Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

PubMed

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks.

Restrictions of physical activity participation in older adults with disability: employing keyword network analysis.

PubMed

Koo, Kyo-Man; Kim, Chun-Jong; Park, Chae-Hee; Byeun, Jung-Kyun; Seo, Geon-Woo

2016-08-01

Older adults with disability might have been increasing due to the rapid aging of society. Many studies showed that physical activity is an essential part for improving quality of life in later lives. Regular physical activity is an efficient means that has roles of primary prevention and secondary prevention. However, there were few studies regarding older adults with disability and physical activity participation. The purpose of this current study was to investigate restriction factors to regularly participate older adults with disability in physical activity by employing keyword network analysis. Two hundred twenty-nine older adults with disability who were over 65 including aging with disability and disability with aging in type of physical disability and brain lesions defined by disabled person welfare law partook in the open questionnaire assessing barriers to participate in physical activity. The results showed that the keyword the most often used was 'Traffic' which was total of 21 times (3.47%) and the same proportion as in the 'personal' and 'economical'. Exercise was considered the most central keyword for participating in physical activity and keywords such as facility, physical activity, disabled, program, transportation, gym, discomfort, opportunity, and leisure activity were associated with exercise. In conclusion, it is necessary to educate older persons with disability about a true meaning of physical activity and providing more physical activity opportunities and decreasing inconvenience should be systematically structured in Korea.

Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

NASA Technical Reports Server (NTRS)

Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

2010-01-01

Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

CoPub: a literature-based keyword enrichment tool for microarray data analysis.

PubMed

Frijters, Raoul; Heupers, Bart; van Beek, Pieter; Bouwhuis, Maurice; van Schaik, René; de Vlieg, Jacob; Polman, Jan; Alkema, Wynand

2008-07-01

Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl.

Restrictions of physical activity participation in older adults with disability: employing keyword network analysis

PubMed Central

Koo, Kyo-Man; Kim, Chun-Jong; Park, Chae-Hee; Byeun, Jung-Kyun; Seo, Geon-Woo

2016-01-01

Older adults with disability might have been increasing due to the rapid aging of society. Many studies showed that physical activity is an essential part for improving quality of life in later lives. Regular physical activity is an efficient means that has roles of primary prevention and secondary prevention. However, there were few studies regarding older adults with disability and physical activity participation. The purpose of this current study was to investigate restriction factors to regularly participate older adults with disability in physical activity by employing keyword network analysis. Two hundred twenty-nine older adults with disability who were over 65 including aging with disability and disability with aging in type of physical disability and brain lesions defined by disabled person welfare law partook in the open questionnaire assessing barriers to participate in physical activity. The results showed that the keyword the most often used was ‘Traffic’ which was total of 21 times (3.47%) and the same proportion as in the ‘personal’ and ‘economical’. Exercise was considered the most central keyword for participating in physical activity and keywords such as facility, physical activity, disabled, program, transportation, gym, discomfort, opportunity, and leisure activity were associated with exercise. In conclusion, it is necessary to educate older persons with disability about a true meaning of physical activity and providing more physical activity opportunities and decreasing inconvenience should be systematically structured in Korea. PMID:27656637

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

PubMed

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%). Several major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

Analyzing the Chinese landscape in anti-diabetic drug research: leading knowledge production institutions and thematic communities.

PubMed

Deng, Junling; Sitou, Kaweng; Zhang, Yongping; Yan, Ru; Hu, Yuanjia

2016-01-01

The discovery of anti-diabetic drugs is an active Chinese medicine research area. This study aims to map out anti-diabetic drug research in China using a network-based systemic approach based on co-authorship of academic publications. We focused on identifying leading knowledge production institutions, analyzing interactions among them, detecting communities with high internal associations, and exploring future research directions. Target articles published in 2009-2013 under the topic "diabetes" and subject category "pharmacology & pharmacy," with "China," "Taiwan," "Hong Kong," or "Macao" (or "Macau") in the authors' address field were retrieved from the science citation index expanded database and their bibliographic information (e.g., article title, authors, keywords, and authors' affiliation addresses) analyzed. A social network approach was used to construct an institutional collaboration network based on co-publications. Gephi software was used to visualize the network and relationships among institutes were analyzed using centrality measurements. Thematic analysis based on article keywords and R sc value was applied to reveal the research hotspots and directions of network communities. The top 50 institutions were identified; these included Shanghai Jiao Tong University, National Taiwan University, Peking University, and China Pharmaceutical University. Institutes from Taiwan tended to cooperate with institutes outside Taiwan, but those from mainland China showed low interest in external collaboration. Fourteen thematic communities were detected with the Louvain algorithm and further labeled by their high-frequency and characteristic keywords, such as Chinese medicines, diabetic complications, oxidative stress, pharmacokinetics, and insulin resistance. The keyword Chinese medicines comprised a range of Chinese medicine-related topics, including berberine, flavonoids, Astragalus polysaccharide, emodin, and ginsenoside. These keywords suggest potential fields for further anti-diabetic drug research. The correlation of -0.641 (P = 0.013) between degree centrality and the R sc value of non-core keywords indicates that communities concentrating on rare research fields are usually isolated by others and have a lower chance of collaboration. With a better understanding of the Chinese landscape in anti-diabetic drug research, researchers and scholars looking for experts and institutions in a specific research area can rapidly spot their target community, then select the most appropriate potential collaborator and suggest preferential research directions for future studies.

Literature-based compound profiling: application to toxicogenomics.

PubMed

Frijters, Raoul; Verhoeven, Stefan; Alkema, Wynand; van Schaik, René; Polman, Jan

2007-11-01

To reduce continuously increasing costs in drug development, adverse effects of drugs need to be detected as early as possible in the process. In recent years, compound-induced gene expression profiling methodologies have been developed to assess compound toxicity, including Gene Ontology term and pathway over-representation analyses. The objective of this study was to introduce an additional approach, in which literature information is used for compound profiling to evaluate compound toxicity and mode of toxicity. Gene annotations were built by text mining in Medline abstracts for retrieval of co-publications between genes, pathology terms, biological processes and pathways. This literature information was used to generate compound-specific keyword fingerprints, representing over-represented keywords calculated in a set of regulated genes after compound administration. To see whether keyword fingerprints can be used for assessment of compound toxicity, we analyzed microarray data sets of rat liver treated with 11 hepatotoxicants. Analysis of keyword fingerprints of two genotoxic carcinogens, two nongenotoxic carcinogens, two peroxisome proliferators and two randomly generated gene sets, showed that each compound produced a specific keyword fingerprint that correlated with the experimentally observed histopathological events induced by the individual compounds. By contrast, the random sets produced a flat aspecific keyword profile, indicating that the fingerprints induced by the compounds reflect biological events rather than random noise. A more detailed analysis of the keyword profiles of diethylhexylphthalate, dimethylnitrosamine and methapyrilene (MPy) showed that the differences in the keyword fingerprints of these three compounds are based upon known distinct modes of action. Visualization of MPy-linked keywords and MPy-induced genes in a literature network enabled us to construct a mode of toxicity proposal for MPy, which is in agreement with known effects of MPy in literature. Compound keyword fingerprinting based on information retrieved from literature is a powerful approach for compound profiling, allowing evaluation of compound toxicity and analysis of the mode of action.

Optimization of OSPF Routing in IP Networks

NASA Astrophysics Data System (ADS)

Bley, Andreas; Fortz, Bernard; Gourdin, Eric; Holmberg, Kaj; Klopfenstein, Olivier; Pióro, Michał; Tomaszewski, Artur; Ümit, Hakan

The Internet is a huge world-wide packet switching network comprised of more than 13,000 distinct subnetworks, referred to as Autonomous Systems (ASs) autonomous system AS . They all rely on the Internet Protocol (IP) internet protocol IP for transport of packets across the network. And most of them use shortest path routing protocols shortest path routing!protocols , such as OSPF or IS-IS, to control the routing of IP packets routing!of IP packets within an AS. The idea of the routing is extremely simple — every packet is forwarded on IP links along the shortest route between its source and destination nodes of the AS. The AS network administrator can manage the routing of packets in the AS by supplying the so-called administrative weights of IP links, which specify the link lengths that are used by the routing protocols for their shortest path computations. The main advantage of the shortest path routing policy is its simplicity, allowing for little administrative overhead. From the network engineering perspective, however, shortest path routing can pose problems in achieving satisfactory traffic handling efficiency. As all routing paths depend on the same routing metric routing!metric , it is not possible to configure the routing paths for the communication demands between different pairs of nodes explicitly or individually; the routing can be controlled only indirectly and only as a whole by modifying the routing metric. Thus, one of the main tasks when planning such networks is to find administrative link weights that induce a globally efficient traffic routing traffic!routing configuration of an AS. It turns out that this task leads to very difficult mathematical optimization problems. In this chapter, we discuss and describe exact integer programming models and solution approaches as well as practically efficient smart heuristics for such shortest path routing problems shortest path routing!problems .

The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

PubMed

Özgür, Arzucan; Hur, Junguk; He, Yongqun

2016-01-01

The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

2016 Year-in-Review of Clinical and Consumer Informatics: Analysis and Visualization of Keywords and Topics.

PubMed

Park, Hyeoun-Ae; Lee, Joo Yun; On, Jeongah; Lee, Ji Hyun; Jung, Hyesil; Park, Seul Ki

2017-04-01

The objective of this study was to review and visualize the medical informatics field over the previous 12 months according to the frequencies of keywords and topics in papers published in the top four journals in the field and in Healthcare Informatics Research (HIR) , an official journal of the Korean Society of Medical Informatics. A six-person team conducted an extensive review of the literature on clinical and consumer informatics. The literature was searched using keywords employed in the American Medical Informatics Association year-in-review process and organized into 14 topics used in that process. Data were analyzed using word clouds, social network analysis, and association rules. The literature search yielded 370 references and 1,123 unique keywords. 'Electronic Health Record' (EHR) (78.6%) was the most frequently appearing keyword in the articles published in the five studied journals, followed by 'telemedicine' (2.1%). EHR (37.6%) was also the most frequently studied topic area, followed by clinical informatics (12.0%). However, 'telemedicine' (17.0%) was the most frequently appearing keyword in articles published in HIR , followed by 'telecommunications' (4.5%). Telemedicine (47.1%) was the most frequently studied topic area, followed by EHR (14.7%). The study findings reflect the Korean government's efforts to introduce telemedicine into the Korean healthcare system and reactions to this from the stakeholders associated with telemedicine.

Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature.

PubMed

Radhakrishnan, Srinivasan; Erbis, Serkan; Isaacs, Jacqueline A; Kamarthi, Sagar

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.

Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature

PubMed Central

Isaacs, Jacqueline A.

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map. PMID:28328983

Mapping the knowledge structure of research on patient adherence: knowledge domain visualization based co-word analysis and social network analysis.

PubMed

Zhang, Juan; Xie, Jun; Hou, Wanli; Tu, Xiaochen; Xu, Jing; Song, Fujian; Wang, Zhihong; Lu, Zuxun

2012-01-01

Patient adherence is an important issue for health service providers and health researchers. However, the knowledge structure of diverse research on treatment adherence is unclear. This study used co-word analysis and social network analysis techniques to analyze research literature on adherence, and to show their knowledge structure and evolution over time. Published scientific papers about treatment adherence were retrieved from Web of Science (2000 to May 2011). A total of 2308 relevant articles were included: 788 articles published in 2000-2005 and 1520 articles published in 2006-2011. The keywords of each article were extracted by using the software Biblexcel, and the synonym and isogenous words were merged manually. The frequency of keywords and their co-occurrence frequency were counted. High frequency keywords were selected to yield the co-words matrix. Finally the decomposition maps were used to comb the complex knowledge structures. Research themes were more general in the first period (2000 to 2005), and more extensive with many more new terms in the second period (2006 to 2011). Research on adherence has covered more and more diseases, populations and methods, but other diseases/conditions are not as hot as HIV/AIDS and have not become specialty themes/sub-directions. Most studies originated from the United States. The dynamic of this field is mainly divergent, with increasing number of new sub-directions of research. Future research is required to investigate specific directions and converge as well to construct a general paradigm in this field.

Mapping the Knowledge Structure of Research on Patient Adherence: Knowledge Domain Visualization Based Co-Word Analysis and Social Network Analysis

PubMed Central

Hou, Wanli; Tu, Xiaochen; Xu, Jing; Song, Fujian; Wang, Zhihong; Lu, Zuxun

2012-01-01

Background Patient adherence is an important issue for health service providers and health researchers. However, the knowledge structure of diverse research on treatment adherence is unclear. This study used co-word analysis and social network analysis techniques to analyze research literature on adherence, and to show their knowledge structure and evolution over time. Methods Published scientific papers about treatment adherence were retrieved from Web of Science (2000 to May 2011). A total of 2308 relevant articles were included: 788 articles published in 2000–2005 and 1520 articles published in 2006–2011. The keywords of each article were extracted by using the software Biblexcel, and the synonym and isogenous words were merged manually. The frequency of keywords and their co-occurrence frequency were counted. High frequency keywords were selected to yield the co-words matrix. Finally the decomposition maps were used to comb the complex knowledge structures. Results Research themes were more general in the first period (2000 to 2005), and more extensive with many more new terms in the second period (2006 to 2011). Research on adherence has covered more and more diseases, populations and methods, but other diseases/conditions are not as hot as HIV/AIDS and have not become specialty themes/sub-directions. Most studies originated from the United States. Conclusion The dynamic of this field is mainly divergent, with increasing number of new sub-directions of research. Future research is required to investigate specific directions and converge as well to construct a general paradigm in this field. PMID:22496819

Patent citation network in nanotechnology (1976-2004)

NASA Astrophysics Data System (ADS)

Li, Xin; Chen, Hsinchun; Huang, Zan; Roco, Mihail C.

2007-06-01

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords "full-text" searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a␣less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters.

Disease gene classification with metagraph representations.

PubMed

Kircali Ata, Sezin; Fang, Yuan; Wu, Min; Li, Xiao-Li; Xiao, Xiaokui

2017-12-01

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics on the PPIK network, we further propose to represent proteins with metagraphs. Different from a traditional network motif or subgraph, a metagraph can capture a particular topological arrangement involving the interactions/associations between both proteins and keywords. Based on the novel metagraph representations for proteins, we further build classifiers for disease protein classification through supervised learning. Our experiments on three different PPI databases demonstrate that the proposed method consistently improves disease protein prediction across various classifiers, by 15.3% in AUC on average. It outperforms the baselines including the diffusion-based methods (e.g., RWR) and the module-based methods by 13.8-32.9% for overall disease protein prediction. For predicting breast cancer genes, it outperforms RWR, PRINCE and the module-based baselines by 6.6-14.2%. Finally, our predictions also turn out to have better correlations with literature findings from PubMed. Copyright © 2017 Elsevier Inc. All rights reserved.

Research progress and hotspot analysis of spatial interpolation

NASA Astrophysics Data System (ADS)

Jia, Li-juan; Zheng, Xin-qi; Miao, Jin-li

2018-02-01

In this paper, the literatures related to spatial interpolation between 1982 and 2017, which are included in the Web of Science core database, are used as data sources, and the visualization analysis is carried out according to the co-country network, co-category network, co-citation network, keywords co-occurrence network. It is found that spatial interpolation has experienced three stages: slow development, steady development and rapid development; The cross effect between 11 clustering groups, the main convergence of spatial interpolation theory research, the practical application and case study of spatial interpolation and research on the accuracy and efficiency of spatial interpolation. Finding the optimal spatial interpolation is the frontier and hot spot of the research. Spatial interpolation research has formed a theoretical basis and research system framework, interdisciplinary strong, is widely used in various fields.

Exploring the Knowledge Structure of Nursing Care for Older Patients With Delirium: Keyword Network Analysis.

PubMed

Choi, Jung Eun; Kim, Mi So

2018-05-01

Prevention of delirium is considered a critical part of the agenda for patient safety and an indicator of healthcare quality for older patients. As the incidence rate of delirium for older patients has increased in recent years, there has been a significant expansion in knowledge relevant to nursing care. The purposes of this study were to analyze the knowledge structure and trends in nursing care for older adults with delirium based on a keyword network analysis, and to provide a foundation for future research. Data analysis showed that knowledge structure in this area consists of three themes of research: postoperative acute care for older patients with delirium, prevention of delirium for older patients in intensive care units, and safety management for the improvement of outcomes for patients with delirium. Through research trend analysis, we found that research on care for patients with delirium has achieved both quantitative and qualitative improvements over the last decades. Concerning future research, we propose the expansion of patient- and family-centered care, community care, specific nursing interventions, and the integration of new technology into care for patients with delirium. These results provide a reference framework for understanding and developing nursing care for older adults with delirium.

What Can We Learn about Mental Health Needs from Tweets Mentioning Dementia on World Alzheimer’s Day?

PubMed Central

Yoon, Sunmoo

2017-01-01

Background Twitter can address the mental health challenges of dementia care. The aims of this study is to explore the contents and user interactions of tweets mentioning dementia to gain insights for dementia care. Methods We collected 35,260 tweets mentioning Alzheimer’s or dementia on World Alzheimer’s Day, September 21st in 2015. Topic modeling and social network analysis were applied to uncover content and structure of user communication. Results Global users generated keywords related to mental health and care including #psychology and #mental health. There were similarities and differences between the UK and the US in tweet content. The macro-level analysis uncovered substantial public interest on dementia. The meso-level network analysis revealed that top leaders of communities were spiritual organizations and traditional media. Conclusions The application of topic modeling and multi-level network analysis while incorporating visualization techniques can promote a global level understanding regarding public attention, interests, and insights regarding dementia care and mental health. PMID:27803262

Utilizing Social Bookmarking Tag Space for Web Content Discovery: A Social Network Analysis Approach

ERIC Educational Resources Information Center

Wei, Wei

2010-01-01

Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based…

Comparing the Hierarchy of Keywords in On-Line News Portals

PubMed Central

Tibély, Gergely; Sousa-Rodrigues, David; Pollner, Péter; Palla, Gergely

2016-01-01

Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals. PMID:27802319

Comparing the Hierarchy of Keywords in On-Line News Portals.

PubMed

Tibély, Gergely; Sousa-Rodrigues, David; Pollner, Péter; Palla, Gergely

2016-01-01

Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals.

Words Analysis of Online Chinese News Headlines about Trending Events: A Complex Network Perspective

PubMed Central

Li, Huajiao; Fang, Wei; An, Haizhong; Huang, Xuan

2015-01-01

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines’ keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words’ networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly. PMID:25807376

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

PubMed

Li, Huajiao; Fang, Wei; An, Haizhong; Huang, Xuan

2015-01-01

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.

Capturing the Interplay of Dynamics and Networks through Parameterizations of Laplacian Operators

DTIC Science & Technology

2016-08-24

important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that...vertices as a potential cluster (or community ) with respect to this process. We show that the subset-quality function generalizes the traditional conductance...compare the different perspectives they create on network structure. Subjects Network Science and Online Social Networks Keywords Network, Community

Multiple Factors-Aware Diffusion in Social Networks

DTIC Science & Technology

2015-05-22

Multiple Factors-Aware Diffusion in Social Networks Chung-Kuang Chou(B) and Ming-Syan Chen Department of Electrical Engineering, National Taiwan...propagates from nodes to nodes over a social network . The behavior that a node adopts an information piece in a social network can be affected by...Twitter dataset. Keywords: Social networks · Diffusion models 1 Introduction Information diffusion in social networks has been an active research field

Aplicación de técnicas de análisis de redes sociales y de co-ocurrencia de palabras en la determinación de frentes de investigación

NASA Astrophysics Data System (ADS)

Boeris, C. E.

A bibliometric study of the scientific production of the IAR researchers has been performed, with the aim of determining the institute's research fronts and groups of researchers working on these fronts. Methods of analysis of co-occurrence of words, authorship analysis and social network analysis (SNA) has been applied by extracting keywords and the names of the authors on the base of published works. The results support the existence of two research fronts within the institution. FULL TEXT IN SPANISH

Forecasting turning trends in knowledge networks

NASA Astrophysics Data System (ADS)

Szántó-Várnagy, Ádám; Farkas, Illés J.

2018-10-01

A large portion of our collective human knowledge is in electronic repositories. These repositories range from "hard fact" databases (e.g., scientific publications and patents) to "soft" knowledge such as news portals. The common denominator between them all is that they can be thought of in terms of topics/keywords. The interest in these topics is constantly changing over time. Their frequency occurrence diagrams can be used for effective prediction by the most straightforward simplification. In this paper, we use these diagrams to produce simple and human-readable rules that are able to predict the future trends of the most important keywords in 5 data sets of different types. A thorough analysis of the necessary input variables and parameters and their relation to the success rate is presented, as well.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

Traits and causes of environmental loss-related chemical accidents in China based on co-word analysis.

PubMed

Wu, Desheng; Song, Yu; Xie, Kefan; Zhang, Baofeng

2018-04-25

Chemical accidents are major causes of environmental losses and have been debated due to the potential threat to human beings and environment. Compared with the single statistical analysis, co-word analysis of chemical accidents illustrates significant traits at various levels and presents data into a visual network. This study utilizes a co-word analysis of the keywords extracted from the Web crawling texts of environmental loss-related chemical accidents and uses the Pearson's correlation coefficient to examine the internal attributes. To visualize the keywords of the accidents, this study carries out a multidimensional scaling analysis applying PROXSCAL and centrality identification. The research results show that an enormous environmental cost is exacted, especially given the expected environmental loss-related chemical accidents with geographical features. Meanwhile, each event often brings more than one environmental impact. Large number of chemical substances are released in the form of solid, liquid, and gas, leading to serious results. Eight clusters that represent the traits of these accidents are formed, including "leakage," "poisoning," "explosion," "pipeline crack," "river pollution," "dust pollution," "emission," and "industrial effluent." "Explosion" and "gas" possess a strong correlation with "poisoning," located at the center of visualization map.

Scientific production on indoor air quality of environments used for physical exercise and sports practice: Bibliometric analysis.

PubMed

Andrade, Alexandro; Dominski, Fábio Hech; Coimbra, Danilo Reis

2017-07-01

In order to minimize adverse health effects and increase the benefits of physical activity, it is important to systematize indoor air quality study in environments used for physical exercise and sports. To investigate and analyze the scientific production related to indoor air quality of environments used for physical exercise and sports practice through a bibliometric analysis. The databases Scielo, Science Direct, Scopus, Lilacs, Medline via Pubmed, and SportDiscus were searched from their inception to March 2016. Bibliometric analysis was performed for authors, institutions, countries, and collaborative networks, in relation to publication year, theme, citation network, funding agency, and analysis of titles and keywords of publications. Country, area, and impact factor of the journals were analyzed. Of 1281 studies screened, 34 satisfied the inclusion criteria. The first publication occurred in 1975. An increase in publications was observed in the last 15 years. Most of the studies were performed by researchers in the USA, followed by Portugal and Italy. Seventeen different scientific journals have published studies on the subject, and most are in the area of Environmental Sciences. It was noted that the categories of author keywords associated with "Pollutants," "Sport Environment," and "Physical Exercise" were the most commonly used in most studies. A total of 68% of the studies had at least one funding agency, and 81% of studies published in the last decade had funding. Our results demonstrate that there is recent exponential growth, driven in the last decade by researchers in environmental science from European institutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

The evolution of publication hotspots in the field of telemedicine from 1962 to 2015 and differences among six countries.

PubMed

Wang, Yanjun; Zhao, Ye; Zheng, Jianzhong; Zhang, Ailian; Dong, Haiyuan

2018-04-01

Introduction Telemedicine has been implemented in many countries and has captured the attention of many researchers. Herein, we aim to quantify publication hotspots in the field of telemedicine, analyse their evolution, compare them in different countries, and provide visual representations. Methods We used software tools to process PubMed entries for a 54-year period and identified publication hotspots using keyword frequency analysis. We employed a keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and network visualization technology. Results The number of Medical Subject Heading (MeSH) terms increased with time. The most common subcategories of telemedicine between 1962 and 2015 were Remote Consultation, Teleradiology, and Telepathology. The most popular information communication technologies in telemedicine publications were related to the Internet and cell phones. The topics of Patient Satisfaction, Treatment Outcomes, and Home Care Services associated with telemedicine were highlighted after the 1990s. Use frequency of the terms Cell Phones and Self-Care increased drastically in the past six years, and the publication focus in six countries that had the highest output was different. Knowledge network maps and perceptual maps show the relationship between high-frequency MeSH terms. Discussion The telemedicine field has experienced significant growth and expansion in knowledge and innovation in the last 54 years. Publication hotspots for telemedicine lean towards clinical treatment, home care services, and personal care, and countries emphasize publishing in areas related to their national characteristics. This study quantitatively discusses publication hotspots, provides an objective and systematic understanding of this field, and suggests directions for future telemedicine research.

Look Who's Talking - The Role of the IARPC Collaborations Website in Supporting Mutli-Institution Dialog on Arctic Research Imperatives

NASA Astrophysics Data System (ADS)

Starkweather, S.; Stephenson, S. N.; Rohde, J. A.; Bowden, S.

2015-12-01

The IARPC Collaborations website (www.iarpccollaborations.org) was developed to support collaborative implementation of the Interagency Arctic Research Policy Committee's (IARPC) 5-Year Plan for Arctic Research. The Plan describes an ambitious agenda for advancing understanding of the changing Arctic, a challenge that requires innovative approaches to integrate disparate research activities. IARPC was created by Congress to address this integration with a mandate that includes developing interagency collaboration and outside partnerships, specifically those with the State of Alaska, indigenous communities, academia, industry and non-governmental organizations. The IARPC Collaborations website was introduced in October of 2014 as an innovative means to address IARPC's mandate. It is an open, social networking platform with member-driven content and features to support dialog and milestone tracking. In its first year, IARPC Collaborations has attracted more than 600 members. Member-supplied content added to the site includes more than 575 research planning documents and scientific presentations and 300 updates on research plans and resources; all content is tagged with descriptive keywords to expedite discovery and elucidate connectivity across members and topics. Applying a social network analysis to metadata from the site reveals the strength and nature of this connectivity. This analysis demonstrates that Collaboration Team phone meetings remain the dominant form of communication. Dialog on the site through comment forums has been slow to emerge despite its merits of persistence and transparency. While more than 80 members have used the comment features at least once, the strong centrality of the IARPC Secretariat to website dialog is apparent. An analysis of content keywords demonstrates the potential for improved dialog based on overlapping interests as revealed by trending topics like "sea ice prediction", "traditional knowledge" and "permafrost carbon". Less than one year into launch, this analysis of IARPC's experiment in collaborative integration reveals the enduring strengths of traditional collaboration tools like secretariat support and phone meetings; the full potential of IARPC's social networking tools remains to be seen.

Wireless Sensor Network Radio Power Management and Simulation Models

DTIC Science & Technology

2010-01-01

The Open Electrical & Electronic Engineering Journal, 2010, 4, 21-31 21 1874-1290/10 2010 Bentham Open Open Access Wireless Sensor Network Radio...Air Force Institute of Technology, Wright-Patterson AFB, OH, USA Abstract: Wireless sensor networks (WSNs) create a new frontier in collecting and...consumption. Keywords: Wireless sensor network , power management, energy-efficiency, medium access control (MAC), simulation pa- rameters. 1

Rapid automatic keyword extraction for information retrieval and analysis

DOEpatents

Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

2012-03-06

Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

A systematic review protocol: social network analysis of tobacco use.

PubMed

Maddox, Raglan; Davey, Rachel; Lovett, Ray; van der Sterren, Anke; Corbett, Joan; Cochrane, Tom

2014-08-08

Tobacco use is the single most preventable cause of death in the world. Evidence indicates that behaviours such as tobacco use can influence social networks, and that social network structures can influence behaviours. Social network analysis provides a set of analytic tools to undertake methodical analysis of social networks. We will undertake a systematic review to provide a comprehensive synthesis of the literature regarding social network analysis and tobacco use. The review will answer the following research questions: among participants who use tobacco, does social network structure/position influence tobacco use? Does tobacco use influence peer selection? Does peer selection influence tobacco use? We will follow the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines and search the following databases for relevant articles: CINAHL (Cumulative Index to Nursing and Allied Health Literature); Informit Health Collection; PsycINFO; PubMed/MEDLINE; Scopus/Embase; Web of Science; and the Wiley Online Library. Keywords include tobacco; smoking; smokeless; cigarettes; cigar and 'social network' and reference lists of included articles will be hand searched. Studies will be included that provide descriptions of social network analysis of tobacco use.Qualitative, quantitative and mixed method data that meets the inclusion criteria for the review, including methodological rigour, credibility and quality standards, will be synthesized using narrative synthesis. Results will be presented using outcome statistics that address each of the research questions. This systematic review will provide a timely evidence base on the role of social network analysis of tobacco use, forming a basis for future research, policy and practice in this area. This systematic review will synthesise the evidence, supporting the hypothesis that social network structures can influence tobacco use. This will also include exploring the relationship between social network structure, social network position, peer selection, peer influence and tobacco use across all age groups, and across different demographics. The research will increase our understanding of social networks and their impact on tobacco use, informing policy and practice while highlighting gaps in the literature and areas for further research.

Analysis of worldwide research in the field of cybernetics during 1997-2011.

PubMed

Singh, Virender; Perdigones, Alicia; García, José Luis; Cañas-Guerrero, Ignacio; Mazarrón, Fernando R

2014-12-01

The study provides an overview of the research activity carried out in the field of cybernetics. To do so, all research papers from 1997 to 2011 (16,445 research papers) under the category of "Computer Science, Cybernetics" of Web of Science have been processed using our in-house software which is developed specifically for this purpose. Among its multiple capabilities, this software analyses individual and compound keywords, quantifies productivity taking into account the work distribution, estimates the impact of each article and determines the collaborations established at different scales. Keywords analysis identifies the evolution of the most important research topics in the field of cybernetics and their specificity in biological aspects, as well as the research topics with lesser interest. The analysis of productivity, impact and collaborations provides a framework to assess research activity in a specific and realistic context. The geographical and institutional distribution of publications reveals the leading countries and research centres, analysing their relation to main research journals. Moreover, collaborations analysis reveals great differences in terms of internationalization and complexity of research networks. The results of this study may be very useful for the characterization and the decisions made by research in the field of cybernetics.

Semantic Web-based Vocabulary Broker for Open Science

NASA Astrophysics Data System (ADS)

Ritschel, B.; Neher, G.; Iyemori, T.; Murayama, Y.; Kondo, Y.; Koyama, Y.; King, T. A.; Galkin, I. A.; Fung, S. F.; Wharton, S.; Cecconi, B.

2016-12-01

Keyword vocabularies are used to tag and to identify data of science data repositories. Such vocabularies consist of controlled terms and the appropriate concepts, such as GCMD1 keywords or the ESPAS2 keyword ontology. The Semantic Web-based mash-up of domain-specific, cross- or even trans-domain vocabularies provides unique capabilities in the network of appropriate data resources. Based on a collaboration between GFZ3, the FHP4, the WDC for Geomagnetism5 and the NICT6 we developed the concept of a vocabulary broker for inter- and trans-disciplinary data detection and integration. Our prototype of the Semantic Web-based vocabulary broker uses OSF7 for the mash-up of geo and space research vocabularies, such as GCMD keywords, ESPAS keyword ontology and SPASE8 keyword vocabulary. The vocabulary broker starts the search with "free" keywords or terms of a specific vocabulary scheme. The vocabulary broker almost automatically connects the different science data repositories which are tagged by terms of the aforementioned vocabularies. Therefore the mash-up of the SKOS9 based vocabularies with appropriate metadata from different domains can be realized by addressing LOD10 resources or virtual SPARQL11 endpoints which maps relational structures into the RDF format12. In order to demonstrate such a mash-up approach in real life, we installed and use a D2RQ13 server for the integration of IUGONET14 data which are managed by a relational database. The OSF based vocabulary broker and the D2RQ platform are installed at virtual LINUX machines at the Kyoto University. The vocabulary broker meets the standard of a main component of the WDS15 knowledge network. The Web address of the vocabulary broker is http://wdcosf.kugi.kyoto-u.ac.jp 1 Global Change Master Directory2 Near earth space data infrastructure for e-science3 German Research Centre for Geosciences4 University of Applied Sciences Potsdam5 World Data Center for Geomagnetism Kyoto6 National Institute of Information and Communications Technology Tokyo7 Open Semantic Framework8 Space Physics Archive Search and Extract9 Simple Knowledge Organization System10 Linked Open Data11 SPARQL Protocol And RDF Query12 Resource Description Framework13 Database to RDF Query14 Inter-university Upper atmosphere Global Observation NETwork15 World Data System

[Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service].

PubMed

Kim, Minji; Choi, Mona; Youm, Yoosik

2017-12-01

As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies. © 2017 Korean Society of Nursing Science

Methods for extracting social network data from chatroom logs

NASA Astrophysics Data System (ADS)

Osesina, O. Isaac; McIntire, John P.; Havig, Paul R.; Geiselman, Eric E.; Bartley, Cecilia; Tudoreanu, M. Eduard

2012-06-01

Identifying social network (SN) links within computer-mediated communication platforms without explicit relations among users poses challenges to researchers. Our research aims to extract SN links in internet chat with multiple users engaging in synchronous overlapping conversations all displayed in a single stream. We approached this problem using three methods which build on previous research. Response-time analysis builds on temporal proximity of chat messages; word context usage builds on keywords analysis and direct addressing which infers links by identifying the intended message recipient from the screen name (nickname) referenced in the message [1]. Our analysis of word usage within the chat stream also provides contexts for the extracted SN links. To test the capability of our methods, we used publicly available data from Internet Relay Chat (IRC), a real-time computer-mediated communication (CMC) tool used by millions of people around the world. The extraction performances of individual methods and their hybrids were assessed relative to a ground truth (determined a priori via manual scoring).

Management Requirements of the 3COM Ethernet Local Area Network

DTIC Science & Technology

1988-09-01

Management Information System . With the introduction of new technology comes the requirement to administer the network. This paper describes LAN services available on the network, management philosophies for the LAN services, and areas of LAN administration considered important to the successful operation and maintenance of a LAN. LAN administration problems identified by users are also addressed. Keywords included; Local area network (LAN); Lan management; Lan administration; 3COM ETHERNET LAN.

Topology Optimization for Energy Management in Underwater Sensor Networks

DTIC Science & Technology

2015-02-01

1 To appear in International Journal of Control as a regular paper Topology Optimization for Energy Management in Underwater Sensor Networks ⋆ Devesh...K. Jha1 Thomas A. Wettergren2 Asok Ray1 Kushal Mukherjee3 Keywords: Underwater Sensor Network , Energy Management, Pareto Optimization, Adaptation...Optimization for Energy Management in Underwater Sensor Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d

Multiscale complex network analysis: An approach to study spatiotemporal rainfall pattern in south Germany

NASA Astrophysics Data System (ADS)

Agarwal, Ankit; Marwan, Norbert; Rathinasamy, Maheswaran; Oeztuerk, Ugur; Merz, Bruno; Kurths, Jürgen

2017-04-01

Understanding of the climate sytems has been of tremendous importance to different branches such as agriculture, flood, drought and water resources management etc. In this regard, complex networks analysis and time series analysis attracted considerable attention, owing to their potential role in understanding the climate system through characteristic properties. One of the basic requirements in studying climate network dynamics is to identify connections in space or time or space-time, depending upon the purpose. Although a wide variety of approaches have been developed and applied to identify and analyse spatio-temporal relationships by climate networks, there is still further need for improvements in particular when considering precipitation time series or interactions on different scales. In this regard, recent developments in the area of network theory, especially complex networks, offer new avenues, both for their generality about systems and for their holistic perspective about spatio-temporal relationships. The present study has made an attempt to apply the ideas developed in the field of complex networks to examine connections in regional climate networks with particular focus on multiscale spatiotemporal connections. This paper proposes a novel multiscale understanding of regional climate networks using wavelets. The proposed approach is applied to daily precipitation records observed at 543 selected stations from south Germany for a period of 110 years (1901-2010). Further, multiscale community mining is performed on the same study region to shed more light on the underlying processes at different time scales. Various network measure and tools so far employed provide micro-level (individual station) and macro-level (community structure) information of the network. It is interesting to investigate how the result of this study can be useful for future climate predictions and for evaluating climate models on their implementation regarding heavy precipitation. Keywords: Complex network, event synchronization, wavelet, regional climate network, multiscale community mining

MYSEA: The Monterey Security Architecture

DTIC Science & Technology

2009-01-01

Security and Protection, Organization and Design General Terms: Design; Security Keywords: access controls, authentication, information flow controls...Applicable environments include: mil- itary coalitions, agencies and organizations responding to security emergencies, and mandated sharing in business ...network architecture affords users the abil- ity to securely access information across networks at dif- ferent classifications using standardized

The comparative risk of developing postoperative complications in patients with distal radius fractures following different treatment modalities

PubMed Central

Qiu, Wen-Jun; Li, Yi-Fan; Ji, Yun-Han; Xu, Wei; Zhu, Xiao-Dong; Tang, Xian-Zhong; Zhao, Huan-Li; Wang, Gui-Bin; Jia, Yue-Qing; Zhu, Shi-Cai; Zhang, Feng-Fang; Liu, Hong-Mei

2015-01-01

In this study, we performed a network meta-analysis to compare the outcomes of seven most common surgical procedures to fix DRF, including bridging external fixation, non-bridging external fixation, K-wire fixation, plaster fixation, dorsal plating, volar plating, and dorsal and volar plating. Published studies were retrieved through PubMed, Embase and Cochrane Library databases. The database search terms used were the following keywords and MeSH terms: DRF, bridging external fixation, non-bridging external fixation, K-wire fixation, plaster fixation, dorsal plating, volar plating, and dorsal and volar plating. The network meta-analysis was performed to rank the probabilities of postoperative complication risks for the seven surgical modalities in DRF patients. This network meta-analysis included data obtained from a total of 19 RCTs. Our results revealed that compared to DRF patients treated with bridging external fixation, marked differences in pin-track infection (PTI) rate were found in patients treated with plaster fixation, volar plating, and dorsal and volar plating. Cluster analysis showed that plaster fixation is associated with the lowest probability of postoperative complication in DRF patients. Plaster fixation is associated with the lowest risk for postoperative complications in DRF patients, when compared to six other common DRF surgical methods examined. PMID:26549312

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California

PubMed Central

Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

2017-01-01

Background Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. PMID:28676471

Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Ihli, Monica; Hendrick, Oscar; Delgado-Arias, Sabrina; Escobar, Vanessa M.; Griffith, Peter

2015-01-01

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.

Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis.

PubMed

Chien, Tsair-Wei; Chang, Yu; Wang, Hsien-Yi

2018-02-01

Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

The Development of a High-Throughput/Combinatorial Workflow for the Study of Porous Polymer Networks

DTIC Science & Technology

2012-04-05

poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared. Changes in opacity of the blends as they cured...allowed for the identification of compositional variables and process variables that enabled the production of porous networks. Keywords: high...in polymer network cross-link density,poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

Forecasting of natural gas consumption with neural network and neuro fuzzy system

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan

2010-05-01

The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting

Yik Yak: a social media sensor

NASA Astrophysics Data System (ADS)

Leskovich, W. Robert

2015-05-01

This is the first academic paper which focuses specifically on the new social media application Yik Yak. To provide a solid foundation, a brief overview of a few anonymous social media platforms is provided. A social media sensor framework is then presented which utilizes a three-layered approach to addressing the use of analytic tools. Specifically the use of keyword, geolocation, sentiment, and network analysis is explored through the perspective of social media as a sensor. Challenges and criticisms are exposed in addition to some possible solutions. A theoretical case study is then offered which outlines a potential use of social media as a senor for emergency managers. The paper culminates with a data collection for the development of a lexicon for Yik Yak. This data collection focuses on an 18 day study which collects Yik Yak posts and Twitter tweets simultaneously. The top 100 keywords for each platform are collected for every 24 hour period and placed through a relative change comparison. Overall, Yik Yak offers a more stable baseline as compared to Twitter.

Influence of fracture network physical properties on stability criteria of density-driven flow in a dual-porosity system

NASA Astrophysics Data System (ADS)

Hassanzadeh, H.; Jafari Raad, S. M.

2017-12-01

Linear stability analysis is conducted to study the onset of buoyancy-driven convection involved in solubility trapping of CO2 into deep fractured aquifers. In this study, the effect of fracture network physical properties on the stability criteria in a brine-rich fractured porous layer is investigated using dual porosity concept for both single and variable matrix block size distributions. Linear stability analysis results show that both fracture interporosity flow and fracture storativity factors play an important role in the stability behavior of the system. It is shown that a diffusive boundary layer under the gravity field in a fractured rock with lower fracture storativity and/or higher fracture interporosity flow coefficient is more stable. We present scaling relations that relate the onset of convective instability in fractured aquifers. These findings improve our understanding of buoyancy driven flow in fractured aquifers and are particularly important in estimation of potential storage capacity, risk assessment, and storage sites characterization and screening.Keywords: CO2 sequestration; fractured rock; buoyancy-driven convection; stability analysis

GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

PubMed

Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

2014-09-01

Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Digital Social Network Mining for Topic Discovery

NASA Astrophysics Data System (ADS)

Moradianzadeh, Pooya; Mohi, Maryam; Sadighi Moshkenani, Mohsen

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer's disease neuroimaging initiative.

PubMed

Yao, Xiaohui; Yan, Jingwen; Ginda, Michael; Börner, Katy; Saykin, Andrew J; Shen, Li

2017-01-01

Alzheimer's disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD.

Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative

PubMed Central

Yao, Xiaohui; Yan, Jingwen; Ginda, Michael; Börner, Katy; Saykin, Andrew J.

2017-01-01

Background Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD. PMID:29095836

Automatic generation of stop word lists for information retrieval and analysis

DOEpatents

Rose, Stuart J

2013-01-08

Methods and systems for automatically generating lists of stop words for information retrieval and analysis. Generation of the stop words can include providing a corpus of documents and a plurality of keywords. From the corpus of documents, a term list of all terms is constructed and both a keyword adjacency frequency and a keyword frequency are determined. If a ratio of the keyword adjacency frequency to the keyword frequency for a particular term on the term list is less than a predetermined value, then that term is excluded from the term list. The resulting term list is truncated based on predetermined criteria to form a stop word list.

Global informetric perspective studies on translational medical research

PubMed Central

2013-01-01

Background Translational medical research literature has increased rapidly in the last few decades and played a more and more important role during the development of medicine science. The main aim of this study is to evaluate the global performance of translational medical research during the past few decades. Methods Bibliometric, social network analysis, and visualization technologies were used for analyzing translational medical research performance from the aspects of subject categories, journals, countries, institutes, keywords, and MeSH terms. Meanwhile, the co-author, co-words and cluster analysis methods were also used to trace popular topics in translational medical research related work. Results Research output suggested a solid development in translational medical research, in terms of increasing scientific production and research collaboration. We identified the core journals, mainstream subject categories, leading countries, and institutions in translational medical research. There was an uneven distribution of publications at authorial, institutional, and national levels. The most commonly used keywords that appeared in the articles were “translational research”, “translational medicine”, “biomarkers”, “stroke”, “inflammation”, “cancer”, and “breast cancer”. Conclusions The subject categories of “Research & Experimental Medicine”, “Medical Laboratory Technology”, and “General & Internal Medicine” play a key role in translational medical research both in production and in its networks. Translational medical research and CTS, etc. are core journals of translational research. G7 countries are the leading nations for translational medical research. Some developing countries, such as P.R China, also play an important role in the communication of translational research. The USA and its institutions play a dominant role in the production, collaboration, citations and high quality articles. The research trends in translational medical research involve drug design and development, pathogenesis and treatment of disease, disease model research, evidence-based research, and stem and progenitor cells. PMID:23885955

On Quantifying Diffusion of Health Information on Twitter.

PubMed

Bakal, Gokhan; Kavuluru, Ramakanth

2017-02-01

With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to retweet particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

PubMed

Peiper, Nicholas C; Baumgartner, Peter M; Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

2017-07-04

Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. ©Nicholas C Peiper, Peter M Baumgartner, Robert F Chew, Yuli P Hsieh, Gayle S Bieler, Georgiy V Bobashev, Christopher Siege, Gary A Zarkin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.07.2017.

Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium falciparum

DTIC Science & Technology

2017-03-22

Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US...2017 Available online 22 March 2017 Keywords: Plasmodium Chloroquine Metabolic network modeling Redox metabolism Carbon fixation* Corresponding... available (Antony and Parija, 2016), their efficacy has declined appreciably in the last few decades owing to widespread drug resistance developed by the

Characteristics and trends on global environmental monitoring research: a bibliometric analysis based on Science Citation Index Expanded.

