DeepDrawing: A deep learning approach to graph drawing

Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.Comment: 11 page

Micro RNAs and the biological clock: a target for diseases associated with a loss of circadian regulation

Background: Circadian clocks are self-sustaining oscillators that coordinate behavior and physiology over a 24 hour period, achieving time-dependent homeostasis with the external environment. The molecular clocks driving circadian rhythmic changes are based on intertwined transcriptional/translational feedback loops that combine with a range of environmental and metabolic stimuli to generate daily internal programing. Understanding how biological rhythms are generated throughout the body and the reasons for their dysregulation can provide avenues for temporally directed therapeutics. Summary: In recent years, microRNAs have been shown to play important roles in the regulation of the circadian clock, particularly in Drosophila , but also in some small animal and human studies. This review will summarize our current understanding of the role of miRNAs during clock regulation, with a particular focus on the control of clock regulated gene expression

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNLens, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.Comment: 15 page

Hainan sport tourism development—A SWOT analysis

Hainan, as a popular tourism destination, is well-promoted by the Chinese central government. In particular, both central and local governments encourage Hainan’s sport tourism-related professionals to develop sport tourism as one of the most important tourist activities in Hainan. However, previous research has not reported on Hainan’s sport tourism strengths, weaknesses, opportunities, and threats as a tourism destination or a sports event host. This study uses SWOT analysis to identify the strengths, weaknesses, opportunities, and threats in the context of Hainan’s sport tourism development. A total of 12 dimensions, including branding, culture, finance, infrastructure, location, market, nature, policy, product, specialty, sustainability, and tourist were generated from our data analysis. In addition, a total of five future directions, including emphasizing event-oriented sport tourism, prioritizing sport motivation, identifying major sport tourism markets, making the rational use of sport tourism resources, and nurturing sport culture, are recommended as a result of this study

Psoralen-loaded lipid-polymer hybrid nanoparticles enhance doxorubicin efficacy in multidrug-resistant HepG2 cells

Background: Psoralen (PSO), a major active component of Psoralea corylifolia, has been shown to overcome multidrug resistance in cancer. A drug carrier comprising a lipid-monolayer shell and a biodegradable polymer core for sustained delivery and improved efficacy of drug have exhibited great potential in efficient treatment of cancers. Methods: The PSO-loaded lipid polymer hybrid nanoparticles were prepared and characterized. In vitro cytotoxicity assay, cellular uptake, cell cycle analysis, detection of ROS level and mitochondrial membrane potential (ΔΨm) and western blot were performed. Results: The P-LPNs enhanced the cytotoxicity of doxorubicin (DOX) 17-fold compared to free DOX in multidrug resistant HepG2/ADR cells. Moreover, P-LPNs displayed pro-apoptotic activity, increased levels of ROS and depolarization of ΔΨm. In addition, there were no significant effects on cellular uptake of DOX, cell cycle arrest, or the expression of P-glycoprotein. Mechanistic studies suggested that P-LPNs enhanced DOX cytotoxicity by increased release of cytochrome c and enhanced caspase3 cleavage, causing apoptosis in HepG2/ADR cells. Conclusion: The lipid-polymer hybrid nanoparticles can be considered a powerful and promising drug delivery system for effective cancer chemotherapy. Keywords: lipid-polymer hybrid nanoparticles, psoralen, drug delivery, HepG2, ADR cells, apoptosis.This work was supported by the National Natural Science Foundation of China (81273707), the Ministry of Education in the New Century Excellent Talents (NECT-12-0677), the Natural Science Foundation of Guangdong (S2013010012880, 2016A030311037), the Science and Technology Program of Guangzhou (2014J4500005, 201704030141), the Science Program of the Department of Education of Guangdong (2013KJCX0021, 2015KGJHZ012), the Science and Technology Program of Guangdong (2015A050502027), and the Special Project of International Scientific and Technological Cooperation in Guangzhou Development District (2017GH16)