Difference in Nitrogen Starvation-Inducible Expression Patterns among Phylogenetically Diverse Ammonium Transporter Genes in the Red Seaweed <i>Pyropia yezoensis</i>

Nitrogen deficiency induces senescence and the expression of genes encoding ammonium transporters (AMTs) in terrestrial plants where the AMT family is subdivided into AMT1 and AMT2 subfamilies. Nitrogen starvation in the red seaweed Pyropia yezoensis causes senescence-like discoloration. In this study, we identified five genes in P. yezoensis encoding AMT domain-containing proteins, which were phylogenetically categorized into the AMT1 subfamily. We also found a gene encoding a Rhesus protein (Rh) that was related to, but diverged from, AMTs. Moreover, our phylogenetic analysis showed that AMT domain-containing proteins from micro- and macro-algae belonged to either the AMT1 or Rh subfamily, indicating the absence of AMT2 in algae. Gene expression analyses revealed the presence of gametophyte- and sporophyte-specific AMT1 genes that were up-regulated transiently and continually, respectively, under nitrogen-deficient conditions. In addition, up-regulated sporophyte-specific gene expression was suppressed when nitrogen was resupplied. Accordingly, an expansion of the ancient AMT gene has produced AMT1 functional variants differing in temporal and nitrogen starvation-inducible expression patterns during the life cycle of P. yezoensis. These findings help elucidate the unique nutrition starvation responses involving functionally diverse AMT1 and Rh subfamilies in red seaweed.

Reference-guided structure-aware deep sketch colorization for cartoons

Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as color guidance,particularly when colorizing related characters or an animation sequence.Reference-guided colorization is more intuitive than colorization with other hints,such as color points or scribbles,or text-based hints.Unfortunately,reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading.In this paper,we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image.Our framework contains a color style extractor to extract the color feature from a color image,a colorization network to generate multi-scale output images by combining a sketch and a color feature,and a multi-scale discriminator to improve the reality of the output image.Extensive qualitative and quantitative evaluations show that our method outperforms existing methods,providing both superior visual quality and style reference consistency in the task of reference-based colorization.

Boundary-aware texture region segmentation from manga

Due to the lack of color in manga(Japanese comics), black-and-white textures are often used to enrich visual experience. With the rising need to digitize manga, segmenting texture regions from manga has become an indispensable basis for almost all manga processing, from vectorization to colorization. Unfortunately, such texture segmentation is not easy since textures in manga are composed of lines and exhibit similar features to structural lines(contour lines). So currently, texture segmentation is still manually performed, which is labor-intensive and time-consuming. To extract a texture region, various texture features have been proposed for measuring texture similarity, but precise boundaries cannot be achieved since boundary pixels exhibit different features from inner pixels. In this paper, we propose a novel method which also adopts texture features to estimate texture regions. Unlike existing methods, the estimated texture region is only regarded an initial, imprecise texture region. We expand the initial texture region to the precise boundary based on local smoothness via a graph-cut formulation. This allows our method to extract texture regions with precise boundaries. We have applied our method to various manga images and satisfactory results were achieved in all cases.