperative to community resiliency. We apply a social network framework to examine information networks that affect perceptions of wildfire and climate change in Central Oregon. Data were collected using a mailed questionnaire. Analysis focused on the residents' information networks that are used to gain awareness of governmental activities and measures of community social capital. A two-mode network analysis was used to uncover information exchanges. Results suggest that the general public develops perceptions about climate change based on complex social and cultural systems rather than as patrons of scientific inquiry and understanding. It appears that perceptions about climate change itself may not be the limiting factor in these communities' adaptive capacity, but rather how they perceive local risks. We provide a novel methodological approach in understanding rural community adaptation and resilience in fire-prone landscapes and offer a framework for future studies.

  9. Community Garden Information Systems: Analyzing and Strengthening Community-Based Resource Sharing Networks

    Loria, Kristen

    2013-01-01

    Extension professionals play an increasingly central role in supporting community garden and other community-based agriculture projects. With growing interest in community gardens as tools to improve community health and vitality, the best strategies for supporting these projects should be explored. Due to the importance of inter-personal networks…

  10. A social network analysis of treatment discoveries in cancer.

    Athanasios Tsalatsanis

    2011-03-01

    Full Text Available Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i at random, ii based on common characteristics, or iii based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers' tendency to interact selectively with established and successful peers. Furthermore, the RCT networks are "small worlds": trials are connected through a small number of ties, yet there is much clustering among subsets of trials. We also find that treatment success (improved survival is proportional to the network centrality measures of closeness and betweenness. Negative correlation exists between survival and the extent to which trials operate within a limited scope of information. Finally, the trials testing curative treatments in solid tumors showed the highest centrality and the most influential group was the ECOG. We conclude that the chances of discovering life-saving treatments are directly related to the richness of social interactions between researchers inherent in a preferential interaction model.

  11. Cluster synchronization of community network with distributed time delays via impulsive control

    Leng Hui; Wu Zhao-Yan

    2016-01-01

    Cluster synchronization is an important dynamical behavior in community networks and deserves further investigations. A community network with distributed time delays is investigated in this paper. For achieving cluster synchronization, an impulsive control scheme is introduced to design proper controllers and an adaptive strategy is adopted to make the impulsive controllers unified for different networks. Through taking advantage of the linear matrix inequality technique and constructing Lyapunov functions, some synchronization criteria with respect to the impulsive gains, instants, and system parameters without adaptive strategy are obtained and generalized to the adaptive case. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results. (paper)

  12. Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

    2014-01-01

    We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...

  13. Fluctuating interaction network and time-varying stability of a natural fish community

    Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

    2018-02-01

    Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

  14. Influence of network communities and transition to web 3.0 on change of approaches

    Ринат Гинаятович Рамазанов

    2018-12-01

    Full Text Available The article presents the genesis of network communities, the transformation of the interaction of participants in the evolution of this form of communication. The main characteristics and stages inherent in changing the formats of network interaction are described. The concept of networked communities is multifaceted and includes various processes of socialization: from the behaviour of people in interest groups to the formation of large international online communities. The evolution of such communities includes the transition from guest books and forums to more complex systems with many additional tools for networking. The introduction of modern technologies in all spheres of life leads to the formation of a qualitatively new distinctive model of education. In such conditions, the roles of participants in the educational process are changing, more attention is paid to self-education and development at the expense of the internal need for learning, the organizational and methodological component of the educational process is being reconstructed, and new conditions are created for the development of distance learning technologies. All this allows us to develop a more competitive system of interaction, make management processes transparent and create conditions for the independent development of each participant in the network space. Online network communities are a powerful tool that must be used in the education system in various interpretations, and as an additional resource that allows the principle of “lifelong learning” to be introduced into the teacher’s self-education system.

  15. Local communities obstruct global consensus: Naming game on multi-local-world networks

    Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

    2018-02-01

    Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

  16. The community structure of the European network of interlocking directorates 2005-2010.

    Eelke M Heemskerk

    Full Text Available The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

  17. The community structure of the European network of interlocking directorates 2005-2010.

    Heemskerk, Eelke M; Daolio, Fabio; Tomassini, Marco

    2013-01-01

    The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

  18. The Community Structure of the European Network of Interlocking Directorates 2005–2010

    Heemskerk, Eelke M.; Daolio, Fabio; Tomassini, Marco

    2013-01-01

    The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer. PMID:23894318

  19. Startup : Philippine Community eCentres Network | IDRC ...

    ... served as a model for networking the telecentres and pointing future directions. ... long-term climate action to reduce social inequality, promote greater gender parity, ... including heat stress, water management, and climate-related migration.

  20. Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

    Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

    2010-01-01

    We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily

  1. Knowledge Assessment of the Dental Community in Texas on the Role of Human Papilloma Virus in Oropharyngeal Cancer.

    Rowan, Stephanie D; Hu, Shirley L; Brotzman, Jacob S; Redding, Spencer W; Rankin, K Vendrell; Vigneswaran, Nadarajah

    2015-08-01

    The epidemiology of oral cancer is changing. From 1988 to 2004, there has been a dramatic increase in Human Papilloma virus (HPV) positive oropharyngeal squamous cell carcinoma (OPC) in the U.S. At the same time there have been decreasing rates of OPC associated with the traditional risk factors of smoking and alcohol consumption. The epidemiology of oral cancer is changing. As the epidemiology changes, it is important that the dental community recognize these factors. The goal of this study was to assess the baseline level of knowledge about HPV and OPC within the Texas dental community. Practicing dentists and dental hygienists from Texas dental professional networks and dental students from the three Texas schools of dentistry were recruited to participate in the study. Participants were requested to access and complete a 7-item online survey. To ensure anonymity, a third party practice facilitator or department administrator disseminated the survey link to participants. Of the 457 surveys completed, 100% of respondents reported conducting oral soft tissue examinations at least annually. However, only 73% included the oropharynx in their exam. Less than 50% of dental professionals selected the correct location of the greatest increase in oral cancer incidence during the last 10 years. Less than 30% of each of the groups answered correctly in indicating the age group with the most rapidly increasing incidence of oral cancer. Approximately 40% of all groups indicated that a biopsy from the posterior oropharynx should be tested for HPV. Survey results across Texas dentists, dental hygienists, and Texas dental students demonstrated a lack of knowledge of the changing profile of oral cancer regarding HPV-associated OPC. This aim of this initial phase was to determine the baseline level of knowledge surrounding the risks associated with oropharyngeal cancer in the survey population. Our goal is to utilize these findings to develop educational interventions that will

  2. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

    Chen Bor-Sen

    2011-01-01

    Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

  3. Altered small-world properties of gray matter networks in breast cancer

    Hosseini S M

    2012-05-01

    Full Text Available Abstract Background Breast cancer survivors, particularly those treated with chemotherapy, are at significantly increased risk for long-term cognitive and neurobiologic impairments. These deficits tend to involve skills that are subserved by distributed brain networks. Additionally, neuroimaging studies have shown a diffuse pattern of brain structure changes in chemotherapy-treated breast cancer survivors that might impact large-scale brain networks. Methods We therefore applied graph theoretical analysis to compare the gray matter structural networks of female breast cancer survivors with a history of chemotherapy treatment and healthy age and education matched female controls. Results Results revealed reduced clustering coefficient and small-world index in the brain network of the breast cancer patients across a range of network densities. In addition, the network of the breast cancer group had less highly interactive nodes and reduced degree/centrality in the frontotemporal regions compared to controls, which may help explain the common impairments of memory and executive functioning among these patients. Conclusions These results suggest that breast cancer and chemotherapy may decrease regional connectivity as well as global network organization and integration, reducing efficiency of the network. To our knowledge, this is the first report of altered large-scale brain networks associated with breast cancer and chemotherapy.

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

    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.

  5. Predicting Genes Involved in Human Cancer Using Network Contextual Information

    Rahmani Hossein

    2012-03-01

    Full Text Available Protein-Protein Interaction (PPI networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1 Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself; and (2 Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.

  6. Social network, social support, and risk of incident stroke: Atherosclerosis Risk in Communities study.

    Nagayoshi, Mako; Everson-Rose, Susan A; Iso, Hiroyasu; Mosley, Thomas H; Rose, Kathryn M; Lutsey, Pamela L

    2014-10-01

    Having a small social network and lack of social support have been associated with incident coronary heart disease; however, epidemiological evidence for incident stroke is limited. We assessed the longitudinal association of a small social network and lack of social support with risk of incident stroke and evaluated whether the association was partly mediated by vital exhaustion and inflammation. The Atherosclerosis Risk in Communities study measured social network and social support in 13 686 men and women (mean, 57 years; 56% women; 24% black; 76% white) without a history of stroke. Social network was assessed by the 10-item Lubben Social Network Scale and social support by a 16-item Interpersonal Support Evaluation List-Short Form. During a median follow-up of 18.6 years, 905 incident strokes occurred. Relative to participants with a large social network, those with a small social network had a higher risk of stroke (hazard ratio [95% confidence interval], 1.44 [1.02-2.04]) after adjustment for demographics, socioeconomic variables, marital status, behavioral risk factors, and major stroke risk factors. Vital exhaustion, but not inflammation, partly mediated the association between a small social network and incident stroke. Social support was unrelated to incident stroke. In this sample of US community-dwelling men and women, having a small social network was associated with excess risk of incident stroke. As with other cardiovascular conditions, having a small social network may be associated with a modestly increased risk of incident stroke. © 2014 American Heart Association, Inc.

  7. Social Media Use for Cancer Education at a Community-Based Cancer Center in South Korea.

    Heo, Jaesung; Chun, Mison; Lee, Hyun Woo; Woo, Jeong-Hee

    2016-12-12

    The main purpose of this study was to evaluate the effectiveness of the education system using social media. Eight educational video clips were developed instructing the viewer on cancer-related issues such as prevention, treatment, and survivorship. Each video was made with participation of medical professors and posted on a YouTube channel. A mobile phone application was produced containing a scheduler function, introduction of a community cancer center program, and cancer information. A medical blog was established to provide stationary materials such as images and articles. Descriptive analysis was done by Google analytics. From May of 2014 to June of 2016, 15,247 total views were recorded on the YouTube channel, and the average view duration was about 3 min. The most popular video was about chemotherapy treatment; 5409 (36%) people watched this video, and 3615 (23.5%) people viewed a video on balanced dietary habits. As well as South Korea, 1,113 (7%) views were confirmed in the United States and 175 (1%) in Japan. The equipment used to watch the contents were mobile phones (59%), laptops (33%), and tablets (6%). Five hundred people installed the smartphone application from March of 2015 to July of 2016. Three hundred eighty-three medical contents were posted on the blog since March of 2015. Cancer education is necessary to address the education needs of patients with cancer and their caregivers. Education based on social media could be an effective method that reaches beyond geographical boundaries.

  8. Barriers to prostate cancer prevention and community recommended health education strategies in an urban African American community in Jackson, Mississippi.

