Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples -both informative to model training and reflective of user real needs.In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledgeaware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS [39] and IRGAN [30], and KG-enhanced recommender models like KGAT [32]. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy. CCS CONCEPTS• Information systems → Recommender systems.

Staphylococcus aureus is a major human pathogen and one of the more prominent pathogens causing biofilm related infections in clinic. Antibiotic resistance in S. aureus such as methicillin resistance is approaching an epidemic level. Antibiotic resistance is widespread among major human pathogens and poses a serious problem for public health. Conventional antibiotics are either bacteriostatic or bacteriocidal, leading to strong selection for antibiotic resistant pathogens. An alternative approach of inhibiting pathogen virulence without inhibiting bacterial growth may minimize the selection pressure for resistance. In previous studies, we identified a chemical series of low molecular weight compounds capable of inhibiting group A streptococcus virulence following this alternative anti-microbial approach. In the current study, we demonstrated that two analogs of this class of novel anti-virulence compounds also inhibited virulence gene expression of S. aureus and exhibited an inhibitory effect on S. aureus biofilm formation. This class of anti-virulence compounds could be a starting point for development of novel anti-microbial agents against S. aureus.

The widespread occurrence of antibiotic resistance among human pathogens is a major public health problem. Conventional antibiotics typically target bacterial killing or growth inhibition, resulting in strong selection for the development of antibiotic resistance. Alternative therapeutic approaches targeting microbial pathogenicity without inhibiting growth might minimize selection for resistant organisms. Compounds inhibiting gene expression of streptokinase (SK), a critical group A streptococcal (GAS) virulence factor, were identified through a high-throughput, growth-based screen on a library of 55,000 small molecules. The lead compound [Center for Chemical Genomics 2979 (CCG-2979)] and an analog (CCG-102487) were confirmed to also inhibit the production of active SK protein. Microarray analysis of GAS grown in the presence of CCG-102487 showed down-regulation of a number of important virulence factors in addition to SK, suggesting disruption of a general virulence gene regulatory network. CCG-2979 and CCG-102487 both enhanced granulocyte phagocytosis and killing of GAS in an in vitro assay, and CCG-2979 also protected mice from GAS-induced mortality in vivo. These data suggest that the class of compounds represented by CCG-2979 may be of therapeutic value for the treatment of GAS and potentially other Gram-positive infections in humans.

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Coronary artery disease (CAD) isbaseline data for each patient. All patients underwent cardioangiography and were divided into four groups according to their Gensini scores: a control group, and groups with a mild, moderate, or severe degree of stenosis. One hundred and five of these patients simultaneously underwent angiography of the lower extremities and were divided into four groups according to the degree of artery stenosis: a control group, and groups with a mild, moderate, or severe degree of stenosis. Grouping of baPWV levels was made according to Japanese surveys. Bilateral baPWV and ABI were measured using non-invasive arterial atherosclerosis measuring equipment. In the coronary artery groups based on Gensini score, ABI in the group with a high degree of stenosis was significantly lower than that in the control and moderate stenosis groups, while the baPWV was significantly higher than that in the control and mild stenosis groups. In the grouping of baPWV levels, it was indicated that the ABI level was significantly different between each group. The ABI <0.9 in groups with baPWV <1,400 cm/s and >2,100 cm/s was higher than that in other groups. In the grouping by angiography of the lower extremities, the ABI level was decreased with increasing degree of artery stenosis while the baPWV levels were increased under the same circumstance (p <0.05 or p <0.01). Logistic regression analysis indicated that relatively low ABI, high baPWV, abnormal fasting blood glucose, and smoking were independent risk factors for the development of cardiovascular diseases. The simultaneous measurement of bilateral baPWV and ABI using the newly developed equipment presented herein is