PubMed

Zhang, Di; Fu, Hui-Zhen; Ho, Yuh-Shan

2017-11-01

A bibliometric analysis based on the Science Citation Index Expanded from Web of Science was carried out to provide insights into research activities and trends of the environmental monitoring from 1993 to 2012. Study emphases covered publication outputs, language, categories, journals, countries/territories, institutions, words, and hot issues. The results indicated that the annual output of environmental monitoring publications increased steadily. The environmental sciences and analytical chemistry were the two most common categories. Environmental Monitoring and Assessment published the most articles. The USA and the UK ranked in the top two in terms of all five indicators. The U.S. Environmental Protection Agency took the leading position of the institutions in terms of publication output. The synthesized analysis by words in title, author keywords, and KeyWords Plus provided important clues for hot issues. Researchers paid more attention on water environment monitoring than other environmental factors. The contaminants including organic contaminants, heavy metal, and radiation were most common research focuses, and the organic contaminants and heavy metal of the degree of concern were gradually rising. Sensor and biosensor played an important role in the field of environmental monitoring devices. In addition to conventional device detection method, the remote sensing, GIS, and wireless sensor networks were the mainstream environmental monitoring methods. The international organization, social awareness, and the countries' positive and effective political and policies promoted the published articles.

Recurrent-neural-network-based Boolean factor analysis and its application to word clustering.

PubMed

Frolov, Alexander A; Husek, Dusan; Polyakov, Pavel Yu

2009-07-01

The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characterized by a set of words which coherently appear in documents dedicated to a given topic. The appearance of each word in a document is coded by the activity of a particular neuron. In accordance with the Hebbian learning rule implemented in the network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the found sets of words can be identified based on the words' semantics. To make the method complete, a special technique based on a Bayesian procedure has been developed for the following purposes: first, to provide a complete description of factors in terms of component probability, and second, to enhance the accuracy of classification of signals to determine whether it contains the factor. Since it is assumed that every word may possibly contribute to several topics, the proposed method might be related to the method of fuzzy clustering. In this paper, we show that the results of Boolean factor analysis and fuzzy clustering are not contradictory, but complementary. To demonstrate the capabilities of this attempt, the method is applied to two types of textual data on neural networks in two different languages. The obtained topics and corresponding words are at a good level of agreement despite the fact that identical topics in Russian and English conferences contain different sets of keywords.

Data on the interaction between thermal comfort and building control research.

PubMed

Park, June Young; Nagy, Zoltan

2018-04-01

This dataset contains bibliography information regarding thermal comfort and building control research. In addition, the instruction of a data-driven literature survey method guides readers to reproduce their own literature survey on related bibliography datasets. Based on specific search terms, all relevant bibliographic datasets are downloaded. We explain the keyword co-occurrences of historical developments and recent trends, and the citation network which represents the interaction between thermal comfort and building control research. Results and discussions are described in the research article entitled "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review" (Park and Nagy, 2018).

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Metadata Effectiveness in Internet Discovery: An Analysis of Digital Collection Metadata Elements and Internet Search Engine Keywords

ERIC Educational Resources Information Center

Yang, Le

2016-01-01

This study analyzed digital item metadata and keywords from Internet search engines to learn what metadata elements actually facilitate discovery of digital collections through Internet keyword searching and how significantly each metadata element affects the discovery of items in a digital repository. The study found that keywords from Internet…

It's a sentence, not a word: insights from a keyword analysis in cancer communication.

PubMed

Taylor, Kimberly; Thorne, Sally; Oliffe, John L

2015-01-01

Keyword analysis has been championed as a methodological option for expanding the insights that can be extracted from qualitative datasets using various properties available in qualitative software. Intrigued by the pioneering applications of Clive Seale and his colleagues in this regard, we conducted keyword analyses for word frequency and "keyness" on a qualitative database of interview transcripts from a study on cancer communication. We then subjected the results from these operations to an in-depth contextual inquiry by resituating word instances within their original speech contexts, finding that most of what had initially appeared as group variations broke down under close analysis. In this article, we illustrate the various threads of analysis, and explain how they unraveled under closer scrutiny. On the basis of this tentative exercise, we conclude that a healthy skepticism for the benefits of keyword analysis within a qualitative investigative process seems warranted. © The Author(s) 2014.

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2016-09-01

networks during resting states. Autism spectrum disorder (ASD) begins prenatal, and early maldevelopment is present in many sites and systems that mediate...molecular and genomic evidence indicates autism spectrum disorder (ASD) begins prenatally, most likely by or before the late second trimester 10-15 as...ages 3 to 4 years. 2. KEYWORDS Autism spectrum disorder, ASD, early brain development, intrinsic functional brain networks, fMRI, infants, toddlers

Two Stage Data Augmentation for Low Resourced Speech Recognition (Author’s Manuscript)

DTIC Science & Technology

2016-09-12

speech recognition, deep neural networks, data augmentation 1. Introduction When training data is limited—whether it be audio or text—the obvious...Schwartz, and S. Tsakalidis, “Enhancing low resource keyword spotting with au- tomatically retrieved web documents,” in Interspeech, 2015, pp. 839–843. [2...and F. Seide, “Feature learning in deep neural networks - a study on speech recognition tasks,” in International Conference on Learning Representations

Is There a Standard Default Keyword Operator? A Bibliometric Analysis of Processing Options Chosen by Libraries To Execute Keyword Searches in Online Public Access Catalogs.

ERIC Educational Resources Information Center

Klein, Gary M.

1994-01-01

Online public access catalogs from 67 libraries using NOTIS software were searched using Internet connections to determine the positional operators selected as the default keyword operator on each catalog. Results indicate the lack of a processing standard for keyword searches. Five tables provide information. (Author/AEF)

Two-stage approach to keyword spotting in handwritten documents

NASA Astrophysics Data System (ADS)

Haji, Mehdi; Ameri, Mohammad R.; Bui, Tien D.; Suen, Ching Y.; Ponson, Dominique

2013-12-01

Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.

Silver Nanoparticles and Ionic Silver Have Opposite Effects on Spontaneous Activity and Pharmacological Responses in Neuronal Networks In Vitro

EPA Science Inventory

CONTROL ID: 1850472 CONTACT (NAME ONLY): Timothy Shafer Abstract Details PRESENTATION TYPE: Platform or Poster CURRENT CATEGORY: Nanotoxicology, In Vitro | Neurotoxicity, General | Neurotoxicity, Metals KEYWORDS: Nanoparticle, Neurotoxicity, microelectrode array. DATE/TIME LAST...

Analysis of scientific production in spanish implantology.

PubMed

Tarazona, Beatriz; Vidal-Infer, Antonio; Tarazona-Alvarez, Pablo; Alonso-Arroyo, Adolfo

2017-05-01

The aim of the study was to quantify the scientific productivity of researchers, organizations, and regions in Spain that publish articles on implantology in dental journals indexed in Journal Citation Reports. A search was conducted among the core collection of Thomson Reuters' Web of Science database, on the basis of its broad thematic and geographic coverage of health sciences. The search identified original articles - the main vehicle for the dissemination of research results. The search was conducted in July 2016, applying the truncated search term 'implant*' to locate original articles on implantology and its derivative forms. The search was conducted within the topic field (title, keywords and abstract) and two inclusion criteria were applied: documents denominated as articles were included; and articles categorized as Web of Science Medicine Dentistry and Oral Surgery. Finally only articles for which one of the participating organizations was located in Spain were selected. The final search identified a total of 774 records. The period 1988 to 2015 saw an exponential growth in scientific production, especially during the last 10 years. Clinical Oral Implants Research and Medicina Oral Patologia Oral y Cirugia Bucal (Oral Medicine, Oral Pathology, and Oral Surgery) were the most productive journals. Collaborative networks among authors and among institutions increased and this increase was related to the improving quality of the publications. Bibliometric analysis revealed a significant growth in the quantity and quality of Spanish implantology literature. Most key bibliometric indicators demonstrated upward trends. Key words: Bibliometric analysis, publication, keywords, implantology, implant.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

PubMed Central

Vadillo, Miguel A; Curcin, Vasa

2017-01-01

Background Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. PMID:28663166

Research Status and Development Trend of Remote Sensing in China Using Bibliometric Analysis

NASA Astrophysics Data System (ADS)

Zeng, Y.; Zhang, J.; Niu, R.

2015-06-01

Remote sensing was introduced into China in 1970s and then began to flourish. At present, China has developed into a big remote sensing country, and remote sensing is increasingly playing an important role in various fields of national economic construction and social development. Based on China Academic Journals Full-text Database and China Citation Database published by China National Knowledge Infrastructure, this paper analyzed academic characteristics of 963 highly cited papers published by 16 professional and academic journals in the field of surveying and mapping from January 2010 to December 2014 in China, which include hot topics, literature authors, research institutions, and fundations. At the same time, it studied a total of 51,149 keywords published by these 16 journals during the same period. Firstly by keyword selection, keyword normalization, keyword consistency and keyword incorporation, and then by analysis of high frequency keywords, the progress and prospect of China's remote sensing technology in data acquisition, data processing and applications during the past five years were further explored and revealed. It can be seen that: highly cited paper analysis and word frequency analysis is complementary on subject progress analysis; in data acquisition phase, research focus is new civilian remote sensing satellite systems and UAV remote sensing system; research focus of data processing and analysis is multi-source information extraction and classification, laser point cloud data processing, objectoriented high resolution image analysis, SAR data and hyper-spectral image processing, etc.; development trend of remote sensing data processing is quantitative, intelligent, automated, and real-time, and the breadth and depth of remote sensing application is gradually increased; parallel computing, cloud computing and geographic conditions monitoring and census are the new research focuses to be paid attention to.

Fake news portrayals of stem cells and stem cell research.

PubMed

Marcon, Alessandro R; Murdoch, Blake; Caulfield, Timothy

2017-10-01

This study examines how stem cells and stem cell research are portrayed on websites deemed to be purveyors of distorted and dubious information. Content analysis was conducted on 224 articles from 2015 to 2016, compiled by searching with the keywords 'stem cell(s)' on a list of websites flagged for containing either 'fake' or 'junk science' news. Articles contained various exaggerated positive and negative claims about stem cells and stem cell science, health and science related conspiracy theories, and statements promoting fear and mistrust of conventional medicine. Findings demonstrate the existence of organized misinformation networks, which may lead the public away from accurate information and facilitate a polarization of public discourse.

Analysis of straw row in the image to control the trajectory of the agricultural combine harvester

NASA Astrophysics Data System (ADS)

Shkanaev, Aleksandr Yurievich; Polevoy, Dmitry Valerevich; Panchenko, Aleksei Vladimirovich; Krokhina, Darya Alekseevna; Nailevish, Sadekov Rinat

2018-04-01

The paper proposes a solution to the automatic operation of the combine harvester along the straw rows by means of the images from the camera, installed in the cab of the harvester. The U-Net is used to recognize straw rows in the image. The edges of the row are approximated in the segmented image by the curved lines and further converted into the harvester coordinate system for the automatic operating system. The "new" network architecture and approaches to the row approximation has improved the quality of the recognition task and the processing speed of the frames up to 96% and 7.5 fps, respectively. Keywords: Grain harvester,

Text analysis of MEDLINE for discovering functional relationships among genes: evaluation of keyword extraction weighting schemes.

PubMed

Liu, Ying; Navathe, Shamkant B; Pivoshenko, Alex; Dasigi, Venu G; Dingledine, Ray; Ciliax, Brian J

2006-01-01

One of the key challenges of microarray studies is to derive biological insights from the gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the functional links among genes. However, the quality of the keyword lists significantly affects the clustering results. We compared two keyword weighting schemes: normalised z-score and term frequency-inverse document frequency (TFIDF). Two gene sets were tested to evaluate the effectiveness of the weighting schemes for keyword extraction for gene clustering. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords outperformed those produced from normalised z-score weighted keywords. The optimised algorithms should be useful for partitioning genes from microarray lists into functionally discrete clusters.

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords.

PubMed

Bentley, R Alexander

2008-08-27

The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.

Random Drift versus Selection in Academic Vocabulary: An Evolutionary Analysis of Published Keywords

PubMed Central

Bentley, R. Alexander

2008-01-01

The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example. PMID:18728786

Mining author relationship in scholarly networks based on tripartite citation analysis

PubMed Central

Wang, Xiaohan; Yang, Siluo

2017-01-01

Following scholars in Scientometrics as examples, we develop five author relationship networks, namely, co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC). The time frame of data sets is divided into two periods: before 2011 (i.e., T1) and after 2011 (i.e., T2). Through quadratic assignment procedure analysis, we found that some authors have ABC or AC relationships (i.e., potential communication relationship, PCR) but do not have actual collaborations or direct citations (i.e., actual communication relationship, ACR) among them. In addition, we noticed that PCR and AKC are highly correlated and that the old PCR and the new ACR are correlated and consistent. Such facts indicate that PCR tends to produce academic exchanges based on similar themes, and ABC bears more advantages in predicting potential relations. Based on tripartite citation analysis, including AC, ABC, and ADC, we also present an author-relation mining process. Such process can be used to detect deep and potential author relationships. We analyze the prediction capacity by comparing between the T1 and T2 periods, which demonstrate that relation mining can be complementary in identifying authors based on similar themes and discovering more potential collaborations and academic communities. PMID:29117198

Statistics of co-occurring keywords in confined text messages on Twitter

NASA Astrophysics Data System (ADS)

Mathiesen, J.; Angheluta, L.; Jensen, M. H.

2014-09-01

Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.

Dimensions and dynamics of citizen observatories: The case of online amateur weather networks

NASA Astrophysics Data System (ADS)

Gharesifard, Mohammad; Wehn, Uta; van der Zaag, Pieter

2016-04-01

Crowd-sourced environmental observations are being increasingly considered as having the potential to enhance the spatial and temporal resolution of current data streams from terrestrial and areal sensors. The rapid diffusion of ICTs during the past decades has facilitated the process of data collection and sharing by the general public (so-called citizen science) and has resulted in the formation of various online environmental citizen observatory networks. Online amateur weather networks are a particular example of such ICT-mediated citizen observatories as one of the oldest and most widely practiced citizen science activities. The objective of this paper is to introduce a conceptual framework that enables a systematic review of different dimensions of these mushrooming/expanding networks. These dimensions include the geographic scope and types of network participants; the network's establishment mechanism, revenue stream(s) and existing communication paradigm; efforts required by citizens and support offered by platform providers; and issues such as data accessibility, availability and quality. An in-depth understanding of these dimensions helps to analyze various dynamics such as interactions between different stakeholders, motivations to run these networks, sustainability of the platforms, data ownership and level of transparency of each network. This framework is then utilized to perform a critical and normative review of six existing online amateur weather networks based on publicly available data. The main findings of this analysis suggest that: (1) There are several key stakeholders such as emergency services and local authorities that are not (yet) engaged in these networks. (2) The revenue stream(s) of online amateur weather networks is one of the least discussed but most important dimensions that is crucial for the sustainability of these networks. (3) Although all of the networks included in this study have one or more explicit pattern of two-way communications, there is no sign (yet) of interactive information exchange among the triangle of weather observers, data aggregators and policy makers. KEYWORDS Citizen Science, Citizen Observatories, ICT-enabled citizen participation, online amateur weather networks

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

PubMed

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

2016-05-05

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

Library and Information Science Research Areas: A Content Analysis of Articles from the Top 10 Journals 2007-8

ERIC Educational Resources Information Center

Aharony, Noa

2012-01-01

The current study seeks to describe and analyze journal research publications in the top 10 Library and Information Science journals from 2007-8. The paper presents a statistical descriptive analysis of authorship patterns (geographical distribution and affiliation) and keywords. Furthermore, it displays a thorough content analysis of keywords and…

Teaching Responsibly with Technology-Mediated Communication

ERIC Educational Resources Information Center

Veltsos, Jennifer R.; Veltsos, Christophe

2010-01-01

Technology-mediated communication, or "new media," such as blogs, Twitter, wikis, and social network sites, can be an endless source of ideas for activities or inspiration for classroom discussion. Many instructors ask students to monitor current events by following keywords and industry leaders on Twitter and reading both corporate and…

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review.

PubMed

Wongkoblap, Akkapon; Vadillo, Miguel A; Curcin, Vasa

2017-06-29

Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. ©Akkapon Wongkoblap, Miguel A Vadillo, Vasa Curcin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.06.2017.

Gender, cancer experience and internet use: a comparative keyword analysis of interviews and online cancer support groups.

PubMed

Seale, Clive; Ziebland, Sue; Charteris-Black, Jonathan

2006-05-01

A new method, comparative keyword analysis, is used to compare the language of men and women with cancer in 97 research interviews and two popular internet based support groups for people with cancer. The method is suited to the conjoint qualitative and quantitative analysis of differences between large bodies of text, an alternative to the 'code and retrieval' approach used in much thematic analysis of qualitative materials. Web forums are a rich source of data about illness experience and gender differences. Marked differences in the performance of gender are evident. These differences follow linguistic and other behavioural patterns (such as social network differences) established in other contexts. Men with prostate cancer indicate in research interviews that they are more likely to seek information on the internet; women with breast cancer that they are more likely to seek social and emotional support. Men's concerns cluster around treatment information, medical personnel and procedures. Their experience of disease is more localised on particular areas of the body, while women's experience is more holistic. Women's forum postings orientate much more towards the exchange of emotional support, including concern with the impact of illness on a wide range of other people. Women's use of superlatives as well as words referring to feelings indicate their enactment of greater emotional expressivity. Web forums are platforms for an intensification of men's knowledge gathering activities. Web forums, though actually quite publicly visible, appear to be subjectively experienced by both sexes as relatively private places for the exchange of intimate personal information. The 'privacy' of the breast cancer forum facilitated interactions found in other studies to be characteristic of women's friendship groups.

Community water fluoridation online: an analysis of the digital media ecosystem.

PubMed

Helmi, Mohammad; Spinella, Mary Kate; Seymour, Brittany

2018-03-30

Research demonstrates the safety and efficacy of community water fluoridation (CWF). Yet, the digitization of communication has triggered the spread of inaccurate information online. The purpose of this study was to analyze patterns of CWF information dissemination by a network of sources on the web. We used Media Cloud, a searchable big data platform of over 550 million stories from 50 thousand sources, along with tools to analyze that archive. We generated a network of fluoridation publishers using Media Cloud's keyword identification from August 1, 2015 to July 31, 2016. We defined the media type and sentiment toward CWF for each source and generated a network map of the most influential sources during our study period based on hyperlinking activity. Media Cloud detected a total of 980 stories from 325 different sources related to water fluoridation. We identified nine different media types participating in the dissemination of information: academic, government, scientific group, natural medicine, blogs, mainstream media, advocacy groups, user-generated (e.g., YouTube), and "other." We detected five sub-networks within the overall fluoridation network map, each with its own characteristics. Twenty-one percent of sources were pro-fluoridation, receiving 57 percent of all inlinks, 22 percent of sources were anti-fluoridation, and the rest were neutral (54 percent). The dominant neutral sentiment of the network may signify that anti- and pro-sides of the debate are viewed as balanced, not just in number but also in quality of information. Despite high inlinks to pro-sources, anti-fluoridation sentiment maintains influence online. © 2018 American Association of Public Health Dentistry.

Improving the precision of the keyword-matching pornographic text filtering method using a hybrid model.

PubMed

Su, Gui-yang; Li, Jian-hua; Ma, Ying-hua; Li, Sheng-hong

2004-09-01

With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision.

Intrasectoral variation in mission and values: the case of the Catholic health systems.

PubMed

White, Kenneth R; Dandi, Roberto

2009-01-01

Catholic health systems represent a unique sector of nonprofit health care delivery organizations because they must be accountable to institutional pressures of the Roman Catholic Church, in addition to responsiveness to market pressures. Mission statements and values are purported to be the driving force of Catholic institutional identity. Central to the understanding of the Catholic health care delivery sector is the exploration of variation in mission and values statements across the homogeneous field of organizations. The purposes of this study were to identify expressed organizational identity variation-in terms of keywords in mission statements and values-of Catholic health systems in the United States by applying a methodology that integrates text and social network analytical techniques. Data were obtained from the Web site of The Catholic Health Association of the United States and the Web sites of 50 Catholic health systems in 2007. Catholic health system mission statements and values were assessed using a cross-sectional study design. Text analysis and social network techniques were employed to identify the most central words in the texts and linkages among mission statement components and values. This study identifies the components of a common mission statement and the most shared and unique values for a Catholic health system. Even with tremendous similarity, there is also evidence of intrasectoral variation between Catholic health system keywords in mission statements and values. Management implications include the consideration of word relationships developing and constructing mission and values statements to form the framework for strategic vision and management decision making, to assess potential partnership arrangements based on expressed mission statements and values, and to use in executing due diligence in mergers and partnerships.

Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field

NASA Astrophysics Data System (ADS)

Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali

2017-04-01

One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .

Monitoring of Deformation in Ground Before and After Tunnel Excavation

NASA Astrophysics Data System (ADS)

Eren, Mehmet; Hilmi Erkoç, Muharrem

2017-04-01

As population increase in metropolitan city, we need transportation and transmission tunnel. In this context, the engineers and administors attach impotance to building and planning underground-tunnel. Moreover, we must at regular intervals monitoring to deformation in underground-tunnel for quality and safety. Firstly, a deformation monitoring network is designed as perpendicular to the tunnel main axis. Secondly, the prescribed number of deformation measurements must be made. Finally, the deformation analysis is evaluated and its results is interpreted. This study investigates how deformation in monitoring network during and after tunnel excavate change.For this purpose, a deformation monitoring network of 18 object point and 4 reference point was established. Object points networks was designed steeply to the tunnel main axis as 3 cross section. Each cross section consisted of 3 point left, 2 point right and 1 point at the flowing line. Initial conditional measurement was made before tunnel excavation. Then the deformation measurement was made 5 period (1 period measured after tunnel excavate). All data sets were adjusted according to free adjustment method. The results from the investigation considering the tunnel line, a symmetrical subsidence was observed. The following day of tunnel excavation, we were observed %68 per of the total deformation. At the end of the last period measurements, %99 per of the total deformation was detected. Keywords: Tunnel, Deformation, Subsidence, Excavation

Precise deformation measurement of prestressed concrete beam during a strain test using the combination of intersection photogrammetry and micro-network measurement

NASA Astrophysics Data System (ADS)

Urban, Rudolf; Braun, Jaroslav; Štroner, Martin

2015-05-01

The prestressed thin-walled concrete elements enable the bridge a relatively large span. These structures are advantageous in economic and environmental way due to their thickness and lower consumption of materials. The bending moments can be effectively influenced by using the pre-stress. The experiment was done to monitor deformation of the under load. During the experiment the discrete points were monitored. To determine a large number of points, the intersection photogrammetry combined with precise micro-network were chosen. Keywords:

A Framework for Achieving Situational Awareness during Crisis based on Twitter Analysis

NASA Astrophysics Data System (ADS)

Zielinski, Andrea; Tokarchuk, Laurissa; Middleton, Stuart; Chaves, Fernando

2013-04-01

Decision Support Systems for Natural Crisis Management increasingly employ Web 2.0 and 3.0 technologies for future collaborative decision making, including the use of social networks like Twitter. However, human sensor data is not readily accessible and interpretable, since the texts are unstructured, noisy and available in various languages. The present work focusses on the detection of crisis events in a multilingual setting as part of the FP7-funded EU project TRIDEC and is motivated by the goal to establish a Tsunami warning system for the Mediterranean. It is integrated into a dynamic spatial-temporal decision making component with a command and control unit's graphical user interface that presents all relevant information to the human operator to support critical decision-support. To this end, a tool for the interactive visualization of geospatial data is implemented: All tweets with an exact timestamp or geo-location are monitored on the map in real-time so that the operator on duty can get an overall picture of the situation. Apart from the human sensor data, the seismic sensor data will appear also on the same screen. Signs of abnormal activity from twitter usage in social networks as well as in sensor networks devices can then be used to trigger official warning alerts according to the CAP message standard. Whenever a certain threshold of relevant tweets in a HASC region (Hierarchical Administrative Subdivision Code) is exceeded, the twitter activity in this administrative region will be shown on a map. We believe that the following functionalities are crucial for monitoring crisis, making use of text mining and network analysis techniques: Focussed crawling, trustworthyness analysis geo-parsing, and multilingual tweet classification. In the first step, the Twitter Streaming API accesses the social data, using an adaptive keyword list (focussed crawling). Then, tweets are filtered and aggregated to form counts for a certain time-span (e.g., an interval of 1-2 minutes). Particularly, we investigate the following novel techniques that help to fulfill this task: trustworthyness analysis (linkage analysis and user network analysis), geo-parsing (locating the event in space), and multilingual tweet classification (filtering out of noisy tweets for various Mediterranean languages). Lastly, an aberration algorithm looks for spikes in the temporal stream of twitter data.

Mapping Rwanda public health research (1975-2014).

PubMed

Poreau, Brice

2014-12-01

Since the genocide occurred in 1994, Rwanda has faced up to the challenge of rebuilding. Public health is a main field to understand this rebuilding. In this paper, the aim was to map the scientific research on public health in Rwanda after the genocide and to present the links between different financing systems. We used bibliographic analyses with Web of Science of papers published during the period 1975-2014. We performed analyses on journals, most cited articles, authors, publication years, organizations, funding companies, countries, and keywords. We obtained 86 articles between 1975 and 2014. Most articles were published after 2007. The main countries of research laboratories were the United States of America, Rwanda, England and Belgium and represented the main network collaboration. The relevant keywords were: HIV, woman, child, program, rural and violence. Public health research on Rwanda appeared 14 years after the genocide. A main field was emerging: the spread of HIV with mother-child transmission, and the policies to take this subject into account in rural zones. The network of institutions developing these studies was USA-Rwanda.

Controlled Vocabularies Boost International Participation and Normalization of Searches

NASA Technical Reports Server (NTRS)

Olsen, Lola M.

2006-01-01

The Global Change Master Directory's (GCMD) science staff set out to document Earth science data and provide a mechanism for it's discovery in fulfillment of a commitment to NASA's Earth Science progam and to the Committee on Earth Observation Satellites' (CEOS) International Directory Network (IDN.) At the time, whether to offer a controlled vocabulary search or a free-text search was resolved with a decision to support both. The feedback from the user community indicated that being asked to independently determine the appropriate 'English" words through a free-text search would be very difficult. The preference was to be 'prompted' for relevant keywords through the use of a hierarchy of well-designed science keywords. The controlled keywords serve to 'normalize' the search through knowledgeable input by metadata providers. Earth science keyword taxonomies were developed, rules for additions, deletions, and modifications were created. Secondary sets of controlled vocabularies for related descriptors such as projects, data centers, instruments, platforms, related data set link types, and locations, along with free-text searches assist users in further refining their search results. Through this robust 'search and refine' capability in the GCMD users are directed to the data and services they seek. The next step in guiding users more directly to the resources they desire is to build a 'reasoning' capability for search through the use of ontologies. Incorporating twelve sets of Earth science keyword taxonomies has boosted the GCMD S ability to help users define and more directly retrieve data of choice.

Analysis of Focal Mechanism and Microseismicity around the Lusi Mud Eruption Site, East Java, Indonesia

NASA Astrophysics Data System (ADS)

Karyono, Karyono; Obermann, Anne; Mazzini, Adriano; Lupi, Matteo; Syafri, Ildrem; Abdurrokhim, Abdurrokhim; Masturyono, Masturyono; Hadi, Soffian

2016-04-01

The 29th of May 2006 numerous eruption sites started in northeast Java, Indonesia following to a M6.3 earthquake striking the island.Within a few weeks an area or nearly 2 km2 was covered by boiling mud and rock fragments and a prominent central crater (named Lusi) has been erupting for the last 9.5 years. The M.6.3 seismic event also triggered the activation of the Watukosek strike slip fault system that originates from the Arjuno-Welirang volcanic complex and extends to the northeast of Java hosting Lusi and other mud volcanoes. Since 2006 this fault system has been reactivated in numerous instances mostly following to regional seismic and volcanic activity. However the mechanism controlling this activity have never been investigated and remain poorly understood. In order to investigate the relationship existing between seismicity, volcanism, faulting and Lusi activity, we have deployed a network of 31 seismometers in the framework of the ERC-Lusi Lab project. This network covers a large region that monitors the Lusi activity, the Watukosek fault system and the neighboring Arjuno-Welirang volcanic complex. In particular, to understand the consistent pattern of the source mechanism, relative to the general tectonic stress in the study area, a detailed analysis has been carried out by performing the moment tensor inversion for the near field data collected from the network stations. Furthermore these data have been combined with the near field data from the regional network of the Meteorological, Climatological and Geophysical Agency of Indonesia that covers the whole country on a broader scale. Keywords: Lusi, microseismic event, focal mechanism

Automatic lithofacies segmentation from well-logs data. A comparative study between the Self-Organizing Map (SOM) and Walsh transform

NASA Astrophysics Data System (ADS)

Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar

2013-04-01

The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html

ABERRANT RESTING-STATE BRAIN ACTIVITY IN POSTTRAUMATIC STRESS DISORDER: A META-ANALYSIS AND SYSTEMATIC REVIEW.

PubMed

Koch, Saskia B J; van Zuiden, Mirjam; Nawijn, Laura; Frijling, Jessie L; Veltman, Dick J; Olff, Miranda

2016-07-01

About 10% of trauma-exposed individuals develop PTSD. Although a growing number of studies have investigated resting-state abnormalities in PTSD, inconsistent results suggest a need for a meta-analysis and a systematic review. We conducted a systematic literature search in four online databases using keywords for PTSD, functional neuroimaging, and resting-state. In total, 23 studies matched our eligibility criteria. For the meta-analysis, we included 14 whole-brain resting-state studies, reporting data on 663 participants (298 PTSD patients and 365 controls). We used the activation likelihood estimation approach to identify concurrence of whole-brain hypo- and hyperactivations in PTSD patients during rest. Seed-based studies could not be included in the quantitative meta-analysis. Therefore, a separate qualitative systematic review was conducted on nine seed-based functional connectivity studies. The meta-analysis showed consistent hyperactivity in the ventral anterior cingulate cortex and the parahippocampus/amygdala, but hypoactivity in the (posterior) insula, cerebellar pyramis and middle frontal gyrus in PTSD patients, compared to healthy controls. Partly concordant with these findings, the systematic review on seed-based functional connectivity studies showed enhanced salience network (SN) connectivity, but decreased default mode network (DMN) connectivity in PTSD. Combined, these altered resting-state connectivity and activity patterns could represent neurobiological correlates of increased salience processing and hypervigilance (SN), at the cost of awareness of internal thoughts and autobiographical memory (DMN) in PTSD. However, several discrepancies between findings of the meta-analysis and systematic review were observed, stressing the need for future studies on resting-state abnormalities in PTSD patients. © 2016 Wiley Periodicals, Inc.

Is autoimmunology a discipline of its own? A big data-based bibliometric and scientometric analyses.

PubMed

Watad, Abdulla; Bragazzi, Nicola Luigi; Adawi, Mohammad; Amital, Howard; Kivity, Shaye; Mahroum, Naim; Blank, Miri; Shoenfeld, Yehuda

2017-06-01

Autoimmunology is a super-specialty of immunology specifically dealing with autoimmune disorders. To assess the extant literature concerning autoimmune disorders, bibliometric and scientometric analyses (namely, research topics/keywords co-occurrence, journal co-citation, citations, and scientific output trends - both crude and normalized, authors network, leading authors, countries, and organizations analysis) were carried out using open-source software, namely, VOSviewer and SciCurve. A corpus of 169,519 articles containing the keyword "autoimmunity" was utilized, selecting PubMed/MEDLINE as bibliographic thesaurus. Journals specifically devoted to autoimmune disorders were six and covered approximately 4.15% of the entire scientific production. Compared with all the corpus (from 1946 on), these specialized journals have been established relatively few decades ago. Top countries were the United States, Japan, Germany, United Kingdom, Italy, China, France, Canada, Australia, and Israel. Trending topics are represented by the role of microRNAs (miRNAs) in the ethiopathogenesis of autoimmune disorders, contributions of genetics and of epigenetic modifications, role of vitamins, management during pregnancy and the impact of gender. New subsets of immune cells have been extensively investigated, with a focus on interleukin production and release and on Th17 cells. Autoimmunology is emerging as a new discipline within immunology, with its own bibliometric properties, an identified scientific community and specifically devoted journals.

Workplace social capital in nursing: an evolutionary concept analysis.

PubMed

Read, Emily A

2014-05-01

To report an analysis of the concept of nurses' workplace social capital. Workplace social capital is an emerging concept in nursing with potential to illuminate the value of social relationships at work. A common definition is needed. Concept analysis. The Cumulative Index to Nursing and Allied Health Literature, PubMed, PsychINFO and ProQuest Nursing. Databases were systematically searched using the keywords: workplace social capital, employee social capital, work environment, social capital and nursing. Sources published between January 1937-November 2012 in English that described or studied social capital of nurses at work were included. A total of 668 resources were found. After removing 241 duplicates, literature was screened in two phases: (1) titles and abstracts were reviewed (n = 427); and (2) remaining data sources were retrieved and read (n = 70). Eight sources were included in the final analysis. Attributes of nurses' workplace social capital included networks of social relationships at work, shared assets and shared ways of knowing and being. Antecedents were communication, trust and positive leadership practices. Nurses' workplace social capital was associated with positive consequences for nurses, their patients and healthcare organizations. Nurses' workplace social capital is defined as nurses' shared assets and ways of being and knowing that are evident in, and available through, nurses' networks of social relationships at work. Future studies should examine and test relationships between antecedents and consequences of nurses' workplace social capital to understand this important aspect of healthy professional practice environments better. © 2013 John Wiley & Sons Ltd.

50 years of Arabidopsis research: highlights and future directions

DOE PAGES

Provart, Nicholas J.; Alonso, Jose; Assmann, Sarah M.; ...

2015-10-14

The year 2014 marked the 25 th International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. In this paper, we present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlightingmore » some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.« less

50 years of Arabidopsis research: highlights and future directions

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

Provart, Nicholas J.; Alonso, Jose; Assmann, Sarah M.

The year 2014 marked the 25 th International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. In this paper, we present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlightingmore » some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.« less

A Step Beyond Simple Keyword Searches: Services Enabled by a Full Content Digital Journal Archive

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

2003-01-01

The problems of managing and searching large archives of scientific journal articles can potentially be addressed through data mining and statistical techniques matured primarily for quantitative scientific data analysis. A journal paper could be represented by a multivariate descriptor, e.g., the occurrence counts of a number key technical terms or phrases (keywords), perhaps derived from a controlled vocabulary ( e . g . , the American Meteorological Society's Glossary of Meteorology) or bootstrapped from the journal archive itself. With this technique, conventional statistical classification tools can be leveraged to address challenges faced by both scientists and professional societies in knowledge management. For example, cluster analyses can be used to find bundles of "most-related" papers, and address the issue of journal bifurcation (when is a new journal necessary, and what topics should it encompass). Similarly, neural networks can be trained to predict the optimal journal (within a society's collection) in which a newly submitted paper should be published. Comparable techniques could enable very powerful end-user tools for journal searches, all premised on the view of a paper as a data point in a multidimensional descriptor space, e.g.: "find papers most similar to the one I am reading", "build a personalized subscription service, based on the content of the papers I am interested in, rather than preselected keywords", "find suitable reviewers, based on the content of their own published works", etc. Such services may represent the next "quantum leap" beyond the rudimentary search interfaces currently provided to end-users, as well as a compelling value-added component needed to bridge the print-to-digital-medium gap, and help stabilize professional societies' revenue stream during the print-to-digital transition.

A Design and Development of Distance Learning Support Environment for Collaborative Problem Solving in Group Learners

ERIC Educational Resources Information Center

Nitta, Takuya; Takaoka, Ryo; Ahama, Shigeki; Shimokawa, Masayuki

2014-01-01

The competency and curriculum for human resource development in knowledge based society are proposed in each country. We think the keywords are "collaborative problem solving" and "effective use of ICT". In particular, the competency to perform the collaborative problem solving and learning with others on the network is…

Therapies for bruxism: a systematic review and network meta-analysis (protocol).

PubMed

Mesko, Mauro Elias; Hutton, Brian; Skupien, Jovito Adiel; Sarkis-Onofre, Rafael; Moher, David; Pereira-Cenci, Tatiana

2017-01-13

Bruxism is a sleep disorder characterized by grinding and clenching of the teeth that may be related to irreversible tooth injuries. It is a prevalent condition occurring in up to 31% of adults. However, there is no definitive answer as to which of the many currently available treatments (including drug therapy, intramuscular injections, physiotherapy, biofeedback, kinesiotherapy, use of intraoral devices, or psychological therapy) is the best for the clinical management of the different manifestations of bruxism. The aim of this systematic review and network meta-analysis is to answer the following question: what is the best treatment for adult bruxists? Comprehensive searches of the Cochrane Library, MEDLINE (via PubMed), Scopus, and LILACS will be completed using the following keywords: bruxism and therapies and related entry terms. Studies will be included, according to the eligibility criteria (Controlled Clinical Trials and Randomized Clinical Trials, considering specific outcome measures for bruxism). The reference lists of included studies will be hand searched. Relevant data will be extracted from included studies using a specially designed data extraction sheet. Risk of bias of the included studies will be assessed, and the overall strength of the evidence will be summarized (i.e., GRADE). A random effects model will be used for all pairwise meta-analyses (with a 95% confidence interval). A Bayesian network meta-analysis will explore the relative benefits between the various treatments. The review will be reported using the Preferred Reporting Items for Systematic Reviews incorporating Network Meta-Analyses (PRISMA-NMA) statement. This systematic review aims at identifying and evaluating therapies to treat bruxism. This systematic review may lead to several recommendations, for both patients and researchers, as which is the best therapy for a specific patient case and how future studies need to be designed, considering what is available now and what is the reality of the patient. PROSPERO CRD42015023308.

Quantitative analysis of the evolution of novelty in cinema through crowdsourced keywords.

PubMed

Sreenivasan, Sameet

2013-09-26

The generation of novelty is central to any creative endeavor. Novelty generation and the relationship between novelty and individual hedonic value have long been subjects of study in social psychology. However, few studies have utilized large-scale datasets to quantitatively investigate these issues. Here we consider the domain of American cinema and explore these questions using a database of films spanning a 70 year period. We use crowdsourced keywords from the Internet Movie Database as a window into the contents of films, and prescribe novelty scores for each film based on occurrence probabilities of individual keywords and keyword-pairs. These scores provide revealing insights into the dynamics of novelty in cinema. We investigate how novelty influences the revenue generated by a film, and find a relationship that resembles the Wundt-Berlyne curve. We also study the statistics of keyword occurrence and the aggregate distribution of keywords over a 100 year period.

Quantitative analysis of the evolution of novelty in cinema through crowdsourced keywords

PubMed Central

Sreenivasan, Sameet

2013-01-01

The generation of novelty is central to any creative endeavor. Novelty generation and the relationship between novelty and individual hedonic value have long been subjects of study in social psychology. However, few studies have utilized large-scale datasets to quantitatively investigate these issues. Here we consider the domain of American cinema and explore these questions using a database of films spanning a 70 year period. We use crowdsourced keywords from the Internet Movie Database as a window into the contents of films, and prescribe novelty scores for each film based on occurrence probabilities of individual keywords and keyword-pairs. These scores provide revealing insights into the dynamics of novelty in cinema. We investigate how novelty influences the revenue generated by a film, and find a relationship that resembles the Wundt-Berlyne curve. We also study the statistics of keyword occurrence and the aggregate distribution of keywords over a 100 year period. PMID:24067890

Social media and flu: Media Twitter accounts as agenda setters.

PubMed

Yun, Gi Woong; Morin, David; Park, Sanghee; Joa, Claire Youngnyo; Labbe, Brett; Lim, Jongsoo; Lee, Sooyoung; Hyun, Daewon

2016-07-01

This paper has two objectives. First, it categorizes the Twitter handles tweeted flu related information based on the amount of replies and mentions within the Twitter network. The collected Twitter accounts are categorized as media, health related individuals, organizations, government, individuals with no background with media or medical field, in order to test the relationship between centrality measures of the accounts and their categories. The second objective is to examine the relationship between the importance of the Twitter accounts in the network, centrality measures, and specific characteristics of each account, including the number of tweets and followers as well as the number of accounts followed and liked. Using Twitter search network API, tweets with "flu" keyword were collected and tabulated. Network centralities were calculated with network analysis tool, NodeXL. The collected Twitters accounts were content analyzed and categorized by multiple coders. When the media or organizational Twitter accounts were present in the list of important Twitter accounts, they were highly effective disseminating flu-related information. Also, they were more likely to stay active one year after the data collection period compared to other influential individual accounts. Health campaigns are recommended to focus on recruiting influential Twitter accounts and encouraging them to retweet or mention in order to produce better results in disseminating information. Although some individual social media users were valuable assets in terms of spreading information about flu, media and organization handles were more reliable information distributors. Thus, health information practitioners are advised to design health campaigns better utilizing media and organizations rather than individuals to achieve consistent and efficient campaign outcomes. Published by Elsevier Ireland Ltd.

Delivering Faster Congestion Feedback with the Mark-Front Strategy

NASA Technical Reports Server (NTRS)

Liu, Chunlei; Jain, Raj

2001-01-01

Computer networks use congestion feedback from the routers and destinations to control the transmission load. Delivering timely congestion feedback is essential to the performance of networks. Reaction to the congestion can be more effective if faster feedback is provided. Current TCP/IP networks use timeout, duplicate Acknowledgement Packets (ACKs) and explicit congestion notification (ECN) to deliver the congestion feedback, each provides a faster feedback than the previous method. In this paper, we propose a markfront strategy that delivers an even faster congestion feedback. With analytical and simulation results, we show that mark-front strategy reduces buffer size requirement, improves link efficiency and provides better fairness among users. Keywords: Explicit Congestion Notification, mark-front, congestion control, buffer size requirement, fairness.

Neural network based chemical structure indexing.

PubMed

Rughooputh, S D; Rughooputh, H C

2001-01-01

Searches on chemical databases are presently dominated by the text-based content of a paper which can be indexed into a keyword searchable form. Such traditional searches can prove to be very time-consuming and discouraging to the less frequent scientist. We report a simple chemical indexing based on the molecular structure alone. The method used is based on a one-to-one correspondence between the chemical structure presented as an image to a neural network and the corresponding binary output. The method is direct and less cumbersome (compared with traditional methods) and proves to be robust, elegant, and very versatile.

Reading the World's Classics Critically: A Keyword-Based Approach to Literary Analysis in Foreign Language Studies

ERIC Educational Resources Information Center

García, Nuria Alonso; Caplan, Alison

2014-01-01

While there are a number of important critical pedagogies being proposed in the field of foreign language study, more attention should be given to providing concrete examples of how to apply these ideas in the classroom. This article offers a new approach to the textual analysis of literary classics through the keyword-based methodology originally…

Video segmentation using keywords

NASA Astrophysics Data System (ADS)

Ton-That, Vinh; Vong, Chi-Tai; Nguyen-Dao, Xuan-Truong; Tran, Minh-Triet

2018-04-01

At DAVIS-2016 Challenge, many state-of-art video segmentation methods achieve potential results, but they still much depend on annotated frames to distinguish between background and foreground. It takes a lot of time and efforts to create these frames exactly. In this paper, we introduce a method to segment objects from video based on keywords given by user. First, we use a real-time object detection system - YOLOv2 to identify regions containing objects that have labels match with the given keywords in the first frame. Then, for each region identified from the previous step, we use Pyramid Scene Parsing Network to assign each pixel as foreground or background. These frames can be used as input frames for Object Flow algorithm to perform segmentation on entire video. We conduct experiments on a subset of DAVIS-2016 dataset in half the size of its original size, which shows that our method can handle many popular classes in PASCAL VOC 2012 dataset with acceptable accuracy, about 75.03%. We suggest widely testing by combining other methods to improve this result in the future.

Vaccine Hesitancy and Online Information: The Influence of Digital Networks.

PubMed

Getman, Rebekah; Helmi, Mohammad; Roberts, Hal; Yansane, Alfa; Cutler, David; Seymour, Brittany

2017-12-01

This article analyzes the digital childhood vaccination information network for vaccine-hesitant parents. The goal of this study was to explore the structure and influence of vaccine-hesitant content online by generating a database and network analysis of vaccine-relevant content. We used Media Cloud, a searchable big-data platform of over 550 million stories from 50,000 media sources, for quantitative and qualitative study of an online media sample based on keyword selection. We generated a hyperlink network map and measured indegree centrality of the sources and vaccine sentiment for a random sample of 450 stories. 28,122 publications from 4,817 sources met inclusion criteria. Clustered communities formed based on shared hyperlinks; communities tended to link within, not among, each other. The plurality of information was provaccine (46.44%, 95% confidence interval [39.86%, 53.20%]). The most influential sources were in the health community (National Institutes of Health, Centers for Disease Control and Prevention) or mainstream media ( New York Times); some user-generated sources also had strong influence and were provaccine (Wikipedia). The vaccine-hesitant community rarely interacted with provaccine content and simultaneously used primary provaccine content within vaccine-hesitant narratives. The sentiment of the overall conversation was consistent with scientific evidence. These findings demonstrate an online environment where scientific evidence online drives vaccine information outside of the vaccine-hesitant community but is also prominently used and misused within the robust vaccine-hesitant community. Future communication efforts should take current context into account; more information may not prevent vaccine hesitancy.

Annual variation in Internet keyword searches: Linking dieting interest to obesity and negative health outcomes.