    Ekúndayò, Olúgbémiga T; Tataw, David B

    2013-01-01

    This article describes the use of survey research in collaboration with the African American urban community of Georgetown, Jackson, Mississippi to identify and understand prostate cancer knowledge, resource utilization, and health education strategies considered most effective in reaching the community with prostate cancer prevention messages. The study revealed profound needs in disease identification and resources awareness and utilization. Barriers to utilization were identified by participants to include lack of self-efficacy, low self-esteem, lack of trust in the health care system, limited knowledge of prostate pathology, and limited ability to pay. Participants' recommended strategies for reaching the community with prostate cancer education include traditional and nontraditional strategies. The list of recommendations exclude modern-day outlets such as handheld devices, Twitter, Facebook, blogs, wikis, and other Internet-based outlets. The findings provide a road map for program development and an intervention research agenda custom-tailored to the Georgetown community of Jackson, Mississippi.

  9. Discursive Deployments: Mobilizing Support for Municipal and Community Wireless Networks in the U.S.

    Alvarez, Rosio; Rodriguez, Juana Maria

    2008-08-16

    This paper examines Municipal Wireless (MW) deployments in the United States. In particular, the interest is in understanding how discourse has worked to mobilize widespread support for MW networks. We explore how local governments discursively deploy the language of social movements to create a shared understanding of the networking needs of communities. Through the process of"framing" local governments assign meaning to the MW networks in ways intended to mobilize support anddemobilize opposition. The mobilizing potential of a frame varies and is dependent on its centrality and cultural resonance. We examine the framing efforts of MW networks by using a sample of Request for Proposals for community wireless networks, semi-structured interviews and local media sources. Prominent values that are central to a majority of the projects and others that are culturally specific are identified and analyzed for their mobilizing potency.

  10. Creation and Sharing of Environmental Knowledge across Communities and Networks

    Søndergård, Bent; Hansen, Ole Erik; Holm, Jesper

    2004-01-01

    Environmental communication is analysed with reference an understanding of knowledge as situated enacted practice. Exchange of environmental knowledge is conceptualised as processes of creating and transfer/translation of knowledge in and between communities of practices and as conditioned...

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

    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.

  12. Evidence gaps in advanced cancer care: community-based clinicians' perspectives and priorities for CER.

    Lowry, Sarah J; Loggers, Elizabeth T; Bowles, Erin J A; Wagner, Edward H

    2012-05-01

    Although much effort has focused on identifying national comparative effectiveness research (CER) priorities, little is known about the CER priorities of community-based practitioners treating patients with advanced cancer. CER priorities of managed care-based clinicians may be valuable as reflections of both payer and provider research interests. We conducted mixed methods interviews with 10 clinicians (5 oncologists and 5 pharmacists) at 5 health plans within the Health Maintenance Organization Cancer Research Network. We asked, "What evidence do you most wish you had when treating patients with advanced cancer" and questioned participants on their impressions and knowledge of CER and pragmatic clinical trials (PCTs). We conducted qualitative analyses to identify themes across interviews. Ninety percent of participants had heard of CER, 20% had heard of PCTs, and all rated CER/PCTs as highly relevant to patient and health plan decision making. Each participant offered between 3 and 10 research priorities. Half (49%) involved head-to-head treatment comparisons; another 20% involved comparing different schedules or dosing regimens of the same treatment. The majority included alternative outcomes to survival (eg, toxicity, quality of life, noninferiority). Participants cited several limitations to existing evidence, including lack of generalizability, funding biases, and rapid development of new treatments. Head-to-head treatment comparisons remain a major evidence need among community- based oncology clinicians, and CER/PCTs are highly valued methods to address the limitations of traditional randomized trials, answer questions of cost-effectiveness or noninferiority, and inform data-driven dialogue and decision making by all stakeholders.

  13. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

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

    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.

  15. Providing interoperability of eHealth communities through peer-to-peer networks.

    Kilic, Ozgur; Dogac, Asuman; Eichelberg, Marco

    2010-05-01

    Providing an interoperability infrastructure for Electronic Healthcare Records (EHRs) is on the agenda of many national and regional eHealth initiatives. Two important integration profiles have been specified for this purpose, namely, the "Integrating the Healthcare Enterprise (IHE) Cross-enterprise Document Sharing (XDS)" and the "IHE Cross Community Access (XCA)." IHE XDS describes how to share EHRs in a community of healthcare enterprises and IHE XCA describes how EHRs are shared across communities. However, the current version of the IHE XCA integration profile does not address some of the important challenges of cross-community exchange environments. The first challenge is scalability. If every community that joins the network needs to connect to every other community, i.e., a pure peer-to-peer network, this solution will not scale. Furthermore, each community may use a different coding vocabulary for the same metadata attribute, in which case, the target community cannot interpret the query involving such an attribute. Yet another important challenge is that each community may (and typically will) have a different patient identifier domain. Querying for the patient identifiers in the target community using patient demographic data may create patient privacy concerns. In this paper, we address each of these challenges and show how they can be handled effectively in a superpeer-based peer-to-peer architecture.

  16. Inferring causal molecular networks: empirical assessment through a community-based effort.

    Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

    2016-04-01

    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

  17. Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

    Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

    2016-04-26

    Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

  18. A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.

    Jacobson, Rebecca S; Becich, Michael J; Bollag, Roni J; Chavan, Girish; Corrigan, Julia; Dhir, Rajiv; Feldman, Michael D; Gaudioso, Carmelo; Legowski, Elizabeth; Maihle, Nita J; Mitchell, Kevin; Murphy, Monica; Sakthivel, Mayurapriyan; Tseytlin, Eugene; Weaver, JoEllen

    2015-12-15

    Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network. ©2015 American Association for Cancer Research.

  19. Local breast cancer spatial patterning: a tool for community health resource allocation to address local disparities in breast cancer mortality.

    Dana M Brantley-Sieders

    Full Text Available Despite available demographic data on the factors that contribute to breast cancer mortality in large population datasets, local patterns are often overlooked. Such local information could provide a valuable metric by which regional community health resources can be allocated to reduce breast cancer mortality. We used national and statewide datasets to assess geographical distribution of breast cancer mortality rates and known risk factors influencing breast cancer mortality in middle Tennessee. Each county in middle Tennessee, and each ZIP code within metropolitan Davidson County, was scored for risk factor prevalence and assigned quartile scores that were used as a metric to identify geographic areas of need. While breast cancer mortality often correlated with age and incidence, geographic areas were identified in which breast cancer mortality rates did not correlate with age and incidence, but correlated with additional risk factors, such as mammography screening and socioeconomic status. Geographical variability in specific risk factors was evident, demonstrating the utility of this approach to identify local areas of risk. This method revealed local patterns in breast cancer mortality that might otherwise be overlooked in a more broadly based analysis. Our data suggest that understanding the geographic distribution of breast cancer mortality, and the distribution of risk factors that contribute to breast cancer mortality, will not only identify communities with the greatest need of support, but will identify the types of resources that would provide the most benefit to reduce breast cancer mortality in the community.

  20. Analysis of the social network development of a virtual community for Australian intensive care professionals.

    Rolls, Kaye Denise; Hansen, Margaret; Jackson, Debra; Elliott, Doug

    2014-11-01

    Social media platforms can create virtual communities, enabling healthcare professionals to network with a broad range of colleagues and facilitate knowledge exchange. In 2003, an Australian state health department established an intensive care mailing list to address the professional isolation experienced by senior intensive care nurses. This article describes the social network created within this virtual community by examining how the membership profile evolved from 2003 to 2009. A retrospective descriptive design was used. The data source was a deidentified member database. Since 2003, 1340 healthcare professionals subscribed to the virtual community with 78% of these (n = 1042) still members at the end of 2009. The membership profile has evolved from a single-state nurse-specific network to an Australia-wide multidisciplinary and multiorganizational intensive care network. The uptake and retention of membership by intensive care clinicians indicated that they appeared to value involvement in this virtual community. For healthcare organizations, a virtual community may be a communications option for minimizing professional and organizational barriers and promoting knowledge flow. Further research is, however, required to demonstrate a link between these broader social networks, enabling the exchange of knowledge and improved patient outcomes.

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

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

    2018-05-01

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

  2. Understanding interactions in virtual HIV communities: a social network analysis approach.

    Shi, Jingyuan; Wang, Xiaohui; Peng, Tai-Quan; Chen, Liang

    2017-02-01

    This study investigated the driving mechanism of building interaction ties among the people living with HIV/AIDS in one of the largest virtual HIV communities in China using social network analysis. Specifically, we explained the probability of forming interaction ties with homophily and popularity characteristics. The exponential random graph modeling results showed that members in this community tend to form homophilous ties in terms of shared location and interests. Moreover, we found a tendency away from popularity effect. This suggests that in this community, resources and information were not disproportionally received by a few of members, which could be beneficial to the overall community.

  3. Use of community engagement strategies to increase research participation in practice-based research networks (PBRNs).

    Spears, William; Tsoh, Janice Y; Potter, Michael B; Weller, Nancy; Brown, Anthony E; Campbell-Voytal, Kimberly; Getrich, Christina M; Sussman, Andrew L; Pascoe, John; Neale, Anne Victoria

    2014-01-01

    Practice-based research networks (PBRNs) are increasingly encouraged to use community engagement approaches. The extent to which PBRNs engage clinic and community partners in strategies to recruit and retain participants from their local communities (specifically racial/ethnic communities) is the focus of this study. The design was a cross-sectional survey of PBRN directors in the United States. Survey respondents indicated whether their research network planned for, implemented, and has capacity for activities that engage clinic and community partners in 7 recommended strategies organized into study phases, called the cycle of trust. The objectives of the national survey were to (1) describe the extent to which PBRNs across the United States routinely implement the strategies recommended for recruiting diverse patient groups and (2) identify factors associated with implementing the recommended strategies. The survey response rate was 63%. Activities that build trust often are used more with clinic partners than with community partners. PBRNs that adopt engagement strategies when working with clinic and community partners have less difficulty in recruiting diverse populations. Multivariate analysis showed that the targeting racial/ethnic communities for study recruitment, Clinical and Translational Science Award affiliation, and planning to use community engagement strategies were independent correlates of PBRN implementation of the recommended strategies. PBRNs that successfully engage racial/ethnic communities as research partners use community engagement strategies. New commitments are needed to support PBRN researchers in developing relationships with the communities in which their patients live. Stable PBRN infrastructure funding that appreciates the value of maintaining community engagement between funded studies is critical to the research enterprise that values translating research findings into generalizable care models for patients in the community.

