Twitter has changed the way of communication and getting news for people's daily life in recent years. Meanwhile, due to the popularity of Twitter, it also becomes a main target for spamming activities. In order to stop spammers, Twitter is using Google SafeBrowsing to detect and block spam links.Despite that blacklists can block malicious URLs embedded in tweets, their lagging time hinders the ability to protect users in real-time. Thus, researchers begin to apply different machine learning algorithms to detect Twitter spam. However, there is no comprehensive evaluation on each algorithms' performance for real-time Twitter spam detection due to the lack of large ground truth. To carry out a thorough evaluation, we collected a large dataset of over 600 million public tweets. We further labelled around 6.5 million spam tweets and extracted 12 light weight features, which can be used for online detection. In addition, we have conducted a number of experiments on six machine learning algorithms under various conditions to better understand their effectiveness and weakness for timely Twitter spam detection. We will make our labelled dataset for researchers who are interested in validating or extending our work.

Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organizing items, generating meaningful tags and improving web search. In this paper, we systematically investigate how user-generated comments can be used to improve the clustering of Web 2.0 items.In our preliminary study of Last.fm, we find that the two data sources extracted from user comments -the textual comments and the commenting users -provide complementary evidence to the items' intrinsic features. These sources have varying levels of quality, but we importantly we find that incorporating all three sources improves clustering. To accommodate such quality imbalance, we invoke multi-view clustering, in which each data source represents a view, aiming to best leverage the utility of different views.To combine multiple views under a principled framework, we propose CoNMF (Co-regularized Non-negative Matrix Factorization), which extends NMF for multi-view clustering by jointly factorizing the multiple matrices through co-regularization. Under our CoNMF framework, we devise two paradigms -pair-wise CoNMF and cluster-wise CoNMF -and propose iterative algorithms for their joint factorization. Experimental results on Last.fm and Yelp datasets demonstrate the effectiveness of our solution. In Last.fm, CoNMF betters k-means with a statistically significant F1 increase of 14%, while achieving comparable performance with the state-ofthe-art multi-view clustering method CoSC [24]. On a Yelp dataset, CoNMF outperforms the best baseline CoSC with a statistically significant performance gain of 7%.

Hydrogels as fire-resistant materials have attracted great attention due to their high water content and tailored shapes that can cover various surfaces.

Background and Aim A causal relationship between changes of the gut microbiome and non‐alcoholic fatty liver disease (NAFLD) remains unclear. We demonstrated that endogenous ethanol (EnEth) produced by intestinal microbiota is likely a causative agent of NAFLD. Methods Two mutants with different alcohol‐producing abilities, namely, W14‐adh and W14Δadh, were constructed using the clinical high alcohol‐producing (HiAlc) Klebsiella pneumoniae strain W14 as a parent. Damage to hepatocytes caused by bacteria with different alcohol‐producing capacities was evaluated (EtOH group as positive control). The ultrastructural changes of mitochondria were assessed via transmission electron microscopy (TEM). Hepatic levels of mitochondrial reactive oxygen species (ROS), DNA damage, and adenosine triphosphate were examined. Results The results illustrated that steatosis was most severe in the W14‐adh group, followed by the W14 group, whereas the W14Δadh group had few fatty droplets. TEM and examination of related protein expression revealed that the mitochondrial integrity of HepG2 hepatocytes was considerably damaged in the EtOH and bacteria treatment groups. The impaired mitochondrial function in HepG2 hepatocytes was evidenced by reduced adenosine triphosphate content and increased mitochondrial ROS accumulation and DNA damage in the EtOH and bacteria treatment groups, especially the W14‐adh group. Meanwhile, liver injury and mitochondrial damage were observed in the hepatocytes of mice. The livers of mice in the W14‐adh group, which had the highest ethanol production, exhibited the most serious damage, similar to that in the EtOH group. Conclusions EnEth produced by HiAlc bacteria induces mitochondrial dysfunction in NAFLD.