Browsing by Autor "Xin Wang"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item type: Item ,
    Identification and expression characterisation of SbERECTA family genes in Sorghum bicolor
    (CSIRO Publishing, 2021) Jiacheng Zheng; Jie Yu; Ting Liu; Xin Wang; Qiu Wen Zhan; Jie Qin Li; Xu Zhao; You Zhi
    ERECTAs are receptor-like kinases that regulate plant biomass and stress resistance. In this study, the wheat (Triticum aestivum) TaERECTA gene was used as a probe to identify the SbERECTA family genes (SbERs) in the sorghum (Sorghum bicolor) genome, analyse their subcellular localisation and characterise their expression. Results showed that the two SbER members, SbER10 with three copies (SbER10_X1, SbER10_X2, and SbER10_X3) and SbER4 with two copies (SbER4_X1 and SbER4_X2), were found on chromosomes 10 and 4 of sorghum, respectively. SbER10 had the highest expression level in the pedicel tissue and showed a remarkable response under treatment with abscisic acid, brassinolide, gibberellin and indole-3-acetic acid. SbER10_X1, functioning on the cell membrane and chloroplast, exhibited abundant transcript in only a few sorghum varieties that are grown in mountainous areas and receive strong light, heat, and water supply. Expression of SbER10_X1 was significantly and positively correlated with plant biomass of 32 sorghum germplasm resources. These results indicate that SbER10 genes have an important regulatory role in sorghum growth, and increasing SbER10 transcription level offers a potential strategic target for breeding or biotechnological approaches to enhance sorghum biomass and environmental adaptability.
  • Thumbnail Image
    Item type: Item ,
    L2C: Describing Visual Differences Needs Semantic Understanding of Individuals
    (2021) An Yan; Xin Wang; Tsu-Jui Fu; William Yang Wang
    Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs. Suppose there are two images, I 1 and I 2 , and the task is to generate a description W 1,2 comparing them, existing methods directly model I 1 , I 2 W 1,2 mapping without the semantic understanding of individuals. In this paper, we introduce a Learningto-Compare (L2C) model, which learns to understand the semantic structures of these two images and compare them while learning to describe each one. We demonstrate that L2C benefits from a comparison between explicit semantic representations and singleimage captions, and generalizes better on the new testing image pairs. It outperforms the baseline on both automatic evaluation and human evaluation for the Birds-to-Words dataset.