Development of a novel homogeneous immunoassay using mutant beta-glucuronidase

Βeta-glucuronidase (GUS) catalyzes breakdown of complex carbohydrates, whose activity can be detected quantitatively and sensitively by using fluorogenic and chromogenic substrates. GUS is a tetramer composed of four identical subunits, and assembly of all these subunits is necessary to attain its activity. Based on a previous study, a set of interface mutations (M516K, Y517E) is known to effectively inhibit the assembly and makes it inactive [1]. Usually, the affinity between the two variable region domains (VH and VL) of an antibody recognizing a small molecule is relatively low. However, in the presence of antigen, this affinity becomes higher so that they bind each other more tightly [2]. This gives the idea that a fusion protein system comprising VH and VL of an antibody as the detector each tethered to a mutant GUS subunit (GUSm) as the reporter can be used as a biosensor for small molecules. In this study, we aimed at detecting 4-hydroxy-3-nitrophenylacetyl (NP) and bone Gla protein (BGP) as targets of this novel immunosensor (Fig. 1). Please click Additional Files below to see the full abstract

Neural Aesthetic Image Reviewer

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high- or low-aesthetic score should be assigned to an image. From such a point of view, we propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, we propose two multi-task architectures based on shared aesthetically semantic layers and task-specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, we collect the AVA-Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA-Reviews dataset. Moreover, we demonstrate experimentally that our model can generate textual reviews related to aesthetics, which are consistent with human perception.Comment: 8 pages, 13 figure