  4. Partnership capacity for community health improvement plan implementation: findings from a social network analysis.

    McCullough, J Mac; Eisen-Cohen, Eileen; Salas, S Bianca

    2016-07-13

    Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. The coalition's overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization's perceived value and trustworthiness as rated by other coalition members. Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high

  5. Comparative indicators for cancer network management in England: Availability, characteristics and presentation

    Coleman Michel P

    2008-02-01

    Full Text Available Abstract Background In 2000, the national cancer plan for England created 34 cancer networks, new organisational structures to coordinate services across populations varying between a half and three million people. We investigated the availability of data sets reflecting measures of structure, process and outcome that could be used to support network management. Methods We investigated the properties of national data sets relating to four common cancers – breast, colorectal, lung and prostate. We reviewed the availability and completeness of these data sets, identified leading items within each set and put them into tables of the 34 cancer networks. We also investigated methods of presentation. Results The Acute Hospitals Portfolio and the Cancer Standards Peer Review recorded structural characteristics at hospital and cancer service level. Process measures included Hospital Episode Statistics, recording admissions, and Hospital Waiting-List data. Patient outcome measures included the National Survey of Patient Satisfaction for cancer, and cancer survival, drawn from cancer registration. Data were drawn together to provide an exemplar indicator set a single network, and methods of graphical presentation were considered. Conclusion While not as yet used together in practice, comparative indicators are available within the National Health Service in England for use in performance assessment by cancer networks.

  6. Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers

    Schneider, Maria V.; Walter, Peter; Blatter, Marie-Claude

    2012-01-01

    and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review...

  7. Growing up with Social Networks and Online Communities

    Strom, Paris; Strom, Robert

    2012-01-01

    This presentation examines child and adolescent social networking with an emphasis on how this unprecedented form of communication can be used to contribute to healthy growth and development. Most literature about child and adolescent relationships reflects yesterday's world, a time when face-to-face encounters were the only concern. Students saw…

  8. How plants connect pollination and herbivory networks and their contribution to community stability.

    Sauve, Alix M C; Thébault, Elisa; Pocock, Michael J O; Fontaine, Colin

    2016-04-01

    Pollination and herbivory networks have mainly been studied separately, highlighting their distinct structural characteristics and the related processes and dynamics. However, most plants interact with both pollinators and herbivores, and there is evidence that both types of interaction affect each other. Here we investigated the way plants connect these mutualistic and antagonistic networks together, and the consequences for community stability. Using an empirical data set, we show that the way plants connect pollination and herbivory networks is not random and promotes community stability. Analyses of the structure of binary and quantitative networks show different results: the plants' generalism with regard to pollinators is positively correlated to their generalism with regard to herbivores when considering binary interactions, but not when considering quantitative interactions. We also show that plants that share the same pollinators do not share the same herbivores. However, the way plants connect pollination and herbivory networks promotes stability for both binary and quantitative networks. Our results highlight the relevance of considering the diversity of interaction types in ecological communities, and stress the need to better quantify the costs and benefits of interactions, as well as to develop new metrics characterizing the way different interaction types are combined within ecological networks.

  9. Networks and learning: communities, practices and the metaphor of networks–a commentary

    Bruce Ingraham

    2004-12-01

    Full Text Available In issue 12(1, Jones (2004 in his article ‘Networks and learning: communities, practices and the metaphor of networks' sets out to address three inter-related sets of issues: … firstly that learning technology needs to take account of the wider debate about networks and secondly that research in this field needs to address the theoretical and practical issues raised by advances in the field of networks. A third point is that the idea of the network acts as a powerful metaphor even if we are able to discount any particular theory generated in its support. The network metaphor can act as a unifying concept allowing us to bring together apparently disparate elements of the field.

  10. Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

    Yufei Liu

    2017-01-01

    Full Text Available Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

  11. Implementing exercise in cancer care: study protocol to evaluate a community-based exercise program for people with cancer

    Cormie, Prue; Lamb, Stephanie; Newton, Robert U.; Valentine, Lani; McKiernan, Sandy; Spry, Nigel; Joseph, David; Taaffe, Dennis R.; Doran, Christopher M.; Galv?o, Daniel A.

    2017-01-01

    Background Clinical research has established the efficacy of exercise in reducing treatment-related side-effects and increasing wellbeing in people with cancer. Major oncology organisations have identified the importance of incorporating exercise in comprehensive cancer care but information regarding effective approaches to translating evidence into practice is lacking. This paper describes the implementation of a community-based exercise program for people with cancer and the protocol for pr...

  12. Closer to Learning: Social Networks, Trust, and Professional Communities

    Liou, Yi-Hwa; Daly, Alan J.

    2014-01-01

    Researchers, educators, and policymakers suggest the use of professional learning communities as one important approach to the improvement of teaching and learning. However, relatively little research examines the interplay of professional interactions (structural social capital) around instructional practices and key elements of professional…

  13. Heterogeneous Community-based mobility model for human opportunistic network

    Hu, Liang; Dittmann, Lars

    2009-01-01

    a heterogeneous community-based random way-point (HC-RWP) mobility model that captures the four important properties of real human mobility. These properties are based on both intuitive observations of daily human mobility and analysis of empirical mobility traces. By discrete event simulation, we show HC...

  14. Social Relation Networks in UT-Online Community Forum

    Farisi, Mohammad Imam

    2012-01-01

    So far, the existence of a virtual community forum has become a reality and social necessity in an era cybertech. It was also viewed as the electronic frontier of 21st century society that was undoubtedly for reorganizing and redefining to awareness of human being, that ways of their perceptions and explorations no longer limited by time, space,…

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

    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.

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

    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.

  17. Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

    Schaub, Franz X.; Dhankani, Varsha; Berger, Ashton C.; Trivedi, Mihir; Richardson, Anne B.; Shaw, Reid; Zhao, Wei; Zhang, Xiaoyang; Ventura, Andrea; Liu, Yuexin; Ayer, Donald E.; Hurlin, Peter J.; Cherniack, Andrew D.; Eisenman, Robert N.; Bernard, Brady; Grandori, Carla; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Angulo Gonzalez, Ana Maria; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Mora Pinero, Edna M.; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz

    2018-01-01

    Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic

  18. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

    The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in

  19. Convolutional neural networks for prostate cancer recurrence prediction

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  20. Efficiency disparities among community hospitals in Tennessee: do size, location, ownership, and network matter?

    Roh, Chul-Young; Moon, M Jae; Jung, Kwangho

    2013-11-01

    This study examined the impact of ownership, size, location, and network on the relative technical efficiency of community hospitals in Tennessee for the 2002-2006 period, by applying data envelopment analysis (DEA) to measure technical efficiency (decomposed into scale efficiency and pure technical efficiency). Data envelopment analysis results indicate that medium-size hospitals (126-250 beds) are more efficient than their counterparts. Interestingly, public hospitals are significantly more efficient than private and nonprofit hospitals in Tennessee, and rural hospitals are more efficient than urban hospitals. This is the first study to investigate whether hospital networks with other health care providers affect hospital efficiency. Results indicate that community hospitals with networks are more efficient than non-network hospitals. From a management and policy perspective, this study suggests that public policies should induce hospitals to downsize or upsize into optional size, and private hospitals and nonprofit hospitals should change their organizational objectives from profit-driven to quality-driven.

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

    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

  2. Loneliness, social support networks, mood and wellbeing in community-dwelling elderly.

    Golden, Jeannette; Conroy, Ronán M; Bruce, Irene; Denihan, Aisling; Greene, Elaine; Kirby, Michael; Lawlor, Brian A

    2009-07-01

    Both loneliness and social networks have been linked with mood and wellbeing. However, few studies have examined these factors simultaneously in community-dwelling participants. The aim of this study was to examine the relationship between social network, loneliness, depression, anxiety and quality of life in community dwelling older people living in Dublin. One thousand two hundred and ninety-nine people aged 65 and over, recruited through primary care practices, were interviewed in their own homes using the GMS-AGECAT. Social network was assessed using Wenger's typology. 35% of participants were lonely, with 9% describing it as painful and 6% as intrusive. Similarly, 34% had a non-integrated social network. However, the two constructs were distinct: 32% of participants with an integrated social network reported being lonely. Loneliness was higher in women, the widowed and those with physical disability and increased with age, but when age-related variables were controlled for this association was non-significant. Wellbeing, depressed mood and hopelessness were all independently associated with both loneliness and non-integrated social network. In particular, loneliness explained the excess risk of depression in the widowed. The population attributable risk (PAR) associated with loneliness was 61%, compared with 19% for non-integrated social network. Taken together they had a PAR of 70% Loneliness and social networks both independently affect mood and wellbeing in the elderly, underlying a very significant proportion of depressed mood.

  3. BioMart Central Portal: an open database network for the biological community

    Guberman, Jonathan M.; Ai, J.; Arnaiz, O.; Baran, Joachim; Blake, Andrew; Baldock, Richard; Chelala, Claude; Croft, David; Cros, Anthony; Cutts, Rosalind J.; Di Genova, A.; Forbes, Simon; Fujisawa, T.; Gadaleta, E.; Goodstein, D. M.

    2011-01-01

    International audience; BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common inte...

  4. Community intervention to increase neighborhood social network among Japanese older adults.

    Harada, Kazuhiro; Masumoto, Kouhei; Katagiri, Keiko; Fukuzawa, Ai; Chogahara, Makoto; Kondo, Narihiko; Okada, Shuichi

    2018-03-01

    Strengthening neighborhood social networks is important for promoting health among older adults. However, effective intervention strategies aimed at increasing older adults' social networks have not yet been established. The present study examined whether a university-led community intervention that provided communication opportunities could increase older Japanese adults' neighborhood social networks. The present study used a quasi-experimental design. Before the intervention, using postal mail, we carried out a baseline questionnaire survey that was sent to all people living in the Tsurukabuto community aged ≥60 years (n = 1769), of whom 1068 responded. For the community intervention, 18 event-based programs were provided over the course of 1 year at Kobe University. Academic staff at Kobe University organized all the programs. During the program, social interactions among participants were promoted. A follow-up survey was distributed to those who responded to the baseline survey, and 710 individuals answered the question about their participation in the intervention programs (138 respondents were participants, 572 were non-participants). The neighborhood social network was measured in both the baseline and follow-up surveys. Analysis of covariance showed that the changes in neighborhood social network among participants in the program was significantly higher than the changes among non-participants (P = 0.046) after adjusting for the baseline score of social network. The present study found that participants of the intervention expanded their neighborhood social network, but non-participants did not. This finding shows that community interventions using university resources could increase older adults' neighborhood social networks. Geriatr Gerontol Int 2018; 18: 462-469. © 2017 Japan Geriatrics Society.