PubMed

Markey, Patrick M; Markey, Charlotte N

2013-07-01

This study investigated the annual variation in Internet searches regarding dieting. Time-series analysis was first used to examine the annual trends of Google keyword searches during the past 7 years for topics related to dieting within the United States. The results indicated that keyword searches for dieting fit a consistent 12-month linear model, peaking in January (following New Year's Eve) and then linearly decreasing until surging again the following January. Additional state-level analyses revealed that the size of the December-January dieting-related keyword surge was predictive of both obesity and mortality rates due to diabetes, heart disease, and stroke.

Analysis of semantic search within the domains of uncertainty: using Keyword Effectiveness Indexing as an evaluation tool.

PubMed

Lorence, Daniel; Abraham, Joanna

2006-01-01

Medical and health-related searches pose a special case of risk when using the web as an information resource. Uninsured consumers, lacking access to a trained provider, will often rely on information from the internet for self-diagnosis and treatment. In areas where treatments are uncertain or controversial, most consumers lack the knowledge to make an informed decision. This exploratory technology assessment examines the use of Keyword Effectiveness Indexing (KEI) analysis as a potential tool for profiling information search and keyword retrieval patterns. Results demonstrate that the KEI methodology can be useful in identifying e-health search patterns, but is limited by semantic or text-based web environments.

SATURN (Situational Awareness Tool for Urban Responder Networks)

DTIC Science & Technology

2012-07-01

timeline. SATURN is applicable to a broad set of law enforcement, security, and counterterrorism missions typically addressed by urban responders...Keywords-video analytics; sensor fusion; video; urban responders I. INTRODUCTION Urban authorities have a broad set of missions . Duties vary in...both the frequency of occurrence and in the complexity of execution. They include everyday public safety missions such as traffic enforcement as

Service Without Servers

DTIC Science & Technology

1993-08-01

Abstract We propose a new style of operating system architecture appropriate for microkernel -based operating sys- tems: services are implemented as a...retaining all the modularity advantages of microkernel technology. Since services reside in libraries, an application is free to use the library that...U.S. Government. 93-23976,. . I~lUI5E NIIA Keywords: Operating Systems, Microkernel , Network communication, File organization 1. Introduction In the

Social networking in nursing education: integrative literature review.

PubMed

Kakushi, Luciana Emi; Évora, Yolanda Dora Martinez

2016-01-01

to identify the use of social networking in nursing education. integrative literature review in the databases: LILACS, IBECS, Cochrane, BDENF, SciELO, CINAHL, Scopus, PubMed, CAPES Periodicals Portal and Web of Science, using the descriptors: social networking and nursing education and the keywords: social networking sites and nursing education, carried out in April 2015. of the 489 articles found, only 14 met the inclusion and exclusion criteria. Most studies were published after 2013 (57%), originating from the United States and United Kingdom (77.8%). It was observed the use of social networking among nursing students, postgraduate students, mentors and nurses, in undergraduate programmes, hybrid education (blended-learning) and in interprofessional education. The social networking sites used in the teaching and learning process were Facebook (42.8%), Ning (28.5%), Twitter (21.4%) and MySpace (7.1%), by means of audios, videos, quizzes, animations, forums, guidance, support, discussions and research group. few experiences of the use of social networking in nursing education were found and their contributions show the numerous benefits and difficulties faced, providing resourses for the improvement and revaluation of their use in the teaching and learning process.

Posttraumatic stress disorder in the nursing population: a concept analysis.

PubMed

Mealer, Meredith; Jones, Jacqueline

2013-01-01

This article is a report of an analysis of the concept of posttraumatic stress disorder (PTSD) and its application to the nursing population. Nurses are at an increased risk for work-related stress resulting in retention issues and impaired functioning in work and home environment. The nursing discipline has been inconsistent with the concepts used to describe the distress and resultant discussions related to the comprehensive nature of the distress experienced, heavily focusing on existing medical language that emphasizes disorders and psychopathology. Walker and Avant's strategy for concept analysis was used in this analysis. A literature review for 1994-2011 was conducted for the following keywords: secondary traumatic stress, compassion fatigue, vicarious traumatization, posttraumatic stress disorder, and nurse. The concept of posttraumatic stress disorder in the nursing population is best described within the context of the Nurse as Wounded Healer theory. Essential attributes include intrusions, avoidance, and hyperarousal. The consequences include worldview changes, retention issues, sleep disruption, and social network disturbances. This concept analysis viewed through The Nurse as Wounded Healer lens, offers clarity to the concept of PTSD within the nursing population and identifies limitations to prior conceptualizations. © 2013 Wiley Periodicals, Inc.

A machine learning pipeline for automated registration and classification of 3D lidar data

NASA Astrophysics Data System (ADS)

Rajagopal, Abhejit; Chellappan, Karthik; Chandrasekaran, Shivkumar; Brown, Andrew P.

2017-05-01

Despite the large availability of geospatial data, registration and exploitation of these datasets remains a persis- tent challenge in geoinformatics. Popular signal processing and machine learning algorithms, such as non-linear SVMs and neural networks, rely on well-formatted input models as well as reliable output labels, which are not always immediately available. In this paper we outline a pipeline for gathering, registering, and classifying initially unlabeled wide-area geospatial data. As an illustrative example, we demonstrate the training and test- ing of a convolutional neural network to recognize 3D models in the OGRIP 2007 LiDAR dataset using fuzzy labels derived from OpenStreetMap as well as other datasets available on OpenTopography.org. When auxiliary label information is required, various text and natural language processing filters are used to extract and cluster keywords useful for identifying potential target classes. A subset of these keywords are subsequently used to form multi-class labels, with no assumption of independence. Finally, we employ class-dependent geometry extraction routines to identify candidates from both training and testing datasets. Our regression networks are able to identify the presence of 6 structural classes, including roads, walls, and buildings, in volumes as big as 8000 m3 in as little as 1.2 seconds on a commodity 4-core Intel CPU. The presented framework is neither dataset nor sensor-modality limited due to the registration process, and is capable of multi-sensor data-fusion.

Emerging Trends in Healthcare Adoption of Wireless Body Area Networks.

PubMed

Rangarajan, Anuradha

2016-01-01

Real-time personal health monitoring is gaining new ground with advances in wireless communications. Wireless body area networks (WBANs) provide a means for low-powered sensors, affixed either on the human body or in vivo, to communicate with each other and with external telecommunication networks. The healthcare benefits of WBANs include continuous monitoring of patient vitals, measuring postacute rehabilitation time, and improving quality of medical care provided in medical emergencies. This study sought to examine emerging trends in WBAN adoption in healthcare. To that end, a systematic literature survey was undertaken against the PubMed database. The search criteria focused on peer-reviewed articles that contained the keywords "wireless body area network" and "healthcare" or "wireless body area network" and "health care." A comprehensive review of these articles was performed to identify adoption dimensions, including underlying technology framework, healthcare subdomain, and applicable lessons-learned. This article benefits healthcare technology professionals by identifying gaps in implementation of current technology and highlighting opportunities for improving products and services.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

PubMed Central

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

Effectiveness of multi-drug regimen chemotherapy treatment in osteosarcoma patients: a network meta-analysis of randomized controlled trials.

PubMed

Wang, Xiaojie; Zheng, Hong; Shou, Tao; Tang, Chunming; Miao, Kun; Wang, Ping

2017-03-29

Osteosarcoma is the most common malignant bone tumour. Due to the high metastasis rate and drug resistance of this disease, multi-drug regimens are necessary to control tumour cells at various stages of the cell cycle, eliminate local or distant micrometastases, and reduce the emergence of drug-resistant cells. Many adjuvant chemotherapy protocols have shown different efficacies and controversial results. Therefore, we classified the types of drugs used for adjuvant chemotherapy and evaluated the differences between single- and multi-drug chemotherapy regimens using network meta-analysis. We searched electronic databases, including PubMed (MEDLINE), EmBase, and the Cochrane Library, through November 2016 using the keywords "osteosarcoma", "osteogenic sarcoma", "chemotherapy", and "random*" without language restrictions. The major outcome in the present analysis was progression-free survival (PFS), and the secondary outcome was overall survival (OS). We used a random effect network meta-analysis for mixed multiple treatment comparisons. We included 23 articles assessing a total of 5742 patients in the present systematic review. The analysis of PFS indicated that the T12 protocol (including adriamycin, bleomycin, cyclophosphamide, dactinomycin, methotrexate, cisplatin) plays a more critical role in osteosarcoma treatment (surface under the cumulative ranking (SUCRA) probability 76.9%), with a better effect on prolonging the PFS of patients when combined with ifosfamide (94.1%) or vincristine (81.9%). For the analysis of OS, we separated the regimens to two groups, reflecting the disconnection. The T12 protocol plus vincristine (94.7%) or the removal of cisplatinum (89.4%) is most likely the best regimen. We concluded that multi-drug regimens have a better effect on prolonging the PFS and OS of osteosarcoma patients, and the T12 protocol has a better effect on prolonging the PFS of osteosarcoma patients, particularly in combination with ifosfamide or vincristine. The OS analysis showed that the T12 protocol plus vincristine or the T12 protocol with the removal of cisplatinum might be a better regimen for improving the OS of patients. However, well-designed randomized controlled trials of chemotherapeutic protocols are still necessary.

Analysis of information on rheumatology from a selected Internet forum in the context of the need for telemedicine solutions.

PubMed

Szpakowski, Rafał; Maślińska, Maria; Dykowska, Grażyna; Zając, Patrycja

2015-01-01

The aim of this study was to determine how often patients and undiagnosed people who complain of musculoskeletal system and rheumatic diseases look for knowledge contained on an Internet forum. Content analysis was used to identify the level of Internet users' activity in the rheumatology section, compared to other areas of medicine. Material included information posted on the Internet forum established at http://medyczka.pl/. The method employed was a quantitative and qualitative analysis of the content. The method was based on qualitative assessment of the first post in each thread presented on the rheumatologic subforum, by assigning keywords, subjectively determined by the researcher, to such a post. For each keyword a specific definition was established, determining a situation in which a given keyword was used. The quantitative analysis qualified rheumatology in the last place in terms of Internet users' activity compared to other branches of medicine. The qualitative assessment of the rheumatologic forum indicated that the three most common keywords were joint pain (32), joints swelling (13), and schoolage (13). The three most common intentional keywords (arranged in order of their decreasing number) were diagnosis based on symptoms (29), interpretation of the laboratory test results (9), and how to deal with symptoms (8). The analysis leads to the conclusion that the rheumatologic subforum, along with other subforums listed above, presents a critically low level of discussion. There is a large disproportion between the number of active and passive forum users, suggesting that numerous individuals search the forum for presented information. Based on the qualitative analysis of the information stocks of the rheumatologic subforum, it was established that most of the questions posted concerned young individuals, who complained of joint pain and swelling, and asked for a possible diagnosis based on the presented symptomatology, interpretation of the laboratory test results and alleviation of disease symptoms.

A rule-based approach for the correlation of alarms to support Disaster and Emergency Management

NASA Astrophysics Data System (ADS)

Gloria, M.; Minei, G.; Lersi, V.; Pasquariello, D.; Monti, C.; Saitto, A.

2009-04-01

Key words: Simple Event Correlator, Agent Platform, Ontology, Semantic Web, Distributed Systems, Emergency Management The importance of recognition of emergency's typology to control the critical situation for security of citizens has been always recognized. It follows this aspect is very important for proper management of a hazardous event. In this work we present a solution for the recognition of emergency's typology adopted by an Italian research project, called CI6 (Centro Integrato per Servizi di Emergenza Innovativi). In our approach, CI6 receives alarms by citizen or people involved in the work (for example: police, operator of 112, and so on). CI6 represents any alarm by a set of information, including a text that describes it and obtained when the user points out the danger, and a pair of coordinates for its location. The system realizes an analysis of text and automatically infers information on the type of emergencies by means a set of parsing rules and rules of inference applied by a independent module: a correlator of events based on their log and called Simple Event Correlator (SEC). SEC, integrated in CI6's platform, is an open source and platform independent event correlation tool. SEC accepts input both files and text derived from standard input, making it flexible because it can be matched to any application that is able to write its output to a file stream. The SEC configuration is stored in text files as rules, each rule specifying an event matching condition, an action list, and optionally a Boolean expression whose truth value decides whether the rule can be applied at a given moment. SEC can produce output events by executing user-specified shell scripts or programs, by writing messages to files, and by various other means. SEC has been successfully applied in various domains like network management, system monitoring, data security, intrusion detection, log file monitoring and analysis, etc; it has been used or integrated with many application as CiscoWorks, HP OpenView NNM and Operation, BMC Patrol, etc. Analysis of text of an alarm can detect some keywords that allow to classify the particular event. The inference rules were developed by means an analysis about news regard real emergency found by web reaserches. We have seen that often a kind of emergency is characterized by more keyword. Keywords are not uniquely associated with a specific emergency, but they can be shared by different types of emergencies (such as. keyword "landslide" can be associated both emergency "landslide" and emergency "Flood"). However, the identification of two or more keywords associated with a particular type of emergency identified in most cases the correct type of emergency. So, for example, if text contains words as "water", "flood", "overflowing", "landslide" o other words belonging to the set of defined keywords or words that have some root of keywords, the system "decides" that this alarm belongs to specific typology, in this case "flood typology". The system has the memory of this information, so if a new alarm is reported and belongs to one of the typology already identified, it proceeds with the comparison of coordinates. The comparison between the centers of the alarms allows to see if they describe an area inscribed in an ideal circle that has centered on the first alarm and radius defined by the typology above mentioned. If this happens the system CI6 creates an emergency that has centered on the centre of that area and typology equal to that of the alarms. It follows that an emergency is represented by at least two alarms. Thus, the system suggests to manager (CI6's user) the possibility that most alarms can concern same events and makes a classification of this event. It is important to stress that CI6 is a system of decision support, hence also this service is limited to providing advice to the user to facilitate his task, leaving him the decision to accept it or not. REFERENCES SEC (Simple Event Correlator), http://kodu.neti.ee/~risto/sec/ M. Gloria,V. Lersi, G. Minei, D. Pasquariello, C. Monti, A. Saitto, "A Semantic WEB Services Platform to support Disaster and Emergency Management", 4th biennial Meeting of International Environmental Modelling and Software Society (iEMSs), 2008

Exploring health information technology education: an analysis of the research.

PubMed

Virgona, Thomas

2012-01-01

This article is an analysis of the Health Information Technology Education published research. The purpose of this study was to examine selected literature using variables such as journal frequency, keyword analysis, universities associated with the research and geographic diversity. The analysis presented in this paper has identified intellectually significant studies that have contributed to the development and accumulation of intellectual wealth of Health Information Technology. The keyword analysis suggests that Health Information Technology research has evolved from establishing concepts and domains of health information systems, technology and management to contemporary issues such as education, outsourcing, web services and security. The research findings have implications for educators, researchers, journal.

Where Am I? Location Archetype Keyword Extraction from Urban Mobility Patterns

PubMed Central

Kostakos, Vassilis; Juntunen, Tomi; Goncalves, Jorge; Hosio, Simo; Ojala, Timo

2013-01-01

Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy. PMID:23704964

Where am I? Location archetype keyword extraction from urban mobility patterns.

PubMed

Kostakos, Vassilis; Juntunen, Tomi; Goncalves, Jorge; Hosio, Simo; Ojala, Timo

2013-01-01

Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy.

Prediction of EST functional relationships via literature mining with user-specified parameters.

PubMed

Wang, Hei-Chia; Huang, Tian-Hsiang

2009-04-01

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

EasyKSORD: A Platform of Keyword Search Over Relational Databases

NASA Astrophysics Data System (ADS)

Peng, Zhaohui; Li, Jing; Wang, Shan

Keyword Search Over Relational Databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. Based on our previous work, we design and implement a novel KSORD platform named EasyKSORD for users and system administrators to use and manage different KSORD systems in a novel and simple manner. EasyKSORD supports advanced queries, efficient data-graph-based search engines, multiform result presentations, and system logging and analysis. Through EasyKSORD, users can search relational databases easily and read search results conveniently, and system administrators can easily monitor and analyze the operations of KSORD and manage KSORD systems much better.

Social networking in nursing education: integrative literature review

PubMed Central

Kakushi, Luciana Emi; Évora, Yolanda Dora Martinez

2016-01-01

Abstract Objective: to identify the use of social networking in nursing education. Method: integrative literature review in the databases: LILACS, IBECS, Cochrane, BDENF, SciELO, CINAHL, Scopus, PubMed, CAPES Periodicals Portal and Web of Science, using the descriptors: social networking and nursing education and the keywords: social networking sites and nursing education, carried out in April 2015. Results: of the 489 articles found, only 14 met the inclusion and exclusion criteria. Most studies were published after 2013 (57%), originating from the United States and United Kingdom (77.8%). It was observed the use of social networking among nursing students, postgraduate students, mentors and nurses, in undergraduate programmes, hybrid education (blended-learning) and in interprofessional education. The social networking sites used in the teaching and learning process were Facebook (42.8%), Ning (28.5%), Twitter (21.4%) and MySpace (7.1%), by means of audios, videos, quizzes, animations, forums, guidance, support, discussions and research group. Conclusion: few experiences of the use of social networking in nursing education were found and their contributions show the numerous benefits and difficulties faced, providing resourses for the improvement and revaluation of their use in the teaching and learning process. PMID:27384465

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis

PubMed Central

Montemurro, Marcelo A.; Zanette, Damián H.

2013-01-01

The Voynich manuscript has remained so far as a mystery for linguists and cryptologists. While the text written on medieval parchment -using an unknown script system- shows basic statistical patterns that bear resemblance to those from real languages, there are features that suggested to some researches that the manuscript was a forgery intended as a hoax. Here we analyse the long-range structure of the manuscript using methods from information theory. We show that the Voynich manuscript presents a complex organization in the distribution of words that is compatible with those found in real language sequences. We are also able to extract some of the most significant semantic word-networks in the text. These results together with some previously known statistical features of the Voynich manuscript, give support to the presence of a genuine message inside the book. PMID:23805215

Analysis of black soil environment based on Arduino

NASA Astrophysics Data System (ADS)

Li, Y.; Zhang, Y. F.; Wu, C. H.; Wang, J. F.

2017-05-01

As everyone knows, the black soil of Heilongjiang bred rice is famous in the world. How to use networking technology to detection the growth environment of Heilongjiang rice, and expands it to the local planting environment to our country is the most important topic. However, the growth environment of rice is complex. In current research, some importnat factors such as carbon dioxide, oxygen, temperature and humidity, pH value and microbial content in black soil that affect the growth of plants are selected, and a kind of black land based on data acquisition and transmission system based on the Arduino development environment and the mechanism construction of Kingview has been realized. The collected data was employed to establish the simulation environment for the growth of rice in Heilongjiang. It can be applied to stimulate the rice growing environment of Heilongjiang province, and gives a improvement of rice quality in other areas. Keywords: Arduino; Kingview; living environment

WGISS-45 International Directory Network (IDN) Report

NASA Technical Reports Server (NTRS)

Morahan, Michael

2018-01-01

The objective of this presentation is to provide IDN (International Directory Network) updates on features and activities to the Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS) and provider community. The following topics will be will be discussed during the presentation: Transition of Providers DIF-9 (Directory Interchange Format-9) to DIF-10 Metadata Records in the Common Metadata Repository (CMR); GCMD (Global Change Master Directory) Keyword Update; DIF-10 and UMM-C (Unified Metadata Model-Collections) Schema Changes; Metadata Validation of Provider Metadata; docBUILDER for Submitting IDN Metadata to the CMR (i.e. Registration); and Mapping WGClimate Essential Climate Variable (ECV) Inventory to IDN Records.

Data mining of mental health issues of non-bone marrow donor siblings.

PubMed

Takita, Morihito; Tanaka, Yuji; Kodama, Yuko; Murashige, Naoko; Hatanaka, Nobuyo; Kishi, Yukiko; Matsumura, Tomoko; Ohsawa, Yukio; Kami, Masahiro

2011-07-20

Allogenic hematopoietic stem cell transplantation is a curative treatment for patients with advanced hematologic malignancies. However, the long-term mental health issues of siblings who were not selected as donors (non-donor siblings, NDS) in the transplantation have not been well assessed. Data mining is useful in discovering new findings from a large, multidisciplinary data set and the Scenario Map analysis is a novel approach which allows extracting keywords linking different conditions/events from text data of interviews even when the keywords appeared infrequently. The aim of this study is to assess mental health issues on NDSs and to find helpful keywords for the clinical follow-up using a Scenario Map analysis. A 47-year-old woman whose younger sister had undergone allogenic hematopoietic stem cell transplantation 20 years earlier was interviewed as a NDS. The text data from the interview transcriptions was analyzed using Scenario Mapping. Four clusters of words and six keywords were identified. Upon review of the word clusters and keywords, both the subject and researchers noticed that the subject has had mental health issues since the disease onset to date with being a NDS. The issues have been alleviated by her family. This single subject study suggested the advantages of data mining in clinical follow-up for mental health issues of patients and/or their families.

The Visions of World-Class Universities

ERIC Educational Resources Information Center

Slyusarenko, Olena

2015-01-01

The visions of the top 26 world-class universities of the first 30 in the Shanghai ranking list have been evaluated and compared with the missions of the world's top 20 universities. Applying the content analysis, a group of 48 keywords, which describe the essence of these visions, has been revealed. The average amount of keywords in one vision is…

A Study of Practical Proxy Reencryption with a Keyword Search Scheme considering Cloud Storage Structure

PubMed Central

Lee, Im-Yeong

2014-01-01

Data outsourcing services have emerged with the increasing use of digital information. They can be used to store data from various devices via networks that are easy to access. Unlike existing removable storage systems, storage outsourcing is available to many users because it has no storage limit and does not require a local storage medium. However, the reliability of storage outsourcing has become an important topic because many users employ it to store large volumes of data. To protect against unethical administrators and attackers, a variety of cryptography systems are used, such as searchable encryption and proxy reencryption. However, existing searchable encryption technology is inconvenient for use in storage outsourcing environments where users upload their data to be shared with others as necessary. In addition, some existing schemes are vulnerable to collusion attacks and have computing cost inefficiencies. In this paper, we analyze existing proxy re-encryption with keyword search. PMID:24693240

A study of practical proxy reencryption with a keyword search scheme considering cloud storage structure.

PubMed

Lee, Sun-Ho; Lee, Im-Yeong

2014-01-01

Data outsourcing services have emerged with the increasing use of digital information. They can be used to store data from various devices via networks that are easy to access. Unlike existing removable storage systems, storage outsourcing is available to many users because it has no storage limit and does not require a local storage medium. However, the reliability of storage outsourcing has become an important topic because many users employ it to store large volumes of data. To protect against unethical administrators and attackers, a variety of cryptography systems are used, such as searchable encryption and proxy reencryption. However, existing searchable encryption technology is inconvenient for use in storage outsourcing environments where users upload their data to be shared with others as necessary. In addition, some existing schemes are vulnerable to collusion attacks and have computing cost inefficiencies. In this paper, we analyze existing proxy re-encryption with keyword search.

Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting.

PubMed

Wöllmer, Martin; Marchi, Erik; Squartini, Stefano; Schuller, Björn

2011-09-01

Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database-a corpus containing emotionally colored conversations with a cognitive system for "Sensitive Artificial Listening".

Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

2010-05-01

Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms

NASA Technical Reports Server (NTRS)

Storrie-Lombardi, Michael C.; Hoover, Richard B.

2005-01-01

Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures

Linagliptin versus sitagliptin in patients with type 2 diabetes mellitus: a network meta-analysis of randomized clinical trials.

PubMed

Keshavarz, Khosro; Lotfi, Farhad; Sanati, Ehsan; Salesi, Mahmood; Hashemi-Meshkini, Amir; Jafari, Mojtaba; Mojahedian, Mohammad M; Najafi, Behzad; Nikfar, Shekoufeh

2017-10-25

Diabetes is one of the most common chronic and costly diseases worldwide and type 2 diabetes is the most common type which accounts for about 90% of cases with diabetes. New medication-therapy regimens such as those containing linagliptin alone or in combination with other medications (within the category of DDP-4 inhibitors) must be evaluated in terms of efficacy and compared with other currently used drugs and then enter the medication list of the country. Hence, this study aimed to compare the clinical efficacy of the two drugs, i.e. linagliptin and sitagliptin, in patients with type 2 diabetes. A systematic review was conducted to identify all clinical trials published by 2015 which compared the two drugs in patients with type 2 diabetes. Using keywords such as "linagliptin", "type 2 diabetes mellitus", "sitagliptin" and related combinations, we searched databases including Scopus, PubMed, and Web of Science. The quality of the selected studies was evaluated using the Jadad score. Considering primary and secondary outcomes extracted from the reviewed studies, a network meta-analysis was used to conduct a systematic comparison between the two studied drugs. This network meta-analysis included 32 studies (Linagliptin vs PLB: n = 8, Sitagliptin vs PLB: n = 13, Linagliptin + MET vs PLB + MET: n = 4, and Sitagliptin + MET vs PLB + MET: n = 7) and a total of 13,747 patients. The results showed no significant difference between linagliptin and sitagliptin in terms of key efficacy and safety outcomes such as HbA1c changes from baseline, body weight change from baseline, percentage of patients achieving HbA1c <7, and percentage of patients experiencing hypoglycemic events (p > 0.05). The results showed that the efficacy of the two drug regimens was the same. Based on the results, there was no significant difference between the two drugs, i.e. linagliptin and sitagliptin, in terms of efficacy; in other words, the efficacy of the two drugs was the same. Therefore, the use of these two drugs depends on their availability and cost. Graphical abstract of the network meta-analysis performed to evaluate the alternatives under the study.

Multiple-Localization and Hub Proteins

PubMed Central

Ota, Motonori; Gonja, Hideki; Koike, Ryotaro; Fukuchi, Satoshi

2016-01-01

Protein-protein interactions are fundamental for all biological phenomena, and protein-protein interaction networks provide a global view of the interactions. The hub proteins, with many interaction partners, play vital roles in the networks. We investigated the subcellular localizations of proteins in the human network, and found that the ones localized in multiple subcellular compartments, especially the nucleus/cytoplasm proteins (NCP), the cytoplasm/cell membrane proteins (CMP), and the nucleus/cytoplasm/cell membrane proteins (NCMP), tend to be hubs. Examinations of keywords suggested that among NCP, those related to post-translational modifications and transcription functions are the major contributors to the large number of interactions. These types of proteins are characterized by a multi-domain architecture and intrinsic disorder. A survey of the typical hub proteins with prominent numbers of interaction partners in the type revealed that most are either transcription factors or co-regulators involved in signaling pathways. They translocate from the cytoplasm to the nucleus, triggered by the phosphorylation and/or ubiquitination of intrinsically disordered regions. Among CMP and NCMP, the contributors to the numerous interactions are related to either kinase or ubiquitin ligase activity. Many of them reside on the cytoplasmic side of the cell membrane, and act as the upstream regulators of signaling pathways. Overall, these hub proteins function to transfer external signals to the nucleus, through the cell membrane and the cytoplasm. Our analysis suggests that multiple-localization is a crucial concept to characterize groups of hub proteins and their biological functions in cellular information processing. PMID:27285823

Integrated Urban Flood Analysis considering Optimal Operation of Flood Control Facilities in Urban Drainage Networks

NASA Astrophysics Data System (ADS)

Moon, Y. I.; Kim, M. S.; Choi, J. H.; Yuk, G. M.

2017-12-01

eavy rainfall has become a recent major cause of urban area flooding due to the climate change and urbanization. To prevent property damage along with casualties, a system which can alert and forecast urban flooding must be developed. Optimal performance of reducing flood damage can be expected of urban drainage facilities when operated in smaller rainfall events over extreme ones. Thus, the purpose of this study is to execute: A) flood forecasting system using runoff analysis based on short term rainfall; and B) flood warning system which operates based on the data from pump stations and rainwater storage in urban basins. In result of the analysis, it is shown that urban drainage facilities using short term rainfall forecasting data by radar will be more effective to reduce urban flood damage than using only the inflow data of the facility. Keywords: Heavy Rainfall, Urban Flood, Short-term Rainfall Forecasting, Optimal operating of urban drainage facilities. AcknowledgmentsThis research was supported by a grant (17AWMP-B066744-05) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Assessing the Validity of Automated Webcrawlers as Data Collection Tools to Investigate Online Child Sexual Exploitation.

PubMed

Westlake, Bryce; Bouchard, Martin; Frank, Richard

2017-10-01

The distribution of child sexual exploitation (CE) material has been aided by the growth of the Internet. The graphic nature and prevalence of the material has made researching and combating difficult. Although used to study online CE distribution, automated data collection tools (e.g., webcrawlers) have yet to be shown effective at targeting only relevant data. Using CE-related image and keyword criteria, we compare networks starting from CE websites to those from similar non-CE sexuality websites and dissimilar sports websites. Our results provide evidence that (a) webcrawlers have the potential to provide valid CE data, if the appropriate criterion is selected; (b) CE distribution is still heavily image-based suggesting images as an effective criterion; (c) CE-seeded networks are more hub-based and differ from non-CE-seeded networks on several website characteristics. Recommendations for improvements to reliable criteria selection are discussed.

Characteristics of High School Students' and Science Teachers' Cognitive Frame about Effective Teaching Method for High School Science Subject

NASA Astrophysics Data System (ADS)

Chung, Duk Ho; Park, Kyeong-Jin; Cho, Kyu Seong

2016-04-01

We investigated the cognitive frame of high school students and inservice high school science teachers about effective teaching method, and we also explored how they understood about the teaching methods suggested by the 2009 revised Science Curriculum. Data were collected from 275 high school science teachers and 275 high school students. We analyzed data in terms of the words and the cognitive frame using the Semantic Network Analysis. The results were as follows. First, the teachers perceived that an activity oriented class was the effective science class that helped improve students'' problem-solving abilities and their inquiry skills. The students had the cognitive frame that their teacher had to present relevant and enough teaching materials to students, and that they should also receive assistance from teachers in science class to better prepare for college entrance exam. Second, both students and teachers retained the cognitive frame about the efficient science class that was not reflected 2009 revised Science Curriculum exactly. Especially, neither groups connected the elements of ''convergence'' as well as ''integration'' embedded across science subject areas to their cognitive frame nor cognized the fact that many science learning contents were closed related to one another. Therefore, various professional development opportunities should be offered so that teachers succinctly comprehend the essential features and the intents of the 2009 revised Science Curriculum and thereby implement it in their science lessons effectively. Keywords : semantic network analysis, cognitive frame, teaching method, science lesson

Trends in Dissertations Involving Technology-Assisted Mathematics Instruction: The Case of Turkey

ERIC Educational Resources Information Center

Tatar, Enver; Akkaya, Adnan; Kagizmanli, Türkan Berrin

2014-01-01

The purpose of this study is to examine thesis studies on technology-supported mathematics education in Turkey in terms of the keywords, mathematical areas, technologies and methodology used and results obtained. Data were obtained from 105 graduate theses. At the end of the analysis of the data, it was determined that most used keywords were from…

Detection of pneumonia using free-text radiology reports in the BioSense system.

PubMed

Asatryan, Armenak; Benoit, Stephen; Ma, Haobo; English, Roseanne; Elkin, Peter; Tokars, Jerome

2011-01-01

Near real-time disease detection using electronic data sources is a public health priority. Detecting pneumonia is particularly important because it is the manifesting disease of several bioterrorism agents as well as a complication of influenza, including avian and novel H1N1 strains. Text radiology reports are available earlier than physician diagnoses and so could be integral to rapid detection of pneumonia. We performed a pilot study to determine which keywords present in text radiology reports are most highly associated with pneumonia diagnosis. Electronic radiology text reports from 11 hospitals from February 1, 2006 through December 31, 2007 were used. We created a computerized algorithm that searched for selected keywords ("airspace disease", "consolidation", "density", "infiltrate", "opacity", and "pneumonia"), differentiated between clinical history and radiographic findings, and accounted for negations and double negations; this algorithm was tested on a sample of 350 radiology reports. We used the algorithm to study 189,246 chest radiographs, searching for the keywords and determining their association with a final International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis of pneumonia. Performance of the search algorithm in finding keywords, and association of the keywords with a pneumonia diagnosis. In the sample of 350 radiographs, the search algorithm was highly successful in identifying the selected keywords (sensitivity 98.5%, specificity 100%). Analysis of the 189,246 radiographs showed that the keyword "pneumonia" was the strongest predictor of an ICD-9-CM diagnosis of pneumonia (adjusted odds ratio 11.8) while "density" was the weakest (adjusted odds ratio 1.5). In general, the most highly associated keyword present in the report, regardless of whether a less highly associated keyword was also present, was the best predictor of a diagnosis of pneumonia. Empirical methods may assist in finding radiology report keywords that are most highly predictive of a pneumonia diagnosis. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Species, habitats, society: an evaluation of research supporting EU's Natura 2000 network.

PubMed

Popescu, Viorel D; Rozylowicz, Laurentiu; Niculae, Iulian M; Cucu, Adina L; Hartel, Tibor

2014-01-01

The Natura 2000 network is regarded as one of the conservation success stories in the global effort to protect biodiversity. However, significant challenges remain in Natura 2000 implementation, owing to its rapid expansion, and lack of a coherent vision for its future. Scientific research is critical for identifying conservation priorities, setting management goals, and reconciling biodiversity protection and society in the complex political European landscape. Thus, there is an urgent need for a comprehensive evaluation of published Natura 2000 research to highlight prevalent research themes, disciplinary approaches, and spatial entities. We conducted a systematic review of 572 scientific articles and conference proceedings focused on Natura 2000 research, published between 1996 and 2014. We grouped these articles into 'ecological' and 'social and policy' categories. Using a novel application of network analysis of article keywords, we found that Natura 2000 research forms a cohesive small-world network, owing to the emphasis on ecological research (79% of studies, with a strong focus on spatial conservation planning), and the underrepresentation of studies addressing 'social and policy' issues (typically focused on environmental impact assessment, multi-level governance, agri-environment policy, and ecosystem services valuation). 'Ecological' and 'social and policy' research shared only general concepts (e.g., Natura 2000, Habitats Directive) suggesting a disconnection between these disciplines. The UK and the Mediterranean basin countries dominated Natura 2000 research, and there was a weak correlation between number of studies and proportion of national territory protected. Approximately 40% of 'social and policy' research and 26% of 'ecological' studies highlighted negative implications of Natura 2000, while 21% of studies found positive social and biodiversity effects. We emphasize the need for designing inter- and transdisciplinary research in order to promote a social-ecological understanding of Natura 2000, and advance EU conservation policies.

Evolution of primary care databases in UK: a scientometric analysis of research output.

PubMed

Vezyridis, Paraskevas; Timmons, Stephen

2016-10-11

To identify publication and citation trends, most productive institutions and countries, top journals, most cited articles and authorship networks from articles that used and analysed data from primary care databases (CPRD, THIN, QResearch) of pseudonymised electronic health records (EHRs) in UK. Descriptive statistics and scientometric tools were used to analyse a SCOPUS data set of 1891 articles. Open access software was used to extract networks from the data set (Table2Net), visualise and analyse coauthorship networks of scholars and countries (Gephi) and density maps (VOSviewer) of research topics co-occurrence and journal cocitation. Research output increased overall at a yearly rate of 18.65%. While medicine is the main field of research, studies in more specialised areas include biochemistry and pharmacology. Researchers from UK, USA and Spanish institutions have published the most papers. Most of the journals that publish this type of research and most cited papers come from UK and USA. Authorship varied between 3 and 6 authors. Keyword analyses show that smoking, diabetes, cardiovascular diseases and mental illnesses, as well as medication that can treat such medical conditions, such as non-steroid anti-inflammatory agents, insulin and antidepressants constitute the main topics of research. Coauthorship network analyses show that lead scientists, directors or founders of these databases are, to various degrees, at the centre of clusters in this scientific community. There is a considerable increase of publications in primary care research from EHRs. The UK has been well placed at the centre of an expanding global scientific community, facilitating international collaborations and bringing together international expertise in medicine, biochemical and pharmaceutical research. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Web-based surveillance of public information needs for informing preconception interventions.

PubMed

D'Ambrosio, Angelo; Agricola, Eleonora; Russo, Luisa; Gesualdo, Francesco; Pandolfi, Elisabetta; Bortolus, Renata; Castellani, Carlo; Lalatta, Faustina; Mastroiacovo, Pierpaolo; Tozzi, Alberto Eugenio

2015-01-01

The risk of adverse pregnancy outcomes can be minimized through the adoption of healthy lifestyles before pregnancy by women of childbearing age. Initiatives for promotion of preconception health may be difficult to implement. Internet can be used to build tailored health interventions through identification of the public's information needs. To this aim, we developed a semi-automatic web-based system for monitoring Google searches, web pages and activity on social networks, regarding preconception health. Based on the American College of Obstetricians and Gynecologists guidelines and on the actual search behaviors of Italian Internet users, we defined a set of keywords targeting preconception care topics. Using these keywords, we analyzed the usage of Google search engine and identified web pages containing preconception care recommendations. We also monitored how the selected web pages were shared on social networks. We analyzed discrepancies between searched and published information and the sharing pattern of the topics. We identified 1,807 Google search queries which generated a total of 1,995,030 searches during the study period. Less than 10% of the reviewed pages contained preconception care information and in 42.8% information was consistent with ACOG guidelines. Facebook was the most used social network for sharing. Nutrition, Chronic Diseases and Infectious Diseases were the most published and searched topics. Regarding Genetic Risk and Folic Acid, a high search volume was not associated to a high web page production, while Medication pages were more frequently published than searched. Vaccinations elicited high sharing although web page production was low; this effect was quite variable in time. Our study represent a resource to prioritize communication on specific topics on the web, to address misconceptions, and to tailor interventions to specific populations.

Web-Based Surveillance of Public Information Needs for Informing Preconception Interventions

PubMed Central

D’Ambrosio, Angelo; Agricola, Eleonora; Russo, Luisa; Gesualdo, Francesco; Pandolfi, Elisabetta; Bortolus, Renata; Castellani, Carlo; Lalatta, Faustina; Mastroiacovo, Pierpaolo; Tozzi, Alberto Eugenio

2015-01-01

Background The risk of adverse pregnancy outcomes can be minimized through the adoption of healthy lifestyles before pregnancy by women of childbearing age. Initiatives for promotion of preconception health may be difficult to implement. Internet can be used to build tailored health interventions through identification of the public's information needs. To this aim, we developed a semi-automatic web-based system for monitoring Google searches, web pages and activity on social networks, regarding preconception health. Methods Based on the American College of Obstetricians and Gynecologists guidelines and on the actual search behaviors of Italian Internet users, we defined a set of keywords targeting preconception care topics. Using these keywords, we analyzed the usage of Google search engine and identified web pages containing preconception care recommendations. We also monitored how the selected web pages were shared on social networks. We analyzed discrepancies between searched and published information and the sharing pattern of the topics. Results We identified 1,807 Google search queries which generated a total of 1,995,030 searches during the study period. Less than 10% of the reviewed pages contained preconception care information and in 42.8% information was consistent with ACOG guidelines. Facebook was the most used social network for sharing. Nutrition, Chronic Diseases and Infectious Diseases were the most published and searched topics. Regarding Genetic Risk and Folic Acid, a high search volume was not associated to a high web page production, while Medication pages were more frequently published than searched. Vaccinations elicited high sharing although web page production was low; this effect was quite variable in time. Conclusion Our study represent a resource to prioritize communication on specific topics on the web, to address misconceptions, and to tailor interventions to specific populations. PMID:25879682

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

PubMed

Kaplan, Noam; Vaaknin, Avishay; Linial, Michal

2003-10-01

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

MRO DKF Post-Processing Tool

NASA Technical Reports Server (NTRS)

Ayap, Shanti; Fisher, Forest; Gladden, Roy; Khanampompan, Teerapat

2008-01-01

This software tool saves time and reduces risk by automating two labor-intensive and error-prone post-processing steps required for every DKF [DSN (Deep Space Network) Keyword File] that MRO (Mars Reconnaissance Orbiter) produces, and is being extended to post-process the corresponding TSOE (Text Sequence Of Events) as well. The need for this post-processing step stems from limitations in the seq-gen modeling resulting in incorrect DKF generation that is then cleaned up in post-processing.

A Hybrid P2P Overlay Network for Non-strictly Hierarchically Categorized Content

NASA Astrophysics Data System (ADS)

Wan, Yi; Asaka, Takuya; Takahashi, Tatsuro

In P2P content distribution systems, there are many cases in which the content can be classified into hierarchically organized categories. In this paper, we propose a hybrid overlay network design suitable for such content called Pastry/NSHCC (Pastry for Non-Strictly Hierarchically Categorized Content). The semantic information of classification hierarchies of the content can be utilized regardless of whether they are in a strict tree structure or not. By doing so, the search scope can be restrained to any granularity, and the number of query messages also decreases while maintaining keyword searching availability. Through simulation, we showed that the proposed method provides better performance and lower overhead than unstructured overlays exploiting the same semantic information.

SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803.

PubMed

Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

2008-01-01

Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactions as well as their protein-level interactions using the model cyanobacterium, Synechocystis sp. PCC 6803. It predicts the protein-protein interactions using public interaction databases that contain mutually complementary and redundant data. Furthermore, SynechoNET provides information on transmembrane topology, signal peptide, and domain structure in order to support the analysis of regulatory membrane proteins. Such biological information can be queried and visualized in user-friendly web interfaces that include the interactive network viewer and search pages by keyword and functional category. SynechoNET is an integrated protein-protein interaction database designed to analyze regulatory membrane proteins in cyanobacteria. It provides a platform for biologists to extend the genomic data of cyanobacteria by predicting interaction partners, membrane association, and membrane topology of Synechocystis proteins. SynechoNET is freely available at http://synechocystis.org/ or directly at http://bioportal.kobic.kr/SynechoNET/.

The Characteristics of Earth System Thinking of Science Gifted Students in relation to Climate Changes

NASA Astrophysics Data System (ADS)

Chung, Duk Ho; Cho, Kyu Seong; Hong, Deok Pyo; Park, Kyeong Jin

2016-04-01

This study aimed to investigate the perception of earth system thinking of science gifted students in future problem solving (FPS) in relation to climate changes. In order to this study, the research problem associated with climate changes was developed through a literature review. The thirty seven science gifted students participated in lessons. The ideas in problem solving process of science gifted students were analyzed using the semantic network analysis method. The results are as follows. In the problem solving processes, science gifted students are ''changes of the sunlight by water layer'', ''changes of the Earth''s temperature'', ''changes of the air pressure'', '' change of the wind and weather''were represented in order. On other hand, regard to earth system thinking for climate changes, while science gifted students were used sub components related to atmospheres frequently, they were used sub components related to biosphere, geosphere, and hydrosphere a little. But, the analytical results of the structural relationship between the sub components related to earth system, they were recognised that biosphere, geosphere, and hydrosphere used very important in network structures. In conclusion, science gifted students were understood well that components of the earth system are influencing each other. Keywords : Science gifted students, Future problem solving, Climate change, Earth system thinking

Family support and the child as health promoting agent in the Arctic - "the Inuit way".

PubMed

Montgomery-Andersen, Ruth A; Borup, Ina

2012-01-01

In the context of the UN's 1990 'Convention on the Right's of the Child' 1990, and the associated definition of health promotion as a community's ability to recognise, define and make decisions on how to create a healthy society, this article describes and analyses how family support networks are conceived and present themselves in perinatal Inuit families. This literature review conducted an initial and secondary search using the keywords and combinations of the keywords: healthy families, health promoting families, resiliency, Arctic, Inuit, Family support, was executed in PubMed, Popline, CSA and CINAHL. The tertiary literature search was then combined with literature gleaned from literature lists, and other relevant articles were selected. Individual members of the family contribute to the health of the family, but the child is often the catalyst for health promotion within the family, not only the siblings to the unborn child, but also the unborn child. Perinatal entities create their own networks that support and develop concepts of family and support systems. Resiliency, kinship and ecocultural process within the family are concomitant to the health of perinatal family and of the children. More research is needed that moves children from being viewed as the receivers of health towards being seen as the promoters of health and an important actor as health promoting agent within the family.

Social support networks and eating disorders: an integrative review of the literature.

PubMed

Leonidas, Carolina; Dos Santos, Manoel Antônio

2014-01-01

This study aimed to analyze the scientific literature about social networks and social support in eating disorders (ED). By combining keywords, an integrative review was performed. It included publications from 2006-2013, retrieved from the MEDLINE, LILACS, PsycINFO, and CINAHL databases. The selection of articles was based on preestablished inclusion and exclusion criteria. A total of 24 articles were selected for data extraction. There was a predominance of studies that used nonexperimental and descriptive designs, and which were published in international journals. This review provided evidence of the fact that fully consolidated literature regarding social support and social networks in patients with ED is not available, given the small number of studies dedicated to the subject. We identified evidence that the family social network of patients with ED has been widely explored by the literature, although there is a lack of studies about other networks and sources of social support outside the family. The evidence presented in this study shows the need to include other social networks in health care. This expansion beyond family networks would include significant others - such as friends, colleagues, neighbors, people from religious groups, among others - who could help the individual coping with the disorder. The study also highlights the need for future research on this topic, as well as a need for greater investment in publications on the various dimensions of social support and social networks.

