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Zinc deficiency is the most prevalent micronutrient disorder in rice and leads to delayed development and decreased yield. Nevertheless, despite its primary importance, how rice responds to zinc deficiency remains poorly understood. Herein, we present genetic evidence that OsbZIP48 is essential for regulating rice responses to zinc deficiency. Using the reverse genetics approach, genetic inactivation ofOsbZIP48in rice seedlings caused a hyper sensitivity to zinc deficiency, associated with a significant decrease in the root-to-shoot translocation of zinc. Consistently,OsbZIP48was constitutively expressed in roots, slightly induced by zinc deficiency in shoots, and localized into nuclei induced by Zn deficiency. Comparative transcriptome analysis of the wild-type plants andosbzip48mutant grown under zinc deficiency enabled the identification of OsbZIP48 target genes, including key zinc transporter genes (OsZIP4andOsZIP8). We demonstrated that OsbZIP48 controlled the expressions of these genes by directly binding to their promoters, specifically to the zinc deficiency response element (ZDRE) motif. Collectively, we showed that theOsbZIP48gene encodes for a transcription factor in rice, and demonstrates its critical role in the response to zinc deficiency in this crop. This knowledge is crucial for the design of rice plants that are resilient to the globally prevalent zinc limitation through zinc bio-fortification programs.

Repetitive environment is a challenging scenario for mobile robot global localization due to its highly similar structures and lack of distinctive features. Existing solutions in such environments rely heavily on pre-installed infrastructures, which are neither flexible nor cost-effective. Besides, few of the previous research have been focused on the implementation of infrastructure-free localization approaches in repetitive scenarios. Thus, this paper serves as a survey to investigate the problem of infrastructure-free mobile robot global localization with low-cost and efficient sensors in repetitive environments. Three of the most popular infrastructure-free localization methods, namely LiDAR-based localization (LBL), vision-based localization (VBL), and magnetic field-based localization (MFL), are analyzed and evaluated. Extensive global localization experiments are conducted in real-world repetitive scenarios and the results demonstrate that VBL methods perform slightly better than LBL and MFL methods. The overall evaluations indicate that infrastructure-free global localization in repetitive environment is still a challenging problem which deserves more research efforts to develop new solutions. National Research Foundation (NRF) Accepted version