  5. Evaluating Community-Academic Partnerships of the South Carolina Healthy Brain Research Network.

    Soltani, Suzan Neda; Kannaley, Kristie; Tang, Weizhou; Gibson, Andrea; Olscamp, Kate; Friedman, Daniela B; Khan, Samira; Houston, Julie; Wilcox, Sara; Levkoff, Sue E; Hunter, Rebecca H

    2017-07-01

    Community-academic partnerships have a long history of support from public health researchers and practitioners as an effective way to advance research and solutions to issues that are of concern to communities and their citizens. Data on the development and evaluation of partnerships focused on healthy aging and cognitive health were limited. The purpose of this article is to examine how community partners view the benefits and barriers of a community-academic partner group established to support activities of the South Carolina Healthy Brain Research Network (SC-HBRN). The SC-HBRN is part of the national Healthy Brain Research Network, a thematic research network funded by the Centers for Disease Control and Prevention (CDC). It is focused on improving the scientific and research translation agenda on cognitive health and healthy aging. Semistructured interviews, conducted at end of Year 2 of the 5-year partnership, were used to collect data from partners of the SC-HBRN. Reported benefits of the partnership were information sharing and networking, reaching a broader audience, and humanizing research. When asked to describe what they perceived as barriers to the collaborative, partners described some lack of clarity regarding goals of the network and opportunities to contribute to the partnership. Study results can guide and strengthen other public health-focused partnerships.

  6. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

    Wang, Mingxun; Carver, Jeremy J.; Pevzner, Pavel

    2016-01-01

    are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization...... and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations...

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

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

    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

  9. Energy Spectral Behaviors of Communication Networks of Open-Source Communities.

    Jianmei Yang

    Full Text Available Large-scale online collaborative production activities in open-source communities must be accompanied by large-scale communication activities. Nowadays, the production activities of open-source communities, especially their communication activities, have been more and more concerned. Take CodePlex C # community for example, this paper constructs the complex network models of 12 periods of communication structures of the community based on real data; then discusses the basic concepts of quantum mapping of complex networks, and points out that the purpose of the mapping is to study the structures of complex networks according to the idea of quantum mechanism in studying the structures of large molecules; finally, according to this idea, analyzes and compares the fractal features of the spectra in different quantum mappings of the networks, and concludes that there are multiple self-similarity and criticality in the communication structures of the community. In addition, this paper discusses the insights and application conditions of different quantum mappings in revealing the characteristics of the structures. The proposed quantum mapping method can also be applied to the structural studies of other large-scale organizations.

  10. Investigating student communities with network analysis of interactions in a physics learning center

    Eric Brewe

    2012-01-01

    Full Text Available Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  11. Investigating student communities with network analysis of interactions in a physics learning center

    Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

    2012-06-01

    Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  12. Epidemic spreading in weighted scale-free networks with community structure

    Chu, Xiangwei; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

    2009-01-01

    Many empirical studies reveal that the weights and community structure are ubiquitous in various natural and artificial networks. In this paper, based on the SI disease model, we investigate the epidemic spreading in weighted scale-free networks with community structure. Two exponents, α and β, are introduced to weight the internal edges and external edges, respectively; and a tunable probability parameter q is also introduced to adjust the strength of community structure. We find the external weighting exponent β plays a much more important role in slackening the epidemic spreading and reducing the danger brought by the epidemic than the internal weighting exponent α. Moreover, a novel result we find is that the strong community structure is no longer helpful for slackening the danger brought by the epidemic in the weighted cases. In addition, we show the hierarchical dynamics of the epidemic spreading in the weighted scale-free networks with communities which is also displayed in the famous BA scale-free networks

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

    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.

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

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

    2014-10-01

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

  15. Breast cancer survivors' barriers and motives for participating in a group-based physical activity program offered in the community.

    Wurz, Amanda; St-Aubin, Anik; Brunet, Jennifer

    2015-08-01

    The purpose of this qualitative study was to explore the barriers and motives experienced by women attending an 8-week group-based physical activity program offered in the community following treatment for breast cancer. Seven women were interviewed during the first and last week of the program. Data were analyzed using thematic analysis. Factors that hindered women's continued participation could be subdivided into situational barriers, which encompassed community (i.e., distance of center and traffic) and institutional factors (i.e., competing roles and responsibilities), and internal barriers, which consisted of cancer-specific limitations. Motives for initial and continued participation were situational (i.e., gaining social support, networking, and being around similar others) and internal (i.e., feeling a sense of personal fulfillment, acquiring health benefits, and recovering from cancer). The findings contribute to a deeper understanding of women's motives to engage in a group-based physical activity program after treatment for breast cancer. Further, they underscore the necessity of considering situational and internal barriers when developing group-based physical activity programs to increase regular participation, optimize adherence, and reduce drop-out.

  16. Community Broadband Networks and the Opportunity for E-Government Services

    Williams, Idongesit

    2017-01-01

    Community Broadband Networks (CBN) facilitate Broadband connectivity in underserved areas in many countries. The lack of Broadband connectivity is one of the reasons for the slow diffusion of e-government services in many countries.This article explains how CBNs can be enabled by governments...... to facilitate the delivery of e–government services in underserved areas in the developed and developing countries.The Community Based Broadband Mobilization (CBNM) models are used as explanatory tools....

  17. Social networks, social support and psychiatric symptoms: social determinants and associations within a multicultural community population.

    Smyth, Natasha; Siriwardhana, Chesmal; Hotopf, Matthew; Hatch, Stephani L

    2015-07-01

    Little is known about how social networks and social support are distributed within diverse communities and how different types of each are associated with a range of psychiatric symptoms. This study aims to address such shortcomings by: (1) describing the demographic and socioeconomic characteristics of social networks and social support in a multicultural population and (2) examining how each is associated with multiple mental health outcomes. Data is drawn from the South East London Community Health Study; a cross-sectional study of 1,698 adults conducted between 2008 and 2010. The findings demonstrate variation in social networks and social support by socio-demographic factors. Ethnic minority groups reported larger family networks but less perceived instrumental support. Older individuals and migrant groups reported lower levels of particular network and support types. Individuals from lower socioeconomic groups tended to report less social networks and support across the indicators measured. Perceived emotional and instrumental support, family and friend network size emerged as protective factors for common mental disorder, personality dysfunction and psychotic experiences. In contrast, both social networks and social support appear less relevant for hazardous alcohol use. The findings both confirm established knowledge that social networks and social support exert differential effects on mental health and furthermore suggest that the particular type of social support may be important. In contrast, different types of social network appear to impact upon poor mental health in a more uniform way. Future psychosocial strategies promoting mental health should consider which social groups are vulnerable to reduced social networks and poor social support and which diagnostic groups may benefit most.

  18. Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

    Grainne Conole

    2011-03-01

    Full Text Available This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and personalisation within an educational context. One of the key challenges in the development of the site has been to understand the user interactions and the changing patterns of user behaviour as it evolves. The paper explores the extent to which four frameworks that have been used in researching networked learning contexts can provide insights into the patterns of user behaviour that we see in Cloudworks. The paper considers this within the current debate about the new types of interactions, networking, and community being observed as users adapt to and appropriate new technologies.

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

    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.

  20. Social Network Structures of Breast Cancer Patients and the Contributing Role of Patient Navigators.

    Gunn, Christine M; Parker, Victoria A; Bak, Sharon M; Ko, Naomi; Nelson, Kerrie P; Battaglia, Tracy A

    2017-08-01

    Minority women in the U.S. continue to experience inferior breast cancer outcomes compared with white women, in part due to delays in care delivery. Emerging cancer care delivery models like patient navigation focus on social barriers, but evidence demonstrating how these models increase social capital is lacking. This pilot study describes the social networks of newly diagnosed breast cancer patients and explores the contributing role of patient navigators. Twenty-five women completed a one hour interview about their social networks related to cancer care support. Network metrics identified important structural attributes and influential individuals. Bivariate associations between network metrics, type of network, and whether the network included a navigator were measured. Secondary analyses explored associations between network structures and clinical outcomes. We identified three types of networks: kin-based, role and/or affect-based, or heterogeneous. Network metrics did not vary significantly by network type. There was a low prevalence of navigators included in the support networks (25%). Network density scores were significantly higher in those networks without a navigator. Network metrics were not predictive of clinical outcomes in multivariate models. Patient navigators were not frequently included in support networks, but provided distinctive types of support. If navigators can identify patients with poorly integrated (less dense) social networks, or who have unmet tangible support needs, the intensity of navigation services could be tailored. Services and systems that address gaps and variations in patient social networks should be explored for their potential to reduce cancer health disparities. This study used a new method to identify the breadth and strength of social support following a diagnosis of breast cancer, especially examining the role of patient navigators in providing support. While navigators were only included in one quarter of patient

  1. A cloud-based data network approach for translational cancer research.

    Xing, Wei; Tsoumakos, Dimitrios; Ghanem, Moustafa

    2015-01-01

    We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.

  2. Environmental lovers group: a networking of Ciliwung Depok Community

    Mambo Tampi, Daniel; Sumabrata, Jachrizal; Darmajanti, Linda

    2018-03-01

    The dynamic of urban development needs is social space to bout the rights of urban society. An environmental lovers group was evoked by the environmental and Social Issues cause natural and man-made disruptions. Komunitas Ciliwung Depok (KCD) is located under Grand Depok City’s Bridge to keep an environment such as land conversion and garbage. This study aims to determine the motivation of KCD’s group and to know the social network in Ciliwung riverbank along Depok city. Data were gathered from in-depth interviews, observation and documentation within 4 months. KCD invited the local people to keep and maintain an environment of Ciliwung riverbank. Their strength lies on the actors of KCD founder and involvement of human resources, with the support of public and private sectors facilities. Activities with the local people’s participation through social medias such as Facebook as a media of communication to get volunteers’ aspirations and involvement. The conclusions of this research are KCD’s Network materialized by individual motivation, group awareness and trust between the actors to realizing the common goal.

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

    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.

  4. Convergent evolution of modularity in metabolic networks through different community structures

    Zhou Wanding

    2012-09-01

    Full Text Available Abstract Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability. Further, our results

  5. Convergent evolution of modularity in metabolic networks through different community structures.

    Zhou, Wanding; Nakhleh, Luay

    2012-09-14

    It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network

  6. Critical Social Network Analysis in Community Colleges: Peer Effects and Credit Attainment

    González Canché, Manuel S.; Rios-Aguilar, Cecilia

    2014-01-01

    This chapter discusses the importance of conducting critical social network analysis (CSNA) in higher education. To illustrate the benefits of CSNA, the authors use existing institutional data to examine peer effects in community colleges. The chapter ends with a discussion of the implications of using a CSNA approach to measure inequities in…

  7. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Brian R Granger

    2016-04-01

    Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  8. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

    2016-04-01

    The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  9. Urban ecological stewardship: understanding the structure, function and network of community-based urban land management

    Erika s. Svendsen; Lindsay K. Campbell

    2008-01-01

    Urban environmental stewardship activities are on the rise in cities throughout the Northeast. Groups participating in stewardship activities range in age, size, and geography and represent an increasingly complex and dynamic arrangement of civil society, government and business sectors. To better understand the structure, function and network of these community-based...

