No abstract

Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users' understandability and acceptance of the novel items as well as support users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a music genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood dimensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users' subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and understandable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualization. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization.

No abstract

A novel gout disease, characterized by visceral urate deposition with high-mortality, with outbreaks in goslings in China since 2016 was caused by a novel goose astrovirus (GoAstV) and resulted in serious economic loss. However, the epidemiology and variation of the GoAstV in goslings in southern China and its evolutionary history as well as the classification of the GoAstV are unclear. In the present study, systematic molecular epidemiology, and phylogenetic analyses of the GoAstV were conducted to address these issues. Our results showed that the GoAstV is widespread in goslings in southern China, and the genomes of six GoAstV strains were obtained. Two amino acid mutations (Y36H and E456D) were identified in capsid proteins in this study, which is the dominant antigen for the GoAstV. In addition, the GoAstV could be divided into two distinct clades, GoAstV-1 and GoAstV-2, and GoAstV-2 is responsible for gout outbreaks in goslings and could be classified into Avastrovirus 3 (AAstV-3), while GoAstV-1 belongs to Avastrovirus 1 (AAstV-1). Moreover, the emergence of GoAstV-2 in geese was estimated to have occurred in January 2010, approximately 12 years ago, while GoAstV-1 emerged earlier than GoAstV-2 and was estimated to have emerged in April 1985 based on Bayesian analysis. The mean evolutionary rate for the GoAstV was also calculated to be approximately 1.42 × 10−3 nucleotide substitutions per site per year. In conclusion, this study provides insight into the epidemiology of the GoAstV in goslings in southern China and is helpful for understanding the origin and evolutionary history as well as the classification of the GoAstV in geese.