Visualization and Analysis of Geology Word Vectors for Efficient Information Extraction

NASA Astrophysics Data System (ADS)

Floyd, J. S.

2016-12-01

When a scientist begins studying a new geographic region of the Earth, they frequently begin by gathering relevant scientific literature in order to understand what is known, for example, about the region's geologic setting, structure, stratigraphy, and tectonic and environmental history. Experienced scientists typically know what keywords to seek and understand that if a document contains one important keyword, then other words in the document may be important as well. Word relationships in a document give rise to what is known in linguistics as the context-dependent nature of meaning. For example, the meaning of the word `strike' in geology, as in the strike of a fault, is quite different from its popular meaning in baseball. In addition, word order, such as in the phrase `Cretaceous-Tertiary boundary,' often corresponds to the order of sequences in time or space. The context of words and the relevance of words to each other can be derived quantitatively by machine learning vector representations of words. Here we show the results of training a neural network to create word vectors from scientific research papers from selected rift basins and mid-ocean ridges: the Woodlark Basin of Papua New Guinea, the Hess Deep rift, and the Gulf of Mexico basin. The word vectors are statistically defined by surrounding words within a given window, limited by the length of each sentence. The word vectors are analyzed by their cosine distance to related words (e.g., `axial' and `magma'), classified by high dimensional clustering, and visualized by reducing the vector dimensions and plotting the vectors on a two- or three-dimensional graph. Similarity analysis of `Triassic' and `Cretaceous' returns `Jurassic' as the nearest word vector, suggesting that the model is capable of learning the geologic time scale. Similarity analysis of `basalt' and `minerals' automatically returns mineral names such as `chlorite', `plagioclase,' and `olivine.' Word vector analysis and visualization allow one to extract information from hundreds of papers or more and find relationships in less time than it would take to read all of the papers. As machine learning tools become more commonly available, more and more scientists will be able to use and refine these tools for their individual needs.

Behavioral Analysis of Visitors to a Medical Institution's Website Using Markov Chain Monte Carlo Methods.

PubMed

Suzuki, Teppei; Tani, Yuji; Ogasawara, Katsuhiko

2016-07-25

Consistent with the "attention, interest, desire, memory, action" (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. In the case of the keyword "clinic name," the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword "clinic name and regional name," the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword "clinic name + medical examination," the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords "mammography screening," the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis.

Behavioral Analysis of Visitors to a Medical Institution’s Website Using Markov Chain Monte Carlo Methods

PubMed Central

Tani, Yuji

2016-01-01

Background Consistent with the “attention, interest, desire, memory, action” (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. Objective We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. Methods We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. Results In the case of the keyword “clinic name,” the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword “clinic name and regional name,” the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword “clinic name + medical examination,” the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords “mammography screening,” the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Conclusions Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis. PMID:27457537

Texture Analysis and Cartographic Feature Extraction.

DTIC Science & Technology

1985-01-01

Investigations into using various image descriptors as well as developing interactive feature extraction software on the Digital Image Analysis Laboratory...system. Originator-supplied keywords: Ad-Hoc image descriptor; Bayes classifier; Bhattachryya distance; Clustering; Digital Image Analysis Laboratory

A Unified Mathematical Approach to Image Analysis.

DTIC Science & Technology

1987-08-31

describes four instances of the paradigm in detail. Directions for ongoing and future research are also indicated. Keywords: Image processing; Algorithms; Segmentation; Boundary detection; tomography; Global image analysis .

Biomimetic oral mucin from polymer micelle networks

NASA Astrophysics Data System (ADS)

Authimoolam, Sundar Prasanth

Mucin networks are formed by the complexation of bottlebrush-like mucin glycoprotein with other small molecule glycoproteins. These glycoproteins create nanoscale strands that then arrange into a nanoporous mesh. These networks play an important role in ensuring surface hydration, lubricity and barrier protection. In order to understand the functional behavior in mucin networks, it is important to decouple their chemical and physical effects responsible for generating the fundamental property-function relationship. To achieve this goal, we propose to develop a synthetic biomimetic mucin using a layer-by-layer (LBL) deposition approach. In this work, a hierarchical 3-dimensional structures resembling natural mucin networks was generated using affinity-based interactions on synthetic and biological surfaces. Unlike conventional polyelectrolyte-based LBL methods, pre-assembled biotin-functionalized filamentous (worm-like) micelles was utilized as the network building block, which from complementary additions of streptavidin generated synthetic networks of desired thickness. The biomimetic nature in those synthetic networks are studied by evaluating its structural and bio-functional properties. Structurally, synthetic networks formed a nanoporous mesh. The networks demonstrated excellent surface hydration property and were able capable of microbial capture. Those functional properties are akin to that of natural mucin networks. Further, the role of synthetic mucin as a drug delivery vehicle, capable of providing localized and tunable release was demonstrated. By incorporating antibacterial curcumin drug loading within synthetic networks, bacterial growth inhibition was also demonstrated. Thus, such bioactive interfaces can serve as a model for independently characterizing mucin network properties and through its role as a drug carrier vehicle it presents exciting future opportunities for localized drug delivery, in regenerative applications and as bio-functional implant coats. KEYWORDS: Biomimic, Bioapplication, Drug delivery, Filomicelle, Mucin, Polymer networks.

Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment

DTIC Science & Technology

2015-10-01

Scoring, Gaussian Backend , etc.) as shown in Fig. 39. The methods in this domain also emphasized the ability to perform data purification for both...investigation using the same infrastructure was undertaken to explore Lombard effect “flavor” detection for improved speaker ID. The study The presence of...dimension selection and compared to a common N-gram frequency based selection. 2.1.2: Exploration on NN/DBN backend : Since Deep Neural Networks (DNN) have

Improving Situation Awareness with the Android Team Awareness Kit (ATAK)

DTIC Science & Technology

2015-04-01

fluid user experience and enhanced data sharing. 19 6.2.2 Esri Esri is a US-based company that sells geospatial information systems and data services...field, Situational Awareness (SA) needs to be conveyed in a de- centralized manner to the users at the edge of the network as well as at operations...that ATAK has built-in, and the ways it is being used by a variety of military, homeland security, and law enforcement users . Keywords: situational

Can Item Keyword Feedback Help Remediate Knowledge Gaps?

PubMed

Feinberg, Richard A; Clauser, Amanda L

2016-10-01

In graduate medical education, assessment results can effectively guide professional development when both assessment and feedback support a formative model. When individuals cannot directly access the test questions and responses, a way of using assessment results formatively is to provide item keyword feedback. The purpose of the following study was to investigate whether exposure to item keyword feedback aids in learner remediation. Participants included 319 trainees who completed a medical subspecialty in-training examination (ITE) in 2012 as first-year fellows, and then 1 year later in 2013 as second-year fellows. Performance on 2013 ITE items in which keywords were, or were not, exposed as part of the 2012 ITE score feedback was compared across groups based on the amount of time studying (preparation). For the same items common to both 2012 and 2013 ITEs, response patterns were analyzed to investigate changes in answer selection. Test takers who indicated greater amounts of preparation on the 2013 ITE did not perform better on the items in which keywords were exposed compared to those who were not exposed. The response pattern analysis substantiated overall growth in performance from the 2012 ITE. For items with incorrect responses on both attempts, examinees selected the same option 58% of the time. Results from the current study were unsuccessful in supporting the use of item keywords in aiding remediation. Unfortunately, the results did provide evidence of examinees retaining misinformation.

The discrepancy in the perception of the public-political speech in Croatia.

PubMed

Tanta, Ivan; Lesinger, Gordana

2014-03-01

Key place in this paper takes the study of political speech in the Republic of Croatia and their impact on voters, or which keywords are in political speeches and public appearances of politicians in Croatia that their voting body wants to hear. Given listed below we will define the research topic in the form of a question - is there a discrepancy in the perception of the public-political speech in Croatia, and which keywords are specific to the two main regions in Croatia and that inhabitant these regions respond. Marcus Tullius Cicero, the most important Roman orator, he used a specific associative mnemonic technique that is called "technique room". He would talk expound on keywords and conceptual terms that he needed for the desired topic and join in these make them, according to the desired order, in a very creative and unique way, the premises of the house or palace, which he knew well. Then, while holding the speech intended to pass through rooms of the house or palace and then put keywords and concepts come to mind, again according to the desired order. Given that this is a specific kind of research political speech that is relatively recent in Croatia, it should be noted that there is still, this kind of political communication is not sufficiently explored. Particularly the emphasis on the impact and use of keywords specific to the Republic of Croatia, in everyday public and political communication. The paper will be analyzed the political, campaign speeches and promises several winning candidates, and now Croatian MEPs, specific keywords related to: economics, culture, science, education and health. The analysis is based on comparison of the survey results on the representation of key words in the speeches of politicians and qualitative analysis of the speeches of politicians on key words during the election campaign.

Searching the ASRS Database Using QUORUM Keyword Search, Phrase Search, Phrase Generation, and Phrase Discovery

NASA Technical Reports Server (NTRS)

McGreevy, Michael W.; Connors, Mary M. (Technical Monitor)

2001-01-01

To support Search Requests and Quick Responses at the Aviation Safety Reporting System (ASRS), four new QUORUM methods have been developed: keyword search, phrase search, phrase generation, and phrase discovery. These methods build upon the core QUORUM methods of text analysis, modeling, and relevance-ranking. QUORUM keyword search retrieves ASRS incident narratives that contain one or more user-specified keywords in typical or selected contexts, and ranks the narratives on their relevance to the keywords in context. QUORUM phrase search retrieves narratives that contain one or more user-specified phrases, and ranks the narratives on their relevance to the phrases. QUORUM phrase generation produces a list of phrases from the ASRS database that contain a user-specified word or phrase. QUORUM phrase discovery finds phrases that are related to topics of interest. Phrase generation and phrase discovery are particularly useful for finding query phrases for input to QUORUM phrase search. The presentation of the new QUORUM methods includes: a brief review of the underlying core QUORUM methods; an overview of the new methods; numerous, concrete examples of ASRS database searches using the new methods; discussion of related methods; and, in the appendices, detailed descriptions of the new methods.

Mapping of drinking water research: a bibliometric analysis of research output during 1992-2011.

PubMed

Fu, Hui-Zhen; Wang, Ming-Huang; Ho, Yuh-Shan

2013-01-15

A bibliometric analysis based on the Science Citation Index Expanded from the Web of Science was carried out to provide insights into research activities and tendencies of the global drinking water from 1992 to 2011. Study emphases included performance of publication covering annual outputs, mainstream journals, Web of Science categories, leading countries, institutions, research tendencies and hotspots. The results indicated that annual output of the related scientific articles increased steadily. Water Research, Environmental Science & Technology, and Journal American Water Works Association were the three most common journals in drinking water research. The USA took a leading position out of 168 countries/territories, followed by Japan and Germany. A summary of the most frequently used keywords obtained from words in paper title analysis, author keyword analysis and KeyWords Plus analysis provided the clues to discover the current research emphases. The mainstream research related to drinking water was water treatment methods and the related contaminants. Disinfection process and consequent disinfection by-products attracted much attention. Ozonation and chlorination in disinfection, and adsorption were common techniques and are getting popular. Commonly researched drinking water contaminants concerned arsenic, nitrate, fluoride, lead, and cadmium, and pharmaceuticals emerged as the frequently studied contaminants in recent years. Disease caused by contaminants strongly promoted the development of related research. Copyright © 2012 Elsevier B.V. All rights reserved.

[Type 1 diabetes mellitus: evidence from the literature for appropriate management in children's perspective].

PubMed

Nascimento, Lucila Castanheira; Amaral, Mariana Junco; Sparapani, Valéria de Cássia; Fonseca, Luciana Mara Monti; Nunes, Michelle Darezzo Rodrigues; Dupas, Giselle

2011-06-01

The objective of this study was to identify the evidence available in the literature that address, for children's perspective, factors that are relevant for an appropriate management of type 1 diabetes mellitus. An integrative review was performed on the PubMed, CINAHL, LILACS, CUIDEN and PsycINFO databases, covering the period from 1998 to 2008 and using the following keywords: type 1 diabetes mellitus, child, prevention and control, triggering factors, emergencies, self care, learning and health education. Nineteen of the surveyed articles were selected, and their analysis revealed the following categories: living with diabetes; self care and glucose profile; the actions of family, friends and health professionals; and school. The evidence show that children appreciate the support they receive from their relatives, which have a direct relationship with being prepared for self care. Other members apart from their network are also valued. Areas that deserve attention are the school, the personal experience of each child, and health education.

Total Electron Content forecast model over Australia

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

The Role of Datasets on Scientific Influence within Conflict Research

PubMed Central

Van Holt, Tracy; Johnson, Jeffery C.; Moates, Shiloh; Carley, Kathleen M.

2016-01-01

We inductively tested if a coherent field of inquiry in human conflict research emerged in an analysis of published research involving “conflict” in the Web of Science (WoS) over a 66-year period (1945–2011). We created a citation network that linked the 62,504 WoS records and their cited literature. We performed a critical path analysis (CPA), a specialized social network analysis on this citation network (~1.5 million works), to highlight the main contributions in conflict research and to test if research on conflict has in fact evolved to represent a coherent field of inquiry. Out of this vast dataset, 49 academic works were highlighted by the CPA suggesting a coherent field of inquiry; which means that researchers in the field acknowledge seminal contributions and share a common knowledge base. Other conflict concepts that were also analyzed—such as interpersonal conflict or conflict among pharmaceuticals, for example, did not form their own CP. A single path formed, meaning that there was a cohesive set of ideas that built upon previous research. This is in contrast to a main path analysis of conflict from 1957–1971 where ideas didn’t persist in that multiple paths existed and died or emerged reflecting lack of scientific coherence (Carley, Hummon, and Harty, 1993). The critical path consisted of a number of key features: 1) Concepts that built throughout include the notion that resource availability drives conflict, which emerged in the 1960s-1990s and continued on until 2011. More recent intrastate studies that focused on inequalities emerged from interstate studies on the democracy of peace earlier on the path. 2) Recent research on the path focused on forecasting conflict, which depends on well-developed metrics and theories to model. 3) We used keyword analysis to independently show how the CP was topically linked (i.e., through democracy, modeling, resources, and geography). Publically available conflict datasets developed early on helped shape the operationalization of conflict. In fact, 94% of the works on the CP that analyzed data either relied on publically available datasets, or they generated a dataset and made it public. These datasets appear to be important in the development of conflict research, allowing for cross-case comparisons, and comparisons to previous works. PMID:27124569

The Role of Datasets on Scientific Influence within Conflict Research.

PubMed

Van Holt, Tracy; Johnson, Jeffery C; Moates, Shiloh; Carley, Kathleen M

2016-01-01

We inductively tested if a coherent field of inquiry in human conflict research emerged in an analysis of published research involving "conflict" in the Web of Science (WoS) over a 66-year period (1945-2011). We created a citation network that linked the 62,504 WoS records and their cited literature. We performed a critical path analysis (CPA), a specialized social network analysis on this citation network (~1.5 million works), to highlight the main contributions in conflict research and to test if research on conflict has in fact evolved to represent a coherent field of inquiry. Out of this vast dataset, 49 academic works were highlighted by the CPA suggesting a coherent field of inquiry; which means that researchers in the field acknowledge seminal contributions and share a common knowledge base. Other conflict concepts that were also analyzed-such as interpersonal conflict or conflict among pharmaceuticals, for example, did not form their own CP. A single path formed, meaning that there was a cohesive set of ideas that built upon previous research. This is in contrast to a main path analysis of conflict from 1957-1971 where ideas didn't persist in that multiple paths existed and died or emerged reflecting lack of scientific coherence (Carley, Hummon, and Harty, 1993). The critical path consisted of a number of key features: 1) Concepts that built throughout include the notion that resource availability drives conflict, which emerged in the 1960s-1990s and continued on until 2011. More recent intrastate studies that focused on inequalities emerged from interstate studies on the democracy of peace earlier on the path. 2) Recent research on the path focused on forecasting conflict, which depends on well-developed metrics and theories to model. 3) We used keyword analysis to independently show how the CP was topically linked (i.e., through democracy, modeling, resources, and geography). Publically available conflict datasets developed early on helped shape the operationalization of conflict. In fact, 94% of the works on the CP that analyzed data either relied on publically available datasets, or they generated a dataset and made it public. These datasets appear to be important in the development of conflict research, allowing for cross-case comparisons, and comparisons to previous works.

Bankruptcy Prediction in the Construction Industry: Financial Ratio Analysis

DTIC Science & Technology

1989-08-01

financial reporting between the two industries. Using this information an effort will be made to modifying the models that can be applicable to the construction industry. Keywords: Analysis of variance,

Research on Social Networking Sites and Social Support from 2004 to 2015: A Narrative Review and Directions for Future Research.

PubMed

Meng, Jingbo; Martinez, Lourdes; Holmstrom, Amanda; Chung, Minwoong; Cox, Jeff

2017-01-01

The article presents a narrative review of scholarship on social support through social networking sites (SNSs) published from 2004 to 2015. By searching keywords related to social support and SNSs in major databases for social sciences, we identified and content analyzed directly relevant articles (N = 88). The article summarizes the prevalence of theory usage; the function of theory usage (e.g., testing a theory, developing a theory); major theories referenced; and methodologies, including research designs, measurement, and the roles of social support and SNS examined in this literature. It also reports four themes identified across the studies, indicating the trends in the current research. Based on the review, the article presents a discussion about study sites, conceptualization of social support, theoretical coherence, the role of social networks, and the dynamic relationships between SNS use and social support, which points out potential avenues for shaping a future research agenda.

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of these gene interaction networks identified top ranked E. coli genes and 6 INO interaction types (e.g., regulation and gene expression). Vaccine-related E. coli gene-gene interaction network was constructed using ontology-based literature mining strategy, which identified important E. coli vaccine genes and their interactions with other genes through specific interaction types.

Social support networks and eating disorders: an integrative review of the literature

PubMed Central

Leonidas, Carolina; dos Santos, Manoel Antônio

2014-01-01

Aims This study aimed to analyze the scientific literature about social networks and social support in eating disorders (ED). Methods By combining keywords, an integrative review was performed. It included publications from 2006–2013, retrieved from the MEDLINE, LILACS, PsycINFO, and CINAHL databases. The selection of articles was based on preestablished inclusion and exclusion criteria. Results A total of 24 articles were selected for data extraction. There was a predominance of studies that used nonexperimental and descriptive designs, and which were published in international journals. This review provided evidence of the fact that fully consolidated literature regarding social support and social networks in patients with ED is not available, given the small number of studies dedicated to the subject. We identified evidence that the family social network of patients with ED has been widely explored by the literature, although there is a lack of studies about other networks and sources of social support outside the family. Conclusion The evidence presented in this study shows the need to include other social networks in health care. This expansion beyond family networks would include significant others – such as friends, colleagues, neighbors, people from religious groups, among others – who could help the individual coping with the disorder. The study also highlights the need for future research on this topic, as well as a need for greater investment in publications on the various dimensions of social support and social networks. PMID:24899810

m2-ABKS: Attribute-Based Multi-Keyword Search over Encrypted Personal Health Records in Multi-Owner Setting.

PubMed

Miao, Yinbin; Ma, Jianfeng; Liu, Ximeng; Wei, Fushan; Liu, Zhiquan; Wang, Xu An

2016-11-01

Online personal health record (PHR) is more inclined to shift data storage and search operations to cloud server so as to enjoy the elastic resources and lessen computational burden in cloud storage. As multiple patients' data is always stored in the cloud server simultaneously, it is a challenge to guarantee the confidentiality of PHR data and allow data users to search encrypted data in an efficient and privacy-preserving way. To this end, we design a secure cryptographic primitive called as attribute-based multi-keyword search over encrypted personal health records in multi-owner setting to support both fine-grained access control and multi-keyword search via Ciphertext-Policy Attribute-Based Encryption. Formal security analysis proves our scheme is selectively secure against chosen-keyword attack. As a further contribution, we conduct empirical experiments over real-world dataset to show its feasibility and practicality in a broad range of actual scenarios without incurring additional computational burden.

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information. Project funded by the European Regional Development Fund and the Occitanie Region.

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

NASA Astrophysics Data System (ADS)

Yumusak, Semih; Dogdu, Erdogan; Kodaz, Halife; Kamilaris, Andreas; Vandenbussche, Pierre-Yves

In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a "search keyword" discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, these search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. Finally, we have developed a new SPARQL endpoint crawler (SpEC) for crawling and link analysis.

[Pruritus in Germany-a Google search engine analysis].

PubMed

Zink, A; Rüth, M; Schuster, B; Darsow, U; Biedermann, T; Ständer, S

2018-06-06

Because affected persons often do not visit a doctor, the prevalence of chronic and acute pruritus in the general population is difficult to determine. The aim of this study is to estimate the frequency and the most common locations of pruritus in German internet users, who-with 62.4 million persons-represent a large majority of the German population, by analysing the Google search volume. Relevant keywords for the subject "pruritus" were identified and analysed using the Google AdWords Keyword Planner. The assessment period was January 2015 to December 2016. In total the Google AdWords Keyword Planner identified 701 keywords for the topic "Juckreiz" (German lay word for pruritus), resulting in 7,531,890 pruritus-related Google searches during the assessment period. Most common search terms were the German lay term for atopic eczema ("Neurodermitis", 23.7%), the German lay term for psoriasis ("Schuppenflechte", 17.8%) and "psoriasis" (13%). The German lay term for pruritus ("Juckreiz") was only the sixth most searched term (3%). Most searches (72%) focused on influencing factors for pruritus, especially on skin diseases and skin conditions. The most commonly searched location was pruritus on the whole body, followed by anal pruritus. Analysis of the temporal course showed a higher monthly search volume during winter. With its unconventional methodology, a Google search engine analysis, this study allows a rough estimation of the medical need of pruritus in the German general population, which seems to be higher than expected. Especially pruritus in the anal area was identified as an unmet medical need.

Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening.

PubMed

Cheng, Paula Glenda Ferrer; Ramos, Roann Munoz; Bitsch, Jó Ágila; Jonas, Stephan Michael; Ix, Tim; See, Portia Lynn Quetulio; Wehrle, Klaus

2016-07-20

Language reflects the state of one's mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one's natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis. The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation. The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs. The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like "sad", "unworthy", or "tired" which are almost always accompanied by personal pronouns, such as "I", "I'm" or "my" and in Filipino, "ako" or "ko". For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version. The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms.

Outdoor air pollution and respiratory health: a bibliometric analysis of publications in peer-reviewed journals (1900 - 2017).

PubMed

Sweileh, Waleed M; Al-Jabi, Samah W; Zyoud, Sa'ed H; Sawalha, Ansam F

2018-01-01

Outdoor air pollution is a major threat to global public health that needs responsible participation of researchers at all levels. Assessing research output is an important step in highlighting national and international contribution and collaboration in a certain field. Therefore, the aim of this study was to analyze globally-published literature in outdoor air pollution - related respiratory health. Outdoor air pollution documents related to respiratory health were retrieved from Scopus database. The study period was up to 2017. Mapping of author keywords was carried out using VOSviewer 1.6.6. Search query yielded 3635 documents with an h -index of 137. There was a dramatic increase in the number of publications in the last decade of the study period. The most frequently encountered author keywords were: air pollution (835 occurrences), asthma (502 occurrences), particulate matter (198 occurrences), and children (203 occurrences). The United States of America ranked first (1082; 29.8%) followed by the United Kingdom (279; 7.7%) and Italy (198; 5.4%). Annual research productivity stratified by income and population size indicated that China ranked first (22.2) followed by the USA (18.8). Analysis of regional distribution of publications indicated that the Mediterranean, African, and South-East Asia regions had the least contribution. Harvard University (92; 2.5%) was the most active institution/organization followed the US Environmental Protection Agency (89; 2.4%). International collaboration was restricted to three regions: Northern America, Europe, and Asia. The top ten preferred journals were in the field of environmental health and respiratory health. Environmental Health Perspective was the most preferred journal for publishing documents in outdoor pollution in relation to respiratory health. Research on the impact of outdoor air pollution on respiratory health had accelerated lately and is receiving a lot of interest. Global research networks that include countries with high level of pollution and limited resources are highly needed to create public opinion in favor of minimizing outdoor air pollution and investing in green technologies.

Video content analysis of surgical procedures.

PubMed

Loukas, Constantinos

2018-02-01

In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations. The review was obtained from PubMed and Google Scholar search on combinations of the following keywords: 'surgery', 'video', 'phase', 'task', 'skills', 'event', 'shot', 'analysis', 'retrieval', 'detection', 'classification', and 'recognition'. The collected articles were categorized and reviewed based on the technical goal sought, type of surgery performed, and structure of the operation. A total of 81 articles were included. The publication activity is constantly increasing; more than 50% of these articles were published in the last 3 years. Significant research has been performed for video task detection and retrieval in eye surgery. In endoscopic surgery, the research activity is more diverse: gesture/task classification, skills assessment, tool type recognition, shot/event detection and retrieval. Recent works employ deep neural networks for phase and tool recognition as well as shot detection. Content-based video analysis of surgical operations is a rapidly expanding field. Several future prospects for research exist including, inter alia, shot boundary detection, keyframe extraction, video summarization, pattern discovery, and video annotation. The development of publicly available benchmark datasets to evaluate and compare task-specific algorithms is essential.

HEALTH TECHNOLOGY ASSESSMENT: THE SCIENTIFIC CAREER OF A POLICY CONCEPT.

PubMed

Benoit, Cyril; Gorry, Philippe

2017-01-01

The aim of this work was to provide a comprehensive overview of the evolution of the health technology assessment (HTA) concept in the scientific literature through a scientometric approach. A literature search was conducted, by selecting publications, as well as news from the media, containing "health technology assessment" in their title, abstracts, or keywords. We then undertook a bibliometric and network analysis on the corpus of 2,865 publications thus obtained. Since a first publication in 1978, interest in HTA remained marginal until a turning point in the late 1980s, when growth of the number of publications took off alongside the creation of the U.K.'s NICE agency. Since then, publications have spread across several journals. The ranking of the organizations that publish such articles does not reflect any hegemonic position. However, HTA-related scientific production is strongly concentrated in Commonwealth and Nordic countries. Despite its transnational aspects, research on HTA has been framed within a small number of scientific networks and by a few opinion leaders. The "career" of the HTA concept may be seen as a scientific-knowledge based institutionalization of a public policy. To succeed in a country, HTA first needs scientific prerequisites, such as an organized scientific community working on the health sector and health services. Then, it appears that the recognition of this research by decision makers plays a key role in the development of the field.

50 years of Arabidopsis research: highlights and future directions.

PubMed

Provart, Nicholas J; Alonso, Jose; Assmann, Sarah M; Bergmann, Dominique; Brady, Siobhan M; Brkljacic, Jelena; Browse, John; Chapple, Clint; Colot, Vincent; Cutler, Sean; Dangl, Jeff; Ehrhardt, David; Friesner, Joanna D; Frommer, Wolf B; Grotewold, Erich; Meyerowitz, Elliot; Nemhauser, Jennifer; Nordborg, Magnus; Pikaard, Craig; Shanklin, John; Somerville, Chris; Stitt, Mark; Torii, Keiko U; Waese, Jamie; Wagner, Doris; McCourt, Peter

2016-02-01

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome-wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25(th) International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Can Item Keyword Feedback Help Remediate Knowledge Gaps?

PubMed Central

Feinberg, Richard A.; Clauser, Amanda L.

2016-01-01

ABSTRACT Background  In graduate medical education, assessment results can effectively guide professional development when both assessment and feedback support a formative model. When individuals cannot directly access the test questions and responses, a way of using assessment results formatively is to provide item keyword feedback. Objective  The purpose of the following study was to investigate whether exposure to item keyword feedback aids in learner remediation. Methods  Participants included 319 trainees who completed a medical subspecialty in-training examination (ITE) in 2012 as first-year fellows, and then 1 year later in 2013 as second-year fellows. Performance on 2013 ITE items in which keywords were, or were not, exposed as part of the 2012 ITE score feedback was compared across groups based on the amount of time studying (preparation). For the same items common to both 2012 and 2013 ITEs, response patterns were analyzed to investigate changes in answer selection. Results  Test takers who indicated greater amounts of preparation on the 2013 ITE did not perform better on the items in which keywords were exposed compared to those who were not exposed. The response pattern analysis substantiated overall growth in performance from the 2012 ITE. For items with incorrect responses on both attempts, examinees selected the same option 58% of the time. Conclusions  Results from the current study were unsuccessful in supporting the use of item keywords in aiding remediation. Unfortunately, the results did provide evidence of examinees retaining misinformation. PMID:27777664

Development of SNS Stream Analysis Based on Forest Disaster Warning Information Service System

NASA Astrophysics Data System (ADS)

Oh, J.; KIM, D.; Kang, M.; Woo, C.; Kim, D.; Seo, J.; Lee, C.; Yoon, H.; Heon, S.

2017-12-01

Forest disasters, such as landslides and wildfires, cause huge economic losses and casualties, and the cost of recovery is increasing every year. While forest disaster mitigation technologies have been focused on the development of prevention and response technologies, they are now required to evolve into evacuation and border evacuation, and to develop technologies fused with ICT. In this study, we analyze the SNS (Social Network Service) stream and implement a system to detect the message that the forest disaster occurred or the forest disaster, and search the keyword related to the forest disaster in advance in real time. It is possible to detect more accurate forest disaster messages by repeatedly learning the retrieved results using machine learning techniques. To do this, we designed and implemented a system based on Hadoop and Spark, a distributed parallel processing platform, to handle Twitter stream messages that open SNS. In order to develop the technology to notify the information of forest disaster risk, a linkage of technology such as CBS (Cell Broadcasting System) based on mobile communication, internet-based civil defense siren, SNS and the legal and institutional issues for applying these technologies are examined. And the protocol of the forest disaster warning information service system that can deliver the SNS analysis result was developed. As a result, it was possible to grasp real-time forest disaster situation by real-time big data analysis of SNS that occurred during forest disasters. In addition, we confirmed that it is possible to rapidly propagate alarm or warning according to the disaster situation by using the function of the forest disaster warning information notification service. However, the limitation of system application due to the restriction of opening and sharing of SNS data currently in service and the disclosure of personal information remains a problem to be solved in the future. Keyword : SNS stream, Big data, Machine learning techniques, CBS, Forest disaster warning information service system Acknowledgement : This research was supported by the Forestry Technology 2015 Forestry Technology Research and Development Project (Planning project).

Ubiquitous picture-rich content representation

NASA Astrophysics Data System (ADS)

Wang, Wiley; Dean, Jennifer; Muzzolini, Russ

2010-02-01

The amount of digital images taken by the average consumer is consistently increasing. People enjoy the convenience of storing and sharing their pictures through online (digital) and offline (traditional) media. A set of pictures can be uploaded to: online photo services, web blogs and social network websites. Alternatively, these images can be used to generate: prints, cards, photo books or other photo products. Through uploading and sharing, images are easily transferred from one format to another. And often, a different set of associated content (text, tags) is created across formats. For example, on his web blog, a user may journal his experiences of his recent travel; on his social network website, his friends tag and comment on the pictures; in his online photo album, some pictures are titled and keyword-tagged. When the user wants to tell a complete story, perhaps in a photo book, he must collect, across all formats: the pictures, writings and comments, etc. and organize them in a book format. The user has to arrange the content of his trip in each format. The arrangement, the associations between the images, tags, keywords and text, cannot be shared with other formats. In this paper, we propose a system that allows the content to be easily created and shared across various digital media formats. We define a uniformed data association structure to connect: images, documents, comments, tags, keywords and other data. This content structure allows the user to switch representation formats without reediting. The framework under each format can emphasize (display or hide) content elements based on preference. For example, a slide show view will emphasize the display of pictures with limited text; a blog view will display highlighted images and journal text; and the photo book will try to fit in all images and text content. In this paper, we will discuss the strategy to associate pictures with text content, so that it can naturally tell a story. We will also list sample solutions on different formats such as: picture view, blog view and photo book view.

Construction of In-house Databases in a Corporation

NASA Astrophysics Data System (ADS)

Okuda, Yasukazu; Yoshikawa, Ichirou; Sasano, Fumio

The authors describe the outline and the construction process of the in-house technical information system of Mitsui Petrochemical Industries Ltd., “MITOLIS”. This system was constructed in 1981 and has been improved since then to make better use of in-house technical reports. Bibliographic data and keywords of technical reports of R & D division are stored in the host computer system in Iwakuni and can be retrieved by the company members on the desk-side terminal connected to the local area network (LAN). The number of stored reports reaches 6100 from 1970 to 1987.

SIRW: A web server for the Simple Indexing and Retrieval System that combines sequence motif searches with keyword searches.

PubMed

Ramu, Chenna

2003-07-01

SIRW (http://sirw.embl.de/) is a World Wide Web interface to the Simple Indexing and Retrieval System (SIR) that is capable of parsing and indexing various flat file databases. In addition it provides a framework for doing sequence analysis (e.g. motif pattern searches) for selected biological sequences through keyword search. SIRW is an ideal tool for the bioinformatics community for searching as well as analyzing biological sequences of interest.

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

NASA Astrophysics Data System (ADS)

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.

A review on equipped hospital beds with wireless sensor networks for reducing bedsores

PubMed Central

Ajami, Sima; Khaleghi, Lida

2015-01-01

At present, the solutions to prevent bedsore include using various techniques for movement and displacement of patients, which is not possible for some patients or dangerous for some of them while it also poses problems for health care providers. On the other hand, development of information technology in the health care system including application of wireless sensor networks (WSNs) has led to easy and quick service-providing. It can provide a solution to prevent bedsore in motionless and disabled patients. Hence, the aim of this article was first to introduce WSNs in hospital beds and second, to identify the benefits and challenges in implementing this technology. This study was a nonsystematic review. The literature was searched for WSNs to reduce and prevent bedsores with the help of libraries, databases (PubMed, SCOPUS, and EMBASE), and also searches engines available at Google Scholar including during 1974-2014 while the inclusion criteria were applied in English and Persian. In our searches, we employed the following keywords and their combinations: “wireless sensor network,” “smart bed,” “information technology,” “smart mattress,” and “bedsore” in the searching areas of titles, keywords, abstracts, and full texts. In this study, more than 45 articles and reports were collected and 37 of them were selected based on their relevance. Therefore, identification and implementation of this technology will be a step toward mechanization of traditional procedures in providing care for hospitalized patients and disabled people. The smart bed and mattress, either alone or in combination with the other technologies, should be capable of providing all of the novel features while still providing the comfort and safety features usually associated with traditional and hospital mattresses. It can eliminate the expense of bedsore in the intensive care unit (ICU) department in the hospital and save much expense there. PMID:26929768

CD-ROM source data uploaded to the operating and storage devices of an IBM 3090 mainframe through a PC terminal.

PubMed

Boros, L G; Lepow, C; Ruland, F; Starbuck, V; Jones, S; Flancbaum, L; Townsend, M C

1992-07-01

A powerful method of processing MEDLINE and CINAHL source data uploaded to the IBM 3090 mainframe computer through an IBM/PC is described. Data are first downloaded from the CD-ROM's PC devices to floppy disks. These disks then are uploaded to the mainframe computer through an IBM/PC equipped with WordPerfect text editor and computer network connection (SONNGATE). Before downloading, keywords specifying the information to be accessed are typed at the FIND prompt of the CD-ROM station. The resulting abstracts are downloaded into a file called DOWNLOAD.DOC. The floppy disks containing the information are simply carried to an IBM/PC which has a terminal emulation (TELNET) connection to the university-wide computer network (SONNET) at the Ohio State University Academic Computing Services (OSU ACS). The WordPerfect (5.1) processes and saves the text into DOS format. Using the File Transfer Protocol (FTP, 130,000 bytes/s) of SONNET, the entire text containing the information obtained through the MEDLINE and CINAHL search is transferred to the remote mainframe computer for further processing. At this point, abstracts in the specified area are ready for immediate access and multiple retrieval by any PC having network switch or dial-in connection after the USER ID, PASSWORD and ACCOUNT NUMBER are specified by the user. The system provides the user an on-line, very powerful and quick method of searching for words specifying: diseases, agents, experimental methods, animals, authors, and journals in the research area downloaded. The user can also copy the TItles, AUthors and SOurce with optional parts of abstracts into papers under edition. This arrangement serves the special demands of a research laboratory by handling MEDLINE and CINAHL source data resulting after a search is performed with keywords specified for ongoing projects. Since the Ohio State University has a centrally founded mainframe system, the data upload, storage and mainframe operations are free.

The Role of Social Network Technologies in Online Health Promotion: A Narrative Review of Theoretical and Empirical Factors Influencing Intervention Effectiveness.

PubMed

Balatsoukas, Panos; Kennedy, Catriona M; Buchan, Iain; Powell, John; Ainsworth, John

2015-06-11

Social network technologies have become part of health education and wider health promotion—either by design or happenstance. Social support, peer pressure, and information sharing in online communities may affect health behaviors. If there are positive and sustained effects, then social network technologies could increase the effectiveness and efficiency of many public health campaigns. Social media alone, however, may be insufficient to promote health. Furthermore, there may be unintended and potentially harmful consequences of inaccurate or misleading health information. Given these uncertainties, there is a need to understand and synthesize the evidence base for the use of online social networking as part of health promoting interventions to inform future research and practice. Our aim was to review the research on the integration of expert-led health promotion interventions with online social networking in order to determine the extent to which the complementary benefits of each are understood and used. We asked, in particular, (1) How is effectiveness being measured and what are the specific problems in effecting health behavior change?, and (2) To what extent is the designated role of social networking grounded in theory? The narrative synthesis approach to literature review was used to analyze the existing evidence. We searched the indexed scientific literature using keywords associated with health promotion and social networking. The papers included were only those making substantial study of both social networking and health promotion—either reporting the results of the intervention or detailing evidence-based plans. General papers about social networking and health were not included. The search identified 162 potentially relevant documents after review of titles and abstracts. Of these, 42 satisfied the inclusion criteria after full-text review. Six studies described randomized controlled trials (RCTs) evaluating the effectiveness of online social networking within health promotion interventions. Most of the trials investigated the value of a "social networking condition" in general and did not identify specific features that might play a role in effectiveness. Issues about the usability and level of uptake of interventions were more common among pilot studies, while observational studies showed positive evidence about the role of social support. A total of 20 papers showed the use of theory in the design of interventions, but authors evaluated effectiveness in only 10 papers. More research is needed in this area to understand the actual effect of social network technologies on health promotion. More RCTs of greater length need to be conducted taking into account contextual factors such as patient characteristics and types of a social network technology. Also, more evidence is needed regarding the actual usability of online social networking and how different interface design elements may help or hinder behavior change and engagement. Moreover, it is crucial to investigate further the effect of theory on the effectiveness of this type of technology for health promotion. Research is needed linking theoretical grounding with observation and analysis of health promotion in online networks.

Accuracy Analysis and Parameters Optimization in Urban Flood Simulation by PEST Model

NASA Astrophysics Data System (ADS)

Keum, H.; Han, K.; Kim, H.; Ha, C.

2017-12-01

The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various rainfall event scenarios and parameter optimization. Keywords: Parameters Optimization; PEST model; Urban area Acknowledgement This research was supported by a grant (17AWMP-B079625-04) from Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Developing convolutional neural networks for measuring climate change opinions from social media data

NASA Astrophysics Data System (ADS)

Mao, H.; Bhaduri, B. L.

2016-12-01

Understanding public opinions on climate change is important for policy making. Public opinion, however, is typically measured with national surveys, which are often too expensive and thus being updated at a low frequency. Twitter has become a major platform for people to express their opinions on social and political issues. Our work attempts to understand if Twitter data can provide complimentary insights about climate change perceptions. Since the nature of social media is real-time, this data source can especially help us understand how public opinion changes over time in response to climate events and hazards, which though is very difficult to be captured by manual surveys. We use the Twitter Streaming API to collect tweets that contain keywords, "climate change" or "#climatechange". Traditional machine-learning based opinion mining algorithms require a significant amount of labeled data. Data labeling is notoriously time consuming. To address this problem, we use hashtags (a significant feature used to mark topics of tweets) to annotate tweets automatically. For example, hashtags, #climatedenial and #climatescam, are negative opinion labels, while #actonclimate and #climateaction are positive. Following this method, we can obtain a large amount of training data without human labor. This labeled dataset is used to train a deep convolutional neural network that classifies tweets into positive (i.e. believe in climate change) and negative (i.e. do not believe). Based on the positive/negative tweets obtained, we will further analyze risk perceptions and opinions towards policy support. In addition, we analyze twitter user profiles to understand the demographics of proponents and opponents of climate change. Deep learning techniques, especially convolutional deep neural networks, have achieved much success in computer vision. In this work, we propose a convolutional neural network architecture for understanding opinions within text. This method is compared with lexicon-based opinion analysis approaches. Results and the advantages/limitations of this method are to be discussed.

Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development.

PubMed

Hart, Mark; Stetten, Nichole E; Islam, Sabrina; Pizarro, Katherine

2017-09-20

The use of social networking sites is increasingly being adopted in public health, in part, because of the barriers to funding and reduced resources. Public health professionals are using social media platforms, specifically Twitter, as a way to facilitate professional development. The objective of this study was to identify public health professionals using Twitter and to analyze how they use this platform to enhance their formal and informal professional development within the context of public health. Keyword searches were conducted to identify and invite potential participants to complete a survey related to their use of Twitter for public health and professional experiences. Data regarding demographic attributes, Twitter usage, and qualitative information were obtained through an anonymous Web-based survey. Open-response survey questions were analyzed using the constant comparison method. "Using Twitter makes it easier to expand my networking opportunities" and "I find Twitter useful for professional development" scored highest, with a mean score of 4.57 (standard deviation [SD] 0.74) and 4.43 (SD 0.76) on a 5-point Likert scale. Analysis of the qualitative data shows the emergence of the following themes for why public health professionals mostly use Twitter: (1) geography, (2) continuing education, (3) professional gain, and (4) communication. For public health professionals in this study, Twitter is a platform best used for their networking and professional development. Furthermore, the use of Twitter allows public health professionals to overcome a series of barriers and enhances opportunities for growth. ©Mark Hart, Nichole E Stetten, Sabrina Islam, Katherine Pizarro. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 20.09.2017.

Finding collaborators: toward interactive discovery tools for research network systems.

PubMed

Borromeo, Charles D; Schleyer, Titus K; Becich, Michael J; Hochheiser, Harry

2014-11-04

Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.

Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies.

PubMed

Coloma, Preciosa M; Becker, Benedikt; Sturkenboom, Miriam C J M; van Mulligen, Erik M; Kors, Jan A

2015-10-01

There is growing interest in whether social media can capture patient-generated information relevant for medicines safety surveillance that cannot be found in traditional sources. The aim of this study was to evaluate the potential contribution of mining social media networks for medicines safety surveillance using the following associations as case studies: (1) rosiglitazone and cardiovascular events (i.e. stroke and myocardial infarction); and (2) human papilloma virus (HPV) vaccine and infertility. We collected publicly accessible, English-language posts on Facebook, Google+, and Twitter until September 2014. Data were queried for co-occurrence of keywords related to the drug/vaccine and event of interest within a post. Messages were analysed with respect to geographical distribution, context, linking to other web content, and author's assertion regarding the supposed association. A total of 2537 posts related to rosiglitazone/cardiovascular events and 2236 posts related to HPV vaccine/infertility were retrieved, with the majority of posts representing data from Twitter (98 and 85%, respectively) and originating from users in the US. Approximately 21% of rosiglitazone-related posts and 84% of HPV vaccine-related posts referenced other web pages, mostly news items, law firms' websites, or blogs. Assertion analysis predominantly showed affirmation of the association of rosiglitazone/cardiovascular events (72%; n = 1821) and of HPV vaccine/infertility (79%; n = 1758). Only ten posts described personal accounts of rosiglitazone/cardiovascular adverse event experiences, and nine posts described HPV vaccine problems related to infertility. Publicly available data from the considered social media networks were sparse and largely untrackable for the purpose of providing early clues of safety concerns regarding the prespecified case studies. Further research investigating other case studies and exploring other social media platforms are necessary to further characterise the usefulness of social media for safety surveillance.

Finding Collaborators: Toward Interactive Discovery Tools for Research Network Systems

PubMed Central

Schleyer, Titus K; Becich, Michael J; Hochheiser, Harry

2014-01-01

Background Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. Objective The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Methods Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Results Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Conclusions Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows. PMID:25370463

Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening

PubMed Central

Ramos, Roann Munoz; Bitsch, Jó Ágila; Jonas, Stephan Michael; Ix, Tim; See, Portia Lynn Quetulio; Wehrle, Klaus

2016-01-01

Background Language reflects the state of one’s mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one’s natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis. Objective The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation. Methods The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs. Results The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like “sad”, “unworthy”, or “tired” which are almost always accompanied by personal pronouns, such as “I”, “I’m” or “my” and in Filipino, “ako” or “ko”. For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version. Conclusions The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms. PMID:27439444

Global Change Master Directory (GCMD) Keywords and Their Applications in Earth Science Data Discovery

NASA Astrophysics Data System (ADS)

Aleman, A.