  10. Must Invisible Colleges Be Invisible? An Approach to Examining Large Communities of Network Users.

    Ruth, Stephen R.; Gouet, Raul

    1993-01-01

    Discussion of characteristics of users of computer-mediated communication systems and scientific networks focuses on a study of the scientific community in Chile. Topics addressed include users and nonusers; productivity; educational level; academic specialty; age; gender; international connectivity; public policy issues; and future research…

  11. Community access networks: how to connect the next billion to the ...

    Community access networks: how to connect the next billion to the Internet ... services is a prerequisite to sustainable socio-economic development. ... It will provide case studies and formulate recommendations with respect to ... An IDRC delegation will join international delegates and city representatives at the ICLEI World ...

  12. Social Networking Technologies as Vehicles of Support for Women in Learning Communities

    Burgess, Kimberly R.

    2009-01-01

    Women have long since used social networking as a means of coping with their struggles, educating and empowering themselves, engaging in broader social movements, and building international advocacy. Internet communities that are designed and facilitated to be inclusive of women's experiences can be important social spaces where women feel…

  13. Case Study IV: Carnegie Foundation for the Advancement of Teaching's Networked Improvement Communities (NICs)

    Coburn, Cynthia E.; Penuel, William R.; Geil, Kimberly E.

    2015-01-01

    The Carnegie Foundation for the Advancement of Teaching is a nonprofit, operating foundation with a long tradition of developing and studying ways to improve teaching practice. For the past three years, the Carnegie Foundation has initiated three different Networked Improvement Communities (NICs). The first, Quantway, is addressing the high…

  14. Community access networks: how to connect the next billion to the ...

    Community access networks: how to connect the next billion to the Internet. Despite recent progress with mobile technology diffusion, more than four billion people worldwide are unconnected and have limited access to global communication infrastructure. The cost of implementing connectivity infrastructure in underserved ...

  15. Cocoon: A lightweight opportunistic networking middleware for community-oriented smart mobile applications

    Türkes, Okan; Scholten, Johan; Havinga, Paul J.M.

    2016-01-01

    Modern society is surrounded by an ample spectrum of smart mobile devices. This ubiquity forms a high potential for community-oriented opportunistic ad hoc networking applications. Nevertheless, today’s smart mobile devices such as smartphones, tablets, and wristbands are still onerous to

  16. How do we make community owned information networks work for the poor?

    Morris, CF

    2009-01-01

    Full Text Available asked in this paper is how do we make community owned information networks work for the poor? A case study from Angola shares key lessons learnt in developing shared cost models in telecentres in the face of exorbitantly high connectivity costs. The real...

  17. Social Embeddedness and Late-Life Parenthood : Community Activity, Close Ties, and Support Networks

    Wenger, G. Clare; Dykstra, Pearl A.; Melkas, Tuula; Knipscheer, Kees C.P.M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people’s involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  18. Social embeddedness and late-life parenthood: community activity, close ties and support networks

    Wenger, G.; Dykstra, P.A.; Melkas, T.; Knipscheer, K.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people’s involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  19. Social embeddedness and late-life parenthood: Community activity, close ties, and support networks

    Wenger, G.C.; Dykstra, P.A.; Melkas, T.; Knipscheer, C.P.M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people's involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  20. Social Embeddedness and Late-Life Parenthood: Community Activity, Close Ties, and Support Networks

    Wenger, G. Clare; Dykstra, Pearl A.; Melkas, Tuula; Knipscheer, Kees C. P. M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people's involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show that childless older adults, regardless of…

  1. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven,

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

    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.

  3. Building a sense of virtual community: the role of the features of social networking sites.

    Chen, Chi-Wen; Lin, Chiun-Sin

    2014-07-01

    In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

  4. Investigating the social configuration of a community to understand how networked learning activities take place: The OERu case study

    Schreurs, Bieke; Van den Beemt, Antoine; Prinsen, Fleur; De Laat, Maarten; Witthaus, Gaby; Conole, Grainne

    2015-01-01

    Examining how OER (Open Educational Resources) communities come to live, function or learn can support in empowering educators in the use of open educational resources. In this paper we investigate how an OER community functions through its networked learning activities. Networked learning

  5. Community-Based Social Networks: Generation of Power Law Degree Distribution and IP Solutions to the KPP

    Wu, Wentao

    2012-01-01

    The objective of this thesis is two-fold: (1) to investigate the degree distribution property of community-based social networks (CSNs) and (2) to provide solutions to a pertinent problem, the Key Player Problem. In the first part of this thesis, we consider a growing community-based network in which the ability of nodes competing for links to new…

  6. Children's cancer camps: a sense of community, a sense of family.

    Laing, Catherine M; Moules, Nancy J

    2014-05-01

    Childhood cancer is a family affair, and each year in Canada, approximately 1,400 children and adolescents under the age of 20 are diagnosed with cancer. Innumerable challenges accompany this diagnosis, and in recognition of the stress of childhood cancer, children's cancer camps arose in the 1970s to help children and their families escape the rigidity and severity of cancer treatment. Very little is known about these cancer camps, and to that end, a philosophical hermeneutic study was conducted to understand the meaning of children's cancer camps for the child with cancer and the family. Six families were interviewed to bring understanding to this topic. While the research included findings related to the concept of play, fit and acceptance, storytelling, and grief, this paper will detail the finding related to the solidarity of the community--the "camp family"--as one that creates intense, healing bonds.

  7. Commentary: Utilizing Community-Engaged Approaches to Investigate and Address Hmong Women’s Cancer Disparities

    Shannon M.A. Sparks

    2014-12-01

    Full Text Available Cancer is a growing concern for women in the Hmong community. Hmong women experience poor health outcomes for both cervical and breast cancer, largely due to low rates of screening and resultant late-stage at diagnosis. Both breast and cervical cancer screening are complicated by a multitude of social, cultural and environmental factors which influence health care decision-making and can otherwise serve to restrict access. We argue that community-engaged research, an orientation which prioritizes collaborative, equitable partnerships and community voice in identifying both problems and solutions, can be a valuable approach to helping address cancer health disparities for Hmong women. Using the Milwaukee-based “Healthy Hmong Women” project as a case example, we detail how the community-engaged approach implemented by the project partners was critical in identifying factors contributing to Hmong cancer disparities and appropriate interventions, as well as the overall acceptance and success of the project. Specifically, we discuss how this approach: (1 promoted community investment and ownership in the project; (2 facilitated the integration of local perspectives and experiences; (3 built capacity to address cancer screening disparities; (4 facilitated the creation of interventions targeting multiple ecological levels; and (5 framed the community as the foundation and driver of positive change.

  8. Effectiveness of an Ongoing, Community-Based Breast Cancer Prevention Program for Korean American Women.

    Koh, Eun; Choi, Ga-Young; Cho, Ji Young

    2016-02-01

    The study evaluates the effectiveness of an ongoing, community-based breast cancer prevention program offered by a local social services agency in the Washington, DC, metropolitan area. Korean American women who participated in this breast cancer prevention program were compared with those who did not participate in their knowledge, attitude, and screening behaviors. The study found that the intervention group was more knowledgeable on breast cancer and related services and reported more positive attitudes toward breast cancer screening services than the comparison group. The participants in the intervention group were also more likely to plan to receive a mammogram than those in the comparison group. However, significant differences were not observed in the two groups in their intention to receive a clinical breast examination. The study findings suggest that an ongoing, community-based breast cancer prevention program can be an effective method of addressing breast cancer prevention disparities observed among Korean American women.

  9. Stress and Depressive Symptoms in Cancer Survivors and Their Family Members: Korea Community Health Survey, 2012.

    Han, Mi Ah

    2017-09-01

    This study examined the prevalence of perceived stress and depressive symptoms in cancer survivors and their family members compared with subjects without cancer and without family members with cancer. The subjects of this cross-sectional study were adults ≥19 years old who participated in the 2012 Korea Community Health Survey. Stress and depressive symptoms in cancer survivors and their family members were assessed and compared to symptoms in control groups by chi-square tests and multiple logistic regression analyses. Of the 6783 cancer survivors, 26.9% and 8.7% reported having stress and depressive symptoms, respectively, and 27.7% and 5.9% of family members of cancer survivors reported having stress and depressive symptoms, respectively. Cancer survivors showed higher adjusted odds ratio (aOR) for stress (aOR = 1.26, 95% confidence interval (CI) = 1.16-1.37) and depressive symptoms (aOR = 1.82, 95% CI = 1.57-2.11) than subjects without cancer history. Family members of cancer survivors showed a higher OR for stress and depressive symptoms than subjects without a family member who survived cancer. Cancer survivors and family members of cancer survivors had more stress and depressive symptoms than controls. Careful management for cancer patients and their family members should include screening for stress and depression to improve mental health associated with cancer survivorship.

  10. Enhancing Cancer Education through the Arts: Building Connections with Alaska Native People, Cultures and Communities

    Cueva, Melany; Kuhnley, Regina; Cueva, Katie

    2012-01-01

    Building upon the dynamic traditions of Alaska Native people, which include the arts as a viable way of knowing, the expressive arts were woven into a five-day cancer education course for Alaska village-based Community Health Workers (CHWs). Cancer is the leading cause of mortality for Alaska Native people. Course learning modalities included…

  11. Puerto Rico NCI Community Oncology Research Program Minority/Underserved | Division of Cancer Prevention

    The Puerto Rico NCI Community Oncology Research Program (PRNCORP) will be the principal organization in the island that promotes cancer prevention, control and screening/post-treatment surveillance clinical trials. It will conduct cancer care delivery research and will provide access to treatment and imaging clinical trials conducted under the reorganization of the National

  12. Differences in cancer mortality rates in Ohio communities with respect to uraniferous geology

    Dzik, A.J.

    1989-01-01

    Populations in areas of uraniferous geology may be at risk from radon emissions. Twenty-eight municipalities were examined as to their location with respect to uraniferous geology. Communities with possible radon risk had higher rates for all cancers and cancer of the respiratory system, but differences were not statistically significant. Some possible reasons for the results are discussed

  13. Understanding the structure of community collaboration: the case of one Canadian health promotion network.

    Barnes, Martha; Maclean, Joanne; Cousens, Laura

    2010-06-01

    In 2004, over 6.8 million Canadians were considered overweight, with an additional 2.4 million labeled clinically obese. Due to these escalating levels of obesity in Canada, physical activity is being championed by politicians, physicians, educators and community members as a means to address this health crisis. In doing so, many organizations are being called upon to provide essential physical activity services and programs to combat rising obesity rates. Yet, strategies for achieving these organizations' mandates, which invariably involve stretching already scarce resources, are difficult to implement and sustain. One strategy for improving the health and physical activity levels of people in communities has been the creation of inter-organizational networks of service providers. Yet, little is known about whether networks are effective in addressing policy issues in non-clinical health settings. The purpose of this investigation was 2-fold; to use whole network analysis to determine the structure of one health promotion network in Canada, and to identify the types of ties shared by actors in the health network. Findings revealed a network wherein information sharing constituted the basis for collaboration, whereas efforts related to sharing resources, marketing and/or fundraising endeavors were less evident.

