Author

Yichen Dong

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Jul 2026

CAP1 regulates root hair ammonium response through RBOHD-dependent ROS homeostasis in Arabidopsis.

Elucidating the molecular mechanisms underlying ammonium (NH4+)-mediated root system development is critical for alleviating NH4+ toxicity in plants. The Arabidopsis thaliana [Ca2+]cyt-associated protein kinase CAP1 regulates root hair growth in response to NH4+ treatment, with the cap1-1 mutant exhibiting hypersensitivity to NH4+ and elevated reactive oxygen species (ROS) levels. Based on our previous phosphoproteomic data, here we investigated the role of Respiratory Burst Oxidase Homolog D (AtRBOHD) in CAP1-mediated root hair growth under NH4+ treatment. Through yeast two-hybrid (Y2H), bimolecular fluorescence complementation (BiFC), co-immunoprecipitation (Co-IP), and in vitro phosphorylation assays, we demonstrate that CAP1 physically interacts with and phosphorylates RBOHD, with Ser347 identified as a critical phosphorylation site in RBOHD that is required for their interaction. Genetic complementation assays revealed that expressing either wild-type RBOHD or its phospho-mimetic variant (RBOHDS347D) partially restored root hair growth in cap1-1 mutants while reducing ROS levels to those of the wild type under NH4+ treatment. Collectively, our findings establish a mechanistic model in which CAP1 phosphorylates RBOHD at Ser347 to regulate NH4+-dependent root hair growth by modulating ROS homeostasis.

Hong Yang, Lianlian Wang, Yichen Dong et al. · Plant Science · 0 citations

Preprint Aug 2026

STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation

Large Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-to-document generation in a single pass frequently suffers from structural misalignment, manifesting as sentence omissions or hallucinations that violate the core requirement of source-target correspondence. To address this, we introduce Sentence Translation Alignment Rate (STAR), an auxiliary metric that explicitly quantifies sentence-level structural fidelity. Building on this, we propose STAR-masked Preference Optimization (StarPO), a framework that ranks document-level hypotheses by structural quality and utilizes a dynamic alignment mask to focus optimization on misaligned segments. Experimental results across news and literary domains demonstrate that StarPO significantly enhances translation quality and structural integrity. Notably, StarPO allows compact models to surpass the performance of massive proprietary systems like GPT-4o while maintaining superior token efficiency.

Yichen Dong, Hao Wang, Junhui Li et al. · 0 citations