2017-12-01

This presentation will provide an overview and discussion of the Global Change Master Directory (GCMD) Keywords and their applications in Earth science data discovery. The GCMD Keywords are a hierarchical set of controlled keywords covering the Earth science disciplines, including: science keywords, service keywords, data centers, projects, location, data resolution, instruments and platforms. Controlled vocabularies (keywords) help users accurately, consistently and comprehensively categorize their data and also allow for the precise search and subsequent retrieval of data. The GCMD Keywords are a community resource and are developed collaboratively with input from various stakeholders, including GCMD staff, keyword users and metadata providers. The GCMD Keyword Landing Page and GCMD Keyword Community Forum provide access to keyword resources and an area for discussion of topics related to the GCMD Keywords. See https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords

New Features in ADS Labs

NASA Astrophysics Data System (ADS)

Accomazzi, Alberto; Kurtz, M. J.; Henneken, E. A.; Grant, C. S.; Thompson, D.; Di Milia, G.; Luker, J.; Murray, S. S.

2013-01-01

The NASA Astrophysics Data System (ADS) has been working hard on updating its services and interfaces to better support our community's research needs. ADS Labs is a new interface built on the old tried-and-true ADS Abstract Databases, so all of ADS's content is available through it. In this presentation we highlight the new features that have been developed in ADS Labs over the last year: new recommendations, metrics, a citation tool and enhanced fulltext search. ADS Labs has long been providing article-level recommendations based on keyword similarity, co-readership and co-citation analysis of its corpus. We have now introduced personal recommendations, which provide a list of articles to be considered based on a individual user's readership history. A new metrics interface provides a summary of the basic impact indicators for a list of records. These include the total and normalized number of papers, citations, reads, and downloads. Also included are some of the popular indices such as the h, g and i10 index. The citation helper tool allows one to submit a set of records and obtain a list of top 10 papers which cite and/or are cited by papers in the original list (but which are not in it). The process closely resembles the network approach of establishing "friends of friends" via an analysis of the citation network. The full-text search service now covers more than 2.5 million documents, including all the major astronomy journals, as well as physics journals published by Springer, Elsevier, the American Physical Society, the American Geophysical Union, and all of the arXiv eprints. The full-text search interface interface allows users and librarians to dig deep and find words or phrases in the body of the indexed articles. ADS Labs is available at http://adslabs.org

Constructing "Nerdiness": Characterisation in "The Big Bang Theory"

ERIC Educational Resources Information Center

Bednarek, Monika

2012-01-01

This paper analyses the linguistic construction of the televisual character Sheldon--the "main nerd" in the sitcom "The Big Bang Theory" (CBS, 2007-), approaching this construction of character through both computerised and "manual" linguistic analysis. More specifically, a computer analysis of dialogue (using concordances and keyword analysis) in…

MULTIPROCESSOR AND DISTRIBUTED PROCESSING BIBLIOGRAPHIC DATA BASE SOFTWARE SYSTEM

NASA Technical Reports Server (NTRS)

Miya, E. N.

1994-01-01

Multiprocessors and distributed processing are undergoing increased scientific scrutiny for many reasons. It is more and more difficult to keep track of the existing research in these fields. This package consists of a large machine-readable bibliographic data base which, in addition to the usual keyword searches, can be used for producing citations, indexes, and cross-references. The data base is compiled from smaller existing multiprocessing bibliographies, and tables of contents from journals and significant conferences. There are approximately 4,000 entries covering topics such as parallel and vector processing, networks, supercomputers, fault-tolerant computers, and cellular automata. Each entry is represented by 21 fields including keywords, author, referencing book or journal title, volume and page number, and date and city of publication. The data base contains UNIX 'refer' formatted ASCII data and can be implemented on any computer running under the UNIX operating system. The data base requires approximately one megabyte of secondary storage. The documentation for this program is included with the distribution tape, although it can be purchased for the price below. This bibliography was compiled in 1985 and updated in 1988.

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases. © The Author(s) 2015. Published by Oxford University Press.

Prediction of S-wave velocity using complete ensemble empirical mode decomposition and neural networks

NASA Astrophysics Data System (ADS)

Gaci, Said; Hachay, Olga; Zaourar, Naima

2017-04-01

One of the key elements in hydrocarbon reservoirs characterization is the S-wave velocity (Vs). Since the traditional estimating methods often fail to accurately predict this physical parameter, a new approach that takes into account its non-stationary and non-linear properties is needed. In this view, a prediction model based on complete ensemble empirical mode decomposition (CEEMD) and a multiple layer perceptron artificial neural network (MLP ANN) is suggested to compute Vs from P-wave velocity (Vp). Using a fine-to-coarse reconstruction algorithm based on CEEMD, the Vp log data is decomposed into a high frequency (HF) component, a low frequency (LF) component and a trend component. Then, different combinations of these components are used as inputs of the MLP ANN algorithm for estimating Vs log. Applications on well logs taken from different geological settings illustrate that the predicted Vs values using MLP ANN with the combinations of HF, LF and trend in inputs are more accurate than those obtained with the traditional estimating methods. Keywords: S-wave velocity, CEEMD, multilayer perceptron neural networks.

A peer-to-peer music sharing system based on query-by-humming

NASA Astrophysics Data System (ADS)

Wang, Jianrong; Chang, Xinglong; Zhao, Zheng; Zhang, Yebin; Shi, Qingwei

2007-09-01

Today, the main traffic in peer-to-peer (P2P) network is still multimedia files including large numbers of music files. The study of Music Information Retrieval (MIR) brings out many encouraging achievements in music search area. Nevertheless, the research of music search based on MIR in P2P network is still insufficient. Query by Humming (QBH) is one MIR technology studied for years. In this paper, we present a server based P2P music sharing system which is based on QBH and integrated with a Hierarchical Index Structure (HIS) to enhance the relation between surface data and potential information. HIS automatically evolving depends on the music related items carried by each peer such as midi files, lyrics and so forth. Instead of adding large amount of redundancy, the system generates a bit of index for multiple search input which improves the traditional keyword-based text search mode largely. When network bandwidth, speed, etc. are no longer a bottleneck of internet serve, the accessibility and accuracy of information provided by internet are being more concerned by end users.

Real-Time Tropospheric Delay Estimation using IGS Products

NASA Astrophysics Data System (ADS)

Stürze, Andrea; Liu, Sha; Söhne, Wolfgang

2014-05-01

The Federal Agency for Cartography and Geodesy (BKG) routinely provides zenith tropospheric delay (ZTD) parameter for the assimilation in numerical weather models since more than 10 years. Up to now the results flowing into the EUREF Permanent Network (EPN) or E-GVAP (EUMETNET EIG GNSS water vapour programme) analysis are based on batch processing of GPS+GLONASS observations in differential network mode. For the recently started COST Action ES1206 about "Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate" (GNSS4SWEC), however, rapid updates in the analysis of the atmospheric state for nowcasting applications require changing the processing strategy towards real-time. In the RTCM SC104 (Radio Technical Commission for Maritime Services, Special Committee 104) a format combining the advantages of Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) is under development. The so-called State Space Representation approach is defining corrections, which will be transferred in real-time to the user e.g. via NTRIP (Network Transport of RTCM via Internet Protocol). Meanwhile messages for precise orbits, satellite clocks and code biases compatible to the basic PPP mode using IGS products are defined. Consequently, the IGS Real-Time Service (RTS) was launched in 2013 in order to extend the well-known precise orbit and clock products by a real-time component. Further messages e.g. with respect to ionosphere or phase biases are foreseen. Depending on the level of refinement, so different accuracies up to the RTK level shall be reachable. In co-operation of BKG and the Technical University of Darmstadt the real-time software GEMon (GREF EUREF Monitoring) is under development. GEMon is able to process GPS and GLONASS observation and RTS product data streams in PPP mode. Furthermore, several state-of-the-art troposphere models, for example based on numerical weather prediction data, are implemented. Hence, it opens the possibility to evaluate the potential of troposphere parameter determination in real-time and its effect to Precise Point Positioning. Starting with an offline investigation of the influence of different RTS products and a priori troposphere models the configuration delivering the best results is used for a real-time processing of the GREF (German Geodetic Reference) network over a suitable period of time. The evaluation of the derived ZTD parameters and station heights is done with respect to well proven GREF, EUREF, IGS, and E-GVAP analysis results. Keywords: GNSS, Zenith Tropospheric Delay, Real-time Precise Point Positioning

Classifying patents based on their semantic content.

PubMed

Bergeaud, Antonin; Potiron, Yoann; Raimbault, Juste

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Classifying patents based on their semantic content

PubMed Central

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. PMID:28445550

Investigation of Pre-Earthquake Ionospheric Disturbances by 3D Tomographic Analysis

NASA Astrophysics Data System (ADS)

Yagmur, M.

2016-12-01

Ionospheric variations before earthquakes have been widely discussed phenomena in ionospheric studies. To clarify the source and mechanism of these phenomena is highly important for earthquake forecasting. To well understanding the mechanical and physical processes of pre-seismic Ionospheric anomalies that might be related even with Lithosphere-Atmosphere-Ionosphere-Magnetosphere Coupling, both statistical and 3D modeling analysis are needed. For these purpose, firstly we have investigated the relation between Ionospheric TEC Anomalies and potential source mechanisms such as space weather activity and lithospheric phenomena like positive surface electric charges. To distinguish their effects on Ionospheric TEC, we have focused on pre-seismically active days. Then, we analyzed the statistical data of 54 earthquakes that M≽6 between 2000 and 2013 as well as the 2011 Tohoku and the 2016 Kumamoto Earthquakes in Japan. By comparing TEC anomaly and Solar activity by Dst Index, we have found that 28 events that might be related with Earthquake activity. Following the statistical analysis, we also investigate the Lithospheric effect on TEC change on selected days. Among those days, we have chosen two case studies as the 2011 Tohoku and the 2016 Kumamoto Earthquakes to make 3D reconstructed images by utilizing 3D Tomography technique with Neural Networks. The results will be presented in our presentation. Keywords : Earthquake, 3D Ionospheric Tomography, Positive and Negative Anomaly, Geomagnetic Storm, Lithosphere

Development of Integrated Flood Analysis System for Improving Flood Mitigation Capabilities in Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Jong-suk

2016-04-01

Recently, the needs of people are growing for a more safety life and secure homeland from unexpected natural disasters. Flood damages have been recorded every year and those damages are greater than the annual average of 2 trillion won since 2000 in Korea. It has been increased in casualties and property damages due to flooding caused by hydrometeorlogical extremes according to climate change. Although the importance of flooding situation is emerging rapidly, studies related to development of integrated management system for reducing floods are insufficient in Korea. In addition, it is difficult to effectively reduce floods without developing integrated operation system taking into account of sewage pipe network configuration with the river level. Since the floods result in increasing damages to infrastructure, as well as life and property, structural and non-structural measures should be urgently established in order to effectively reduce the flood. Therefore, in this study, we developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting for supporting synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information in Korea. Keywords: Flooding, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ011686022015)" Rural Development Administration, Republic of Korea

The Evolution of Topics and Leading Trends over the Past 15 Years of Research on the Quality of Higher Education in China: Based on Keyword Co-Occurrence Knowledge Map Analysis of the Research Papers Published from 2000 to 2014 in the CSSCI Database

ERIC Educational Resources Information Center

Qu, Xia; Yang, Xiaotong

2016-01-01

Using CiteSpace to draw a keyword co-occurrence knowledge map for 1,048 research papers on the quality of higher education from 2000 to 2014 in the Chinese Social Sciences Citation Index database, we found that over the past 15 years, research on the quality of Chinese higher education was clearly oriented toward policies, and a good interactive…

Earth Science Keyword Stewardship: Access and Management through NASA's Global Change Master Directory (GCMD) Keyword Management System (KMS)

NASA Astrophysics Data System (ADS)

Stevens, T.; Olsen, L. M.; Ritz, S.; Morahan, M.; Aleman, A.; Cepero, L.; Gokey, C.; Holland, M.; Cordova, R.; Areu, S.; Cherry, T.; Tran-Ho, H.

2012-12-01

Discovering Earth science data can be complex if the catalog holding the data lacks structure. Controlled keyword vocabularies within metadata catalogues can improve data discovery. NASA's Global Change Master Directory's (GCMD) Keyword Management System (KMS) is a recently released a RESTful web service for managing and providing access to controlled keywords (science keywords, service keywords, platforms, instruments, providers, locations, projects, data resolution, etc.). The KMS introduces a completely new paradigm for the use and management of the keywords and allows access to these keywords as SKOS Concepts (RDF), OWL, standard XML, and CSV. A universally unique identifier (UUID) is automatically assigned to each keyword, which uniquely identifies each concept and its associated information. A component of the KMS is the keyword manager, an internal tool that allows GCMD science coordinators to manage concepts. This includes adding, modifying, and deleting broader, narrower, or related concepts and associated definitions. The controlled keyword vocabulary represents over 20 years of effort and collaboration with the Earth science community. The maintenance, stability, and ongoing vigilance in maintaining mutually exclusive and parallel keyword lists is important for a "normalized" search and discovery, and provides a unique advantage for the science community. Modifications and additions are made based on community suggestions and internal review. To help maintain keyword integrity, science keyword rules and procedures for modification of keywords were developed. This poster will highlight the use of the KMS as a beneficial service for the stewardship and access of the GCMD keywords. Users will learn how to access the KMS and utilize the keywords. Best practices for managing an extensive keyword hierarchy will also be discussed. Participants will learn the process for making keyword suggestions, which subsequently help in building a controlled keyword vocabulary to improve earth science data discovery and access.

Analysis of the intellectual structure of human space exploration research using a bibliometric approach: Focus on human related factors

NASA Astrophysics Data System (ADS)

Lee, Tai Sik; Lee, Yoon-Sun; Lee, Jaeho; Chang, Byung Chul

2018-02-01

Human space exploration (HSE) is an interdisciplinary field composed of a range of subjects that have developed dramatically over the last few decades. This paper investigates the intellectual structure of HSE research with a focus on human related factors. A bibliometric approach with quantitative analytical techniques is applied to study the development and growth of the research. This study retrieves 1921 papers on HSE related to human factors from the year 1990 to the year 2016 from Web of Science and constructs a critical citation network composed of 336 papers. Edge-betweenness-based clustering is used to classify the citation network into twelve distinct research clusters based on four research themes: "biological risks from space radiation," "health and performance during long-duration spaceflight," "program and in-situ resources for HSE missions," and "habitat and life support systems in the space environment." These research themes are also similar to the classification results of a co-occurrence analysis on keywords for a total of 1921 papers. Papers with high centrality scores are identified as important papers in terms of knowledge flow. Moreover, the intermediary role of papers in exchanging knowledge between HSE sub-areas is identified using brokerage analysis. The key-route main path highlights the theoretical development trajectories. Due to the recent dramatic increase in investment by international governments and the private sector, the theoretical development trajectories of key research themes have been expanding from furthering scientific and technical knowledge to include various social and economic issues, thus encouraging massive public participation. This study contributes to an understanding of research trends and popular issues in the field of HSE by introducing a powerful way of determining major research themes and development trajectories. This study will help researchers seek the underlying knowledge diffusion flow from multifaceted aspects to establish future research directions.

Developing a database for pedestrians' earthquake emergency evacuation in indoor scenarios.

PubMed

Zhou, Junxue; Li, Sha; Nie, Gaozhong; Fan, Xiwei; Tan, Jinxian; Li, Huayue; Pang, Xiaoke

2018-01-01

With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians' earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models.

Overview of technical trend of optical fiber/cable and research and development strategy of Samsung

NASA Astrophysics Data System (ADS)

Kim, Jin H.

2005-01-01

Fiber-to-the-Premise (FTTP), a keyword in the current fiber and cable industry, leads us variegated directions of the research and development activities. In fact, this momentum of industry seems to be weak yet, since the bandwidth demand by market is still unbalanced to the capacity in the several market segments. However, the recent gradual recovery in metro and access network indicates a positive sign for FTTP deployment projects. It is the very preferable for us to optimize R&D strategy applicable to the current market trend of sequential investment.

FITSManager: Management of Personal Astronomical Data

NASA Astrophysics Data System (ADS)

Cui, Chenzhou; Fan, Dongwei; Zhao, Yongheng; Kembhavi, Ajit; He, Boliang; Cao, Zihuang; Li, Jian; Nandrekar, Deoyani

2011-07-01

With the increase of personal storage capacity, it is easy to find hundreds to thousands of FITS files in the personal computer of an astrophysicist. Because Flexible Image Transport System (FITS) is a professional data format initiated by astronomers and used mainly in the small community, data management toolkits for FITS files are very few. Astronomers need a powerful tool to help them manage their local astronomical data. Although Virtual Observatory (VO) is a network oriented astronomical research environment, its applications and related technologies provide useful solutions to enhance the management and utilization of astronomical data hosted in an astronomer's personal computer. FITSManager is such a tool to provide astronomers an efficient management and utilization of their local data, bringing VO to astronomers in a seamless and transparent way. FITSManager provides fruitful functions for FITS file management, like thumbnail, preview, type dependent icons, header keyword indexing and search, collaborated working with other tools and online services, and so on. The development of the FITSManager is an effort to fill the gap between management and analysis of astronomical data.

Trends in Social Science: The Impact of Computational and Simulative Models

NASA Astrophysics Data System (ADS)

Conte, Rosaria; Paolucci, Mario; Cecconi, Federico

This paper discusses current progress in the computational social sciences. Specifically, it examines the following questions: Are the computational social sciences exhibiting positive or negative developments? What are the roles of agent-based models and simulation (ABM), network analysis, and other "computational" methods within this dynamic? (Conte, The necessity of intelligent agents in social simulation, Advances in Complex Systems, 3(01n04), 19-38, 2000; Conte 2010; Macy, Annual Review of Sociology, 143-166, 2002). Are there objective indicators of scientific growth that can be applied to different scientific areas, allowing for comparison among them? In this paper, some answers to these questions are presented and discussed. In particular, comparisons among different disciplines in the social and computational sciences are shown, taking into account their respective growth trends in the number of publication citations over the last few decades (culled from Google Scholar). After a short discussion of the methodology adopted, results of keyword-based queries are presented, unveiling some unexpected local impacts of simulation on the takeoff of traditionally poorly productive disciplines.

Twitter communication during 2014 flood in Malaysia: Informational or emotional?

NASA Astrophysics Data System (ADS)

Supian, Muhammad Nazirul Aiman Abu; Razak, Fatimah Abdul; Bakar, Sakhinah Abu

2017-04-01

Twitter has become one of the most important medium in spreading information due to its powerful capability reaching beyond the original tweet's follower. Not to mention, it is effective and easy to get viral especially during extreme events. The behaviour of information dissemination during a natural disaster, specifically flood has been an interest to this study. This paper examines the dynamics of social networks and the formation and evolution of Twitter communities in response to this event. A Twitter dataset of early days during 2014 flood in Malaysia were harnessed. The datasets were harnessed based on the keyword "banjir" in Malay which define as flood from 18 December 2014 until 31 December 2014. The analysis shows that the evolution of Twitter conversation during this range of time tends to focus on flood issue. We get to distinguish the informational and emotional tweets from the content analysis. The dynamics of these informational and emotional tweets are then analysed to observe information dissemination in the community. Emotional tweets are likely to be related to community concern and motivational support. Informational tweets are mostly about the flood condition from time to time, numbers of flood victims, and the flood relief from the government organization, aid organization and news organization.

Qualitative and quantitative analysis of solar hydrogen generation literature from 2001 to 2014.

PubMed

Maghami, Mohammad Reza; Asl, Shahin Navabi; Rezadad, Mohammad Esmaeil; Ale Ebrahim, Nader; Gomes, Chandima

Solar hydrogen generation is one of the new topics in the field of renewable energy. Recently, the rate of investigation about hydrogen generation is growing dramatically in many countries. Many studies have been done about hydrogen generation from natural resources such as wind, solar, coal etc. In this work we evaluated global scientific production of solar hydrogen generation papers from 2001 to 2014 in any journal of all the subject categories of the Science Citation Index compiled by Institute for Scientific Information (ISI), Philadelphia, USA. Solar hydrogen generation was used as keywords to search the parts of titles, abstracts, or keywords. The published output analysis showed that hydrogen generation from the sun research steadily increased over the past 14 years and the annual paper production in 2013 was about three times 2010-paper production. The number of papers considered in this research is 141 which have been published from 2001 to this date. There are clear distinctions among author keywords used in publications from the five most high-publishing countries such as USA, China, Australia, Germany and India in solar hydrogen studies. In order to evaluate this work quantitative and qualitative analysis methods were used to the development of global scientific production in a specific research field. The analytical results eventually provide several key findings and consider the overview hydrogen production according to the solar hydrogen generation.

HST Keyword Dictionary

NASA Astrophysics Data System (ADS)

Swade, D. A.; Gardner, L.; Hopkins, E.; Kimball, T.; Lezon, K.; Rose, J.; Shiao, B.

STScI has undertaken a project to place all HST keyword information in one source, the keyword database, and to provide a mechanism for making this keyword information accessible to all HST users, the keyword dictionary, which is a WWW interface to the keyword database.

Automated indexing for making of a newspaper article database

NASA Astrophysics Data System (ADS)

Kamio, Tatsuo

Automated indexing has been widely employed in the process of making newspaper article databases. It is essential to speed up the compiling time of the said databases for the large amount of articles come out daily, and save manpower involved in it, with the aid of computers. However, indexed terms which are extracted by the current automated indexing systems have no links with subject analysis, so that they are not considered to be keywords in a strict sense. Thus, the system of Nihon Keizai Shimbun KK enables to justify keywords to certain extent based on the two clues ; 1) at which location the extracted term occurred, and 2) whether or not subject area of the article corresponds to thesaurus class of the extracted term, by using characteristics peculiar to newspaper articles. Also the experiment of assigning keywords which are not occurred in articles was conducted. The fairly good result was obtained.

[Brazilian biomedical and epidemiological research vis-à-vis the UNGASS targets].

PubMed

Bastos, Francisco Inácio; Hacker, Mariana A

2006-04-01

The focus of the present study is the Brazilian response within science, technology and innovation to the targets formulated in the UNGASS document. An analysis was made of items 70-73 of the UNGASS Draft Declaration of Commitment on HIV/AIDS (2001), which defined science, technology and innovation targets relating to HIV/AIDS. The main topics listed in these items were put into operation in the form of keywords, in order to guide systematic searches within the standard biomedicine databases, also including the subdivisions of the Web of Science relating to natural and social sciences. The success of Brazilian research within the field of characterization and isolation of HIV-1 is undeniable. Phase II/III vaccine studies have been developed in Rio de Janeiro, Belo Horizonte and São Paulo. Empirical studies on the monitoring of primary resistance have been developed in specific populations, through the Brazilian HIV Resistance Monitoring Network. Within the field of monitoring secondary resistance, initiatives such as the National Genotyping Network have been highlighted. Two national systems--the Mortality Information System and the Notifiable Diseases Information System (Aids)--and some studies with wider coverage have given rise to work on trends within the epidemic. The production of high-quality generic medications and their free distribution to patients have been highlighted. Brazil has implemented a consistent and diversified response within the field of HIV/AIDS, with studies relating to the development of vaccines, new medications and monitoring of the epidemic.

Drug discovery of neurodegenerative disease through network pharmacology approach in herbs.

PubMed

Ke, Zhipeng; Zhang, Xinzhuang; Cao, Zeyu; Ding, Yue; Li, Na; Cao, Liang; Wang, Tuanjie; Zhang, Chenfeng; Ding, Gang; Wang, Zhenzhong; Xu, Xiaojie; Xiao, Wei

2016-03-01

Neurodegenerative diseases, referring to as the progressive loss of structure and function of neurons, constitute one of the major challenges of modern medicine. Traditional Chinese herbs have been used as a major preventive and therapeutic strategy against disease for thousands years. The numerous species of medicinal herbs and Traditional Chinese Medicine (TCM) compound formulas in nervous system disease therapy make it a large chemical resource library for drug discovery. In this work, we collected 7362 kinds of herbs and 58,147 Traditional Chinese medicinal compounds (Tcmcs). The predicted active compounds in herbs have good oral bioavailability and central nervous system (CNS) permeability. The molecular docking and network analysis were employed to analyze the effects of herbs on neurodegenerative diseases. In order to evaluate the predicted efficacy of herbs, automated text mining was utilized to exhaustively search in PubMed by some related keywords. After that, receiver operator characteristic (ROC) curves was used to estimate the accuracy of predictions. Our study suggested that most herbs were distributed in family of Asteraceae, Fabaceae, Lamiaceae and Apocynaceae. The predictive model yielded good sensitivity and specificity with the AUC values above 0.800. At last, 504 kinds of herbs were obtained by using the optimal cutoff values in ROC curves. These 504 herbs would be the most potential herb resources for neurodegenerative diseases treatment. This study would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-neurodegenerative disease. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Self-Organization on Social Media: Endo-Exo Bursts and Baseline Fluctuations

PubMed Central

Oka, Mizuki; Hashimoto, Yasuhiro; Ikegami, Takashi

2014-01-01

A salient dynamic property of social media is bursting behavior. In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes. We call this threshold the critical threshold. Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge. Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two. Having a threshold for an input value for activating the system implies that Twitter is an excitable medium. These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media. PMID:25329610

A Workbench for Discovering Task-Specific Theories of Learning

DTIC Science & Technology

1989-03-03

mind (the cognitive architecture) will not be of much use to educators who wish to perform a cognitive task analysis of their subject matter before...analysis packages that can be added to a cognitive architecture, thus creating a ’workbench’ for performing cognitive task analysis . Such tools becomes...learning theories have been. Keywords: Cognitive task analysis , Instructional design, Cognitive modelling, Learning.

Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis

PubMed Central

2016-01-01

Background After the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media. Objective The objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern. Methods We retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived. Results A Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32% (880,108/1,168,542) tweeted the word radioactive and 9.20% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year. Conclusions Although the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk. PMID:27888168

The Role of Social Network Technologies in Online Health Promotion: A Narrative Review of Theoretical and Empirical Factors Influencing Intervention Effectiveness

PubMed Central

Kennedy, Catriona M; Buchan, Iain; Powell, John; Ainsworth, John

2015-01-01

Background Social network technologies have become part of health education and wider health promotion—either by design or happenstance. Social support, peer pressure, and information sharing in online communities may affect health behaviors. If there are positive and sustained effects, then social network technologies could increase the effectiveness and efficiency of many public health campaigns. Social media alone, however, may be insufficient to promote health. Furthermore, there may be unintended and potentially harmful consequences of inaccurate or misleading health information. Given these uncertainties, there is a need to understand and synthesize the evidence base for the use of online social networking as part of health promoting interventions to inform future research and practice. Objective Our aim was to review the research on the integration of expert-led health promotion interventions with online social networking in order to determine the extent to which the complementary benefits of each are understood and used. We asked, in particular, (1) How is effectiveness being measured and what are the specific problems in effecting health behavior change?, and (2) To what extent is the designated role of social networking grounded in theory? Methods The narrative synthesis approach to literature review was used to analyze the existing evidence. We searched the indexed scientific literature using keywords associated with health promotion and social networking. The papers included were only those making substantial study of both social networking and health promotion—either reporting the results of the intervention or detailing evidence-based plans. General papers about social networking and health were not included. Results The search identified 162 potentially relevant documents after review of titles and abstracts. Of these, 42 satisfied the inclusion criteria after full-text review. Six studies described randomized controlled trials (RCTs) evaluating the effectiveness of online social networking within health promotion interventions. Most of the trials investigated the value of a “social networking condition” in general and did not identify specific features that might play a role in effectiveness. Issues about the usability and level of uptake of interventions were more common among pilot studies, while observational studies showed positive evidence about the role of social support. A total of 20 papers showed the use of theory in the design of interventions, but authors evaluated effectiveness in only 10 papers. Conclusions More research is needed in this area to understand the actual effect of social network technologies on health promotion. More RCTs of greater length need to be conducted taking into account contextual factors such as patient characteristics and types of a social network technology. Also, more evidence is needed regarding the actual usability of online social networking and how different interface design elements may help or hinder behavior change and engagement. Moreover, it is crucial to investigate further the effect of theory on the effectiveness of this type of technology for health promotion. Research is needed linking theoretical grounding with observation and analysis of health promotion in online networks. PMID:26068087

Analysis of Explosives in Soil Using Solid Phase Microextraction and Gas Chromatography: Environmental Analysis

DTIC Science & Technology

2006-01-01

ENVIRONMENTAL ANALYSIS Analysis of Explosives in Soil Using Solid Phase Microextraction and Gas Chromatography Howard T. Mayfield Air Force Research...Abstract: Current methods for the analysis of explosives in soils utilize time consuming sample preparation workups and extractions. The method detection...chromatography/mass spectrometry to provide a con- venient and sensitive analysis method for explosives in soil. Keywords: Explosives, TNT, solid phase

Analysis of breath volatile organic compounds as a screening tool for detection of Tuberculosis in cattle

USDA-ARS?s Scientific Manuscript database

• Keywords: bovine tuberculosis; Mycobacterium bovis; breath analysis; volatile organic compound; gas chromatography; mass spectrometry; NaNose • Introduction: This presentation describes two studies exploring the use of breath VOCs to identify Mycobacterium bovis infection in cattle. • Methods: ...

Computational Methods for Analyzing Health News Coverage

ERIC Educational Resources Information Center

McFarlane, Delano J.

2011-01-01

Researchers that investigate the media's coverage of health have historically relied on keyword searches to retrieve relevant health news coverage, and manual content analysis methods to categorize and score health news text. These methods are problematic. Manual content analysis methods are labor intensive, time consuming, and inherently…

Formalizing An Approach to Curate the Global Change Master Directory (GCMD)'s Controlled Vocabularies (Keywords) Through a Keyword Governance Process and Community Involvement

NASA Astrophysics Data System (ADS)

Stevens, T.

2016-12-01

NASA's Global Change Master Directory (GCMD) curates a hierarchical set of controlled vocabularies (keywords) covering Earth sciences and associated information (data centers, projects, platforms, and instruments). The purpose of the keywords is to describe Earth science data and services in a consistent and comprehensive manner, allowing for precise metadata search and subsequent retrieval of data and services. The keywords are accessible in a standardized SKOS/RDF/OWL representation and are used as an authoritative taxonomy, as a source for developing ontologies, and to search and access Earth Science data within online metadata catalogs. The keyword curation approach involves: (1) receiving community suggestions; (2) triaging community suggestions; (3) evaluating keywords against a set of criteria coordinated by the NASA Earth Science Data and Information System (ESDIS) Standards Office; (4) implementing the keywords; and (5) publication/notification of keyword changes. This approach emphasizes community input, which helps ensure a high quality, normalized, and relevant keyword structure that will evolve with users' changing needs. The Keyword Community Forum, which promotes a responsive, open, and transparent process, is an area where users can discuss keyword topics and make suggestions for new keywords. Others could potentially use this formalized approach as a model for keyword curation.

SE Asian freshwater fish population and networks: the impacts of climatic and environmental change on a vital resource

NASA Astrophysics Data System (ADS)

Santos, Rita; Parsons, Daniel; Cowx, Ian

2016-04-01

The Mekong River is the 10th largest freshwater river in the world, with the second highest biodiversity wealth, behind the much larger Amazon basin. The fisheries activity in the Lower Mekong countries counts for 2.7 million tons of fish per year, with an estimated value worth up to US 7 billion. For the 60 million people living in the basin, fish represent their primary source of economic income and protein intake, with an average per capita consumption estimated at 45.4 Kg. The proposed hydropower development in the basin is threatening its sustainability and resilience. Such developments affect fish migration patterns, hydrograph flood duration and magnitudes and sediment flux. Climate change is also likely to impact the basin, exacerbating the issues created by development. As a monsoonal system, the Mekong River's pronounced annual flood pulse cycle is important in creating variable habitat for fish productivity. Moreover, the annual flood also triggers fish migration and provides vital nutrients carried by the sediment flux. This paper examines the interactions between both dam development and climate change scenarios on fish habitat and habitat connectivity, with the aim of predicting how these will affect fish species composition and fisheries catch. The project will also employ Environmental DNA (eDNA) to quantify and understand the species composition of this complex and large freshwater system. By applying molecular analysis, it is possible to trace species abundance and migration patterns of fish and evaluate the ecological networks establish between an inland system. The aim of this work is to estimate, using process-informed models, the impacts of the proposed dam development and climate change scenarios on the hydrological and hydraulic conditions of habitat availability for fish. Furthermore, it will evaluate the connectivity along the Mekong and its tributaries, and the importance of maintaining these migration pathways, used by a great diversity of fish species. It will also present the preliminary findings on eDNA analysis for species composition and the ecological networks established along the river and particularly on the fish hotspot place for biodiversity, the Tonle Sap system in Cambodia. Keywords: Mekong River, climate change, fish production, dams, eDNA analysis, numerical modelling.

The Effects of Stress on Pilot Judgment in a MIDIS Simulator

DTIC Science & Technology

1989-02-01

stress were relatively independent of problem demands for working memory and knowledge. Keywords: Decision making; Stress psychology; Pilot judgment; Divided attention; Cognitive task analysis ; Flight simulators.

U.S.-MEXICO BORDER PROGRAM ARIZONA BORDER STUDY--STANDARD OPERATING PROCEDURE FOR LABORATORY ANALYSIS OF HAIR SAMPLES FOR MERCURY (RTI-L-1.0)

EPA Science Inventory

The purpose of this protocol is to provide guidelines for the analysis of hair samples for total mercury by cold vapor atomic fluorescence (CVAFS) spectrometry. This protocol describes the methodology and all other analytical aspects involved in the analysis. Keywords: hair; s...

Thoracic Surgery Information on the Internet: A Multilingual Quality Assessment

PubMed Central

Davaris, Myles; Barnett, Stephen; Abouassaly, Robert

2017-01-01

Background Previous data suggest that quality of Internet information regarding surgical conditions and their treatments is variable. However, no comprehensive analysis of website quality exists for thoracic surgery. Objective The aim of this study was to quantify website quality in a multilingual setting using an international standard for assessment. Methods Health On the Net (HON) principles may be applied to websites using an automated toolbar function. We used the English, French, Spanish, and German Google search engines to identify 12,000 websites using keywords related to thoracic conditions and procedures. The first 150 websites returned by each keyword in each language were examined. We compared website quality to assess for tertile (is the quality better in first, second, or third 50 websites returned) and language differences. A further analysis of the English site types was undertaken performing a comparative analysis of website provider types. Results Overall, there are a considerable number of websites devoted to thoracic surgery: “lung cancer” returned over 150 million websites. About 7.85% (940/11,967) of websites are HON-accredited with differences by search term (P<.001) and tertiles (P<.001) of the first 150 websites, but not between languages. Oncological keywords regarding conditions and procedures were found to return a higher percentage of HON-accreditation. The percentage of HON-accredited sites was similar across all four languages (P=.77). In general, the first tertile contained a higher percentage of HON-accredited sites for every keyword. Conclusions Clinicians should appreciate the lack of validation of the majority of thoracic websites, with discrepancies in quality and number of websites across conditions and procedures. These differences appear similar regardless of language. An opportunity exists for clinicians to participate in the development of informative, ethical, and reliable health websites on the Internet and direct patients to them. PMID:28500021

Thoracic Surgery Information on the Internet: A Multilingual Quality Assessment.

PubMed

Davaris, Myles; Barnett, Stephen; Abouassaly, Robert; Lawrentschuk, Nathan

2017-05-12

Previous data suggest that quality of Internet information regarding surgical conditions and their treatments is variable. However, no comprehensive analysis of website quality exists for thoracic surgery. The aim of this study was to quantify website quality in a multilingual setting using an international standard for assessment. Health On the Net (HON) principles may be applied to websites using an automated toolbar function. We used the English, French, Spanish, and German Google search engines to identify 12,000 websites using keywords related to thoracic conditions and procedures. The first 150 websites returned by each keyword in each language were examined. We compared website quality to assess for tertile (is the quality better in first, second, or third 50 websites returned) and language differences. A further analysis of the English site types was undertaken performing a comparative analysis of website provider types. Overall, there are a considerable number of websites devoted to thoracic surgery: "lung cancer" returned over 150 million websites. About 7.85% (940/11,967) of websites are HON-accredited with differences by search term (P<.001) and tertiles (P<.001) of the first 150 websites, but not between languages. Oncological keywords regarding conditions and procedures were found to return a higher percentage of HON-accreditation. The percentage of HON-accredited sites was similar across all four languages (P=.77). In general, the first tertile contained a higher percentage of HON-accredited sites for every keyword. Clinicians should appreciate the lack of validation of the majority of thoracic websites, with discrepancies in quality and number of websites across conditions and procedures. These differences appear similar regardless of language. An opportunity exists for clinicians to participate in the development of informative, ethical, and reliable health websites on the Internet and direct patients to them. ©Myles Davaris, Stephen Barnett, Robert Abouassaly, Nathan Lawrentschuk. Originally published in the Interactive Journal of Medical Research (http://www.i-jmr.org/), 12.05.2017.

MAC/GMC 4.0 User's Manual: Keywords Manual. Volume 2

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2002-01-01

This document is the second volume in the three volume set of User's Manuals for the Micromechanics Analysis Code with Generalized Method of Cells Version 4.0 (MAC/GMC 4.0). Volume 1 is the Theory Manual, this document is the Keywords Manual, and Volume 3 is the Example Problem Manual. MAC/GMC 4.0 is a composite material and laminate analysis software program developed at the NASA Glenn Research Center. It is based on the generalized method of cells (GMC) micromechanics theory, which provides access to the local stress and strain fields in the composite material. This access grants GMC the ability to accommodate arbitrary local models for inelastic material behavior and various types of damage and failure analysis. MAC/GMC 4.0 has been built around GMC to provide the theory with a user-friendly framework, along with a library of local inelastic, damage, and failure models. Further, applications of simulated thermo-mechanical loading, generation of output results, and selection of architectures to represent the composite material have been automated in MAC/GMC 4.0. Finally, classical lamination theory has been implemented within MAC/GMC 4.0 wherein GMC is used to model the composite material response of each ply. Consequently, the full range of GMC composite material capabilities is available for analysis of arbitrary laminate configurations as well. This volume describes the basic information required to use the MAC/GMC 4.0 software, including a 'Getting Started' section, and an in-depth description of each of the 22 keywords used in the input file to control the execution of the code.

Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses.

PubMed

Falagas, Matthew E; Pitsouni, Eleni I; Malietzis, George A; Pappas, Georgios

2008-02-01

The evolution of the electronic age has led to the development of numerous medical databases on the World Wide Web, offering search facilities on a particular subject and the ability to perform citation analysis. We compared the content coverage and practical utility of PubMed, Scopus, Web of Science, and Google Scholar. The official Web pages of the databases were used to extract information on the range of journals covered, search facilities and restrictions, and update frequency. We used the example of a keyword search to evaluate the usefulness of these databases in biomedical information retrieval and a specific published article to evaluate their utility in performing citation analysis. All databases were practical in use and offered numerous search facilities. PubMed and Google Scholar are accessed for free. The keyword search with PubMed offers optimal update frequency and includes online early articles; other databases can rate articles by number of citations, as an index of importance. For citation analysis, Scopus offers about 20% more coverage than Web of Science, whereas Google Scholar offers results of inconsistent accuracy. PubMed remains an optimal tool in biomedical electronic research. Scopus covers a wider journal range, of help both in keyword searching and citation analysis, but it is currently limited to recent articles (published after 1995) compared with Web of Science. Google Scholar, as for the Web in general, can help in the retrieval of even the most obscure information but its use is marred by inadequate, less often updated, citation information.

Publications - GMC 30 | Alaska Division of Geological & Geophysical Surveys

Science.gov Websites

DGGS GMC 30 Publication Details Title: Geochemical analysis (total organic carbon, rock-eval pyrolysis , Geochemical analysis (total organic carbon, rock-eval pyrolysis, vitrinite reflectance and gc/ms chromato (1.3 M) Keywords Pyrolysis; Rock-Eval Pyrolysis; Total Organic Carbon; Vitrinite Reflectance Top of

Publications - GMC 19 | Alaska Division of Geological & Geophysical Surveys

Science.gov Websites

DGGS GMC 19 Publication Details Title: Geochemical analysis (total organic carbon-rock-eval, vitrinite information. Bibliographic Reference Unknown, [n.d.], Geochemical analysis (total organic carbon-rock-eval K) Keywords Total Organic Carbon; Vitrinite Reflectance Top of Page Department of Natural Resources

A Scheme for Text Analysis Using Fortran.

ERIC Educational Resources Information Center

Koether, Mary E.; Coke, Esther U.

Using string-manipulation algorithms, FORTRAN computer programs were designed for analysis of written material. The programs measure length of a text and its complexity in terms of the average length of words and sentences, map the occurrences of keywords or phrases, calculate word frequency distribution and certain indicators of style. Trials of…

Analysis of DISMS (Defense Integrated Subsistence Management System) Increment 4

DTIC Science & Technology

1988-12-01

response data entry; and rationale supporting an on-line system based on real time management information needs. Keywords: Automated systems; Subsistence; Workload capacity; Bid response; Contract administration; Computer systems.

Community Involvement in Enhancing the Global Change Master Directory (GCMD) Controlled Vocabularies (Keywords)

NASA Technical Reports Server (NTRS)

Stevens, T.; Ritz, S.; Aleman, A.; Genazzio, M.; Morahan, M.; Wharton, S.

2016-01-01

NASA's Global Change Master Directory (GCMD) develops and expands a hierarchical set of controlled vocabularies (keywords) covering the Earth sciences and associated information (data centers, projects, platforms, instruments, etc.). The purpose of the keywords is to describe Earth science data and services in a consistent and comprehensive manner, allowing for the precise searching of metadata and subsequent retrieval of data and services. The keywords are accessible in a standardized SKOSRDFOWL representation and are used as an authoritative taxonomy, as a source for developing ontologies, and to search and access Earth Science data within online metadata catalogues. The keyword development approach involves: (1) receiving community suggestions, (2) triaging community suggestions, (3) evaluating the keywords against a set of criteria coordinated by the NASA ESDIS Standards Office, and (4) publication/notification of the keyword changes. This approach emphasizes community input, which helps ensure a high quality, normalized, and relevant keyword structure that will evolve with users changing needs. The Keyword Community Forum, which promotes a responsive, open, and transparent processes, is an area where users can discuss keyword topics and make suggestions for new keywords. The formalized approach could potentially be used as a model for keyword development.

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

PubMed

Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

2015-01-01

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Designing to Support Command and Control in Urban Firefighting

DTIC Science & Technology

2008-06-01

complex human- machine systems. Keywords: Command and control, firefighting, cognitive systems engineering, cognitive task analysis 1...Elm, W. (2000). Bootstrapping multiple converging cognitive task analysis techniques for system design. In J.M.C. Schraagen, S.F. Chipman, & V.L...Shalin, (Eds.), Cognitive Task Analysis . (pp. 317-340). Mahwah, NJ: Lawrence Erlbaum. Rasmussen, J., Pejtersen, A., Goodman, L. (1994). Cognitive

Qualitative Data Analysis: A Compendium of Techniques and a Framework for Selection for School Psychology Research and Beyond

ERIC Educational Resources Information Center

Leech, Nancy L.; Onwuegbuzie, Anthony J.

2008-01-01

Qualitative researchers in school psychology have a multitude of analyses available for data. The purpose of this article is to present several of the most common methods for analyzing qualitative data. Specifically, the authors describe the following 18 qualitative analysis techniques: method of constant comparison analysis, keywords-in-context,…

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

Solving Integer Programs from Dependence and Synchronization Problems

DTIC Science & Technology

1993-03-01

DEFF.NSNE Solving Integer Programs from Dependence and Synchronization Problems Jaspal Subhlok March 1993 CMU-CS-93-130 School of Computer ScienceT IC...method Is an exact and efficient way of solving integer programming problems arising in dependence and synchronization analysis of parallel programs...7/;- p Keywords: Exact dependence tesing, integer programming. parallelilzng compilers, parallel program analysis, synchronization analysis Solving

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords

PubMed Central

Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

2015-01-01

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as “bind” or “interact” plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction. PMID:26783534

Percolator: Scalable Pattern Discovery in Dynamic Graphs

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

Choudhury, Sutanay; Purohit, Sumit; Lin, Peng

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walkingmore » through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.« less

Monitoring User Search Success through Transaction Log Analysis: The WolfPAC Example.

ERIC Educational Resources Information Center

Zink, Steven D.

1991-01-01

Describes the use of transaction log analysis of the online catalog at the University of Nevada, Reno, libraries to help evaluate reasons for unsuccessful user searches. Author, title, and subject searches are examined; problems with Library of Congress subject headings are discussed; and title keyword searching is suggested. (11 references) (LRW)

A Selected Annotated Bibliography on the Analysis of Water Resources System, Volume 2.

ERIC Educational Resources Information Center

Kriss, Carol; And Others

Presented is an annotated bibliography of some recent selected publications pertaining to the application of systems analysis techniques for defining and evaluating alternative solutions to water resource problems. Both subject and author indices are provided. Keywords are listed at the end of each abstract. The abstracted material emphasizes the…

A Selected Annotated Bibliography on the Analysis of Water Resource Systems.