  14. Classification of breast cancer cytological specimen using convolutional neural network

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  15. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-01-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  16. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-11-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  17. Endogenous Molecular-Cellular Network Cancer Theory: A Systems Biology Approach.

    Wang, Gaowei; Yuan, Ruoshi; Zhu, Xiaomei; Ao, Ping

    2018-01-01

    In light of ever apparent limitation of the current dominant cancer mutation theory, a quantitative hypothesis for cancer genesis and progression, endogenous molecular-cellular network hypothesis has been proposed from the systems biology perspective, now for more than 10 years. It was intended to include both the genetic and epigenetic causes to understand cancer. Its development enters the stage of meaningful interaction with experimental and clinical data and the limitation of the traditional cancer mutation theory becomes more evident. Under this endogenous network hypothesis, we established a core working network of hepatocellular carcinoma (HCC) according to the hypothesis and quantified the working network by a nonlinear dynamical system. We showed that the two stable states of the working network reproduce the main known features of normal liver and HCC at both the modular and molecular levels. Using endogenous network hypothesis and validated working network, we explored genetic mutation pattern in cancer and potential strategies to cure or relieve HCC from a totally new perspective. Patterns of genetic mutations have been traditionally analyzed by posteriori statistical association approaches in light of traditional cancer mutation theory. One may wonder the possibility of a priori determination of any mutation regularity. Here, we found that based on the endogenous network theory the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. Normal hepatocyte and cancerous hepatocyte stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable

  18. TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks.

    Chua, Huey Eng; Bhowmick, Sourav S; Zheng, Jie

    2017-09-22

    Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, a user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove. hechua@ntu.edu.sg or assourav@ntu.edu.sg. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  19. Results of the Community Health Applied Research Network (CHARN) National Research Capacity Survey of Community Health Centers.

    Song, Hui; Li, Vivian; Gillespie, Suzanne; Laws, Reesa; Massimino, Stefan; Nelson, Christine; Singal, Robbie; Wagaw, Fikirte; Jester, Michelle; Weir, Rosy Chang

    2015-01-01

    The mission of the Community Health Applied Research Network (CHARN) is to build capacity to carry out Patient-Centered Outcomes Research at community health centers (CHCs), with the ultimate goal to improve health care for vulnerable populations. The CHARN Needs Assessment Staff Survey investigates CHCs' involvement in research, as well as their need for research training and resources. Results will be used to guide future training. The survey was developed and implemented in partnership with CHARN CHCs. Data were collected across CHARN CHCs. Data analysis and reports were conducted by the CHARN data coordinating center (DCC). Survey results highlighted gaps in staff research training, and these gaps varied by staff role. There is considerable variation in research involvement, partnerships, and focus both within and across CHCs. Development of training programs to increase research capacity should be tailored to address the specific needs and roles of staff involved in research.

  20. Modeling community succession and assembly: A novel method for network evolution

    WenJun Zhang

    2012-06-01

    Full Text Available The process of modeling community succession and assembly is in some sense a method for network evolution, as done by Barabasi and Albert (1999. It is also one of the methods to create a sample networkfrom the statistic network I proposed earlier. I think that the mechanism of network evolution supposed by Barabasi and Albert is most likely applicable to the natural phenomena with emergency property. For natural phenomena without emergency property, the present study indicated that a scale-free network may be produced through a new mechanism, i.e., whether the connection of a taxon x occurs, dependent on the type and property of taxon y (in particular, the degree of its direct correlation with x to be connected but not necessarily the existing number of connections of taxon y, as proposed in present study.

  1. Breast cancer detection via Hu moment invariant and feedforward neural network

    Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

    2018-04-01

    One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.

  2. The Community Child Health Network Life Stress Interview: a brief chronic stress measure for community health research.

    Tanner Stapleton, Lynlee R; Dunkel Schetter, Christine; Dooley, Larissa N; Guardino, Christine M; Huynh, Jan; Paek, Cynthia; Clark-Kauffman, Elizabeth; Schafer, Peter; Woolard, Richard; Lanzi, Robin Gaines

    2016-07-01

    Chronic stress is implicated in many theories as a contributor to a wide range of physical and mental health problems. The current study describes the development of a chronic stress measure that was based on the UCLA Life Stress Interview (LSI) and adapted in collaboration with community partners for use in a large community health study of low-income, ethnically diverse parents of infants in the USA (Community Child Health Network [CCHN]). We describe the instrument, its purpose and adaptations, implementation, and results of a reliability study in a subsample of the larger study cohort. Interviews with 272 mothers were included in the present study. Chronic stress was assessed using the CCHN LSI, an instrument designed for administration by trained community interviewers to assess four domains of chronic stress, each rated by interviewers. Significant correlations ranging from small to moderate in size between chronic stress scores on this measure, other measures of stress, biomarkers of allostatic load, and mental health provide initial evidence of construct and concurrent validity. Reliability data for interviewer ratings are also provided. This relatively brief interview (15 minutes) is available for use and may be a valuable tool for researchers seeking to measure chronic stress reliably and validly in future studies with time constraints.

  3. Reducing Cancer Health Disparities through Community Engagement: Working with Faith-Based Organizations (Project CHURCH) | Division of Cancer Prevention

    Speaker | "Reducing Cancer Health Disparities through Community Engagement: Working with Faith-Based Organizations (Project CHURCH)" will be presented by Lorna H. McNeill, PhD, MPH, Chair of the Department of Health Disparities at the University of Texas MD Anderson Cancer Center in Houston, TX. Date: 2/20/2018; Time: 11:00am - 12:00pm; Location: NCI Shady Grove Campus,

  4. Connecting primary care clinics and community pharmacies through a nationwide electronic prescribing network: A qualitative study

    Marie-Pierre Gagnon

    2015-10-01

    Full Text Available Background The use of medication is at the heart of primary care, but is also the cause for major health concerns. It is therefore important to examine the prescription of medication process.Objective This study identifies the barriers and facilitators perceived by community pharmacists and primary care physicians concerning the adoption of a nationwide electronic prescribing (e-prescribing network in the province of Quebec, Canada.Methods We used purposive sampling to identify the most intensive users of the e-prescribing network. We conducted phone and in-person interviews. Interviews were transcribed, and we analysed their content with NVivo, using the clinical adoption framework (CAF for the codification of the data.Results We interviewed 33 pharmacists, 2 pharmacy technicians, 11 physicians and 3 clinic managers. Adoption of the e-prescribing network was fairly low. The respondents underlined adaptation of their work environment, openness to change and perception of benefits as facilitators to the adoption of the network. However, important barriers were perceived, including system quality issues and paper prescriptions being the only legal document in the prescribing process. Even if respondents recognised that the e-prescribing network can offer substantial benefits to the prescribing process, issues still persisted and raised barriers to the full use of such a network, especially in a context where different local information systems are connected within a nationwide e-prescribing network.Conclusion This study, based on the CAF, provides a better understanding of the factors related to the adoption of a nationwide e-prescribing network connecting primary care clinics and community pharmacies. 

  5. Prostate cancer survivors as community health educators: implications for informed decision making and cancer communication.

    Vijaykumar, Santosh; Wray, Ricardo J; Jupka, Keri; Clarke, Ryan; Shahid, Mellve

    2013-12-01

    Recent evidence questioning the effectiveness of prostate-specific antigen testing leave community-based prostate cancer (CaP) outreach programs with a dilemma between promoting screening and highlighting screening risks. CaP survivors are uniquely positioned to address this problem by drawing upon real-life experiences to share nuanced information and perspectives. While CaP survivors have historically been incorporated into outreach programs, little is known about their impact on psychosocial outcomes and their effectiveness compared to professional health educators. This study addressed these gaps through a quasi-experimental design where African American men attended a CaP screening session conducted by a health educator (HE) or survivor educator (SV). The presentation included prostate cancer statistics, CaP information, and descriptions of CaP screening tests. SV were encouraged to bolster their presentations with personal stories whereas HE maintained fidelity to the curriculum content. All participants completed pre- and post-test questionnaires. Our sample comprised a total of 63 participants (HE group = 32; SV group = 31) with an age range of 40-70 years. Decision self-efficacy increased significantly in the SV group (p = 0.01) whereas perceived screening risks reduced significantly in the HE group (p communication efforts confronted by the CaP screening controversy. We discuss conceptual and programmatic implications of our findings and present directions for future research.

  6. Benefits of Peer Support in Online Japanese Breast Cancer Communities: Differences Between Lurkers and Posters

    Setoyama, Yoko; Yamazaki, Yoshihiko; Namayama, Kazuhiro

    2011-01-01

    Background Web 2.0 has improved interactions among peers on the Internet, especially for the many online patient communities that have emerged over the past decades. Online communities are said to be particularly beneficial peer support resources for patients with breast cancer. However, most studies of online patient communities have focused on those members who post actively (posters), even though there are many members who participate without posting (lurkers). In addition, little attentio...

  7. Transduction motif analysis of gastric cancer based on a human signaling network

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  8. Context-Aware Community Construction in Proximity-Based Mobile Networks

    Na Yu

    2015-01-01

    Full Text Available Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

  9. Detecting community structure using label propagation with consensus weight in complex network

    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. Migrant Workers' Community in China: Relationships among Social Networks, Life Satisfaction and Political Participation

    Qingwen Xu

    2011-11-01

    Full Text Available The millions of persons migrating from China´s rural areas to urban spaces have contributed greatly to the country´s decades-long economic growth, and the influx of migrants has changed the fabric of China´s urban social and economic life. These internal migrants, similar to many international immigrants, depend heavily on their social networks, which are often developed in their rural villages, for jobs, housing, financial assistance, and social support both during and after migration. Consequently, migrants´ networks function distinctly in well-being and behavior. Using data from the 2006 China General Social Survey, this article seeks to 1 investigate the existence of migrant sub-groups in China, 2 understand the characteristics of social networks among sub-groups, and 3 explore the relationships social networks hold to life satisfaction and political participation among China´s migrant population. This article asserts that China´s migrant population includes several sub-groups emerging on the basis of gender, education, age, and marital status, which in turn produce different patterns of ties and social interactions among their social networks. While this article finds very different employment patterns among migrant sub-groups, migrant networks do not appear to strongly influence perceptions and behaviors, such as life satisfaction and political participation. This article also argues that individual networks could facilitate the development of migrant communities in cities.

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

    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

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

    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

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

    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

  14. Livelihood diversification in tropical coastal communities: a network-based approach to analyzing 'livelihood landscapes'.

