Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential.We address the problem of using standard topic models in microblogging environments by studying how the models can be trained on the dataset. We propose several schemes to train a standard topic model and compare their quality and effectiveness through a set of carefully designed experiments from both qualitative and quantitative perspectives. We show that by training a topic model on aggregated messages we can obtain a higher quality of learned model which results in significantly better performance in two realworld classification problems. We also discuss how the state-ofthe-art Author-Topic model fails to model hierarchical relationships between entities in Social Media.

Social network services have become a viable source of information for users. In Twitter, information deemed important by the community propagates through retweets. Studying the characteristics of such popular messages is important for a number of tasks, such as breaking news detection, personalized message recommendation, viral marketing and others. This paper investigates the problem of predicting the popularity of messages as measured by the number of future retweets and sheds some light on what kinds of factors influence information propagation in Twitter. We formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages, temporal information, metadata of messages and users, as well as structural properties of the users' social graph on a large scale dataset. We show that our method can successfully predict messages which will attract thousands of retweets with good performance.

Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with latitudes and longitudes. This functionality allows researchers to address an exciting set of questions: 1) How is information created and shared across geographical locations, 2) How do spatial and linguistic characteristics of people vary across regions, and 3) How to model human mobility. Although many attempts have been made for tackling these problems, previous methods are either complicated to be implemented or oversimplified that cannot yield reasonable performance. It is a challenge task to discover topics and identify users' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services. In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user. Furthermore, we take the Markovian nature of a user's location into account. Our model exploits sparse factorial coding of the attributes, thus allowing us to deal with a large and diverse set of covariates efficiently. Our approach is vital for applications such as user profiling, content recommendation and topic tracking. We show high accuracy in location estimation based on our model. Moreover, the algorithm identifies interesting topics based on location and language.

Hyperuricemia is prevalent in the economically developed areas of China. Our report indicates the feasibility of studying the influence that economic changes have on the prevalence of hyperuricemia.

Understanding the roles of noncoding RNAs (ncRNA) in tumorigenesis and metastasis would establish novel avenues to identify diagnostic and therapeutic targets. Here, we aimed to identify hepatocellular carcinoma (HCC)-specific ncRNA and to investigate their roles in hepatocarcinogenesis and metastasis. RNA-seq of xenografts generated by lung metastasis identified long noncoding RNA small nucleolar RNA host gene 10 (SNHG10) and its homolog SCARNA13 as novel drivers for the development and metastasis of HCC. SNHG10 expression positively correlated with SCARNA13 expression in 64 HCC cases, and high expression of SNHG10 or SCARNA13 was associated with poor overall survival. As SCARNA13 showed significant rise and decline after overexpression and knockdown of SNHG10, respectively, we hypothesized that SNHG10 might act as an upstream regulator of SCARNA13. SNHG10 and SCARNA13 coordinately contributed to the malignant phenotype of HCC cells, where SNHG10 served as a sponge for miR-150-5p and interacted with RPL4 mRNA to increase the expression and activity of c-Myb. Reciprocally, upregulated and hyperactivated c-Myb enhanced SNHG10 and SCARNA13 expression by regulating SNHG10 promoter activity, forming a positive feedback loop and continuously stimulating SCARNA13 expression. SCARNA13 mediated SNHG10-driven HCC cell proliferation, invasion, and migration and facilitated the cell cycle and epithelial-mesenchymal transition of HCC cells by regulating SOX9. Overall, we identified a complex circuitry underlying the concomitant upregulation of SNHG10 and its homolog SCARNA13 in HCC in the process of hepatocarcinogenesis and metastasis. Significance: These findings unveil the role of a noncoding RNA in carcinogenesis and metastasis of hepatocellular carcinoma.

SummaryThis study describes a novel mechanism of the inflammatory cytokine IL-6 induced Fra-1 upregulation through activating STAT3 by phosphorylation and acetylation, and demonstrates that this signaling pathway plays a critical role in promoting epithelial–mesenchymal transition and aggressiveness of colorectal cancer.

Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing stateof-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the useritem interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to ×30 times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graphbased loss functions, and would also enable more research under the neural network-based recommendation framework.

MicroRNAs (miRNAs or miRs) are endogenous, small RNA molecules that suppress expression of targeted mRNA. miR-21, one of the most extensively studied miRNAs, is importantly involved in divergent pathophysiological processes relating to ischemia/reperfusion (I/R) injury, such as inflammation and angiogenesis. The role of miR-21 in renal I/R is complex, with both protective and pathological pathways being regulated by miR-21. Preconditioning-induced upregulation of miR-21 contributes to the protection against subsequent renal I/R injury through the targeting of genes such as the proapoptotic gene programmed cell death 4 and interactions between miR-21 and hypoxia-inducible factor. Conversely, long-term elevation of miR-21 may be detrimental to the organ by promoting the development of renal interstitial fibrosis following I/R injury. miR-21 is importantly involved in several pathophysiological processes related to I/R injury including inflammation and angiogenesis as well as the biology of stem cells that could be used to treat I/R injury; however, the effect of miR-21 on these processes in renal I/R injury remains to be studied.