ERIC Educational Resources Information Center

Gysi, Marshall; And Others

Presented is an annotated bibliography of some selected publications pertaining to the application of systems analysis techniques to water resource problems. The majority of the references included in this bibliography have been published within the last five years. About half of the entries have informative abstracts and keywords following the…

Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis.

PubMed

Nishimoto, Naoki; Ota, Mizuki; Yagahara, Ayako; Ogasawara, Katsuhiko

2016-11-25

After the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media. The objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern. We retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived. A Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32% (880,108/1,168,542) tweeted the word radioactive and 9.20% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year. Although the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk. ©Naoki Nishimoto, Mizuki Ota, Ayako Yagahara, Katsuhiko Ogasawara. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 25.11.2016.

Standardization of Keyword Search Mode

ERIC Educational Resources Information Center

Su, Di

2010-01-01

In spite of its popularity, keyword search mode has not been standardized. Though information professionals are quick to adapt to various presentations of keyword search mode, novice end-users may find keyword search confusing. This article compares keyword search mode in some major reference databases and calls for standardization. (Contains 3…

Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

PubMed

Young, Sean D; Rivers, Caitlin; Lewis, Bryan

2014-06-01

Recent availability of "big data" might be used to study whether and how sexual risk behaviors are communicated on real-time social networking sites and how data might inform HIV prevention and detection. This study seeks to establish methods of using real-time social networking data for HIV prevention by assessing 1) whether geolocated conversations about HIV risk behaviors can be extracted from social networking data, 2) the prevalence and content of these conversations, and 3) the feasibility of using HIV risk-related real-time social media conversations as a method to detect HIV outcomes. In 2012, tweets (N=553,186,061) were collected online and filtered to include those with HIV risk-related keywords (e.g., sexual behaviors and drug use). Data were merged with AIDSVU data on HIV cases. Negative binomial regressions assessed the relationship between HIV risk tweeting and prevalence by county, controlling for socioeconomic status measures. Over 9800 geolocated tweets were extracted and used to create a map displaying the geographical location of HIV-related tweets. There was a significant positive relationship (p<.01) between HIV-related tweets and HIV cases. Results suggest the feasibility of using social networking data as a method for evaluating and detecting Human immunodeficiency virus (HIV) risk behaviors and outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Prognosis: the "missing link" within the CanMEDS competency framework.

PubMed

Maida, Vincent; Cheon, Paul M

2014-05-13

The concept of prognosis dates back to antiquity. Quantum advances in diagnostics and therapeutics have relegated this once highly valued core competency to an almost negligible role in modern medical practice. Medical curricula are devoid of teaching opportunities focused on prognosis. This void is driven by a corresponding relative dearth within physician competency frameworks. This study aims to assess the level of content related to prognosis within CanMEDS (Canadian Medical Education Directives for Specialists), a leading and prototypical physician competency framework. A quantitative content analysis of CanMEDS competency framework was carried out to measure the extent of this deficiency. Foxit Reader 5.1 (Foxit Corporation), a keyword scanning software, was used to assess the CanMEDS 2005 framework documents of 29 physician specialties and 37 subspecialties across the seven physician roles (medical expert, communicator, collaborator, manager, health advocate, scholar, and professional). The keywords used in the search included prognosis, prognostic, prognosticate, and prognostication. Of the 29 specialties six (20.7%) contained at least one citation of the keyword "prognosis", and one (3.4%) contained one citation of the keyword "prognostic". Of the 37 subspecialties, sixteen (43.2%) contained at least one citation of the keyword "prognosis", and three (8.1%) contained at least one citation of the keyword "prognostic". The terms "prognosticate" and "prognostication" were completely absent from all CanMEDS 2005 documents. Overall, the combined citations for "prognosis" and "prognostic" were linked with the following competency roles: Medical Expert (80.3%), Scholar (11.5%), and Communicator (8.2%). Given the fundamental and foundational importance of prognosis within medical practice, it is recommended that physicians develop appropriate attitudes, skills and knowledge related to the formulation and communication of prognosis. The deficiencies within CanMEDS, demonstrated by this study, should be addressed in advance of the launch of its updated version in 2015.

Prognosis: the “missing link” within the CanMEDS competency framework

PubMed Central

2014-01-01

Background The concept of prognosis dates back to antiquity. Quantum advances in diagnostics and therapeutics have relegated this once highly valued core competency to an almost negligible role in modern medical practice. Medical curricula are devoid of teaching opportunities focused on prognosis. This void is driven by a corresponding relative dearth within physician competency frameworks. This study aims to assess the level of content related to prognosis within CanMEDS (Canadian Medical Education Directives for Specialists), a leading and prototypical physician competency framework. Methods A quantitative content analysis of CanMEDS competency framework was carried out to measure the extent of this deficiency. Foxit Reader 5.1 (Foxit Corporation), a keyword scanning software, was used to assess the CanMEDS 2005 framework documents of 29 physician specialties and 37 subspecialties across the seven physician roles (medical expert, communicator, collaborator, manager, health advocate, scholar, and professional). The keywords used in the search included prognosis, prognostic, prognosticate, and prognostication. Results Of the 29 specialties six (20.7%) contained at least one citation of the keyword “prognosis”, and one (3.4%) contained one citation of the keyword “prognostic”. Of the 37 subspecialties, sixteen (43.2%) contained at least one citation of the keyword “prognosis”, and three (8.1%) contained at least one citation of the keyword “prognostic”. The terms “prognosticate” and “prognostication” were completely absent from all CanMEDS 2005 documents. Overall, the combined citations for “prognosis” and “prognostic” were linked with the following competency roles: Medical Expert (80.3%), Scholar (11.5%), and Communicator (8.2%). Conclusions Given the fundamental and foundational importance of prognosis within medical practice, it is recommended that physicians develop appropriate attitudes, skills and knowledge related to the formulation and communication of prognosis. The deficiencies within CanMEDS, demonstrated by this study, should be addressed in advance of the launch of its updated version in 2015. PMID:24886446

Detecting depression stigma on social media: A linguistic analysis.

PubMed

Li, Ang; Jiao, Dongdong; Zhu, Tingshao

2018-05-01

Efficient detection of depression stigma in mass media is important for designing effective stigma reduction strategies. Using linguistic analysis methods, this paper aims to build computational models for detecting stigma expressions in Chinese social media posts (Sina Weibo). A total of 15,879 Weibo posts with keywords were collected and analyzed. First, a content analysis was conducted on all 15,879 posts to determine whether each of them reflected depression stigma or not. Second, using four algorithms (Simple Logistic Regression, Multilayer Perceptron Neural Networks, Support Vector Machine, and Random Forest), two groups of classification models were built based on selected linguistic features; one for differentiating between posts with and without depression stigma, and one for differentiating among posts with three specific types of depression stigma. First, 967 of 15,879 posts (6.09%) indicated depression stigma. 39.30%, 15.82%, and 14.99% of them endorsed the stigmatizing view that "People with depression are unpredictable", "Depression is a sign of personal weakness", and "Depression is not a real medical illness", respectively. Second, the highest F-Measure value for differentiating between stigma and non-stigma reached 75.2%. The highest F-Measure value for differentiating among three specific types of stigma reached 86.2%. Due to the limited and imbalanced dataset of Chinese Weibo posts, the findings of this study might have limited generalizability. This paper confirms that incorporating linguistic analysis methods into online detection of stigma can be beneficial to improve the performance of stigma reduction programs. Copyright © 2018 Elsevier B.V. All rights reserved.

Artificial intelligence in medicine.

PubMed Central

Ramesh, A. N.; Kambhampati, C.; Monson, J. R. T.; Drew, P. J.

2004-01-01

INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting. PMID:15333167

Artificial intelligence in medicine.

PubMed

Ramesh, A N; Kambhampati, C; Monson, J R T; Drew, P J

2004-09-01

Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

PubMed

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-09-03

DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.

The Keck keyword layer

NASA Technical Reports Server (NTRS)

Conrad, A. R.; Lupton, W. F.

1992-01-01

Each Keck instrument presents a consistent software view to the user interface programmer. The view consists of a small library of functions, which are identical for all instruments, and a large set of keywords, that vary from instrument to instrument. All knowledge of the underlying task structure is hidden from the application programmer by the keyword layer. Image capture software uses the same function library to collect data for the image header. Because the image capture software and the instrument control software are built on top of the same keyword layer, a given observation can be 'replayed' by extracting keyword-value pairs from the image header and passing them back to the control system. The keyword layer features non-blocking as well as blocking I/O. A non-blocking keyword write operation (such as setting a filter position) specifies a callback to be invoked when the operation is complete. A non-blocking keyword read operation specifies a callback to be invoked whenever the keyword changes state. The keyword-callback style meshes well with the widget-callback style commonly used in X window programs. The first keyword library was built for the two Keck optical instruments. More recently, keyword libraries have been developed for the infrared instruments and for telescope control. Although the underlying mechanisms used for inter-process communication by each of these systems vary widely (Lick MUSIC, Sun RPC, and direct socket I/O, respectively), a basic user interface has been written that can be used with any of these systems. Since the keyword libraries are bound to user interface programs dynamically at run time, only a single set of user interface executables is needed. For example, the same program, 'xshow', can be used to display continuously the telescope's position, the time left in an instrument's exposure, or both values simultaneously. Less generic tools that operate on specific keywords, for example an X display that controls optical instrument exposures, have also been written using the keyword layer.

Scoliosis and the Social Media: Facebook as a Means of Information Exchange.

PubMed

Ng, Jonathan P; Tarazi, Nadim; Byrne, Damien P; Baker, Joseph F; McCabe, John P

2017-03-01

Over the last decade, the emergence of social networking websites such as Facebook have revolutionized information dissemination and broadened opportunities to engage in discussions. In particular, having been widely adopted in the younger generation, the use of this medium has become more prevalent in health disorders such as scoliosis in the adolescent population. However, the quality of information on Facebook is unregulated and variable, which may mislead patients in their decision making. To document the various types of information available and assess the quality of information on Facebook discussion boards using recognized scoring systems. To evaluate the quality of information on the social network. A search for the keyword "scoliosis" on Facebook was performed and the first 100 pages generated were reviewed. SCSS and DISCERN score. Content analysis was performed on discussion boards and personal blogs. Two independent examiners evaluated each site according to scoliosis-specific content score (SCSS) and the DISCERN criteria, both previously used instruments to judge the quality of information on the Internet pertaining to scoliosis. The SCSS range from 0 to 32 (higher score better) and the DISCERN 16 to 80 (higher score better). Of the 100 sites reviewed, 33 were discussion boards and personal blogs. Of these, the overall average SCSS was 5.7 (SD 5.8, range 0-20) and the DISCERN was 22.5 (SD 7.6, range 16-45), indicating that using general scoring systems the quality of information provided was overall poor. Using recognized scoring systems to analyze Facebook pages used as discussion forums or blogs, we showed that the quality in general was poor. For modern practices to adapt to an era of information exchange via the social network, the orthopedic community should develop ways to incorporate the social media in future patient education. Copyright © 2016 Scoliosis Research Society. All rights reserved.

Integrative genetic analysis of transcription modules: towards filling the gap between genetic lociand inherited traits

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

Li, Hongqiang; Chen, Hao; Bao, Lei

2005-01-01

Genetic loci that regulate inherited traits are routinely identified using quantitative trait locus (QTL) mapping methods. However, the genotype-phenotype associations do not provide information on the gene expression program through which the genetic loci regulate the traits. Transcription modules are 'selfconsistent regulatory units' and are closely related to the modular components of gene regulatory network [Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y. and Barkai, N. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370-377; Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Module networks: identifyingmore » regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166-176]. We used genome-wide genotype and gene expression data of a genetic reference population that consists of mice of 32 recombinant inbred strains to identify the transcription modules and the genetic loci regulating them. Twenty-nine transcription modules defined by genetic variations were identified. Statistically significant associations between the transcription modules and 18 classical physiological and behavioral traits were found. Genome-wide interval mapping showed that major QTLs regulating the transcription modules are often co-localized with the QTLs regulating the associated classical traits. The association and the possible co-regulation of the classical trait and transcription module indicate that the transcription module may be involved in the gene pathways connecting the QTL and the classical trait. Our results show that a transcription module may associate with multiple seemingly unrelated classical traits and a classical trait may associate with different modules. Literature mining results provided strong independent evidences for the relations among genes of the transcription modules, genes in the regions of the QTLs regulating the transcription modules and the keywords representing the classical traits.« less

Integrated corridor management initiative : demonstration phase evaluation, San Diego traveler response analysis test plan.

DOT National Transportation Integrated Search

1995-10-01

REAL-TIME TRAFFIC INFORMATION, ROUTE GUIDANCE, ROUTE PLANNING, INTELLIGENT VEHICLE INITIATIVE OR IVI ">">KEYWORDS: OPERATIONAL TESTS, TRAVTEK, ADVANCED TRAVELER INFORMATION SYSTEMS OR ATIS, ADVANCED TRAFFIC MANAGEMENT SYSTEMS OR ATMS, INTELLI...

KAGLVis - On-line 3D Visualisation of Earth-observing-satellite Data

NASA Astrophysics Data System (ADS)

Szuba, Marek; Ameri, Parinaz; Grabowski, Udo; Maatouki, Ahmad; Meyer, Jörg

2015-04-01

One of the goals of the Large-Scale Data Management and Analysis project is to provide a high-performance framework facilitating management of data acquired by Earth-observing satellites such as Envisat. On the client-facing facet of this framework, we strive to provide visualisation and basic analysis tool which could be used by scientists with minimal to no knowledge of the underlying infrastructure. Our tool, KAGLVis, is a JavaScript client-server Web application which leverages modern Web technologies to provide three-dimensional visualisation of satellite observables on a wide range of client systems. It takes advantage of the WebGL API to employ locally available GPU power for 3D rendering; this approach has been demonstrated to perform well even on relatively weak hardware such as integrated graphics chipsets found in modern laptop computers and with some user-interface tuning could even be usable on embedded devices such as smartphones or tablets. Data is fetched from the database back-end using a ReST API and cached locally, both in memory and using HTML5 Web Storage, to minimise network use. Computations, calculation of cloud altitude from cloud-index measurements for instance, can depending on configuration be performed on either the client or the server side. Keywords: satellite data, Envisat, visualisation, 3D graphics, Web application, WebGL, MEAN stack.

A study on real-time low-quality content detection on Twitter from the users' perspective.

PubMed

Chen, Weiling; Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users' content browsing experience most. The aim of our work is to detect low-quality content from the users' perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users' opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content.

A study on real-time low-quality content detection on Twitter from the users’ perspective

PubMed Central

Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users’ content browsing experience most. The aim of our work is to detect low-quality content from the users’ perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users’ opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content. PMID:28793347

A Structure-Activity Analysis of the Variation in Oxime Efficacy Against Nerve Agents

DTIC Science & Technology

2008-01-01

Literature 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER A structure- activity analysis of the variation in oxime...structure– activity analysis of the variation in oxime efficacy against nerve agents☆ Donald M. Maxwell a,⁎, Irwin Koplovitz a, Franz Worek b, Richard E...structure– activity analysi Received 22 February 2008 Revised 8 April 2008 Accepted 13 April 2008 Available online 22 April 2008 Keywords: Organophosphorus

Facilitation of receptive and productive foreign vocabulary learning using the keyword method: the role of image quality.

PubMed

Beaton, Alan A; Gruneberg, Michael M; Hyde, Christopher; Shufflebottom, Alex; Sykes, Robert N

2005-07-01

Ellis and Beaton (1993a) reported that the keyword method of learning enhanced memory of foreign vocabulary items when receptive learning was measured. However, for productive learning, rote repetition was superior to the keyword method. The first two experiments reported here show that, in comparison with rote repetition, both receptive and productive learning can be enhanced by the keyword method, provided that the quality of the keyword images is adequate. In a third experiment using a subset of words from Ellis and Beaton (1993a), the finding they reported, that for productive learning rote repetition was superior to the keyword method, was reversed. The quality of keyword images will vary from study to study and any generalisation regarding the efficacy of the keyword method must take this into account.

Brain functional connectivity network studies of acupuncture: a systematic review on resting-state fMRI.

PubMed

Cai, Rong-Lin; Shen, Guo-Ming; Wang, Hao; Guan, Yuan-Yuan

2018-01-01

Functional magnetic resonance imaging (fMRI) is a novel method for studying the changes of brain networks due to acupuncture treatment. In recent years, more and more studies have focused on the brain functional connectivity network of acupuncture stimulation. To offer an overview of the different influences of acupuncture on the brain functional connectivity network from studies using resting-state fMRI. The authors performed a systematic search according to PRISMA guidelines. The database PubMed was searched from January 1, 2006 to December 31, 2016 with restriction to human studies in English language. Electronic searches were conducted in PubMed using the keywords "acupuncture" and "neuroimaging" or "resting-state fMRI" or "functional connectivity". Selection of included articles, data extraction and methodological quality assessments were respectively conducted by two review authors. Forty-four resting-state fMRI studies were included in this systematic review according to inclusion criteria. Thirteen studies applied manual acupuncture vs. sham, four studies applied electro-acupuncture vs. sham, two studies also compared transcutaneous electrical acupoint stimulation vs. sham, and nine applied sham acupoint as control. Nineteen studies with a total number of 574 healthy subjects selected to perform fMRI only considered healthy adult volunteers. The brain functional connectivity of the patients had varying degrees of change. Compared with sham acupuncture, verum acupuncture could increase default mode network and sensorimotor network connectivity with pain-, affective- and memory-related brain areas. It has significantly greater connectivity of genuine acupuncture between the periaqueductal gray, anterior cingulate cortex, left posterior cingulate cortex, right anterior insula, limbic/paralimbic and precuneus compared with sham acupuncture. Some research had also shown that acupuncture could adjust the limbic-paralimbic-neocortical network, brainstem, cerebellum, subcortical and hippocampus brain areas. It can be presumed that the functional connectivity network is closely related to the mechanism of acupuncture, and central integration plays a critical role in the acupuncture mechanism. Copyright © 2017 Shanghai Changhai Hospital. Published by Elsevier B.V. All rights reserved.

Publications - GMC 385 | Alaska Division of Geological & Geophysical

Science.gov Websites

DGGS GMC 385 Publication Details Title: Porosity, permeability, and capillary pressure core analysis Shimer, G., 2011, Porosity, permeability, and capillary pressure core analysis results (2,124'-2,193 -capilar.xls (108.0 K) gmc385-cores-water.xls (19.0 K) Keywords Oil and Gas; Permeability; Porosity Top of Page

Publications - GMC 114 | Alaska Division of Geological & Geophysical

Science.gov Websites

DGGS GMC 114 Publication Details Title: Total organic carbon and rock eval pyrolysis data and analysis and Ruth Laboratories, Inc., 1989, Total organic carbon and rock eval pyrolysis data and analysis for gmc114.pdf (171.0 K) Keywords Pyrolysis; Rock-Eval Pyrolysis Top of Page Department of Natural Resources

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Social network analysis: Presenting an underused method for nursing research.

PubMed

Parnell, James Michael; Robinson, Jennifer C

2018-06-01

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

Socio-Ecological Changes and Human Mobility in Landslide Zones of Chamoli District of Uttarakhand

NASA Astrophysics Data System (ADS)

Singh, Desh Deepak

2017-04-01

Disaster displacement represents one of the biggest humanitarian challenges of the 21st century. Between 2008 and 2014, 184.6 million people were forced from their homes due to different natural disasters, with 19.3 million newly displaced in 2014, according to the latest available data from the Internal Displacement Monitoring Centre (IDMC). In Uttarakhand state in India, hill slopes are known for their instability as they are ecologically fragile, tectonically and seismically active, and geologically sensitive that makes it prone to landslide hazards. Coupled to this, the rapid expansion of human societies often forces people to occupy highly dynamic and unstable environments. Repeated instances of landslide in highly populated areas have now forced many people to out migrate from vulnerable and high risk areas of Uttarakhand. The present study overlays the maps of geology, vegetation, route network, and settlement of Chamoli district of Uttarakhand to find out through overlay analysis, the landslide risk zonation map of Chamoli. Further, through primary survey in the high risk zones, the migration pattern and migration intensity has been analysed and a model for determining long term trend of migration in ecologically changing location has been developed. Keywords: Landslides, Uttarakhand, Migration, Risk Zonation Mapping

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications.

PubMed

Coelho, Camila Henriques; Durigan, Maurício; Leal, Diego Averaldo Guiguet; Schneider, Adriano de Bernardi; Franco, Regina Maura Bueno; Singer, Steven M

2017-10-01

Giardiasis is an intestinal infection that affects more than two hundred million people annually worldwide; it is caused by the flagellated protozoan Giardia duodenalis. In tropical countries and in low or middle-income settings, like Brazil, its prevalence can be high. There is currently no systematic review on the presence of G. duodenalis in patients, animals or water sources in Brazil. This systematic review was performed according to recommendations established by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). As databases for our searches, we have used PubMed, Embase, Scopus and the Brazilian database SciELO using the keywords «Giardia*» and «Brazil». This systematic review identified research studies related to G. duodenalis in water, giardiasis in animals, prevalence of giardiasis across Brazilian regions, genotyping of strains isolated in humans, and giardiasis in indigenous populations. We also propose a network of G. duodenalis transmission in Brazil based on genotypes analyses. This is the first time within the last twenty years that a review is being published on the occurrence of G. duodenalis in Brazil, addressing relevant issues such as prevalence, molecular epidemiology and analytical methods for parasite detection.

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications

PubMed Central

Durigan, Maurício; Leal, Diego Averaldo Guiguet; Schneider, Adriano de Bernardi; Franco, Regina Maura Bueno; Singer, Steven M.

2017-01-01

Introduction Giardiasis is an intestinal infection that affects more than two hundred million people annually worldwide; it is caused by the flagellated protozoan Giardia duodenalis. In tropical countries and in low or middle-income settings, like Brazil, its prevalence can be high. There is currently no systematic review on the presence of G. duodenalis in patients, animals or water sources in Brazil. Methods This systematic review was performed according to recommendations established by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). As databases for our searches, we have used PubMed, Embase, Scopus and the Brazilian database SciELO using the keywords Giardia* and Brazil. Results This systematic review identified research studies related to G. duodenalis in water, giardiasis in animals, prevalence of giardiasis across Brazilian regions, genotyping of strains isolated in humans, and giardiasis in indigenous populations. We also propose a network of G. duodenalis transmission in Brazil based on genotypes analyses. Conclusion This is the first time within the last twenty years that a review is being published on the occurrence of G. duodenalis in Brazil, addressing relevant issues such as prevalence, molecular epidemiology and analytical methods for parasite detection. PMID:29065126

Ethnic differences in self reported health in Malmö in southern Sweden

PubMed Central

Lindstrom, M; Sundquist, J; Ostergren, P

2001-01-01

STUDY OBJECTIVE—The aim of this study was to investigate ethnic differences in self reported health in the city of Malmö, Sweden, and whether these differences could be explained by psychosocial and economic conditions.
DESIGN/SETTING/PARTICIPANTS—The public health survey in Malmö 1994 was a cross sectional study. A total of 5600 people aged 20-80 years completed a postal questionnaire. The participation rate was 71%. The population was categorised according to country of origin: born in Sweden, other Western countries, Yugoslavia, Poland, Arabic speaking countries and all other countries. The multivariate analysis was performed using a logistic regression model in order to investigate the importance of possible confounders on the differences by country of origin in self reported health. Finally, variables measuring psychosocial and economic conditions were introduced into the model.
MAIN RESULTS—The odds ratios of having poor self reported health were significantly higher among men born in other Western countries, Yugoslavia, Arabic speaking countries and in the category all other countries, as well as among women born in Yugoslavia, Poland and all other countries, compared with men and women born in Sweden. The multivariate analysis including age and education did not change these results. A huge reduction of the odds ratios was observed for men and women born in Yugoslavia, Arabic speaking countries and all other countries, and for women born in Poland after the introduction of the social network, social support and economic factors into the multivariate model.
CONCLUSIONS—There were significant ethnic group differences in self reported health. These differences were greatly reduced by psychosocial and economic factors, which suggest that these factors may be important determinants of self rated health in certain minority groups.


Keywords: self reported health; social network; social support PMID:11154248

An efficient scheme for automatic web pages categorization using the support vector machine

NASA Astrophysics Data System (ADS)

Bhalla, Vinod Kumar; Kumar, Neeraj

2016-07-01

In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.

How do nurses record pedagogical activities? Nurses' documentation in patient records in a cardiac rehabilitation unit for patients who have undergone coronary artery bypass surgery.

PubMed

Bergh, Anne-Louise; Bergh, Claes-Håkan; Friberg, Febe

2007-10-01

To describe the use of pedagogically related keywords and the content of notes connected to these keywords, as they appear in nursing records in a coronary artery bypass graft (CABG) surgery rehabilitation unit. Nursing documentation is an important component of clinical practice and is regulated by law in Sweden. Studies have been carried out in order to evaluate the educational and rehabilitative needs of patients following CABG surgery but, as yet, no study has contained an in-depth evaluation of how nurses document pedagogical activities in the records of these patients. The records of 265 patients admitted to a rehabilitation unit following CABG surgery were analysed. The records were structured in accordance with the VIPS model. Using this model, pedagogically related keywords: communication, cognition/development and information/education were selected. The analysis of the data consisted of three parts: the frequency with which pedagogically related keywords are used, the content and the structure of the notes. Apart from the term 'communication', pedagogically related keywords were seldom used. Communication appeared in all records describing limitations, although no explicit reference was made to pedagogical activities. The notes related to cognition/development were grouped into the following themes: nurses' actions, assessment of knowledge and provision of information, advice and instructions as well as patients' wishes and experiences. The themes related to information were the provision of information and advice in addition to relevant nursing actions. The structure of the documentation was simple. The documentation of pedagogical activities in nursing records was infrequent and inadequate. The patients' need for knowledge and the nurses' teaching must be documented in the patient records so as to clearly reflect the frequency and quality of pedagogical activities.

The importance of the keyword-generation method in keyword mnemonics.

PubMed

Campos, Alfredo; Amor, Angeles; González, María Angeles

2004-01-01

Keyword mnemonics is under certain conditions an effective approach for learning foreign-language vocabulary. It appears to be effective for words with high image vividness but not for words with low image vividness. In this study, two experiments were performed to assess the efficacy of a new keyword-generation procedure (peer generation). In Experiment 1, a sample of 363 high-school students was randomly into four groups. The subjects were required to learn L1 equivalents of a list of 16 Latin words (8 with high image vividness, 8 with low image vividness), using a) the rote method, or the keyword method with b) keywords and images generated and supplied by the experimenter, c) keywords and images generated by themselves, or d) keywords and images previously generated by peers (i.e., subjects with similar sociodemographic characteristics). Recall was tested immediately and one week later. For high-vivideness words, recall was significantly better in the keyword groups than the rote method group. For low-vividness words, learning method had no significant effect. Experiment 2 was basically identical, except that the word lists comprised 32 words (16 high-vividness, 16 low-vividness). In this experiment, the peer-generated-keyword group showed significantly better recall of high-vividness words than the rote method groups and the subject generated keyword group; again, however, learning method had no significant effect on recall of low-vividness words.

Supply Support of Air Force 463L Equipment: An Analysis of the 463L equipment Spare Parts Pipeline

DTIC Science & Technology

1989-09-01

service; and 4) the order processing system created inherent delays in the pipeline because of outdated and indirect information systems and technology. Keywords: Materials handling equipment, Theses. (AW)

A Descriptive Study of the Prevalence and Typology of Alcohol-Related Posts in an Online Social Network for Smoking Cessation.

PubMed

Cohn, Amy M; Zhao, Kang; Cha, Sarah; Wang, Xi; Amato, Michael S; Pearson, Jennifer L; Papandonatos, George D; Graham, Amanda L

2017-09-01

Alcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX.org. Data were analyzed from 814,258 posts (January 2012 to May 2015). Posts containing alcohol keywords were coded via supervised machine-learning text classification for information about the user's personal experience with drinking, whether the user self-identified as a problem drinker or indicated problem drinking, and negative sentiment about drinking in the context of a quit attempt (i.e., alcohol should be avoided during a quit attempt). Less than 1% of posts were related to alcohol, contributed by 13% of users. Roughly a third of alcohol posts described a personal experience with drinking; very few (3%) indicated "problem drinking." The majority (70%) of alcohol posts did not express negative sentiment about drinking alcohol during a quit attempt. Users who did express negative sentiment about drinking were more centrally located within the network compared with those who did not. Discussion of alcohol was rare, and most posts did not signal the need to quit or abstain from drinking during a quit attempt. Featuring expert information or highlighting discussions that are consistent with treatment guidelines may be important steps to ensure smokers are educated about drinking risks.

A multi-scale automatic observatory of soil moisture and temperature served for satellite product validation in Tibetan Plateau

NASA Astrophysics Data System (ADS)

Tang, S.; Dong, L.; Lu, P.; Zhou, K.; Wang, F.; Han, S.; Min, M.; Chen, L.; Xu, N.; Chen, J.; Zhao, P.; Li, B.; Wang, Y.

2016-12-01

Due to the lack of observing data which match the satellite pixel size, the inversion accuracy of satellite products in Tibetan Plateau(TP) is difficult to be evaluated. Hence, the in situ observations are necessary to support the calibration and validation activities. Under the support of the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) projec a multi-scale automatic observatory of soil moisture and temperature served for satellite product validation (TIPEX-III-SMTN) were established in Tibetan Plateau. The observatory consists of two regional scale networks, including the Naqu network and the Geji network. The Naqu network is located in the north of TP, and characterized by alpine grasslands. The Geji network is located in the west of TP, and characterized by marshes. Naqu network includes 33 stations, which are deployed in a 75KM*75KM region according to a pre-designed pattern. At Each station, soil moisture and temperature are measured by five sensors at five soil depths. One sensor is vertically inserted into 0 2 cm depth to measure the averaged near-surface soil moisture and temperature. The other four sensors are horizontally inserted at 5, 10, 20, and 30 cm depths, respectively. The data are recorded every 10 minutes. A wireless transmission system is applied to transmit the data in real time, and a dual power supply system is adopted to keep the continuity of the observation. The construction of Naqu network has been accomplished in August, 2015, and Geji network will be established before Oct., 2016. Observations acquired from TIPEX-III-SMTN can be used to validate satellite products with different spatial resolution, and TIPEX-III-SMTN can also be used as a complementary of the existing similar networks in this area, such as CTP-SMTMN (the multiscale Soil Moistureand Temperature Monitoring Network on the central TP) . Keywords: multi-scale soil moisture soil temperature, Tibetan Plateau Acknowledgments: This work was jointly supported by CMA Special Fund for Scientific Research in the Public Interest (Grant No. GYHY201406001, GYHY201206008-01), and Climate change special fund (QHBH2014)'

Toward a Virtual Solar Observatory: Starting Before the Petabytes Fall

NASA Technical Reports Server (NTRS)

Gurman, J. B.; Fisher, Richard R. (Technical Monitor)

2002-01-01

NASA is currently engaged in the study phase of a modest effort to establish a Virtual Solar Observatory (VSO). The VSO would serve ground- and space-based solar physics data sets from a distributed network of archives through a small number of interfaces to the scientific community. The basis of this approach, as of all planned virtual observatories, is the translation of metadata from the various sources via source-specific dictionaries so the user will not have to distinguish among keyword usages. A single Web interface should give access to all the distributed data. We present the current status of the VSO, its initial scope, and its relation to the European EGSO effort.

Extracting Inter-business Relationship from World Wide Web

NASA Astrophysics Data System (ADS)

Jin, Yingzi; Matsuo, Yutaka; Ishizuka, Mitsuru

Social relation plays an important role in a real community. Interaction patterns reveal relations among actors (such as persons, groups, companies), which can be merged into valuable information as a network structure. In this paper, we propose a new approach to extract inter-business relationship from the Web. Extraction of relation between a pair of companies is realized by using a search engine and text processing. Since names of companies co-appear coincidentaly on the Web, we propose an advanced algorithm which is characterized by addition of keywords (or we call relation words) to a query. The relation words are obtained from either an annotated corpus or the Web. We show some examples and comprehensive evaluations on our approach.

Contemporary Network Proteomics and Its Requirements

PubMed Central

Goh, Wilson Wen Bin; Wong, Limsoon; Sng, Judy Chia Ghee

2013-01-01

The integration of networks with genomics (network genomics) is a familiar field. Conventional network analysis takes advantage of the larger coverage and relative stability of gene expression measurements. Network proteomics on the other hand has to develop further on two critical factors: (1) expanded data coverage and consistency, and (2) suitable reference network libraries, and data mining from them. Concerning (1) we discuss several contemporary themes that can improve data quality, which in turn will boost the outcome of downstream network analysis. For (2), we focus on network analysis developments, specifically, the need for context-specific networks and essential considerations for localized network analysis. PMID:24833333

A model for the determination of pollen count using google search queries for patients suffering from allergic rhinitis.

PubMed

König, Volker; Mösges, Ralph

2014-01-01

Background. The transregional increase in pollen-associated allergies and their diversity have been scientifically proven. However, patchy pollen count measurement in many regions is a worldwide problem with few exceptions. Methods. This paper used data gathered from pollen count stations in Germany, Google queries using relevant allergological/biological keywords, and patient data from three German study centres collected in a prospective, double-blind, randomised, placebo-controlled, multicentre immunotherapy study to analyse a possible correlation between these data pools. Results. Overall, correlations between the patient-based, combined symptom medication score and Google data were stronger than those with the regionally measured pollen count data. The correlation of the Google data was especially strong in the groups of severe allergy sufferers. The results of the three-centre analyses show moderate to strong correlations with the Google keywords (up to >0.8 cross-correlation coefficient, P < 0.001) in 10 out of 11 groups (three averaged patient cohorts and eight subgroups of severe allergy sufferers: high IgE class, high combined symptom medication score, and asthma). Conclusion. For countries with a good Internet infrastructure but no dense network of pollen traps, this could represent an alternative for determining pollen levels and, forecasting the pollen count for the next day.

The Exponential Expansion of Simulation in Research

DTIC Science & Technology

2012-12-01

exponential growth of computing power. Although other analytic approaches also benefit from this trend, keyword searches of several scholarly search ... engines reveal that the reliance on simulation is increasing more rapidly. A descriptive analysis paints a compelling picture: simulation is frequently

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Publications - GMC 406 | Alaska Division of Geological & Geophysical

Science.gov Websites

DGGS GMC 406 Publication Details Title: Carbon isotope and total organic carbon (TOC) analysis of organic carbon (TOC) analysis of washed and unwashed cuttings from the South Barrow Test #3 well: Alaska Table(s) gmc406_toc.xls (196.0 K) gmc406_samples.xls (144.0 K) Keywords Isotopes; Oil and Gas; Organic

Multidimensional System Analysis of Electro-Optic Sensors with Sampled Deterministic Output.

DTIC Science & Technology

1987-12-18

System descriptions of scanning and staring electro - optic sensors with sampled output are developed as follows. Functions representing image...to complete the system descriptions. The results should be useful for designing electro - optic sensor systems and correcting data for instrumental...effects and other experimental conditions. Keywords include: Electro - optic system analysis, Scanning sensors, Staring sensors, Spatial sampling, and Temporal sampling.

U.S.-MEXICO BORDER PROGRAM ARIZONA BORDER STUDY--STANDARD OPERATING PROCEDURE FOR PERFORMANCE OF ANALYSES ON NHEXAS DATA (IIT-A-3.0)

EPA Science Inventory

The purpose of this SOP is to define the procedures for the analysis of NHEXAS and Border study data. These methods were used for all data analysis associated with the Arizona NHEXAS project and the Border study at the Illinois Institute of Technology (IIT) site. Keywords: data;...

Examining Students' Reflective Thinking from Keywords Tagged to Blogs: Using Map Analysis as a Content Analysis Method

ERIC Educational Resources Information Center

Xie, Ying; Sharma, Priya

2013-01-01

Reflective learning refers to a learner's purposeful and conscious manipulation of ideas toward meaningful learning. Blogs have been used to support reflective thinking, but the commonly seen blog software usually does not provide overt mechanisms for students' high-level reflections. A new tool was designed to support the reflective…

An Analysis of a Decade of Research in 10 Instructional Design and Technology Journals

ERIC Educational Resources Information Center

West, Richard Edward; Borup, Jered

2014-01-01

In this paper, we review findings from an analysis of the past decade (2001-10) of research in 10 major journals in the field of instructional design and technology. Each research paper published in these journals during this decade was categorized according to its focus or methodology, topical keywords, authorship and citation trends; and the…

Immunological Targeting of Tumor Initiating Prostate Cancer Cells

DTIC Science & Technology

2014-10-01

clinically using well-accepted immuno-competent animal models. 2) Keywords: Prostate Cancer, Lymphocyte, Vaccine, Antibody 3) Overall Project Summary...castrate animals . Task 1: Identify and verify antigenic targets from CAstrate Resistant Luminal Epithelial Cells (CRLEC) (months 1-16... animals per group will be processed to derive sufficient RNA for microarray analysis; the experiment will be repeated x 3. Microarray analysis will

A bibliometric analysis of occupational therapy publications.

PubMed

Brown, Ted; Gutman, Sharon A; Ho, Yuh-Shan; Fong, Kenneth N K

2018-01-01

Bibliometrics involves the statistical analysis of the publications in a specific discipline or subject area. A bibliometric analysis of the occupational therapy refereed literature is needed. A bibliometric analysis was completed of the occupational therapy literature from 1991-2014, indexed in the Science Citation Index-Expanded or the Social Sciences Citation Index. Publications were searched by title, abstract, keywords, and KeyWords Plus. Total number of article citations, citations per journal, and contributions per country, individual authors, and institution were calculated. 5,315 occupational therapy articles were published in 821 journals. It appears that there is a citation window of an approximate 10-year period between the time of publication and the peak number of citations an article receives. The top three most highly cited articles were published in Developmental Medicine and Child Neurology, JAMA, and Lancet. AJOT, BJOT and AOTJ published the largest number of occupational therapy articles with the United States, Australia, and Canada producing the highest number of publications. McMaster University, the University of Queensland, and the University of Toronto were the institutions that published the largest number of occupational therapy journal articles. The occupational therapy literature is growing and the frequency of article citation is increasing.

Digital Health Communication and Global Public Influence: A Study of the Ebola Epidemic.

PubMed

Roberts, Hal; Seymour, Brittany; Fish, Sands Alden; Robinson, Emily; Zuckerman, Ethan

2017-01-01

Scientists and health communication professionals expressed frustration over the relationship between misinformation circulating on the Internet and global public perceptions of and responses to the Ebola epidemic originating in West Africa. Using the big data platform Media Cloud, we analyzed all English-language stories about keyword "Ebola" published from 1 July 2014 to 17 November 2014 from the media sets U.S. Mainstream Media, U.S. Regional Media, U.S. Political Blogs, U.S. Popular Blogs, Europe Media Monitor, and Global Voices to understand how social network theory and models of the networked global public may have contributed to health communication efforts. 109,400 stories met our inclusion criteria. The CDC and WHO were the two media sources with the most inlinks (hyperlinks directed to their sites). Twitter was fourth Significantly more public engagement on social media globally was directed toward stories about risks of U.S. domestic Ebola infections than toward stories focused on Ebola infections in West Africa or on science-based information. Corresponding public sentiments about Ebola were reflected in the policy responses of the international community, including violations of the International Health Regulations and the treatment of potentially exposed individuals. The digitally networked global public may have influenced the discourse, sentiment, and response to the Ebola epidemic.

Network meta-analysis: an introduction for clinicians.

PubMed

Rouse, Benjamin; Chaimani, Anna; Li, Tianjing

2017-02-01

Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Web page sorting algorithm based on query keyword distance relation

NASA Astrophysics Data System (ADS)

Yang, Han; Cui, Hong Gang; Tang, Hao

2017-08-01

In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.

Exploration of the integration of care for persons with a traumatic brain injury using social network analysis methodology.

PubMed

Lamontagne, Marie-Eve

2013-01-01

Integration is a popular strategy to increase the quality of care within systems of care. However, there is no common language, approach or tool allowing for a valid description, comparison and evaluation of integrated care. Social network analysis could be a viable methodology to provide an objective picture of integrated networks. To illustrate social network analysis use in the context of systems of care for traumatic brain injury. We surveyed members of a network using a validated questionnaire to determine the links between them. We determined the density, centrality, multiplexity, and quality of the links reported. The network was described as moderately dense (0.6), the most prevalent link was knowledge, and four organisation members of a consortium were central to the network. Social network analysis allowed us to create a graphic representation of the network. Social network analysis is a useful methodology to objectively characterise integrated networks.

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Reexamining the Relationship between Verbal Knowledge Background and Keyword Training for Vocabulary Acquisition

PubMed

Hogben; Lawson

1997-07-01

The literature on keyword training presents a confusing picture of the usefulness of the keyword method for foreign language vocabulary learning by students with strong verbal knowledge backgrounds. This paper reviews research which notes the existence of conflicting sets of findings concerning the verbal background-keyword training relationship and presents the results of analyses which argue against the assertion made by McDaniel and Pressley (1984) that keyword training will have minimal effect on students with high verbal ability. Findings from regression analyses of data from two studies did not show that the relationship between keyword training and immediate recall performance was moderated by verbal knowledge background. The disparate sets of findings related to the keyword training-verbal knowledge relationship and themes emerging from other research suggest that this relationship requires further examination.

21 CFR 99.201 - Manufacturer's submission to the agency.

Code of Federal Regulations, 2011 CFR

2011-04-01

... dissemination, and unpublished manuscripts, abstracts, and data analyses from completed or ongoing..., completion of data collection, completion of data analysis, and submission of the supplemental application...., the databases or sources and criteria (i.e., subject headings/keywords) used to generate the...

Global Citizenship

ERIC Educational Resources Information Center

Osiadacz, Evelina

2018-01-01

This article draws attention to the keyword "global citizenship" through an analysis of the ambiguity of expectations of teachers from the Ontario curriculum documents. Particular reference is drawn to the citizenship education framework, an addition to the 2013 revision of "Ontario Curriculum: Social Studies, Grades 1 to 6;…

Fiber and prebiotic supplementation in enteral nutrition: A systematic review and meta-analysis

PubMed Central

Kamarul Zaman, Mazuin; Chin, Kin-Fah; Rai, Vineya; Majid, Hazreen Abdul

2015-01-01

AIM: To investigate fiber and prebiotic supplementation of enteral nutrition (EN) for diarrhea, fecal microbiota and short-chain fatty acids (SCFAs). METHODS: MEDLINE, EMBASE, Cochrane Library, CINAHL, Academic Search Premier, and Web of Science databases were searched for human experimental and observational cohort studies conducted between January 1990 and June 2014. The keywords used for the literature search were fiber, prebiotics and enteral nutrition. English language studies with adult patient populations on exclusive EN were selected. Abstracts and/or full texts of selected studies were reviewed and agreed upon by two independent researchers for inclusion in the meta-analysis. Tools used for the quality assessment were Jadad Scale and the Scottish Intercollegiate Guidelines Network Critical Appraisal of the Medical Literature. RESULTS: A total of 456 possible articles were retrieved, and 430 were excluded due to lack of appropriate data. Of the 26 remaining studies, only eight investigated the effects of prebiotics. Results of the meta-analysis indicated that overall, fiber reduces diarrhea in patients receiving EN (OR = 0.47; 95%CI: 0.29-0.77; P = 0.02). Subgroup analysis revealed a positive effect of fiber supplementation in EN towards diarrhea in stable patients (OR = 0.31; 95%CI: 0.19-0.51; P < 0.01), but not in critically ill patients (OR = 0.89; 95%CI: 0.41-1.92; P = 0.77). Prebiotic supplementation in EN does not improve the incidence of diarrhea despite its manipulative effect on bifidobacteria concentrations and SCFA in healthy humans. In addition, the effect of fiber and/or prebiotic supplementation towards fecal microbiota and SCFA remain disputable. CONCLUSION: Fiber helps minimize diarrhea in patients receiving EN, particularly in non-critically ill patients. However, the effect of prebiotics in moderating diarrhea is inconclusive. PMID:25954112

Occupational therapy publications by Australian authors: A bibliometric analysis.

PubMed

Brown, Ted; Gutman, Sharon A; Ho, Yuh-Shan

2018-01-18

Bibliometrics refers to the collection and measurement of publishing and citation data configurations with the goal of quantifying the influence of scholarly activities. Advantages of bibliometrics include the generation of quantitative indicators of impact, productivity, quality and collaboration. Those parties who benefit from the results of bibliometric analysis include researchers, educators, journal publishers, employers and research funding bodies. A bibliometric analysis was completed of peer-reviewed literature from 1991 to 2015, written by Australian occupational therapists (who were able to be identified as such), and indexed in the Science Citation Index-Expanded (SCI-Expanded) or the Social Sciences Citation Index (SSCI) databases. "Occupational therapy" and "occupational therapist(s)" were used as keywords to search journal articles' publication title, abstract, author details, keywords and KeyWord Plus. Between 1991 and 2015, 752 peer-reviewed journal articles were published by Australian occupational therapy authors. On average, those articles had 3.7 authors, 35 references, and were nine pages in length. The top four journals in which Australian occupational therapists published were Australian Occupational Therapy Journal, British Journal of Occupational Therapy, American Journal of Occupational Therapy, and Physical and Occupational Therapy in Paediatrics. The four Australian institutions that generated the largest number of occupational therapy articles were the University of Queensland, University of Sydney, La Trobe University, and Monash University. The top four countries with whom Australian authors collaborated in manuscript writing were the United Kingdom, United States, Canada and Sweden. The volume of occupational therapy peer-reviewed literature has grown over the last two decades. Australian authors have and continue to make significant contributions to the occupational therapy body of knowledge nationally and internationally. © 2018 Occupational Therapy Australia.