    Joshua E Cinner

    Full Text Available BACKGROUND: Diverse livelihood portfolios are frequently viewed as a critical component of household economies in developing countries. Within the context of natural resources governance in particular, the capacity of individual households to engage in multiple occupations has been shown to influence important issues such as whether fishers would exit a declining fishery, how people react to policy, the types of resource management systems that may be applicable, and other decisions about natural resource use. METHODOLOGY/PRINCIPAL FINDINGS: This paper uses network analysis to provide a novel methodological framework for detailed systemic analysis of household livelihood portfolios. Paying particular attention to the role of natural resource-based occupations such as fisheries, we use network analyses to map occupations and their interrelationships- what we refer to as 'livelihood landscapes'. This network approach allows for the visualization of complex information about dependence on natural resources that can be aggregated at different scales. We then examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities in 5 western Indian Ocean countries. Network statistics, including in- and out-degree centrality, the density of the network, and the level of network centralization are compared along a multivariate index of community-level socioeconomic development and a gradient of human population density. The combination of network analyses suggests an increase in household-level specialization with development for most occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified. CONCLUSIONS/SIGNIFICANCE: The novel modeling approach introduced here provides for various types of livelihood portfolio analyses at different scales of social aggregation. Our livelihood landscapes approach provides insights

  15. The Quake-Catcher Network: Improving Earthquake Strong Motion Observations Through Community Engagement

    Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.; Chung, A. I.; Neighbors, C.; Saltzman, J.

    2010-12-01

    The Quake-Catcher Network (QCN) involves the community in strong motion data collection by utilizing volunteer computing techniques and low-cost MEMS accelerometers. Volunteer computing provides a mechanism to expand strong-motion seismology with minimal infrastructure costs, while promoting community participation in science. Micro-Electro-Mechanical Systems (MEMS) triaxial accelerometers can be attached to a desktop computer via USB and are internal to many laptops. Preliminary shake table tests show the MEMS accelerometers can record high-quality seismic data with instrument response similar to research-grade strong-motion sensors. QCN began distributing sensors and software to K-12 schools and the general public in April 2008 and has grown to roughly 1500 stations worldwide. We also recently tested whether sensors could be quickly deployed as part of a Rapid Aftershock Mobilization Program (RAMP) following the 2010 M8.8 Maule, Chile earthquake. Volunteers are recruited through media reports, web-based sensor request forms, as well as social networking sites. Using data collected to date, we examine whether a distributed sensing network can provide valuable seismic data for earthquake detection and characterization while promoting community participation in earthquake science. We utilize client-side triggering algorithms to determine when significant ground shaking occurs and this metadata is sent to the main QCN server. On average, trigger metadata are received within 1-10 seconds from the observation of a trigger; the larger data latencies are correlated with greater server-station distances. When triggers are detected, we determine if the triggers correlate to others in the network using spatial and temporal clustering of incoming trigger information. If a minimum number of triggers are detected then a QCN-event is declared and an initial earthquake location and magnitude is estimated. Initial analysis suggests that the estimated locations and magnitudes are

  16. Establishment of the Fox Chase Network Breast Cancer Risk Registry

    Daly, Mary

    1997-01-01

    .... The development of the Fox Chase Cancer Center Breast Cancer Risk Registry was proposed to facilitate research in the epidemiologic and genetic predictors of disease and will permit evaluation...

  17. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

    Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

    2015-08-01

    Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  19. Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.

    Herman F Fumiã

    Full Text Available A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.

  20. A CLEAN Network Initiative - Accelerating Transition to Post Carbon and Resilient Communities through Education and Engagement

    Ledley, T. S.; Niepold, F., III; Bozuwa, J.; Davis, A.; Fraser, J.; Kretser, J.; Poppleton, K. L. I.; Qusba, L.; Ruggiero, K.; Spitzer, W.; Stylinski, C.

    2016-12-01

    The Climate Literacy and Energy Awareness Network (CLEAN) was formed in 2008 to help climate and energy literacy stakeholders implement the Climate and Energy Literacy Essential Principles to enable effective and responsible decisions with regard to actions that may affect climate. The ongoing conversations of the CLEAN Network have cultivated a culture of shared resources and expertise and allowed for the development of collective impact strategies. However, it has become clear that to accelerate and scale change, effective mitigation, adaptation, and resilience strategies must be developed by a diverse network of stakeholders at the community level to deal with the local impacts of climate change and move toward decarbonized and resilient economies. A group of CLEAN Network members, experienced in establishing effective networks and representing mature climate change education programs, came together to discuss at the community level 1) how we can collectively enable larger scale efforts to 2) develop effective strategies, 3) identify gaps in the system that limit action, and 4) coordinate possible vectors for interceding to advance community level decisions related to climate. We will describe our Theory of Change, based on both the power of communities and increasing climate literacy as a key requirement for sustained progress on the crisis climate change presents. From our Theory of Change, we have begun to outline a national monitoring strategy that can provide communities a measured way to understand their local readiness to respond to the impacts of climate change and understand the magnitude of those impacts in relation to their political and ecological economies. The scale would help describe the robustness of their programs and partnerships to address those impacts, the political climate for working in advance of pending change, and the degree of citizen engagement in resilience planning and action. The goal is to provide a common tool equivalent to GDP

  1. Cancer screening information at community health fairs: What the participants do with information they receive.

    Monrose, Erica; Ledergerber, Jessica; Acheampong, Derrick; Jandorf, Lina

    2017-09-21

    To assess participants' reasons for seeking cancer screening information at community health fairs and what they do with the information they receive. Mixed quantitative and qualitative approach was used. Community health fairs are organized in underserved New York City neighbourhoods. From June 14, 2016 to August 26, 2016, cancer prevention tables providing information about various cancer screenings were established at 12 local community health fairs in New York City. In-person and follow up telephone surveys assessing interest in the cancer prevention table, personal cancer screening adherence rates, information-sharing behaviours and demographic variables have been taken into account. Statistical analyses were performed using IBM SPSS 22.0: frequencies, descriptive, cross tabulations. All qualitative data was coded by theme so that it could be analysed through SPSS. For example, Were you interested in a specific cancer? may be coded as 2 for yes , breast cancer . One hundred and sixteen patrons participated in the initial survey. Of those, 88 (78%) agreed to give their contact information for the follow-up survey and 60 follow-up surveys were completed (68%). Of those who reported reading the material, 45% shared the information; 15% subsequently spoke to a provider about cancer screenings and 40% intended to speak to a provider. Participants disseminated information without prompting; suggesting the reach of these fairs extends beyond the people who visit our table. Future studies should look at whether patrons would share information at higher rates when they are explicitly encouraged to share the information.

  2. Resource-stratified implementation of a community-based breast cancer management programme in Peru.

    Duggan, Catherine; Dvaladze, Allison L; Tsu, Vivien; Jeronimo, Jose; Constant, Tara K Hayes; Romanoff, Anya; Scheel, John R; Patel, Shilpen; Gralow, Julie R; Anderson, Benjamin O

    2017-10-01

    Breast cancer incidence and mortality rates continue to rise in Peru, with related deaths projected to increase from 1208 in 2012, to 2054 in 2030. Despite improvements in national cancer control plans, various barriers to positive breast cancer outcomes remain. Multiorganisational stakeholder collaboration is needed for the development of functional, sustainable early diagnosis, treatment and supportive care programmes with the potential to achieve measurable outcomes. In 2011, PATH, the Peruvian Ministry of Health, the National Cancer Institute in Lima, and the Regional Cancer Institute in Trujillo collaborated to establish the Community-based Program for Breast Health, the aim of which was to improve breast health-care delivery in Peru. A four-step, resource-stratified implementation strategy was used to establish an effective community-based triage programme and a practical early diagnosis scheme within existing multilevel health-care infrastructure. The phased implementation model was initially developed by the Breast Cancer Initiative 2·5: a group of health and non-governmental organisations who collaborate to improve breast cancer outcomes. To date, the Community-based Program for Breast Health has successfully implemented steps 1, 2, and 3 of the Breast Cancer Initiative 2·5 model in Peru, with reports of increased awareness of breast cancer among women, improved capacity for early diagnosis among health workers, and the creation of stronger and more functional linkages between the primary levels (ie, local or community) and higher levels (ie, district, region, and national) of health care. The Community-based Program for Breast Health is a successful example of stakeholder and collaborator involvement-both internal and external to Peru-in the design and implementation of resource-appropriate interventions to increase breast health-care capacity in a middle-income Latin American country. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. A community intervention: AMBER: Arab American breast cancer education and referral program.

    Ayash, Claudia; Axelrod, Deborah; Nejmeh-Khoury, Sana; Aziz, Arwa; Yusr, Afrah; Gany, Francesca M

    2011-12-01

    Although the number of Arab Americans is growing in the United States, there is very little data available on this population's cancer incidence and screening practices. Moreover, there are few interventions addressing their unique needs. This study aims to determine effective strategies for increasing breast cancer screening in at-risk underserved Arab American women. AMBER utilizes a community based participatory approach to conduct formative research and program interventions, including culturally appropriate Arabic language breast cancer education, screening coordination, and cultural competency training for healthcare professionals in New York City. In 2 years, 597 women were educated, 189 underserved women were identified as being in need of assistance, 68 were screened, one new case of breast cancer was detected, and four active cases in need of follow-up reconnected with care. The AMBER model is an important intervention for breast cancer screening and care in the underserved Arab American community.

  4. Dissemination of colorectal cancer screening by Filipino American community health advisors: a feasibility study.

    Maxwell, Annette E; Danao, Leda L; Bastani, Roshan

    2013-07-01

    Filipino Americans underutilize life-saving screening tests for colorectal cancer, resulting in late stage of diagnosis and poor survival relative to other racial/ethnic groups. Education regarding colorectal cancer screening and distribution of free fecal occult blood test (FOBT) kits are evidence-based interventions that can significantly increase screening. However, this community will only benefit if the intervention is broadly disseminated. We assessed the feasibility of promoting colorectal cancer screening in Filipino American community settings working with community health advisors, and the practicality of conducting one-on-one or small group education, in addition to passing out free FOBT kits. Twenty community health advisors from 4 organizations engaged in recruitment and education activities with 132 participants. Community health advisors consistently completed screening questionnaires to establish eligibility and kept logs of FOBT distribution. However, they did not consistently record eligible participants who did not consent to participate. Process checklists that indicated what information was covered in each educational session and postsession follow-up logs were partially completed. Almost all participants reported receipt of intervention components and receipt of screening at 4-month follow-up and reported high acceptability of the program. The pilot study established the feasibility of working with community health advisors to promote colorectal cancer screening in Filipino American community settings. Findings informed the design of a dissemination trial that is currently ongoing with regards to monitoring recruitment, intervention implementation and follow-up and allowing flexibility regarding one-on-one or small group education.

