15 research outputs found
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).
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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