Analgesic Effects of Tetrodotoxin Combined with Acetylsalicylic Acid on Acetic Acid-induced Abdominal Constriction and Formalin Test in Mice

Aim To study the effects of tetrodotoxin （TTX） combined with acetylsalicylic acid （ASA） on nociceptive stimulus in mice. Methods To assess the antinociceptive effects of TTX, ASA or TTX plus ASA, the acetic acid-induced abdominal constriction test and formalin pain test were used. Results TTX （0.5 - 4.0 μg· kg^-1 ） or ASA （25 - 200 mg· kg^-1 ） im produced a significant inhibition of acetic acid-induced abdominal constriction. The median inhibitory doses （ID508） were 2.1 μg· kg^-1 for TTX（ and 64 mg· kg^-1 for ASA. TTX and ASA also showed a dose-dependent inhibition of the second phase response in the formalin pain model, the ID508, being 2.3μg·kg^-1 and 74.2 mg· kg^-1, respectively. The ihteraction between TTX and ASA was synergistic, as evidenced by the fact that （1） when ASA alone compared with the combination of TTX （0.79 μg · kg^-1 or 0.39μg· kg^-1 ） and ASA, the ID508, of ASA reduced from 64.0 mg· kg^-1 to 5.8 mg· kg^-1 or 12.6 mg· kg^-1, and from 74.2 mg· kg^-1 to 7.4 mg· kg^-1 or 13.0 mg· kg^-1 on tile two models of nociceptive tests, respectively; and that （2） synergism in the analgesic effects was shown by isobiolographic analysis. Conclusion TTX, ASA and the combination of the two drags produce analgesic effects in acetic acid-induced abdominal constriction test and formalin-induced pain test. The interactions between TTX and ASA may be useful in developing novel analgesic agents.

A Probabilistic Rating Prediction and Explanation Inference Model for Recommender Systems

Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.

多模态语料库驱动的中国大学EFL教师课堂语伴手势的产出量研究

话语与伴随手势的数量协同问题长期缺乏实证研究数据支持。在功能协同(同样的语义和语用功能)、结构协同(同在支架话序中)的前提下,本研究使用语料库量化研究方法,探讨了语力话语形式(powerful language forms)与其伴随手势的数量协同机制。研究结果发现,在相同教学功能语境之下,语力话语与伴随手势数量比小于1.3∶1大于1∶1.3之间时,能实现最佳的交际效果;反之,则会对交际效果产生消极作用。

A Note on "Modules, Comodules, and Cotensor Products over Frobenius Algebras"

This is a note on Abrams＇ paper ＂Modules, Comodules, and Cotensor Products over Frobenius Algebras, Journal of Algebras＂ （1999）. With the application of Frobenius coordinates developed recently by Kadison, one has a direct proof of Abrams＇ characterization for Frobenius algebras in terms of comultiplication （see L. Kadison （1999））. For any Frobenius algebra, by using the explicit comultiplication, the explicit correspondence between the category of modules and the category of comodules is obtained. Moreover, with this we give very simplified proofs and improve Abrams＇ results on the Hom functor description of cotensor functor.