  5. Design to Thrive Creating Social Networks and Online Communities that Last

    Howard, Tharon W

    2010-01-01

    Social networks and online communities are reshaping the way people communicate, both in their personal and professional lives. What makes some succeed and others fail? What draws a user in? What makes them join? What keeps them coming back? Entrepreneurs and businesses are turning to user experience practitioners to figure this out. Though they are well-equipped to evaluate and create a variety of interfaces, social networks require a different set of design principles and ways of thinking about the user in order to be successful. .. .. Design to Thrive presents tried and tested design method

  6. Characterization of the Usage of the Serine Metabolic Network in Human Cancer

    Mahya Mehrmohamadi

    2014-11-01

    Full Text Available The serine, glycine, one-carbon (SGOC metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

  7. Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network

    Michelle Wong

    2018-03-01

    Full Text Available Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

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

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

    2013-06-01

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

  9. An investigation into possibilities for implementation of a virtual community of practice delivered via a mobile social network for rural community media in the Eastern Cape, South Africa

    Oliva Muwanga-Zake

    2017-03-01

    Full Text Available Background: The purpose of this article is to provide an overview of how a virtual community of practice can be delivered via a mobile social networking framework to support rural community media in the Eastern Cape Province of South Africa. Objectives: The article presents the results of a study conducted to ascertain the possibilities of utilising mobile social networking as a means to provide access to required information and knowledge to rural community media through creation of a virtual community of practice. Improving the operational effectiveness of rural community media as a component of the rural community communication process would serve to improve the entire rural community communication process as well, making them more effective tools for availing relevant news and information to rural communities and reflecting the realities of rural communities to their broader environment. Method: The study was conducted on rural community media small micro and medium enterprises (SMMEs in the Eastern Cape Province of South Africa. The study applied an interpretive research philosophy, qualitative research design and multiple–case study approach. Primary data were collected through semi-structured interviews supported by a questionnaire, with secondary data collected via literature review, observation and documentation analysis. Results: Findings were that rural community media do make use of social media and mobile devices in operating their business, require access to generic and domain specific support services and actively engage their peers and stakeholders in this respect, although no formalised structure existed. The authors’ recommendation is to create a formalised virtual community of practice through the establishment of a mobile social network. Conclusion: Because of the fact that rural community SMMEs already utilise mobile devices and social media to operate their businesses, development of a solution based on a mobile social

  10. Applying Social Network Analysis to Identify the Social Support Needs of Adolescent and Young Adult Cancer Patients and Survivors.

    Koltai, Kolina; Walsh, Casey; Jones, Barbara; Berkelaar, Brenda L

    2018-04-01

    This article examines how theoretical and clinical applications of social network analysis (SNA) can inform opportunities for innovation and advancement of social support programming for adolescent and young adult (AYA) cancer patients and survivors. SNA can help address potential barriers and challenges to initiating and sustaining AYA peer support by helping to identify the diverse psychosocial needs among individuals in the AYA age range; find strategic ways to support and connect AYAs at different phases of the cancer trajectory with resources and services; and increase awareness of psychosocial resources and referrals from healthcare providers. Network perspectives on homophily, proximity, and evolution provide a foundational basis to explore the utility of SNA in AYA clinical care and research initiatives. The uniqueness of the AYA oncology community can also provide insight into extending and developing current SNA theories. Using SNA in AYA psychosocial cancer research has the potential to create new ideas and pathways for supporting AYAs across the continuum of care, while also extending theories of SNA. SNA may also prove to be a useful tool for examining social support resources for AYAs with various chronic health conditions and other like groups.

  11. Culturally-Relevant Online Cancer Education Modules Empower Alaska's Community Health Aides/Practitioners to Disseminate Cancer Information and Reduce Cancer Risk.

    Cueva, Katie; Revels, Laura; Cueva, Melany; Lanier, Anne P; Dignan, Mark; Viswanath, K; Fung, Teresa T; Geller, Alan C

    2017-04-12

    To address a desire for timely, medically accurate cancer education in rural Alaska, ten culturally relevant online learning modules were developed with, and for, Alaska's Community Health Aides/Practitioners (CHA/Ps). The project was guided by the framework of Community-Based Participatory Action Research, honored Indigenous Ways of Knowing, and was informed by Empowerment Theory. A total of 428 end-of-module evaluation surveys were completed by 89 unique Alaska CHA/Ps between January and December 2016. CHA/Ps shared that as a result of completing the modules, they were empowered to share cancer information with their patients, families, friends, and communities, as well as engage in cancer risk reduction behaviors such as eating healthier, getting cancer screenings, exercising more, and quitting tobacco. CHA/Ps also reported the modules were informative and respectful of their diverse cultures. These results from end-of-module evaluation surveys suggest that the collaboratively developed, culturally relevant, online cancer education modules have empowered CHA/Ps to reduce cancer risk and disseminate cancer information. "brought me to tears couple of times, and I think it will help in destroying the silence that surrounds cancer".

  12. Networking between community health programs: a case study outlining the effectiveness, barriers and enablers

    Grills Nathan J

    2012-07-01

    Full Text Available Abstract Background In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Methods Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. Results The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. Conclusions In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of

  13. Networking between community health programs: a case study outlining the effectiveness, barriers and enablers.

    Grills, Nathan J; Robinson, Priscilla; Phillip, Maneesh

    2012-07-19

    In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships) that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of potential health networks in India and other developing world countries.

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

    Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

    2016-12-01

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

  15. Development of the Moffitt Cancer Network as a Telemedicine and Teleconferencing Educational Tool for Health Care Providers

    Krischer, Jeffrey

    2002-01-01

    The Moffitt Cancer Network's (MCN) goal is to provide up-to-date oncology related information, resources, and education to oncology health care providers and researchers for the prevention and cure of cancer...

  16. Promoting community practitioners' use of evidence-based approaches to increase breast cancer screening.

    Leeman, Jennifer; Moore, Alexis; Teal, Randall; Barrett, Nadine; Leighton, Ashely; Steckler, Allan

    2013-07-01

    Many women do not get mammography screenings at the intervals recommended for early detection and treatment of breast cancer. The Guide to Community Preventive Services (Community Guide) recommends a range of evidence-based strategies to improve mammography rates. However, nurses and others working in community-based settings make only limited use of these strategies. We report on a dissemination intervention that partnered the University of North Carolina with the Susan G. Komen Triangle Affiliate to disseminate Community Guide breast cancer screening strategies to community organizations. The intervention was guided by social marketing and diffusion of innovation theory and was designed to provide evidence and support via Komen's existing relationships with grantee organizations. The present study reports the findings from a formative evaluation of the intervention, which included a content analysis of 46 grant applications pre- and post intervention and focus groups with 20 grant recipients. © 2013 Wiley Periodicals, Inc.

  17. Construction and analysis of circular RNA molecular regulatory networks in liver cancer.

    Ren, Shuangchun; Xin, Zhuoyuan; Xu, Yinyan; Xu, Jianting; Wang, Guoqing

    2017-01-01

    Liver cancer is the sixth most prevalent cancer, and the third most frequent cause of cancer-related deaths. Circular RNAs (circRNAs), a kind of special endogenous ncRNAs, have been coming back to the forefront of cancer genomics research. In this study, we used a systems biology approach to construct and analyze the circRNA molecular regulatory networks in the context of liver cancer. We detected a total of 127 differentially expressed circRNAs and 3,235 differentially expressed mRNAs. We selected the top-5 upregulated circRNAs to construct a circRNA-miRNA-mRNA network. We enriched the pathways and gene ontology items and determined their participation in cancer-related pathways such as p53 signaling pathway and pathways involved in angiogenesis and cell cycle. Quantitative real-time PCR was performed to verify the top-five circRNAs. ROC analysis showed circZFR, circFUT8, circIPO11 could significantly distinguish the cancer samples, with an AUC of 0.7069, 0.7575, and 0.7103, respectively. Our results suggest the circRNA-miRNA-mRNA network may help us further understand the molecular mechanisms of tumor progression in liver cancer, and reveal novel biomarkers and therapeutic targets.

  18. Construction of HMI Network System for Individualized Maternity Intervention Service against Birth Defects in Community

    Xu-huai HU

    2007-01-01

    The paper expounds the community maternity service system against birth defects,from the viewpoint of individualized service in family planning. We have utilized modern information technology to develop health management information (HMI) network with individualized maternity, and to establish the community service system for intervention of birth defects. The service system applied the concept of modern health management information to implementing informational management for screening,treatment, following up, outcome monitoring, so as to provide a base for promotion of health, diagnosis, treatment as well as scientific research, with the prenatal screening of Down's syndrome as a model. The introduction to informational network during the processes of service has been carried out with regards to its composition, function and application, while introducing the effects of computerized case record individualized in prevention, management and research of Down's syndrome.

  19. It Takes More than a Village: Building a Network of Safety in Nepal's Mountain Communities.

    Adams, Vincanne; Craig, Sienna; Samen, Arlene; Bhatta, Surya

    2016-12-01

    Purpose This report from the field details the ways that one small maternal child health NGO, which began its work in Tibet and now works in the mountain communities of Nepal, has established a model for integrated healthcare delivery and support it calls the "network of safety." Description It discusses some of the challenges faced both by the NGO and by the rural mountain communities with whom it partners, as well as with the government of Nepal. Conclusion This report describes and analyzes successful efforts to reduce maternal and infant mortality in a culturally astute, durable, and integrated way, as well as examples of innovation and success experienced by enacting the network of safety model.

  20. The Community Integration Questionnaire - Revised: Australian normative data and measurement of electronic social networking.

    Callaway, Libby; Winkler, Dianne; Tippett, Alice; Herd, Natalie; Migliorini, Christine; Willer, Barry

    2016-06-01

    Consideration of the relationship between meaningful participation, health and wellbeing underpins occupational therapy intervention, and drives measurement of community integration following acquired brain injury (ABI). However, utility of community integration measures has been limited to date by lack of normative data against which to compare outcomes, and none examine the growing use of electronic social networking (ESN) for social participation. This research had four aims: (i) develop and pilot items assessing ESN to add to the Community Integration Questionnaire, producing the Community Integration Questionnaire-Revised (CIQ-R); (ii) examine factor structure of the CIQ-R; (iii) collect Australian CIQ-R normative data; and (iv) assess test-retest reliability of the revised measure. Australia. A convenience sample of adults without ABI (N = 124) was used to develop and pilot ESN items. A representative general population sample of adults without ABI aged 18-64 years (N = 1973) was recruited to gather normative CIQ-R data. Cross-sectional survey. Demographic items and the CIQ-R. The CIQ-R demonstrated acceptable psychometric properties, with minor modification to the original scoring based on the factor analyses provided. Large representative general population CIQ-R normative data have been established, detailing contribution of a range of independent demographic variables to community integration. The addition of electronic social networking items to the CIQ-R offers a contemporary method of assessing community integration following ABI. Normative CIQ-R data enhance the understanding of community integration in the general population, allowing occupational therapists and other clinicians to make more meaningful comparisons between groups. © 2016 Occupational Therapy Australia.