An Improved Model of Physical and Emotional Social Defeat: Different Effects on Social Behavior and Body Weight of Adolescent Mice by Interaction With Social Support

Social stress is a prevalent etiological environmental factor that can affect health, especially during adolescence. Either experiencing or witnessing a traumatic event during adolescence can increase the risk of psychiatric disorders, such as PTSD. The present study attempted to establish an improved social stress model to better distinguish the effects of physical and emotional social stress on the behavior and physiology of adolescent mice. In addition, we investigated how social support affected these stress-induced changes in social behavior. On PND 28, male littermates were exposed to either physical stress (PS) or emotional stress (ES), afterwards, half of them were paired-housed and the others were singly housed. The PS exposed mice were directly confronted with a violent aggressor using the social defeat stress (SDS) paradigm for 15 min/trial (with the total of 10 trials randomly administered over a week), while the ES exposed mice were placed in a neighboring compartment to witness the PS procedure. Our results indicate that both stressors induced an effective stress response in adolescent mice, but PS and ES had differential influence in the context of relevant social anxiety/fear and social interaction with peers. Additionally, social support following stress exposure exerted beneficial effects on the social anxiety/fear in ES exposed mice, but not on PS exposed mice, suggesting that the type of stressor may affect the intervention efficacy of social support. These findings provide extensive evidence that physical and emotional stressors induce different effects. Moreover, ES exposed mice, rather than PS exposed mice, seemed to benefit from social support. In summary, the study suggests that this paradigm will be helpful in investigating the effects of psychological intervention for the treatment of stress-related psychiatric disorders

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201