In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the useruser social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at https: //github.com/wenqifan03/GraphRec-WWW19

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The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders (Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.

Many viruses interact with the host cell division cycle to favor their own growth. In this study, we examined the ability of influenza A virus to manipulate cell cycle progression. Our results show that influenza A virus A/WSN/33 (H1N1) replication results in G0/G1-phase accumulation of infected cells and that this accumulation is caused by the prevention of cell cycle entry from G0/G1 phase into S phase. Consistent with the G0/G1-phase accumulation, the amount of hyperphosphorylated retinoblastoma protein, a necessary active form for cell cycle progression through late G1 into S phase, decreased after infection with A/WSN/33 (H1N1) virus. In addition, other key molecules in the regulation of the cell cycle, such as p21, cyclin E, and cyclin D1, were also changed and showed a pattern of G0/G1-phase cell cycle arrest. It is interesting that increased viral protein expression and progeny virus production in cells synchronized in the G0/G1 phase were observed compared to those in either unsynchronized cells or cells synchronized in the G2/M phase. G0/G1-phase cell cycle arrest is likely a common strategy, since the effect was also observed in other strains, such as H3N2, H9N2, PR8 H1N1, and pandemic swine H1N1 viruses. These findings, in all, suggest that influenza A virus may provide favorable conditions for viral protein accumulation and virus production by inducing a G0/G1-phase cell cycle arrest in infected cells.