Motor vehicle crashes in roadway construction workzones: an analysis using narrative text from insurance claims.

PubMed

Sorock, G S; Ranney, T A; Lehto, M R

1996-01-01

Motor vehicle travel through roadway construction workzones has been shown to increase the risk of a crash. The number of workzones has increased due to recent congressional funding in 1991 for expanded roadway maintenance and repair. In this paper, we describe the characteristics and costs of motor vehicle crashes in roadway construction workzones. As opposed to using standard accident codes to identify accident types, automobile insurance claims files from 1990-93 were searched to identify records with the keyword "construction" in the accident narrative field. A total of 3,686 claims were used for the analysis of crashes. Keywords from the accident narrative field were used to identify five pre-crash vehicle activities and five crash types. We evaluated misclassification error by reading 560 randomly selected claims and found it to be only 5%. For each of four years, 1990-93, there was a total of 648,996,977 and 1,065 crashes, respectively. There was a 70% increase in the crash rate per 10,000 personal insured vehicles from 1990-93 (2.1-3.6). Most crashes (26%) involved a stopped or slowing vehicle in the workzone. The most common crash (31%) was a rear-end collision. The most costly pre-crash activity was a major judgment error on the part of a driver (n = 120, median cost = $2,628). An overturned vehicle was the most costly crash type (n = 16, median cost = $4,745). In summary, keyword text analysis of accident narrative data used in this study demonstrated its utility and potential for enhancing injury epidemiology. The results suggest interventions are needed to respond to growing traffic hazards in construction workzones.

Understanding complex interactions using social network analysis.

PubMed

Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

2012-10-01

The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

ESIP Documentation Cluster Session: GCMD Keyword Update

NASA Technical Reports Server (NTRS)

Stevens, Tyler

2018-01-01

The Global Change Master Directory (GCMD) Keywords are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data and services are described in a consistent and comprehensive manner and allow for the precise searching of collection-level metadata and subsequent retrieval of data and services. Initiated over twenty years ago, the GCMD Keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. This talk explores the current status of the GCMD keywords, the value and usage that the keywords bring to different tools/agencies as it relates to data discovery, and how the keywords relate to SWEET (Semantic Web for Earth and Environmental Terminology) Ontologies.

Identifying changes in the support networks of end-of-life carers using social network analysis

PubMed Central

Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

2015-01-01

End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. PMID:24644162

Spectrum-Based and Collaborative Network Topology Analysis and Visualization

ERIC Educational Resources Information Center

Hu, Xianlin

2013-01-01

Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

Telecommunication Support System Using Keywords and Their Relevant Information in Videoconferencing — Presentation Method for Keeping Audience's Concentration at Distance Lectures

NASA Astrophysics Data System (ADS)

Asai, Kikuo; Kondo, Kimio; Kobayashi, Hideaki; Saito, Fumihiko

We developed a prototype system to support telecommunication by using keywords selected by the speaker in a videoconference. In the traditional presentation style, a speaker talks and uses audiovisual materials, and the audience at remote sites looks at these materials. Unfortunately, the audience often loses concentration and attention during the talk. To overcome this problem, we investigate a keyword presentation style, in which the speaker holds keyword cards that enable the audience to see additional information. Although keyword captions were originally intended for use in video materials for learning foreign languages, they can also be used to improve the quality of distance lectures in videoconferences. Our prototype system recognizes printed keywords in a video image at a server, and transfers the data to clients as multimedia functions such as language translation, three-dimensional (3D) model visualization, and audio reproduction. The additional information is collocated to the keyword cards in the display window, thus forming a spatial relationship between them. We conducted an experiment to investigate the properties of the keyword presentation style for an audience. The results suggest the potential of the keyword presentation style for improving the audience's concentration and attention in distance lectures by providing an environment that facilitates eye contact during videoconferencing.

Attribute-Based Proxy Re-Encryption with Keyword Search

PubMed Central

Shi, Yanfeng; Liu, Jiqiang; Han, Zhen; Zheng, Qingji; Zhang, Rui; Qiu, Shuo

2014-01-01

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for , and propose two concrete constructions for : key-policy and ciphertext-policy . In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography. PMID:25549257

Attribute-based proxy re-encryption with keyword search.

PubMed

Shi, Yanfeng; Liu, Jiqiang; Han, Zhen; Zheng, Qingji; Zhang, Rui; Qiu, Shuo

2014-01-01

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (ABRKS), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, ABRKS allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for ABRKS, and propose two concrete constructions for ABRKS: key-policy ABRKS and ciphertext-policy ABRKS. In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography.

Analysis on the Correlation of Traffic Flow in Hainan Province Based on Baidu Search

NASA Astrophysics Data System (ADS)

Chen, Caixia; Shi, Chun

2018-03-01

Internet search data records user’s search attention and consumer demand, providing necessary database for the Hainan traffic flow model. Based on Baidu Index, with Hainan traffic flow as example, this paper conduct both qualitative and quantitative analysis on the relationship between search keyword from Baidu Index and actual Hainan tourist traffic flow, and build multiple regression model by SPSS.

The Effect of Learning Cycle Models on Achievement of Students: A Meta-Analysis Study

ERIC Educational Resources Information Center

Sarac, Hakan

2018-01-01

In the study, a meta-analysis was conducted to determine the effect of the use of the learning cycle model on the achievements of the students. Doctorate and master theses, made between 2007 and 2016, were searched using the keywords in Turkish and English. As a result of the screening, a total of 123 dissertations, which used learning cycle…

Identifying and monitoring urban heat island in Bucharest using satellite time series and low cost meteorological sensors

NASA Astrophysics Data System (ADS)

Sandric, Ionut; Onose, Diana; Vanau, Gabriel; Ioja, Cristian

2016-04-01

The present study is focusing on the identification of urban heat island in Bucharest using both remote sensing products and low cost temperature sensors. The urban heat island in Bucharest was analyzed through a network of sensors located in 56 points (47 inside the administrative boundary of the city, 9 outside) 2009-2011. The network lost progressively its initial density, but was reformed during a new phase, 2013-2015. Time series satellite images from MODIS were intersected with the sensors for both phases. Statistical analysis were conducted to identify the temporal and spatial pattern of extreme temperatures in Bucharest. Several environmental factors like albedou, presence and absence of vegetation were used to fit a regression model between MODIS satellite products sensors in order to upscale the temperatures values recorded by MODIS For Bucharest, an important role for air temperature values in urban environments proved to have the local environmental conditions that leads to differences in air temperature at Bucharest city scale between 3-5 °C (both in the summer and in the winter). The UHI maps shows a good correlation with the presence of green areas. Differences in air temperature between higher tree density areas and isolated trees can reach much higher values, averages over 24 h periods still are in the 3-5 °C range The results have been obtained within the project UCLIMESA (Urban Heat Island Monitoring under Present and Future Climate), ongoing between 2013 and 2015 in the framework of the Programme for Research-DevelopmentInnovation for Space Technology and Advanced Research (STAR), administrated by the Romanian Space Agency Keywords: time series, urban heat island

Exploration of the integration of care for persons with a traumatic brain injury using social network analysis methodology

PubMed Central

Lamontagne, Marie-Eve

2013-01-01

Introduction Integration is a popular strategy to increase the quality of care within systems of care. However, there is no common language, approach or tool allowing for a valid description, comparison and evaluation of integrated care. Social network analysis could be a viable methodology to provide an objective picture of integrated networks. Goal of the article To illustrate social network analysis use in the context of systems of care for traumatic brain injury. Method We surveyed members of a network using a validated questionnaire to determine the links between them. We determined the density, centrality, multiplexity, and quality of the links reported. Results The network was described as moderately dense (0.6), the most prevalent link was knowledge, and four organisation members of a consortium were central to the network. Social network analysis allowed us to create a graphic representation of the network. Conclusion Social network analysis is a useful methodology to objectively characterise integrated networks. PMID:24250281

A Text-Mining Framework for Supporting Systematic Reviews.

PubMed

Li, Dingcheng; Wang, Zhen; Wang, Liwei; Sohn, Sunghwan; Shen, Feichen; Murad, Mohammad Hassan; Liu, Hongfang

2016-11-01

Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

What Research Says about Vocabulary Instruction for Students with Learning Disabilities

ERIC Educational Resources Information Center

Jitendra, Asha K.; Edwards, Lana L.; Sacks, Gabriell; Jacobson, Lisa A.

2004-01-01

This article summarizes published research on vocabulary instruction involving students with learning disabilities. Nineteen vocabulary studies that comprised 27 investigations were located. Study interventions gleaned from the review included keyword or mnemonic approaches, cognitive strategy instruction (e.g., semantic features analysis), direct…

A Bibliometric Analysis of Highly Cited and High Impact Occupational Therapy Publications by American Authors.

PubMed

Gutman, Sharon A; Brown, Ted; Ho, Yuh-Shan

2017-07-01

A bibliometric analysis was completed of peer-reviewed literature from 1991-2015, written by American occupational therapists, to examine US high impact scholarship with "occupational therapy" and "occupational therapist(s)" used as keywords to search journal articles' publication title, abstract, author details, and keywords. Results included 1,889 journal articles from 1991-2015 published by American occupational therapists as first or corresponding author. Sixty-nine articles attained a TotalCitation 2015 ≥ 50 and 151 attained a Citation 2015 ≥ 5 indicating that they were the most highly cited literature produced in this period. Although the majority (58%) of this literature was published in occupational therapy-specific journals, 41% was published in interdisciplinary journals. Results illustrate that the volume of highly cited American occupational therapy peer-reviewed literature has grown over the last two decades. There is need for the profession to strategize methods to enhance the publication metrics of occupational therapy-specific journals to reduce the loss of high quality publications to external periodicals.

What Performance Analysts Need to Know About Research Trends in Association Football (2012-2016): A Systematic Review.

PubMed

Sarmento, Hugo; Clemente, Filipe Manuel; Araújo, Duarte; Davids, Keith; McRobert, Allistair; Figueiredo, António

2018-04-01

Evolving patterns of match analysis research need to be systematically reviewed regularly since this area of work is burgeoning rapidly and studies can offer new insights to performance analysts if theoretically and coherently organized. The purpose of this paper was to conduct a systematic review of published articles on match analysis in adult male football, identify and organize common research topics, and synthesize the emerging patterns of work between 2012 and 2016, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The Web of Science database was searched for relevant published studies using the following keywords: 'football' and 'soccer', each one associated with the terms 'match analysis', 'performance analysis', 'notational analysis', 'game analysis', 'tactical analysis' and 'patterns of play'. Of 483 studies initially identified, 77 were fully reviewed and their outcome measures extracted and analyzed. Results showed that research mainly focused on (1) performance at set pieces, i.e. corner kicks, free kicks, penalty kicks; (2) collective system behaviours, captured by established variables such as team centroid (geometrical centre of a set of players) and team dispersion (quantification of how far players are apart), as well as tendencies for team communication (establishing networks based on passing sequences), sequential patterns (predicting future passing sequences), and group outcomes (relationships between match-related statistics and final match scores); and (3) activity profile of players, i.e. playing roles, effects of fatigue, substitutions during matches, and the effects of environmental constraints on performance, such as heat and altitude. From the previous review, novel variables were identified that require new measurement techniques. It is evident that the complexity engendered during performance in competitive soccer requires an integrated approach that considers multiple aspects. A challenge for researchers is to align these new measures with the needs of the coaches through a more integrated relationship between coaches and researchers, to produce practical and usable information that improves player performance and coach activity.

Level statistics of words: Finding keywords in literary texts and symbolic sequences

NASA Astrophysics Data System (ADS)

Carpena, P.; Bernaola-Galván, P.; Hackenberg, M.; Coronado, A. V.; Oliver, J. L.

2009-03-01

Using a generalization of the level statistics analysis of quantum disordered systems, we present an approach able to extract automatically keywords in literary texts. Our approach takes into account not only the frequencies of the words present in the text but also their spatial distribution along the text, and is based on the fact that relevant words are significantly clustered (i.e., they self-attract each other), while irrelevant words are distributed randomly in the text. Since a reference corpus is not needed, our approach is especially suitable for single documents for which no a priori information is available. In addition, we show that our method works also in generic symbolic sequences (continuous texts without spaces), thus suggesting its general applicability.

Neuroimaging the neural correlates of increased risk for substance use disorders in attention-deficit/hyperactivity disorder-A systematic review.

PubMed

Adisetiyo, Vitria; Gray, Kevin M

2017-03-01

Children with attention-deficit/hyperactivity disorder (ADHD) are nearly three times more likely to develop substance use disorders (SUD) than their typically developing peers. Our objective was to review the existing neuroimaging research on high-risk ADHD (ie, ADHD with disruptive behavior disorders, familial SUD and/or early substance use), focusing on impulsivity as one possible mechanism underlying SUD risk. A PubMed literature search was conducted using combinations of the keywords "ADHD," "substance use," "substance use disorder," "SUD," "addiction," "dependence," "abuse," "risk," "brain" "MRI," "imaging" and "neuroimaging." Studies had to include cohorts that met diagnostic criteria for ADHD; studies of individuals with ADHD who all met criteria for SUD were excluded. Eight studies met the search criteria. Individuals with high-risk ADHD have hyperactivation in the motivation-reward processing brain network during tasks of impulsive choice, emotion processing, and risky decision-making. During response inhibition tasks, they have hypoactivation in the inhibitory control brain network. However, studies focusing on this latter circuit found hypoactivation during inhibitory control tasks, decreased white matter microstructure coherence and reduced cortical thickness in ADHD independent of substance use history. An exaggerated imbalance between the inhibitory control network and the motivation-reward processing network is theorized to distinguish individuals with high-risk ADHD. Preliminary findings suggest that an exaggerated aberrant reward processing network may be the driving neural correlate of increased SUD risk in ADHD. Neural biomarkers of increased SUD risk in ADHD could help clinicians identify which patients may benefit most from SUD prevention. Thus, more neuroimaging research on this vulnerable population is needed. (Am J Addict 2017;26:99-111). © 2017 American Academy of Addiction Psychiatry.

Packet spacing : an enabling mechanism for delivering multimedia content in computational grids /

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

Feng, A. C.; Feng, W. C.; Belford, Geneva G.

2001-01-01

Streaming multimedia with UDP has become increasingly popular over distributed systems like the Internet. Scientific applications that stream multimedia include remote computational steering of visualization data and video-on-demand teleconferencing over the Access Grid. However, UDP does not possess a self-regulating, congestion-control mechanism; and most best-efort traflc is served by congestion-controlled TCF! Consequently, UDP steals bandwidth from TCP such that TCP$ows starve for network resources. With the volume of Internet traffic continuing to increase, the perpetuation of UDP-based streaming will cause the Internet to collapse as it did in the mid-1980's due to the use of non-congestion-controlled TCP. To address thismore » problem, we introduce the counterintuitive notion of inter-packet spacing with control feedback to enable UDP-based applications to perform well in the next-generation Internet and computational grids. When compared with traditional UDP-based streaming, we illustrate that our approach can reduce packet loss over SO% without adversely afecting delivered throughput. Keywords: network protocol, multimedia, packet spacing, streaming, TCI: UDlq rate-adjusting congestion control, computational grid, Access Grid.« less

Collective dynamics of social annotation

PubMed Central

Cattuto, Ciro; Barrat, Alain; Baldassarri, Andrea; Schehr, Gregory; Loreto, Vittorio

2009-01-01

The enormous increase of popularity and use of the worldwide web has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with keywords known as “tags.” Understanding the rich emergent structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks (RWs), and complex networks theory, can effectively contribute to the mathematical modeling of social annotation systems. Here, we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of RWs. This modeling framework reproduces several aspects, thus far unexplained, of social annotation, among which are the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and that are typically hard to access. PMID:19506244

The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

PubMed

Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y; Kantelhardt, Jan W

2015-01-01

Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

PubMed Central

Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y.; Kantelhardt, Jan W.

2015-01-01

Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic’s importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends. PMID:26720074

A Statistical Ontology-Based Approach to Ranking for Multiword Search

ERIC Educational Resources Information Center

Kim, Jinwoo

2013-01-01

Keyword search is a prominent data retrieval method for the Web, largely because the simple and efficient nature of keyword processing allows a large amount of information to be searched with fast response. However, keyword search approaches do not formally capture the clear meaning of a keyword query and fail to address the semantic relationships…

The Keyword Method of Vocabulary Acquisition: An Experimental Evaluation.

ERIC Educational Resources Information Center

Griffith, Douglas

The keyword method of vocabulary acquisition is a two-step mnemonic technique for learning vocabulary terms. The first step, the acoustic link, generates a keyword based on the sound of the foreign word. The second step, the imagery link, ties the keyword to the meaning of the item to be learned, via an interactive visual image or other…

A critical review of the development, current hotspots, and future directions of Lake Taihu research from the bibliometrics perspective.

PubMed

Zhang, Yunlin; Yao, Xiaolong; Qin, Boqiang

2016-07-01

Lake Taihu, as the important drinking water source of the Yangtze River Delta urban agglomeration and the third largest freshwater lake in China, has experienced serious lake eutrophication and water quality deterioration in the past three decades. Growing scientific, political, and public attention has been given to the water quality of Lake Taihu. This study aimed to conduct a comparative quantitative and qualitative analysis of the development, current hotspots, and future directions of Lake Taihu research using a bibliometric analysis of eight well-studied lakes (Lake Taihu, Lake Baikal, Lake Biwa, Lake Erie, Lake Michigan, Lake Ontario, Lake Superior and Lake Victoria) around the world based on the Science Citation Index (SCI) database. A total of 1582 papers discussing Lake Taihu research were published in 322 journals in the past three decades. However, the first paper about Lake Taihu research was not found in the SCI database until 1989, and there were only zero, one, or two papers each year from 1989 to 1995. There had been rapid development in Lake Taihu research since 1996 and a sharp increase in papers since 2005. A keyword analysis showed that "sediment," "eutrophication", "Microcystis aeruginosa", "cyanobacterial blooms", and "remote sensing" were the most frequently used keywords of the study subject. Owing to its significant impact on aquatic ecosystems, a crucial emphasis has been placed on climate change recently. In addition, the future focuses of research directions, including (1) environmental effects of physical processes; (2) nutrient cycles and control and ecosystem responses; (3) cyanobacteria bloom monitoring, causes, forecast and management; (4) eutrophication and climate change interactions; and (5) ecosystem degradation mechanism and ecological practice of lake restoration, are presented based on the keyword analysis. Through multidisciplinary fields (physics, chemistry, and biology) cross and synthesis study of Lake Taihu, the development of shallow lake limnology will be largely promoted.

The Application of Social Network Analysis to Team Sports

ERIC Educational Resources Information Center

Lusher, Dean; Robins, Garry; Kremer, Peter

2010-01-01

This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting…

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

On the topological structure of multinationals network

NASA Astrophysics Data System (ADS)

Joyez, Charlie

2017-05-01

This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.

Association between occupation and contact allergy to the fragrance mix: a multifactorial analysis of national surveillance data

PubMed Central

Uter, W; Schnuch, A; Geier, J; Pfahlberg, A; Gefeller, O

2001-01-01

OBJECTIVES—To assess the role of potential (occupational) risk factors for fragrance contact allergy (FCA). Most studies assessing the range of contact sensitisation in various clinical populations found the fragrance mix, a good screening tool for the detection of FCA in general, to be one of the leading allergens. The role of occupational exposure to fragrances is, however, yet unclear.
METHODS—Firstly, crude analyses of the prevalence of FCA in various occupational fields including all 57 779 patients patch tested in the participating centres of the Information Network of Departments of Dermatology (IVDK) between January 1992 and December 1998. Secondly, a multifactorial Poisson regression analysis of these patients, including several potential risk factors.
RESULTS—(a) The proportion of patients with FCA varied greatly between different occupational groups from 2.5% to 17.4%, (b) the highest occupational risk of FCA was associated with work as a masseur or physiotherapist, metal furnace operator, potter or glass maker etc, or geriatric nurse, (c) non-occupational factors that influenced risk of FCA included atopy, female sex, several sites, in particular the axillae, and increasing age.
CONCLUSIONS—Occupations with a high risk of FCA were identified as targets of preventive action—that is, the substitution of scented products with fragrance free materials with which to work (skin disinfectants, cleaning solutions, personal care products) wherever possible.


Keywords: contact allergy; occupational risk factors; fragrances PMID:11351055

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Multidimensional Analysis of Linguistic Networks

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Banisch, Sven

Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network.

PubMed

De Brún, Aoife; McAuliffe, Eilish

2018-03-13

Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering.

PubMed

Liu, Ying; Ciliax, Brian J; Borges, Karin; Dasigi, Venu; Ram, Ashwin; Navathe, Shamkant B; Dingledine, Ray

2004-01-01

One of the key challenges of microarray studies is to derive biological insights from the unprecedented quatities of data on gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the nature of the functional links among genes within the derived clusters. However, the quality of the keyword lists extracted from biomedical literature for each gene significantly affects the clustering results. We extracted keywords from MEDLINE that describes the most prominent functions of the genes, and used the resulting weights of the keywords as feature vectors for gene clustering. By analyzing the resulting cluster quality, we compared two keyword weighting schemes: normalized z-score and term frequency-inverse document frequency (TFIDF). The best combination of background comparison set, stop list and stemming algorithm was selected based on precision and recall metrics. In a test set of four known gene groups, a hierarchical algorithm correctly assigned 25 of 26 genes to the appropriate clusters based on keywords extracted by the TDFIDF weighting scheme, but only 23 og 26 with the z-score method. To evaluate the effectiveness of the weighting schemes for keyword extraction for gene clusters from microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle were used as a second test set. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords had higher purity, lower entropy, and higher mutual information than those produced from normalized z-score weighted keywords. The optimized algorithms should be useful for sorting genes from microarray lists into functionally discrete clusters.

Development of Communication Conventions in Instructional Electronic Chats.

ERIC Educational Resources Information Center

Collins, Mauri P.; Murphy, Karen L.

1997-01-01

This study used content analysis to identify the communication conventions and protocols that real-time, interactive electronic chat users developed in instructional settings. Most frequently used conventions included sharing information/techniques for conveying meaning and indicating interest in a topic, using keywords and names of individuals,…

The Exponential Expansion of Simulation: How Simulation has Grown as a Research Tool

DTIC Science & Technology

2012-09-01

exponential growth of computing power. Although other analytic approaches also benefit from this trend, keyword searches of several scholarly search ... engines reveal that the reliance on simulation is increasing more rapidly. A descriptive analysis paints a compelling picture: simulation is frequently

Discovering Semantic Patterns in Bibliographically Coupled Documents.

ERIC Educational Resources Information Center

Qin, Jian

1999-01-01

An example of semantic pattern analysis, based on keywords selected from documents grouped by bibliographical coupling, is used to demonstrate the methodological aspects of knowledge discovery in bibliographic databases. Frequency distribution patterns suggest the existence of a common intellectual base with a wide range of specialties and…

The relative efficiency of modular and non-modular networks of different size

PubMed Central

Tosh, Colin R.; McNally, Luke

2015-01-01

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity. PMID:25631996

A unified framework for image retrieval using keyword and visual features.

PubMed

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

NASA Astrophysics Data System (ADS)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

[Nailfold capillaroscopy in children and adolescents with rheumatic diseases].

PubMed

Petry, Daniela G; Terreri, Maria T; Len, Cláudio A; Hilário, Maria O

2008-01-01

Nailfold capillaroscopy is a simple, noninvasive and inexpensive method which allows a functional and morphological study of the capillary network through direct visualization of the distal row of periungueal capillaries of the fingers. This method has been used as a diagnostic auxiliary in diseases such as scleroderma, dermatomyositis, systemic lupus erythematosus and mixed connective tissue disease. It has also been used to differentiate between active and non active diseases, especially dermatomyositis, and to distinguish between primary and secondary Raynaud's phenomenon. Most reports of nailfold capillaroscopy are qualitative and semi-quantitative. Manuscripts describing quantitative methods (video-capillaroscopy) are scarce, particularly in childhood. The authors did a literature review based on Medline, Lilacs and Pubmed data using the keywords: nailfold capillaroscopy, colagenosis, Raynaud, children and adolescents.

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

PubMed

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.

Visualizing Collaboration Characteristics and Topic Burst on International Mobile Health Research: Bibliometric Analysis.

PubMed

Shen, Lining; Xiong, Bing; Li, Wei; Lan, Fuqiang; Evans, Richard; Zhang, Wei

2018-06-05

In the last few decades, mobile technologies have been widely adopted in the field of health care services to improve the accessibility to and the quality of health services received. Mobile health (mHealth) has emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported. The purpose of this study was to analyze the current state of research, including publication outputs, in the field of mHealth to uncover in-depth collaboration characteristics and topic burst of international mHealth research. The authors collected literature that has been published in the last 20 years and indexed by Thomson Reuters Web of Science Core Collection (WoSCC). Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author, institution, and country collaboration level. The temporal visualization map of burst terms was drawn, and the co-occurrence matrix of these burst terms was analyzed by hierarchical cluster analysis and social network analysis. A total of 2704 bibliographic records on mHealth were collected. The earliest paper centered on mHealth was published in 1997, with the number of papers rising continuously since then. A total of 21.28% (2318/10,895) of authors publishing mHealth research were first author, whereas only 1.29% (141/10,895) of authors had published one paper. The total degree of author collaboration was 4.42 (11,958/2704) and there are 266 core authors who have collectively published 53.07% (1435/2704) of the total number of publications, which means that the core group of authors has fundamentally been formed based on the Law of Price. The University of Michigan published the highest number of mHealth-related publications, but less collaboration among institutions exits. The United States is the most productive country in the field and plays a leading role in collaborative research on mHealth. There are 5543 different identified keywords in the cleaned records. The temporal bar graph clearly presents overall topic evolutionary process over time. There are 12 important research directions identified, which are in the imbalanced development. Moreover, the density of the network was 0.007, a relatively low level. These 12 topics can be categorized into 4 areas: (1) patient engagement and patient intervention, (2) health monitoring and self-care, (3) mobile device and mobile computing, and (4) security and privacy. The collaboration of core authors on mHealth research is not tight and stable. Furthermore, collaboration between institutions mainly occurs in the United States, although country collaboration is seen as relatively scarce. The focus of research topics on mHealth is decentralized. Our study might provide a potential guide for future research in mHealth. ©Lining Shen, Bing Xiong, Wei Li, Fuqiang Lan, Richard Evans, Wei Zhang. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 05.06.2018.

Central On-Line Data Directory

NASA Technical Reports Server (NTRS)

Thieman, J.

1986-01-01

The National Space Science Data Center (NSSDC) Central On-Line Data Directory (CODD), which allows the general scientist remote access to information about data sets available not only at NSSDC, but throughout the scientific community, is discussed. A user may search for data set information within CODD by specifying spacecraft name, experiment name, investigator name, and/or keywords. CODD will include information on atmospheric science data sets contained not only within the PCDS, but also within other data sets that are deemed important. Keywords to be used in locating these data sets are currently being formulated. The main type of keyword to be used for categorization of data sets will be discipline related. The primary discipline keyword for PCDS-type data sets would be ATMOSPHERIC SCIENCE. A good set of subdiscipline keywords is needed under this discipline to subdivide the data sets. A sheet containing a strawman set of subdiscipline keywords was distributed, and a request was made for the knowledgeable scientists to modify or replace the proposed keywords.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery

PubMed Central

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-01-01

Background DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. Results GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. Conclusion GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at . PMID:19728865

A Case Study: Optimal Stage Gauge NetworkUsing Multi Objective Genetic Algorithm

NASA Astrophysics Data System (ADS)

Joo, H. J.; Han, D.; Jung, J.; Kim, H. S.

2017-12-01

Recently, the possibility of occurrence of localized strong heavy rainfall due to climate change is increasing and flood damage is also increasing trend in Korea. Therefore we need more precise hydrologic analysis for preparing alternatives or measures for flood reduction by considering climate conditions which we have difficulty in the prediction. To do this, obtaining reliable hydrologic data, for an example, stage data, is very important. However, the existing stage gauge stations are scattered around the country, making it difficult to maintain them in a stable manner, and subsequently hard to acquire the hydrologic data that could be used for reflecting the localized hydrologic characteristics. In order to overcome such restrictions, this paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For that, this paper identifies the current status of the stage gauge stations installed in the Chung-Ju dam in Han river, Korea and extract the factors related to the division and characteristics of basins. Then, the obtained factors are used to develop the representative unit hydrograph that shows the characteristics of flow. After that, the data are converted into the probability density function and the stations at individual basins are selected by using the entropy theory. In last step, we establish the optimized stage gauge network by the location of the stage station and grade using the Multi Objective Genetic Algorithm(MOGA) technique that takes into account for the combinations of the number of the stations. It is expected that this paper can help establish an optimal observational network of stage guages as it can be applied usefully not only for protecting against floods in a stable manner, but also for acquiring the hydrologic data in an efficient manner. Keywords : Unit Hydrograph, Entropy, Grade of Stage Gauge Station, Multi Objective Genetic Algorithm(MOGA), Optimal Stage Guage Network Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

PubMed

Hosseini, S M Hadi; Hoeft, Fumiko; Kesler, Shelli R

2012-01-01

In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

Automatic Keyword Extraction from Individual Documents

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

Rose, Stuart J.; Engel, David W.; Cramer, Nicholas O.

2010-05-03

This paper introduces a novel and domain-independent method for automatically extracting keywords, as sequences of one or more words, from individual documents. We describe the method’s configuration parameters and algorithm, and present an evaluation on a benchmark corpus of technical abstracts. We also present a method for generating lists of stop words for specific corpora and domains, and evaluate its ability to improve keyword extraction on the benchmark corpus. Finally, we apply our method of automatic keyword extraction to a corpus of news articles and define metrics for characterizing the exclusivity, essentiality, and generality of extracted keywords within a corpus.

Sample size and power considerations in network meta-analysis

PubMed Central

2012-01-01

Background Network meta-analysis is becoming increasingly popular for establishing comparative effectiveness among multiple interventions for the same disease. Network meta-analysis inherits all methodological challenges of standard pairwise meta-analysis, but with increased complexity due to the multitude of intervention comparisons. One issue that is now widely recognized in pairwise meta-analysis is the issue of sample size and statistical power. This issue, however, has so far only received little attention in network meta-analysis. To date, no approaches have been proposed for evaluating the adequacy of the sample size, and thus power, in a treatment network. Findings In this article, we develop easy-to-use flexible methods for estimating the ‘effective sample size’ in indirect comparison meta-analysis and network meta-analysis. The effective sample size for a particular treatment comparison can be interpreted as the number of patients in a pairwise meta-analysis that would provide the same degree and strength of evidence as that which is provided in the indirect comparison or network meta-analysis. We further develop methods for retrospectively estimating the statistical power for each comparison in a network meta-analysis. We illustrate the performance of the proposed methods for estimating effective sample size and statistical power using data from a network meta-analysis on interventions for smoking cessation including over 100 trials. Conclusion The proposed methods are easy to use and will be of high value to regulatory agencies and decision makers who must assess the strength of the evidence supporting comparative effectiveness estimates. PMID:22992327

Analysis and Design of a High Power Laser Adaptive Phased Array Transmitter

DTIC Science & Technology

1977-12-01

Report Number Assigned by Contract Monitor: SLL 80-711; CR75-537/501 Comments on Document: Archive, RRI, DEW Descriptors, Keywords: Analysis Design...Document: DEW j ^ yrf^N H tpm *■ ww CRT»5g:’’""J{ it.. 2. Government Accession No. 3. Recipient’s Catalog No. 4. Title and Subtitle Analysis...Target Plane (e = 0, Range = 185000 M, Five 0.96 M Subapertures, Pointing Plus Phase Adaption for 10.6 pm Propagation in Vacuum) ... 80 :il-14

An Application of Social Network Analysis on Military Strategy, System Networks and the Phases of War

DTIC Science & Technology

2015-03-26

1977. [29] J. D. Guzman, R. F. Deckro, M. J. Robbins, J. F. Morris and N. A. Ballester, “An Analytical Comparison of Social Network Measures,” IEEE...AN APPLICATION OF SOCIAL NETWORK ANALYSIS ON MILITARY STRATEGY, SYSTEM NETWORKS AND THE PHASES OF...subject to copyright protection in the United States. AFIT-ENS-MS-15-M-117 AN APPLICATION OF SOCIAL NETWORK ANALYSIS ON MILITARY STRATEGY

Co-authorship network analysis in health research: method and potential use.

PubMed

Fonseca, Bruna de Paula Fonseca E; Sampaio, Ricardo Barros; Fonseca, Marcus Vinicius de Araújo; Zicker, Fabio

2016-04-30

Scientific collaboration networks are a hallmark of contemporary academic research. Researchers are no longer independent players, but members of teams that bring together complementary skills and multidisciplinary approaches around common goals. Social network analysis and co-authorship networks are increasingly used as powerful tools to assess collaboration trends and to identify leading scientists and organizations. The analysis reveals the social structure of the networks by identifying actors and their connections. This article reviews the method and potential applications of co-authorship network analysis in health. The basic steps for conducting co-authorship studies in health research are described and common network metrics are presented. The application of the method is exemplified by an overview of the global research network for Chikungunya virus vaccines.

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

Integrated network capacity analysis for freight railroads.

DOT National Transportation Integrated Search

2016-02-23

Rail network capacity analysis should consider all network infrastructures in an integrated way, with the challenges of the nonlinear relationships at each network element, a link or a node, and complexity of the interaction between various network e...

Social network type and morale in old age.

PubMed

Litwin, H

2001-08-01

The aim of this research was to derive network types among an elderly population and to examine the relationship of network type to morale. Secondary analysis of data compiled by the Israeli Central Bureau of Statistics (n = 2,079) was employed, and network types were derived through K-means cluster analysis. Respondents' morale scores were regressed on network types, controlling for background and health variables. Five network types were derived. Respondents in diverse or friends networks reported the highest morale; those in exclusively family or restricted networks had the lowest. Multivariate regression analysis underscored that certain network types were second among the study variables in predicting respondents' morale, preceded only by disability level (Adjusted R(2) =.41). Classification of network types allows consideration of the interpersonal environments of older people in relation to outcomes of interest. The relative effects on morale of elective versus obligated social ties, evident in the current analysis, is a case in point.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

PubMed Central

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A.; Zhang, Wenbo

2016-01-01

Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions. PMID:27740473

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

PubMed

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A; Zhang, Wenbo; He, Bin

2016-12-01

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Network meta-analysis: an introduction for pharmacists.

PubMed

Xu, Yina; Amiche, Mohamed Amine; Tadrous, Mina

2018-05-21

Network meta-analysis is a new tool used to summarize and compare studies for multiple interventions, irrespective of whether these interventions have been directly evaluated against each other. Network meta-analysis is quickly becoming the standard in conducting therapeutic reviews and clinical guideline development. However, little guidance is available to help pharmacists review network meta-analysis studies in their practice. Major institutions such as the Cochrane Collaboration, Agency for Healthcare Research and Quality, Canadian Agency for Drugs and Technologies in Health, and National Institute for Health and Care Excellence Decision Support Unit have endorsed utilizing network meta-analysis to establish therapeutic evidence and inform decision making. Our objective is to introduce this novel technique to pharmacy practitioners, and highlight key assumptions behind network meta-analysis studies.

The Complex Dynamics of Sponsored Search Markets

NASA Astrophysics Data System (ADS)

Robu, Valentin; La Poutré, Han; Bohte, Sander

This paper provides a comprehensive study of the structure and dynamics of online advertising markets, mostly based on techniques from the emergent discipline of complex systems analysis. First, we look at how the display rank of a URL link influences its click frequency, for both sponsored search and organic search. Second, we study the market structure that emerges from these queries, especially the market share distribution of different advertisers. We show that the sponsored search market is highly concentrated, with less than 5% of all advertisers receiving over 2/3 of the clicks in the market. Furthermore, we show that both the number of ad impressions and the number of clicks follow power law distributions of approximately the same coefficient. However, we find this result does not hold when studying the same distribution of clicks per rank position, which shows considerable variance, most likely due to the way advertisers divide their budget on different keywords. Finally, we turn our attention to how such sponsored search data could be used to provide decision support tools for bidding for combinations of keywords. We provide a method to visualize keywords of interest in graphical form, as well as a method to partition these graphs to obtain desirable subsets of search terms.

Fighting Dark Networks: Using Social Network Analysis to Implement the Special Operations Targeting Process for Direct and Indirect Approaches

DTIC Science & Technology

2013-03-01

Wouter De Nooy, Andrej Mrvar and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, (New York: Cambridge University Press, 2005), 5...Granovetter, “The Strength of Weak Ties,” 1350–1368. 151 de Nooy, Mrvar , and Batagelj , Exploratory Social Network Analysis with Pajek, 151. 152...Spacetime Wrinkles Exhibit (1995). de Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek. Cambridge

Publications - GMC 274 | Alaska Division of Geological & Geophysical

Science.gov Websites

DGGS GMC 274 Publication Details Title: Total organic carbon analysis with leaching factor from for more information. Bibliographic Reference Brown and Ruth Laboratories, Inc., 1996, Total organic Information gmc274.pdf (109.0 K) Keywords Organic Carbon Top of Page Department of Natural Resources, Division

Web Tutorials on Systems Thinking Using the Driver-Pressure-State-Impact-Response (DPSIR) Framework

EPA Science Inventory

This set of tutorials provides an overview of incorporating systems thinking into decision-making, an introduction to the DPSIR framework as one approach that can assist in the decision analysis process, and an overview of DPSIR tools, including concept mapping and keyword lists,...

Characteristics of "Music Education" Videos Posted on Youtube

ERIC Educational Resources Information Center

Whitaker, Jennifer A.; Orman, Evelyn K.; Yarbrough, Cornelia

2014-01-01

This content analysis sought to determine information related to users uploading, general content, and specific characteristics of music education videos on YouTube. A total of 1,761 videos from a keyword search of "music education" were viewed and categorized. Results for relevant videos indicated users posted videos under 698 different…

Honeycomb: Visual Analysis of Large Scale Social Networks

NASA Astrophysics Data System (ADS)

van Ham, Frank; Schulz, Hans-Jörg; Dimicco, Joan M.

The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

Disposal and improvement of contaminated by waste extraction of copper mining in chile

NASA Astrophysics Data System (ADS)

Naranjo Lamilla, Pedro; Blanco Fernández, David; Díaz González, Marcos; Robles Castillo, Marcelo; Decinti Weiss, Alejandra; Tapia Alvarez, Carolina; Pardo Fabregat, Francisco; Vidal, Manuel Miguel Jordan; Bech, Jaume; Roca, Nuria

2016-04-01

This project originated from the need of a mining company, which mines and processes copper ore. High purity copper is produced with an annual production of 1,113,928 tons of concentrate to a law of 32%. This mining company has generated several illegal landfills and has been forced by the government to make a management center Industrial Solid Waste (ISW). The forecast volume of waste generated is 20,000 tons / year. Chemical analysis established that the studied soil has a high copper content, caused by nature or from the spread of contaminants from mining activities. Moreover, in some sectors, soil contamination by mercury, hydrocarbons and oils and fats were detected, likely associated with the accumulation of waste. The waters are also impacted by mining industrial tasks, specifically copper ores, molybdenum, manganese, sulfates and have an acidic pH. The ISW management center dispels the pollution of soil and water and concentrating all activities in a technically suitable place. In this center the necessary guidelines for the treatment and disposal of soil contamination caused by uncontrolled landfills are given, also generating a leachate collection system and a network of fluid monitoring physicochemical water quality and soil environment. Keywords: Industrial solid waste, soil contamination, Mining waste

SPARK: Adapting Keyword Query to Semantic Search

NASA Astrophysics Data System (ADS)

Zhou, Qi; Wang, Chong; Xiong, Miao; Wang, Haofen; Yu, Yong

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships.

PubMed

Zong, Nansu; Lee, Sungin; Ahn, Jinhyun; Kim, Hong-Gee

2017-08-01

The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search-improving search with inputs, keywords and preferences, under different topics. This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visual analysis and exploration of complex corporate shareholder networks

NASA Astrophysics Data System (ADS)

Tekušová, Tatiana; Kohlhammer, Jörn

2008-01-01

The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.

Evaluating the Quality of Evidence from a Network Meta-Analysis

PubMed Central

Salanti, Georgia; Del Giovane, Cinzia; Chaimani, Anna; Caldwell, Deborah M.; Higgins, Julian P. T.

2014-01-01

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses. PMID:24992266

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

2016-04-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

Organizational network analysis for two networks in the Washington State Department of Transportation.

DOT National Transportation Integrated Search

2010-10-01

Organizational network analysis (ONA) consists of gathering data on information sharing and : connectivity in a group, calculating network measures, creating network maps, and using this : information to analyze and improve the functionality of the g...

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

PubMed

Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

2016-08-18

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Variational dynamic background model for keyword spotting in handwritten documents

NASA Astrophysics Data System (ADS)

Kumar, Gaurav; Wshah, Safwan; Govindaraju, Venu

2013-12-01

We propose a bayesian framework for keyword spotting in handwritten documents. This work is an extension to our previous work where we proposed dynamic background model, DBM for keyword spotting that takes into account the local character level scores and global word level scores to learn a logistic regression classifier to separate keywords from non-keywords. In this work, we add a bayesian layer on top of the DBM called the variational dynamic background model, VDBM. The logistic regression classifier uses the sigmoid function to separate keywords from non-keywords. The sigmoid function being neither convex nor concave, exact inference of VDBM becomes intractable. An expectation maximization step is proposed to do approximate inference. The advantage of VDBM over the DBM is multi-fold. Firstly, being bayesian, it prevents over-fitting of data. Secondly, it provides better modeling of data and an improved prediction of unseen data. VDBM is evaluated on the IAM dataset and the results prove that it outperforms our prior work and other state of the art line based word spotting system.

Infodemiology of alcohol use in Hong Kong mentioned on blogs: infoveillance study.

PubMed

Chan, Kl; Ho, Sy; Lam, Th

2013-09-02

In 2007 and 2008, the beer and wine tax in Hong Kong was halved and then abolished, resulting in an increase of alcohol consumption. The prevalence of the Internet and a high blogging rate by adolescents and adults present a unique opportunity to study drinking patterns by infodemiology. To assess and explain the online use of alcohol-related Chinese keywords and to validate blog searching as an infoveillance method for surveying changes in drinking patterns (eg, alcohol type) in Hong Kong people (represented by bloggers on a Hong Kong-based Chinese blogging site) in 2005-2010. Blog searching was done using a blog search engine, Google Blog Search, in the archives of a Hong Kong-based blog service provider, MySinaBlog from 2005-2010. Three groups of Chinese keywords, each representing a specific alcohol-related concept, were used: (1) "alcohol" (ie, the control concept), (2) "beer or wine", and (3) "spirit". The resulting blog posts were analyzed quantitatively using infodemiological metrics and correlation coefficients, and qualitatively by manual effort. The infodemiological metrics were (1) apparent prevalence, (2) actual prevalence, (3) prevalence rate, and (4) prevalence ratio. Pearson and Spearman correlations were calculated for prevalence rates and ratios with respect to per capita alcohol consumption. Manual analysis focused on (1) blog author characteristics (ie, authorship, sex, and age), and (2) blog content (ie, frequency of keywords, description of a discrete episode of alcohol drinking, drinking amount, and genres). The online use of alcohol-related concepts increased noticeably for "alcohol" in 2008 and "spirit" in 2008-2009 but declined for "beer or wine" over the years. Correlation between infodemiological and epidemiological data was only significant for the "alcohol" prevalence rate. Most blogs were managed by single authors. Their sex distribution was even, and the majority were aged 18 and above. Not all Chinese keywords were found. Many of the blog posts did not describe a discrete episode of alcohol drinking and were classified as personal diary, opinion, or emotional outlets. The rest lacked information on drinking amount, which hindered assessment of binge drinking. The prevalence of alcohol-related Chinese keywords online was attributed to many different factors, including spam, and hence not a specific reflection of local drinking patterns. Correlation between infodemiological data (represented by prevalence rates and ratios of alcohol-related concepts) and epidemiological data (represented by per capita alcohol consumption) was poor. Many blog posts were affective rather than informative in nature. Semantic analysis of blog content was recommended given enough expertise and resources.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

PubMed

Theodosiou, Theodosios; Efstathiou, Georgios; Papanikolaou, Nikolas; Kyrpides, Nikos C; Bagos, Pantelis G; Iliopoulos, Ioannis; Pavlopoulos, Georgios A

2017-07-14

Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Nested Neural Networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1992-01-01

Report presents analysis of nested neural networks, consisting of interconnected subnetworks. Analysis based on simplified mathematical models more appropriate for artificial electronic neural networks, partly applicable to biological neural networks. Nested structure allows for retrieval of individual subpatterns. Requires fewer wires and connection devices than fully connected networks, and allows for local reconstruction of damaged subnetworks without rewiring entire network.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

Pediatric Gastroenterology in Cuba: Evolution and Challenges.

