Identifying Influential Users Of Micro-Blogging Services: A Dynamic Action-Based Network Approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our action-based user influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered

Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

<p>Abstract</p> <p>Background</p> <p>Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks.</p> <p>Results</p> <p>Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks.</p> <p>Conclusion</p> <p>Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.</p

Propagating Surface Plasmon Polaritons: Towards Applications for Remote-Excitation Surface Catalytic Reactions

Plasmonics is a well-established field, exploiting the interaction of light and metals at the nanoscale; with the help of surface plasmon polaritons, remote-excitation can also be observed by using silver or gold plasmonic waveguides. Recently, plasmonic catalysis was established as a new exciting platform for heterogeneous catalytic reactions. Recent reports present remote-excitation surface catalytic reactions as a route to enhance the rate of chemical reactions, and offer a pathway to control surface catalytic reactions. In this review, we focus on recent advanced reports on silver plasmonic waveguide for remote-excitation surface catalytic reactions. First, the synthesis methods and characterization techniques of sivelr nanowire plasmonic waveguides are summarized, and the properties and physical mechanisms of plasmonic waveguides are presented in detail. Then, the applications of plasmonic waveguides including remote excitation fluorescence and SERS are introduced, and we focus on the field of remote-excitation surface catalytic reactions. Finally, forecasts are made for possible future applications for the remote-excitation surface catalysis by plasmonic waveguides in living cells

A Fully Automated Robotic System for Microinjection of Zebrafish Embryos

As an important embodiment of biomanipulation, injection of foreign materials (e.g., DNA, RNAi, sperm, protein, and drug compounds) into individual cells has significant implications in genetics, transgenics, assisted reproduction, and drug discovery. This paper presents a microrobotic system for fully automated zebrafish embryo injection, which overcomes the problems inherent in manual operation, such as human fatigue and large variations in success rates due to poor reproducibility. Based on computer vision and motion control, the microrobotic system performs injection at a speed of 15 zebrafish embryos (chorion unremoved) per minute, with a survival rate of 98% (n = 350 embryos), a success rate of 99% (n = 350 embryos), and a phenotypic rate of 98.5% (n = 210 embryos). The sample immobilization technique and microrobotic control method are applicable to other biological injection applications such as the injection of mouse oocytes/embryos and Drosophila embryos to enable high-throughput biological and pharmaceutical research