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Microbial-driven N turnover is important in regulating N fertilizer use efficiency through the secretion of metabolites like glycolipids. Currently, our understanding of the potential of glycolipids to partially reduce N fertilizer use and the effects of glycolipids on crop yield and N use efficiency is still limited. Here, a three-year in situ field experiment was conducted with seven treatments: no fertilization (CK); chemical N, phosphorus and potassium (NPK); NPK plus glycolipids (N+PKT); and PK plus glycolipids with 10% (0.9 N+PKT), 20% (0.8 N+PKT), 30% (0.7 N+PKT), and 100% (PKT) N reduction. Compared with NPK, glycolipids with 0–20% N reduction did not significantly reduce maize yields, and also increased N uptake by 6.26–11.07%, but no significant changes in grain or straw N uptake. The N resorption efficiency under 0.9 N+PKT was significantly greater than that under NPK, while the apparent utilization rates of N fertilizer and partial factor productivity of N under 0.9 N+PKT were significantly greater than those under NPK. Although 0.9 N+PKT led to additional labor and input costs, compared with NPK, it had a greater net economic benefit. Our study demonstrates the potential for using glycolipids in agroecosystem management and provides theoretical support for optimizing fertilization strategies. [ABSTRACT FROM AUTHOR]

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be challenging due to the complexity and ambiguity of natural language. This research proposes PromptMagician, a visual analysis system that helps users explore the image results and refine the input prompts. The backbone of our system is a prompt recommendation model that takes user prompts as input, retrieves similar prompt-image pairs from DiffusionDB, and identifies special (important and relevant) prompt keywords. To facilitate interactive prompt refinement, PromptMagician introduces a multi-level visualization for the cross-modal embedding of the retrieved images and recommended keywords, and supports users in specifying multiple criteria for personalized exploration. Two usage scenarios, a user study, and expert interviews demonstrate the effectiveness and usability of our system, suggesting it facilitates prompt engineering and improves the creativity support of the generative text-to-image model.