Therapeutic and Radiosensitizing Effects of Armillaridin on Human Esophageal Cancer Cells

Background. Armillaridin (AM) is isolated from Armillaria mellea. We examined the anticancer activity and radiosensitizing effect on human esophageal cancer cells. Methods. Human squamous cell carcinoma (CE81T/VGH and TE-2) and adenocarcinoma (BE-3 and SKGT-4) cell lines were cultured. The MTT assay was used for cell viability. The cell cycle was analyzed using propidium iodide staining. Mitochondrial transmembrane potential was measured by DiOC6(3) staining. The colony formation assay was performed for estimation of the radiation surviving fraction. Human CE81T/VGH xenografts were established for evaluation of therapeutic activity in vivo. Results. AM inhibited the viability of four human esophageal cancer cell lines with an estimated concentration of 50% inhibition (IC50) which was 3.4–6.9 μM. AM induced a hypoploid cell population and morphological alterations typical of apoptosis in cells. This apoptosis induction was accompanied by a reduction of mitochondrial transmembrane potential. AM accumulated cell cycle at G2/M phase and enhanced the radiosensitivity in CE81T/VGH cells. In vivo, AM inhibited the growth of CE81T/VGH xenografts without significant impact on body weight and white blood cell counts. Conclusion. Armillaridin could inhibit growth and enhance radiosensitivity of human esophageal cancer cells. There might be potential to integrate AM with radiotherapy for esophageal cancer treatment

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Service Failure and Recovery: The Role of Customer Forgiveness and Perceived Justice in Customers’ Coping Behaviors

This research investigated the role of customer forgiveness as the underlying mechanism of the effect of service failure severity on customers’ coping behaviors. It also investigated the moderating role of customers’ justice perceptions in the proposed model. The findings showed that customer forgiveness is essential in mending the relationships and lowering customer avoidance. Customer forgiveness was less negatively affected by service failure severity when customer perceived service providers’ recovery efforts with higher levels of justice. Additionally, this research explored the moderating effects of three dimensions of justice on the relationship between service failure severity and customer forgiveness. The findings demonstrated that the higher levels of distributive justice weakened the negative effect of service failure severity on customer forgiveness, especially when customer perceived lower levels of interactional justice. However, such effect was lessened when customer perceived higher levels of interactional justice

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org