Aureane-type sesquiterpene tetraketides as a novel class of immunomodulators with interleukin-17A inhibitory activity

Interleukin(IL)-17A,a pro-inflammatory cytokine,is a fundamental function in the onset and advancement of multiple immune diseases.To uncover the primary compounds with IL-17A inhibitory activity,a large-scale screening of the library of traditional Chinese medicine constituents and microbial secondary metabolites was conducted using splenic cells from IL-17A-GFP reporter mice cultured under Th17-priming conditions.Our results indicated that some aureane-type sesquiterpene tetraketides isolated from a wetland mud-derived fungus,Myrothecium gramineum,showed remarkable IL-17A inhibitory activity.Nine new aureane-type sesquiterpene tetraketides,myrogramins A-I(1,4-11),and two known ones(2 and 3)were isolated and identified from the strain.Compounds 1,3,4,10,and 11 exhibited significant IL-17A inhibitory activity.Among them,compound 3,with a high fermentation yield dosedependently inhibited the generation of IL-17A and suppressed glycolysis in splenic cells under Th17-priming conditions.Strikingly,compound 3 suppressed immunopathology in both IL-17A-mediated animal models of experimental autoimmune encephalomyelitis and pulmonary hypertension.Our results revealed that aureane-type sesquiterpene tetraketides are a novel class of immunomodulators with IL-17A inhibitory activity,and hold great promise applications in treating IL-17A-mediated immune diseases.

Visual polysemy and synonymy:toward near-duplicate image retrieval

that are duplicate or near duplicate to a query image.One of the most popular and practical methods in near-duplicate image retrieval is based on bag-of-words(BoW)model.However,the fundamental deficiency of current BoW method is the gap between visual word and image’s semantic meaning.Similar problem also plagues existing text retrieval.A prevalent method against such issue in text retrieval is to eliminate text synonymy and polysemy and therefore improve the whole performance.Our proposed approach borrows ideas from text retrieval and tries to overcome these deficiencies of BoW model by treating the semantic gap problem as visual synonymy and polysemy issues.We use visual synonymy in a very general sense to describe the fact that there are many different visual words referring to the same visual meaning.By visual polysemy,we refer to the general fact that most visual words have more than one distinct meaning.To eliminate visual synonymy,we present an extended similarity function to implicitly extend query visual words.To eliminate visual polysemy,we use visual pattern and prove that the most efficient way of using visual pattern is merging visual word vector together with visual pattern vector and obtain the similarity score by cosine function.In addition,we observe that there is a high possibility that duplicates visual words occur in an adjacent area.Therefore,we modify traditional Apriori algorithm to mine quantitative pattern that can be defined as patterns containing duplicate items.Experiments prove quantitative patterns improving mean average precision(MAP)significantly.