PubMed

Castañeda-Guillot, Carlos

2016-10-01

INTRODUCTION The professional practice of pediatric gastroenterology arose in Cuba as an expression of the specialty's development internationally and Cuba's new strategies in public health, and in response to national needs for health care expertise in digestive diseases of infants, older children and adolescents. OBJECTIVES Describe the history of pediatric gastroenterology's development in Cuba since its inception at the National Gastroenterology Institute in the early 1970s, its contributions, and efforts to extend it to pediatric hospitals throughout Cuba. EVIDENCE ACQUISITION This is a historical review based on document analysis. Institutional sources from the National Gastroenterology Institute and Ministry of Public Health were reviewed, as well as international and national literature on the history of pediatric gastroenterology and unpublished texts since its emergence in 1972. DEVELOPMENT Although pediatric gastroenterology has not been formally recognized as a medical specialty in Cuba, there have been important achievements in establishing a network of specialized health care services for digestive diseases of children and adolescents. Gastrointestinal endoscopy and other auxiliary diagnostic modalities have been introduced for children and play a major role in clinical trials and research. This article describes the international context that promoted the specialty's development in Cuba. Reference is made to specialized training from its initial stages in 1972, its consolidation as an emerging discipline in Cuban medicine, and its diffusion in Latin American and Caribbean countries. Professional development and specialized training to meet health human resource needs in pediatric hospitals are described, as well as Cuban participation in the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition. National and international milestones, publications, awards and recognitions that indicate advances despite difficulties are also presented. CONCLUSIONS Since 1972, there have been major strides in the development of pediatric gastroenterology practice in Cuba. The establishment of a national network of specialized services in pediatric hospitals throughout Cuba has provided quality health care for digestive diseases of childhood. Pediatric gastroenterology's development and achievements in healthcare and research are such that it deserves official recognition as a medical specialty Cuba. KEYWORDS Pediatric gastroenterology, history of medicine, national health system, service network, hepatology, children, adolescents, Cuba.

[Efficacy of the keyword mnemonic method in adults].

PubMed

Campos, Alfredo; Pérez-Fabello, María José; Camino, Estefanía

2010-11-01

Two experiments were used to assess the efficacy of the keyword mnemonic method in adults. In Experiment 1, immediate and delayed recall (at a one-day interval) were assessed by comparing the results obtained by a group of adults using the keyword mnemonic method in contrast to a group using the repetition method. The mean age of the sample under study was 59.35 years. Subjects were required to learn a list of 16 words translated from Latin into Spanish. Participants who used keyword mnemonics that had been devised by other experimental participants of the same characteristics, obtained significantly higher immediate and delayed recall scores than participants in the repetition method. In Experiment 2, other participants had to learn a list of 24 Latin words translated into Spanish by using the keyword mnemonic method reinforced with pictures. Immediate and delayed recall were significantly greater in the keyword mnemonic method group than in the repetition method group.

A keyword spotting model using perceptually significant energy features

NASA Astrophysics Data System (ADS)

Umakanthan, Padmalochini

The task of a keyword recognition system is to detect the presence of certain words in a conversation based on the linguistic information present in human speech. Such keyword spotting systems have applications in homeland security, telephone surveillance and human-computer interfacing. General procedure of a keyword spotting system involves feature generation and matching. In this work, new set of features that are based on the psycho-acoustic masking nature of human speech are proposed. After developing these features a time aligned pattern matching process was implemented to locate the words in a set of unknown words. A word boundary detection technique based on frame classification using the nonlinear characteristics of speech is also addressed in this work. Validation of this keyword spotting model was done using widely acclaimed Cepstral features. The experimental results indicate the viability of using these perceptually significant features as an augmented feature set in keyword spotting.

Limitations of the mnemonic-keyword method.

PubMed

Campos, Alfredo; González, María Angeles; Amor, Angeles

2003-10-01

The effectiveness of the mnemonic-keyword method was investigated in 4 experiments in which participants were required to learn the 1st-language (L1, Spanish) equivalents of a list of 30 2nd-language words (L2, Latin). Experiments 1 (adolescents) and 2 (adults) were designed to assess whether the keyword method was more effective than the rote method; the researcher supplied the keyword, and the participants were allowed to pace themselves through the list. Experiments 3 (adolescents) and 4 (adults) were similar to Experiments 1 and 2 except that the participants were also supplied with a drawing that illustrated the relationship between the keyword and the L1 target word. All the experiments were performed with groups of participants in their classrooms (i.e., not in a laboratory context). In all experiments, the rote method was significantly more effective than was the keyword method.

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

A methodological approach to the analysis of egocentric social networks in public health research: a practical example.

PubMed

Djomba, Janet Klara; Zaletel-Kragelj, Lijana

2016-12-01

Research on social networks in public health focuses on how social structures and relationships influence health and health-related behaviour. While the sociocentric approach is used to study complete social networks, the egocentric approach is gaining popularity because of its focus on individuals, groups and communities. One of the participants of the healthy lifestyle health education workshop 'I'm moving', included in the study of social support for exercise was randomly selected. The participant was denoted as the ego and members of her/his social network as the alteri. Data were collected by personal interviews using a self-made questionnaire. Numerical methods and computer programmes for the analysis of social networks were used for the demonstration of analysis. The size, composition and structure of the egocentric social network were obtained by a numerical analysis. The analysis of composition included homophily and homogeneity. Moreover, the analysis of the structure included the degree of the egocentric network, the strength of the ego-alter ties and the average strength of ties. Visualisation of the network was performed by three freely available computer programmes, namely: Egonet.QF, E-net and Pajek. The computer programmes were described and compared by their usefulness. Both numerical analysis and visualisation have their benefits. The decision what approach to use is depending on the purpose of the social network analysis. While the numerical analysis can be used in large-scale population-based studies, visualisation of personal networks can help health professionals at creating, performing and evaluation of preventive programmes, especially if focused on behaviour change.

Statistical parsimony networks and species assemblages in Cephalotrichid nemerteans (nemertea).

PubMed

Chen, Haixia; Strand, Malin; Norenburg, Jon L; Sun, Shichun; Kajihara, Hiroshi; Chernyshev, Alexey V; Maslakova, Svetlana A; Sundberg, Per

2010-09-21

It has been suggested that statistical parsimony network analysis could be used to get an indication of species represented in a set of nucleotide data, and the approach has been used to discuss species boundaries in some taxa. Based on 635 base pairs of the mitochondrial protein-coding gene cytochrome c oxidase I (COI), we analyzed 152 nemertean specimens using statistical parsimony network analysis with the connection probability set to 95%. The analysis revealed 15 distinct networks together with seven singletons. Statistical parsimony yielded three networks supporting the species status of Cephalothrix rufifrons, C. major and C. spiralis as they currently have been delineated by morphological characters and geographical location. Many other networks contained haplotypes from nearby geographical locations. Cladistic structure by maximum likelihood analysis overall supported the network analysis, but indicated a false positive result where subnetworks should have been connected into one network/species. This probably is caused by undersampling of the intraspecific haplotype diversity. Statistical parsimony network analysis provides a rapid and useful tool for detecting possible undescribed/cryptic species among cephalotrichid nemerteans based on COI gene. It should be combined with phylogenetic analysis to get indications of false positive results, i.e., subnetworks that would have been connected with more extensive haplotype sampling.

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.

PubMed

Mustafin, Zakhar Sergeevich; Lashin, Sergey Alexandrovich; Matushkin, Yury Georgievich; Gunbin, Konstantin Vladimirovich; Afonnikov, Dmitry Arkadievich

2017-01-27

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged 'network evolution' found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results. Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface. Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

NEAT: an efficient network enrichment analysis test.

PubMed

Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

2016-09-05

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

A bibliometric analysis of the published road traffic injuries research in India, post-1990.

PubMed

Sharma, Neeraj; Bairwa, Mohan; Gowthamghosh, B; Gupta, S D; Mangal, D K

2018-03-01

Globally, road traffic injuries are the leading cause of death among those aged 15-29 years. However, road traffic injury research has not received adequate attention from the scientific community in low- and middle-income countries, including India. The present study aims to provide a bibliometric overview of research assessing road traffic injuries in India. We used Scopus to extract relevant research in road traffic injuries published from 1991 to 2017. This study presented the key bibliometric indicators such as trends of annual publications and citations, top 10 authors, journals, institutions and highly cited articles, citation analysis of articles, co-occurrence of keywords, etc. Analysis was performed using Scopus, Microsoft Excel, and VOS-viewer. A total of 242 articles were retrieved with an h-index of 18, excluding self-citations. A steadfast growth of publications was documented in last decade, especially after the year 2010. The h-index of the top 10 authors, institutions, journals and highly cited articles did not surpass single digits. A network visualisation map showed that 'traffic accident', 'male', 'adolescent' and 'child' were the most commonly encountered key terms. The prominent authors were Gururaj G, Dandona R, and Hyder AA, whereas the top journals were the Indian Journal of Forensic Medicine and Toxicology, Medico Legal Update, and the International Journal of Applied Engineering Research and top institutions were the All India Institute of Medical Sciences, New Delhi, the Indian Institute of Technology, Delhi, and the Administrative Staff College of India. In India, road traffic injuries research is inadequate in quantity and quality, warranting greater attention from researchers and policy planners to address the burden of road traffic injuries.

A bibliometric and visual analysis of global geo-ontology research

NASA Astrophysics Data System (ADS)

Li, Lin; Liu, Yu; Zhu, Haihong; Ying, Shen; Luo, Qinyao; Luo, Heng; Kuai, Xi; Xia, Hui; Shen, Hang

2017-02-01

In this paper, the results of a bibliometric and visual analysis of geo-ontology research articles collected from the Web of Science (WOS) database between 1999 and 2014 are presented. The numbers of national institutions and published papers are visualized and a global research heat map is drawn, illustrating an overview of global geo-ontology research. In addition, we present a chord diagram of countries and perform a visual cluster analysis of a knowledge co-citation network of references, disclosing potential academic communities and identifying key points, main research areas, and future research trends. The International Journal of Geographical Information Science, Progress in Human Geography, and Computers & Geosciences are the most active journals. The USA makes the largest contributions to geo-ontology research by virtue of its highest numbers of independent and collaborative papers, and its dominance was also confirmed in the country chord diagram. The majority of institutions are in the USA, Western Europe, and Eastern Asia. Wuhan University, University of Munster, and the Chinese Academy of Sciences are notable geo-ontology institutions. Keywords such as "Semantic Web," "GIS," and "space" have attracted a great deal of attention. "Semantic granularity in ontology-driven geographic information systems, "Ontologies in support of activities in geographical space" and "A translation approach to portable ontology specifications" have the highest cited centrality. Geographical space, computer-human interaction, and ontology cognition are the three main research areas of geo-ontology. The semantic mismatch between the producers and users of ontology data as well as error propagation in interdisciplinary and cross-linguistic data reuse needs to be solved. In addition, the development of geo-ontology modeling primitives based on OWL (Web Ontology Language）and finding methods to automatically rework data in Semantic Web are needed. Furthermore, the topological relations between geographical entities still require further study.

Software Defined Network Monitoring Scheme Using Spectral Graph Theory and Phantom Nodes

DTIC Science & Technology

2014-09-01

networks is the emergence of software - defined networking ( SDN ) [1]. SDN has existed for the...Chapter III for network monitoring. A. SOFTWARE DEFINED NETWORKS SDNs provide a new and innovative method to simplify network hardware by logically...and R. Giladi, “Performance analysis of software - defined networking ( SDN ),” in Proc. of IEEE 21st International Symposium on Modeling, Analysis

Transportation Network Analysis and Decomposition Methods

DOT National Transportation Integrated Search

1978-03-01

The report outlines research in transportation network analysis using decomposition techniques as a basis for problem solutions. Two transportation network problems were considered in detail: a freight network flow problem and a scheduling problem fo...

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Topological Vulnerability Analysis

NASA Astrophysics Data System (ADS)

Jajodia, Sushil; Noel, Steven

Traditionally, network administrators rely on labor-intensive processes for tracking network configurations and vulnerabilities. This requires a great deal of expertise, and is error prone because of the complexity of networks and associated security data. The interdependencies of network vulnerabilities make traditional point-wise vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we compute attack graphs that convey the impact of individual and combined vulnerabilities on overall security. TVA finds potential paths of vulnerability through a network, showing exactly how attackers may penetrate a network. From this, we identify key vulnerabilities and provide strategies for protection of critical network assets.

Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2.

PubMed

Hoaglin, David C; Hawkins, Neil; Jansen, Jeroen P; Scott, David A; Itzler, Robbin; Cappelleri, Joseph C; Boersma, Cornelis; Thompson, David; Larholt, Kay M; Diaz, Mireya; Barrett, Annabel

2011-06-01

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Social Media's Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors.

PubMed

Fung, Isaac Chun-Hai; Fu, King-Wa; Chan, Chung-Hong; Chan, Benedict Shing Bun; Cheung, Chi-Ngai; Abraham, Thomas; Tse, Zion Tsz Ho

2016-01-01

We analyzed misinformation about Ebola circulating on Twitter and Sina Weibo, the leading Chinese microblog platform, at the outset of the global response to the 2014-2015 Ebola epidemic to help public health agencies develop their social media communication strategies. We retrieved Twitter and Sina Weibo data created within 24 hours of the World Health Organization announcement of a Public Health Emergency of International Concern (Batch 1 from August 8, 2014, 06:50:00 Greenwich Mean Time [GMT] to August 9, 2014, 06:49:59 GMT) and seven days later (Batch 2 from August 15, 2014, 06:50:00 GMT to August 16, 2014, 06:49:59 GMT). We obtained and analyzed a 1% random sample of tweets containing the keyword Ebola. We retrieved all Sina Weibo posts with Chinese keywords for Ebola for analysis. We analyzed changes in frequencies of keywords, hashtags, and Web links using relative risk (RR) and c(2) feature selection algorithm. We identified misinformation by manual coding and categorizing randomly selected sub-datasets. We identified two speculative treatments (i.e., bathing in or drinking saltwater and ingestion of Nano Silver, an experimental drug) in our analysis of changes in frequencies of keywords and hashtags. Saltwater was speculated to be protective against Ebola in Batch 1 tweets but their mentions decreased in Batch 2 (RR=0.11 for "salt" and RR=0.14 for "water"). Nano Silver mentions were higher in Batch 2 than in Batch 1 (RR=10.5). In our manually coded samples, Ebola-related misinformation constituted about 2% of Twitter and Sina Weibo content. A range of 36%-58% of the posts were news about the Ebola outbreak and 19%-24% of the posts were health information and responses to misinformation in both batches. In Batch 2, 43% of Chinese microblogs focused on the Chinese government sending medical assistance to Guinea. Misinformation about Ebola was circulated at a very low level globally in social media in either batch. Qualitative and quantitative analyses of social media posts can provide relevant information to public health agencies during emergency responses.

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Interdependent Multi-Layer Networks: Modeling and Survivability Analysis with Applications to Space-Based Networks

PubMed Central

Castet, Jean-Francois; Saleh, Joseph H.

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks. PMID:23599835

Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

PubMed

Castet, Jean-Francois; Saleh, Joseph H

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks.

Stochastic Control of Multi-Scale Networks: Modeling, Analysis and Algorithms

DTIC Science & Technology

2014-10-20

Theory, (02 2012): 0. doi: B. T. Swapna, Atilla Eryilmaz, Ness B. Shroff. Throughput-Delay Analysis of Random Linear Network Coding for Wireless ... Wireless Sensor Networks and Effects of Long-Range Dependent Data, Sequential Analysis , (10 2012): 0. doi: 10.1080/07474946.2012.719435 Stefano...Sequential Analysis , (10 2012): 0. doi: John S. Baras, Shanshan Zheng. Sequential Anomaly Detection in Wireless Sensor Networks andEffects of Long

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Network Analysis: Applications for the Developing Brain

PubMed Central

Chu-Shore, Catherine J.; Kramer, Mark A.; Bianchi, Matt T.; Caviness, Verne S.; Cash, Sydney S.

2011-01-01

Development of the human brain follows a complex trajectory of age-specific anatomical and physiological changes. The application of network analysis provides an illuminating perspective on the dynamic interregional and global properties of this intricate and complex system. Here, we provide a critical synopsis of methods of network analysis with a focus on developing brain networks. After discussing basic concepts and approaches to network analysis, we explore the primary events of anatomical cortical development from gestation through adolescence. Upon this framework, we describe early work revealing the evolution of age-specific functional brain networks in normal neurodevelopment. Finally, we review how these relationships can be altered in disease and perhaps even rectified with treatment. While this method of description and inquiry remains in early form, there is already substantial evidence that the application of network models and analysis to understanding normal and abnormal human neural development holds tremendous promise for future discovery. PMID:21303762

An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation.

PubMed

Karahalios, Amalia Emily; Salanti, Georgia; Turner, Simon L; Herbison, G Peter; White, Ian R; Veroniki, Areti Angeliki; Nikolakopoulou, Adriani; Mckenzie, Joanne E

2017-06-24

Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods. We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk. The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.

Publications - GMC 20 | Alaska Division of Geological & Geophysical Surveys

Science.gov Websites

, rock-eval/pyrolysis, total organic carbon) and core logs for the David River USA #1-A, Hoodoo Lake Unit , 1969, Geochemical analysis (vitrinite reflectance, visual kerogen, rock-eval/pyrolysis, total organic gmc020.pdf (3.2 M) Keywords Kerogen; Pyrolysis; Rock-Eval Pyrolysis; Total Organic Carbon; Vitrinite

Flipped Classroom Research and Trends from Different Fields of Study

ERIC Educational Resources Information Center

Zainuddin, Zamzami; Halili, Siti Hajar

2016-01-01

This paper aims to analyse the trends and contents of flipped classroom research based on 20 articles that report on flipped learning classroom initiatives from 2013-2015. The content analysis was used as a methodology to investigate methodologies, area of studies, technology tools or online platforms, the most frequently used keywords and works…

Credentialing Structures, Pedagogies, Practices, and Curriculum Goals: Trajectories of Change in Community College Mission Statements

ERIC Educational Resources Information Center

Ayers, David F.

2015-01-01

Objective: To examine the discursive strategies deployed by community colleges to sustain legitimacy in an evolving and contradictory institutional environment. Method: Using corpus linguistics software, I compared 1,009 mission statements from 2012-2013 with a reference corpus of 427 mission statements from 2004. Results: Keywords analysis,…

Research Trends in Turkish Distance Education: A Content Analysis of Dissertations, 1986-2014

ERIC Educational Resources Information Center

Bozkurt, Aras; Kumtepe, Evrim Genc; Kumtepe, Alper Tolga; Aydin, Irem Erdem; Bozkaya, Müjgan; Aydin, Cengiz Hakan

2015-01-01

This paper presents a content analytic approach on doctoral dissertations in the field of distance education in Turkish Higher Education context from the years of 1986 through 2014. A total of 61 dissertations were examined to explore keywords, academic discipline, research areas, theoretical/conceptual frameworks, research designs, research…

A Network Optimization Approach for Improving Organizational Design

DTIC Science & Technology

2004-01-01

functions, Dynamic Network Analysis, Social Network Analysis Abstract Organizations are frequently designed and redesigned, often in...links between sites on the web. Hence a change in any one of the four networks in which people are involved can potentially result in a cascade of...in terms of a set of networks that open the possibility of using all networks (both social and dynamic network measures) as indicators of potential

Advantages of Social Network Analysis in Educational Research

ERIC Educational Resources Information Center

Ushakov, K. M.; Kukso, K. N.

2015-01-01

Currently one of the main tools for the large scale studies of schools is statistical analysis. Although it is the most common method and it offers greatest opportunities for analysis, there are other quantitative methods for studying schools, such as network analysis. We discuss the potential advantages that network analysis has for educational…

Key Spatial Relations-based Focused Crawling (KSRs-FC) for Borderlands Situation Analysis

NASA Astrophysics Data System (ADS)

Hou, D. Y.; Wu, H.; Chen, J.; Li, R.

2013-11-01

Place names play an important role in Borderlands Situation topics, while current focused crawling methods treat them in the same way as other common keywords, which may lead to the omission of many useful web pages. In the paper, place names in web pages and their spatial relations were firstly discussed. Then, a focused crawling method named KSRs-FC was proposed to deal with the collection of situation information about borderlands. In this method, place names and common keywords were represented separately, and some of the spatial relations related to web pages crawling were used in the relevance calculation between the given topic and web pages. Furthermore, an information collection system for borderlands situation analysis was developed based on KSRs-FC. Finally, F-Score method was adopted to quantitatively evaluate this method by comparing with traditional method. Experimental results showed that the F-Score value of the proposed method increased by 11% compared to traditional method with the same sample data. Obviously, KSRs-FC method can effectively reduce the misjudgement of relevant webpages.

Venous thromboembolism and coffee: critical review and meta-analysis.

PubMed

Lippi, Giuseppe; Mattiuzzi, Camilla; Franchini, Massimo

2015-07-01

Among the various risk factors of venous thromboembolism (VTE), nutrients seem to play a significant role in the pathogenesis of this condition. This study aimed to clarify the relationship between coffee intake and venous thrombosis, and we performed a critical review of clinical studies that have been published so far. An electronic search was carried out in Medline, Scopus and ISI Web of Science with the keywords "coffee" AND "venous thromboembolism" OR "deep vein thrombosis" OR "pulmonary embolism" in "Title/Abstract/Keywords", with no language and date restriction. According to our criteria, three studies (two prospective and one case-control) were finally selected (inter-study heterogeneity: 78%; P<0.001). Cumulative data suggests that a modest intake of coffee (i.e., 1-4 cups/day) may be associated with an 11% increased risk of VTE compared to abstainers, whereas a larger intake (i.e., ≥5 coffee/day) may be associated with a 25% decreased risk. Our analysis of published data seemingly confirm the existence of a U-shape relationship between coffee intake and VTE, thus exhibiting a trend that overlaps with that previously reported for cardiovascular disease (CVD).

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

PubMed Central

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

2018-01-01

Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Outcomes and analysis Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. Conclusions In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. PMID:29490922

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Structural Properties of the Brazilian Air Transportation Network.

PubMed

Couto, Guilherme S; da Silva, Ana Paula Couto; Ruiz, Linnyer B; Benevenuto, Fabrício

2015-09-01

The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City) is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

Comparing Networks from a Data Analysis Perspective

NASA Astrophysics Data System (ADS)

Li, Wei; Yang, Jing-Yu

To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information and exhaustible computing. Here, we present an approach to compare networks from the perspective of data analysis. Initially, the approach projects each node of original network as a high-dimensional data point, and the network is seen as clouds of data points. Then the dispersion information of the principal component analysis (PCA) projection of the generated data clouds can be used to distinguish networks. We applied this node projection method to the yeast protein-protein interaction networks and the Internet Autonomous System networks, two types of networks with several similar higher properties. The method can efficiently distinguish one from the other. The identical result of different datasets from independent sources also indicated that the method is a robust and universal framework.

Performance analysis of Aloha networks with power capture and near/far effect

NASA Astrophysics Data System (ADS)

McCartin, Joseph T.

1989-06-01

An analysis is presented for the throughput characteristics for several classes of Aloha packet networks. Specifically, the throughput for variable packet length Aloha utilizing multiple power levels to induce receiver capture is derived. The results are extended to an analysis of a selective-repeat ARQ Aloha network. Analytical results are presented which indicate a significant increase in throughput for a variable packet network implementing a random two power level capture scheme. Further research into the area of the near/far effect on Aloha networks is included. Improvements in throughput for mobile radio Aloha networks which are subject to the near/far effect are presented. Tactical Command, Control and Communications (C3) systems of the future will rely on Aloha ground mobile data networks. The incorporation of power capture and the near/far effect into future tactical networks will result in improved system analysis, design, and performance.

Semantic message oriented middleware for publish/subscribe networks

NASA Astrophysics Data System (ADS)

Li, Han; Jiang, Guofei

2004-09-01

The publish/subscribe paradigm of Message Oriented Middleware provides a loosely coupled communication model between distributed applications. Traditional publish/subscribe middleware uses keywords to match advertisements and subscriptions and does not support deep semantic matching. To this end, we designed and implemented a Semantic Message Oriented Middleware system to provide such capabilities for semantic description and matching. We adopted the DARPA Agent Markup Language and Ontology Inference Layer, a formal knowledge representation language for expressing sophisticated classifications and enabling automated inference, as the topic description language in our middleware system. A simple description logic inference system was implemented to handle the matching process between the subscriptions of subscribers and the advertisements of publishers. Moreover our middleware system also has a security architecture to support secure communication and user privilege control.

[Mechanism of pain sensation].

PubMed

Gyulaházi, Judit

2009-11-15

Pain, as subjective content of consciousness, is an essential attention-calling sign that helps to survive. Pain relieve is obligatory for every physician, thus, its individual appearance can make the analgesia difficult to carry out. The improving neuroimaging techniques allow understanding the development of pain sensation. Through the 24 articles on the PubMed found with keywords 'pain' and 'neuroimaging', we review here the parts of the pain neuron matrix, their tasks and the assumed mechanism of the acute pain sensation. The mechanism of the individual pain sensation is illustrated by the view of the modular function of the medial part of the pain matrix. Experimental results of empathic pain suggest that pain sensation may occur without real damage of the tissues, as well. The pain network plays main role in chronic pain.

Score Normalization for Keyword Search

DTIC Science & Technology

2016-06-23

Anahtar Sözcük Arama için Skor Düzgeleme Score Normalization for Keyword Search Leda Sarı, Murat Saraçlar Elektrik ve Elektronik Mühendisliği Bölümü...skor düzgeleme. Abstract—In this work, keyword search (KWS) is based on a symbolic index that uses posteriorgram representation of the speech data...For each query, sum-to-one normalization or keyword specific thresholding is applied to the search results. The effect of these methods on the proposed

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.

PubMed

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 smoking or quitting is discussed. Results indicated that smoking and quitting discussions were relatively rare, but nevertheless provide insight into barriers that may need to be addressed in cessation interventions for survivors. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Abiraterone acetate/androgen deprivation therapy combination versus docetaxel/androgen deprivation therapy combination in advanced hormone-sensitive prostate cancer: a network meta-analysis on safety and efficacy.

PubMed

Kassem, Loay; Shohdy, Kyrillus S; Abdel-Rahman, Omar

2018-05-01

A major, yet precisely studied, shift has occurred in the treatment of advanced hormone-sensitive prostate cancer (HSPC) by the addition of docetaxel to androgen deprivation therapy (ADT) in the first line. Recently, two landmark trials showed that abiraterone acetate (AA) can be an effective alternative along with ADT in the same setting. We implemented a network meta-analysis to compare the safety and efficacy of the two combinations. PubMed database, ASCO and ESMO meeting library databases of all results published until June 2017 were searched using the keywords: "prostate cancer" AND "docetaxel" OR "abiraterone acetate". Efficacy endpoints including progression-free survival (PFS) and overall survival (OS), and safety endpoints (including treatment related deaths and selected adverse events) were assessed. Twenty relevant studies were retrieved and assessed for eligibility. Of those trials, eight were found potentially eligible. Inconsistent reporting of efficacy outcomes limited our analysis to M1 HSPC. The pooled hazard ratios (HRs) of OS and PFS of the direct comparison of abiraterone acetate plus ADT versus ADT were 0.63 (95% CI: 0.545-0.717) and 0.38 (95% CI: 0.34-0.43), respectively. Meanwhile, in the trials of docetaxel plus ADT the pooled HRs of OS and PFS were 0.75 (95% CI: 0.65-0.86) and 0.634 (95% CI: 0.57-0.70), respectively. The indirect comparison showed that the HRs of OS and PFS in DOC + ADT in comparison to AA + ADT were 1.2 (95% CI: 0.98-1.46) and 1.65 (1.40-1.94), respectively. The pooled RR of treatment-related mortality in docetaxel + ADT versus AA + ADT was 1.438 (95% CI: 0.508-4.075). Patients with metastatic HSPC (mHSPC) who received abiraterone acetate with ADT had better PFS and less toxicity compared to those receiving docetaxel with ADT. A trend towards superior OS and fewer treatment-related deaths was also observed, but was statistically non-significant. In view of lacking clear OS advantage, the choice between docetaxel and AA should include a discussion with the patient about the potential toxicities and impact on quality of life of each regimen.

Angle of Arrival Detection Through Artificial Neural Network Analysis of Optical Fiber Intensity Patterns

DTIC Science & Technology

1990-12-01

ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS THESIS Scott Thomas Captain, USAF AFIT/GE/ENG/90D-62 DTIC...ELECTE ao • JAN08 1991 Approved for public release; distribution unlimited. AFIT/GE/ENG/90D-62 ANGLE OF ARRIVAL DETECTION THROUGH ARTIFICIAL NEURAL NETWORK ANALYSIS... ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS L Introduction The optical sensors of United States Air Force reconnaissance

Evaluation of the Components of the North Carolina Syndromic Surveillance System Heat Syndrome Case Definition.

PubMed

Harduar Morano, Laurel; Waller, Anna E

To improve heat-related illness surveillance, we evaluated and refined North Carolina's heat syndrome case definition. We analyzed North Carolina emergency department (ED) visits during 2012-2014. We evaluated the current heat syndrome case definition (ie, keywords in chief complaint/triage notes or International Classification of Diseases, Ninth Revision, Clinical Modification [ ICD-9-CM] codes) and additional heat-related inclusion and exclusion keywords. We calculated the positive predictive value and sensitivity of keyword-identified ED visits and manually reviewed ED visits to identify true positives and false positives. The current heat syndrome case definition identified 8928 ED visits; additional inclusion keywords identified another 598 ED visits. Of 4006 keyword-identified ED visits, 3216 (80.3%) were captured by 4 phrases: "heat ex" (n = 1674, 41.8%), "overheat" (n = 646, 16.1%), "too hot" (n = 594, 14.8%), and "heatstroke" (n = 302, 7.5%). Among the 267 ED visits identified by keyword only, a burn diagnosis or the following keywords resulted in a false-positive rate >95%: "burn," "grease," "liquid," "oil," "radiator," "antifreeze," "hot tub," "hot spring," and "sauna." After applying the revised inclusion and exclusion criteria, we identified 9132 heat-related ED visits: 2157 by keyword only, 5493 by ICD-9-CM code only, and 1482 by both (sensitivity = 27.0%, positive predictive value = 40.7%). Cases identified by keywords were strongly correlated with cases identified by ICD-9-CM codes (rho = .94, P < .001). Revising the heat syndrome case definition through the use of additional inclusion and exclusion criteria substantially improved the accuracy of the surveillance system. Other jurisdictions may benefit from refining their heat syndrome case definition.

Friendship Group Composition and Juvenile Institutional Misconduct.

PubMed

Reid, Shannon E

2017-02-01

The present study examines both the patterns of friendship networks and how these network characteristics relate to the risk factors of institutional misconduct for incarcerated youth. Using friendship networks collected from males incarcerated with California's Division of Juvenile Justice (DJJ), latent profile analysis was utilized to create homogeneous groups of friendship patterns based on alter attributes and network structure. The incarcerated youth provided 144 egocentric networks reporting 558 social network relationships. Latent profile analysis identified three network profiles: expected group (67%), new breed group (20%), and model citizen group (13%). The three network profiles were integrated into a multiple group analysis framework to examine the relative influence of individual-level risk factors on their rate of institutional misconduct. The analysis finds variation in predictors of institutional misconduct across profile types. These findings suggest that the close friendships of incarcerated youth are patterned across the individual characteristics of the youth's friends and that the friendship network can act as a moderator for individual risk factors for institutional misconduct.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-09-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-04-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Challenges in Identifying Refugees in National Health Data Sets.

PubMed

Semere, Wagahta; Yun, Katherine; Ahalt, Cyrus; Williams, Brie; Wang, Emily A

2016-07-01

To evaluate publicly available data sets to determine their utility for studying refugee health. We searched for keywords describing refugees in data sets within the Society of General Internal Medicine Dataset Compendium and the Inter-University Consortium for Political and Social Research database. We included in our analysis US-based data sets with publicly available documentation and a self-defined, health-related focus that allowed for an examination of patient-level factors. Of the 68 data sets that met the study criteria, 37 (54%) registered keyword matches related to refugees, but only 2 uniquely identified refugees. Few health data sets identify refugee status among participants, presenting barriers to understanding refugees' health and health care needs. Information about refugee status in national health surveys should include expanded demographic questions and focus on mental health and chronic disease.

Use of keyword hierarchies to interpret gene expression patterns.

PubMed

Masys, D R; Welsh, J B; Lynn Fink, J; Gribskov, M; Klacansky, I; Corbeil, J

2001-04-01

High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.

Chinese Social Media Reaction to Information about 42 Notifiable Infectious Diseases.

PubMed

Fung, Isaac Chun-Hai; Hao, Yi; Cai, Jingxian; Ying, Yuchen; Schaible, Braydon James; Yu, Cynthia Mengxi; Tse, Zion Tsz Ho; Fu, King-Wa

2015-01-01

This study aimed to identify what information triggered social media users' responses regarding infectious diseases. Chinese microblogs in 2012 regarding 42 infectious diseases were obtained through a keyword search in the Weiboscope database. Qualitative content analysis was performed for the posts pertinent to each keyword of the day of the year with the highest daily count. Similar posts were grouped and coded. We identified five categories of information that increased microblog traffic pertaining to infectious diseases: news of an outbreak or a case; health education/information; alternative health information/Traditional Chinese Medicine; commercial advertisement/entertainment; and social issues. News unrelated to the specified infectious diseases also led to elevated microblog traffic. Our study showcases the diverse contexts from which increased social media traffic occur. Our results will facilitate better health communication as causes underlying increased social media traffic are revealed.

Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis

PubMed Central

Zare-Farashbandi, Firoozeh; Geraei, Ehsan; Siamaki, Saba

2014-01-01

Background: Co-authorship is one of the most tangible forms of research collaboration. A co-authorship network is a social network in which the authors through participation in one or more publication through an indirect path have linked to each other. The present research using the social network analysis studied co-authorship network of 681 articles published in Journal of Research in Medical Sciences (JRMS) during 2008-2012. Materials and Methods: The study was carried out with the scientometrics approach and using co-authorship network analysis of authors. The topology of the co-authorship network of 681 published articles in JRMS between 2008 and 2012 was analyzed using macro-level metrics indicators of network analysis such as density, clustering coefficient, components and mean distance. In addition, in order to evaluate the performance of each authors and countries in the network, the micro-level indicators such as degree centrality, closeness centrality and betweenness centrality as well as productivity index were used. The UCINET and NetDraw softwares were used to draw and analyze the co-authorship network of the papers. Results: The assessment of the authors productivity in this journal showed that the first ranks were belonged to only five authors, respectively. Furthermore, analysis of the co-authorship of the authors in the network demonstrated that in the betweenness centrality index, three authors of them had the good position in the network. They can be considered as the network leaders able to control the flow of information in the network compared with the other members based on the shortest paths. On the other hand, the key role of the network according to the productivity and centrality indexes was belonged to Iran, Malaysia and United States of America. Conclusion: Co-authorship network of JRMS has the characteristics of a small world network. In addition, the theory of 6° separation is valid in this network was also true. PMID:24672564

Spectral Analysis of Rich Network Topology in Social Networks

ERIC Educational Resources Information Center

Wu, Leting

2013-01-01

Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…

Techno-Economic Analysis of FiWi Access Networks Based on 802.11ac WLAN and NG-PON2 Networks

NASA Astrophysics Data System (ADS)

Breskovic, Damir; Begusic, Dinko

2017-05-01

In this article, techno-economic analysis of a fiber-wireless access network is presented. With high bandwidth capacity of the gigabit passive optical network and with cost-effectiveness of very high throughput 802.11ac wireless local area networks that enable user mobility in the wireless segment, fiber-wireless access networks can be considered as an alternative to the fiber-to-the-home architecture for next generation access networks. Analysis based on the proposed scenario here, shows that a fiber-wireless access network is a more cost-effective solution in densely populated areas, but with some introduced improvements, even other geotypes can be considered as a commercially-viable solution.

Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

PubMed Central

Stringer, Clive; Beeknoo, Neeraj

2017-01-01

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13–17% of all edges channelled 83–90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing. PMID:28968472

Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

PubMed

Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

2016-03-01

Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings. © Society for Community Research and Action 2016.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

PubMed

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

2018-02-28

To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

[Reliability theory based on quality risk network analysis for Chinese medicine injection].

PubMed

Li, Zheng; Kang, Li-Yuan; Fan, Xiao-Hui

2014-08-01

A new risk analysis method based upon reliability theory was introduced in this paper for the quality risk management of Chinese medicine injection manufacturing plants. The risk events including both cause and effect ones were derived in the framework as nodes with a Bayesian network analysis approach. It thus transforms the risk analysis results from failure mode and effect analysis (FMEA) into a Bayesian network platform. With its structure and parameters determined, the network can be used to evaluate the system reliability quantitatively with probabilistic analytical appraoches. Using network analysis tools such as GeNie and AgenaRisk, we are able to find the nodes that are most critical to influence the system reliability. The importance of each node to the system can be quantitatively evaluated by calculating the effect of the node on the overall risk, and minimization plan can be determined accordingly to reduce their influences and improve the system reliability. Using the Shengmai injection manufacturing plant of SZYY Ltd as a user case, we analyzed the quality risk with both static FMEA analysis and dynamic Bayesian Network analysis. The potential risk factors for the quality of Shengmai injection manufacturing were identified with the network analysis platform. Quality assurance actions were further defined to reduce the risk and improve the product quality.

Artificial Neural Network Analysis System

DTIC Science & Technology

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

Google matrix analysis of directed networks

NASA Astrophysics Data System (ADS)

Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.

2015-10-01

In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.

Social network models predict movement and connectivity in ecological landscapes

USGS Publications Warehouse

Fletcher, R.J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, W.M.

2011-01-01

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Plain Language to Communicate Physical Activity Information: A Website Content Analysis.

PubMed

Paige, Samantha R; Black, David R; Mattson, Marifran; Coster, Daniel C; Stellefson, Michael

2018-04-01

Plain language techniques are health literacy universal precautions intended to enhance health care system navigation and health outcomes. Physical activity (PA) is a popular topic on the Internet, yet it is unknown if information is communicated in plain language. This study examined how plain language techniques are included in PA websites, and if the use of plain language techniques varies according to search procedures (keyword, search engine) and website host source (government, commercial, educational/organizational). Three keywords ("physical activity," "fitness," and "exercise") were independently entered into three search engines (Google, Bing, and Yahoo) to locate a nonprobability sample of websites ( N = 61). Fourteen plain language techniques were coded within each website to examine content formatting, clarity and conciseness, and multimedia use. Approximately half ( M = 6.59; SD = 1.68) of the plain language techniques were included in each website. Keyword physical activity resulted in websites with fewer clear and concise plain language techniques ( p < .05), whereas fitness resulted in websites with more clear and concise techniques ( p < .01). Plain language techniques did not vary by search engine or the website host source. Accessing PA information that is easy to understand and behaviorally oriented may remain a challenge for users. Transdisciplinary collaborations are needed to optimize plain language techniques while communicating online PA information.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Comparative analysis of quantitative efficiency evaluation methods for transportation networks

PubMed Central

He, Yuxin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess’s Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified. PMID:28399165

Comparative analysis of quantitative efficiency evaluation methods for transportation networks.

PubMed

He, Yuxin; Qin, Jin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess's Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter.

PubMed

Oscar, Nels; Fox, Pamela A; Croucher, Racheal; Wernick, Riana; Keune, Jessica; Hooker, Karen

2017-09-01

Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset. Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability". This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Research trends in electrochemical technology for water and wastewater treatment

NASA Astrophysics Data System (ADS)

Zheng, Tianlong; Wang, Juan; Wang, Qunhui; Meng, Huimin; Wang, Lihong

2017-03-01

It is difficult to completely degrade wastewater containing refractory pollutants without secondary pollution by biological treatment, as well as physical-chemical process. Therefore, electrochemical technology has attracted much attention for its environmental compatibility, high removal efficiency, and potential cost effectiveness, especially on the industrial wastewater treatment. An effective bibliometric analysis based on the Science Citation Index Core Collection database was conducted to evaluate electrochemical technology for water and wastewater treatment related research from 1994 to 2013. The amount of publications significantly increased in the last two decades. Journal of the Electrochemical Society published the most articles in this field with a top h-index of 90, taking 5.8 % of all, followed by Electrochimica Acta and Journal of Electroanalytical Chemistry. The researchers focused on categories of chemistry, electrochemistry, and materials science. China and Chinese Academy of Sciences were the most productive country and institution, respectively, while the USA, with the most international collaborative articles and highest h-index of 130, was the major collaborator with 15 other countries in top 20 most productive countries. Moreover, based on the analysis of author keywords, title, abstract, and `KeyWords Plus', a new method named "word cluster analysis" was successfully applied to trace the research hotspot. Nowadays, researchers mainly focused on novel anodic electrode, especially on its physiochemical and electrochemical properties.

[Scale effect of Nanjing urban green infrastructure network pattern and connectivity analysis.

PubMed

Yu, Ya Ping; Yin, Hai Wei; Kong, Fan Hua; Wang, Jing Jing; Xu, Wen Bin

2016-07-01

Based on ArcGIS, Erdas, GuidosToolbox, Conefor and other software platforms, using morphological spatial pattern analysis (MSPA) and landscape connectivity analysis methods, this paper quantitatively analysed the scale effect, edge effect and distance effect of the Nanjing urban green infrastructure network pattern in 2013 by setting different pixel sizes (P) and edge widths in MSPA analysis, and setting different dispersal distance thresholds in landscape connectivity analysis. The results showed that the type of landscape acquired based on the MSPA had a clear scale effect and edge effect, and scale effects only slightly affected landscape types, whereas edge effects were more obvious. Different dispersal distances had a great impact on the landscape connectivity, 2 km or 2.5 km dispersal distance was a critical threshold for Nanjing. When selecting the pixel size 30 m of the input data and the edge wide 30 m used in the morphological model, we could get more detailed landscape information of Nanjing UGI network. Based on MSPA and landscape connectivity, analysis of the scale effect, edge effect, and distance effect on the landscape types of the urban green infrastructure (UGI) network was helpful for selecting the appropriate size, edge width, and dispersal distance when developing these networks, and for better understanding the spatial pattern of UGI networks and the effects of scale and distance on the ecology of a UGI network. This would facilitate a more scientifically valid set of design parameters for UGI network spatiotemporal pattern analysis. The results of this study provided an important reference for Nanjing UGI networks and a basis for the analysis of the spatial and temporal patterns of medium-scale UGI landscape networks in other regions.

How Social Network Position Relates to Knowledge Building in Online Learning Communities

ERIC Educational Resources Information Center

Wang, Lu

2010-01-01

Social Network Analysis, Statistical Analysis, Content Analysis and other research methods were used to research online learning communities at Capital Normal University, Beijing. Analysis of the two online courses resulted in the following conclusions: (1) Social networks of the two online courses form typical core-periphery structures; (2)…

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

PubMed Central

Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

2015-01-01

Background The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. Objective The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. Methods In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. Results The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. Conclusions It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively. PMID:27731860

Recommendations and Extraction of Clinical Variables of Pediatric Multiple Sclerosis Using Common Data Elements.

PubMed

Newland, Pamela; Newland, John M; Hendricks-Ferguson, Verna L; Smith, Judith M; Oliver, Brant J

2018-06-01

The purpose of this article was to demonstrate the feasibility of using common data elements (CDEs) to search for information on the pediatric patient with multiple sclerosis (MS) and provide recommendations for future quality improvement and research in the use of CDEs for pediatric MS symptom management strategies Methods: The St. Louis Children's Hospital (SLCH), Washington University (WU) pediatrics data network was evaluated for use of CDEs identified from a database to identify variables in pediatric MS, including the key clinical features from the disease course of MS. The algorithms used were based on International Classification of Diseases, Ninth/Tenth Revision, codes and text keywords to identify pediatric patients with MS from a de-identified database. Data from a coordinating center of SLCH/WU pediatrics data network, which houses inpatient and outpatient records consisting of patients (N = 498 000), were identified, and detailed information regarding the clinical course of MS were located from the text of the medical records, including medications, presence of oligoclonal bands, year of diagnosis, and diagnosis code. There were 466 pediatric patients with MS, with a few also having the comorbid diagnosis of anxiety and depression. St. Louis Children's Hospital/WU pediatrics data network is one of the largest databases in the United States of detailed data, with the ability to query and validate clinical data for research on MS. Nurses and other healthcare professionals working with pediatric MS patients will benefit from having common disease identifiers for quality improvement, research, and practice. The increased knowledge of big data from SLCH/WU pediatrics data network has the potential to provide information for intervention and decision-making that can be personalized to the pediatric MS patient.