Splicing is a critical step in post-transcriptional processing of genes, and its accuracy directly determines whether mature mRNA can be correctly formed. Current strategies for identifying functional mutations from EMS mutant populations have largely focused on premature termination codons and canonical splice sites, while intronic regions—central to splicing regulation—contain potential regulatory variants that create new GT/AG dinucleotides affecting splicing efficiency, which remain largely unexplored. In this study, we developed a workflow integrating in silico screening, PlantCaduceus deep learning-based prediction, and experimental validation to identify intronic GT/AG gain variants in maizeEMSDB mutant library. Screening candidate sites yielded a validation rate of 71.4% (5/7). AG gain variants showed a distinct positional bias: newly created AG sites clustered within 50 bp upstream of canonical acceptors, matching the branchpoint-AG region, and their splicing efficiency decreased as distance from the canonical site increased. This pattern is highly consistent with pathogenic AG-gain variants reported in human genetic studies. By contrast, GT gain variants showed no such positional or distance-dependent effects. Among the validated sites, an AGG→AAG mutation at a canonical splice acceptor site (cl30719_1) revealed a competitive splicing phenomenon: the mutation reduced splicing efficiency at the canonical AG, allowing nearby sites to compete and activating a downstream AG that produced aberrant transcripts. Together, this study establishes a screening method for functional gene research in maize and advances understanding of plant splicing regulation.

The rapid expansion of the AlphaFold Database has provided unprecedented structural coverage of plant proteomes, thereby creating new opportunities for the computational design of functional proteins tailored for agricultural and biotechnological applications. However, existing deep learning-based antibody design methods encounter substantial computational bottlenecks when scaled for high-throughput screening of plant targets. We present PhytoNB, an automated, parallel-accelerated framework for the de novo design of plant nanobodies. This pipeline integrates domain-level segmentation via Chainsaw, multi-view binding site prediction using GPSite and MVGNN, generative nanobody design with IgGM, and large-scale energy-based filtering through Rosetta. To address the considerable computational demands of large-scale generative models, we developed a parallel acceleration engine incorporating dynamic GPU scheduling and multi-threading optimization. This engine enables efficient task allocation across multiple GPUs and CPU cores. Starting from structural inputs, PhytoNB autonomously performs structure prediction, epitope localization, sequence-structure co-generation, and biophysical validation. The pipeline thereby identifies high-stability nanobody candidates that target key functional regions of plant proteins. Benchmarks demonstrate that the parallelized workflow achieves orders-of-magnitude acceleration in design throughput while maintaining structural fidelity and binding specificity. PhytoNB provides an efficient and scalable platform for plant nanobody discovery, with an interface that supports streamlined application of the design workflow. Extensible applications of this platform include multi-epitope targeting, multi-specific binder design, and cross-species applications.

Particulate organic matter (POM) represents the most active carbon (C) pool in agroecosystems. However, the contribution of POM to soil C sequestration under different straw return methods remains unclear, owing to its substantial interactions with soil physicochemical properties and microbial communities. This review gives a brief overview of the biochemical characteristics and analytical techniques of POM and then focuses on the actuation mechanisms regulating its spatiotemporal dynamics. It also discusses the application of POM management strategies, particularly through straw deep incorporation, to increase soil C stock and sequestration potential. In contrast to straw mulching and shallow incorporation, which concentrate C in the topsoil layer prone to C saturation, deep incorporation introduces fresh straw-derived C into the subsoil horizons. This process reshapes the pore networks by increasing the abundance of pores in the 30–90 µm range with high connectivity, and induces changes in soil aggregates, microbial communities, and enzyme activities. In the low-oxygen and thermally buffered environment of the subsoil, deep incorporation substantially increases POM-derived C and enhances C sequestration efficiency by 15%–30% compared with the traditional practices. Spatial optimization framework for agricultural POM management presented here provides a theoretical basis for enhancing C sequestration and improving ecosystem services in Mollisols of Northeast China.

Maize, a crucial global food and feed crop, requires continuous yield improvement to ensure food security. High-density planting has become a core strategy for maize yield improvement. While modifying plant architecture to enhance density tolerance in maize has been well reviewed, the regulatory mechanisms of ear development under high-density conditions remain far less clear. Therefore, this review aims to deepen the understanding of the mechanisms regulating ear traits, providing key insights into how ear architecture adapts to high-density planting. It presents the ideal ear architecture for high-density planting and offers a comprehensive overview of current morphological, genetic, and molecular insights into ear development. Furthermore, this review systematically analyzes the functions, breeding implications, and high-density regulatory roles of key ear-trait regulators and associated physiological determinants. Finally, prospective research directions are outlined, focusing on regulatory networks, multi-omics integration, ideotype design, and precision breeding to guide the coordinated improvement of density tolerance. This review not only advances the mechanistic understanding of maize ear morphogenesis but also provides a theoretical framework for breeding elite maize adapted to a high-density cultivation system.

Maize (Zea mays) is a global staple crop that plays a critical role in food security; however, its production is increasingly threatened by insect pests. These challenges have intensified due to globalization, climate change, and agricultural intensification. Invasive species such as Spodoptera frugiperda have caused substantial yield losses across Africa and Asia, while native pests including Ostrinia furnacalis and Diabrotica virgifera virgifera continue to develop resistance to pesticides and Bt maize. Pest profiles differ considerably across regions, resulting in different management programs. The Americas rely heavily on integrated strategies combining Bacillus thuringiensis (Bt) maize, crop rotation, and precision pesticide applications; Africa emphasizes low-cost solutions such as climate-adapted push–pull systems and microbial biopesticides; Asia prioritizes policy-driven integrated pest management (IPM) and large-scale releases of Trichogramma spp.; Europe focuses on agroecological practices and precision monitoring technologies; and Oceania adopts integrated approaches tailored to irrigated and rainfed production systems. Integrated pest management remains the cornerstone of sustainable maize protection, combining advanced monitoring technologies (e.g., remote sensing and AI-based models), cultural practices (such as intercropping and improved resistant varieties), biological control (including natural enemies and microbial agents), behavioral manipulation (e.g., pheromone traps and push–pull strategies), and regulated chemical control with resistance management. Emerging technologies, including digital agriculture, CRISPR-based breeding, and ecological engineering, offer promising opportunities for future pest management. However, major challenges remain, including insufficient cross-border collaboration, widespread pesticide resistance, unequal access to advanced technologies, and uncertainties in pest dynamics under climate change. Addressing these issues will require coordinated global monitoring networks, equitable technology transfer, promotion of agroecological practices, and standardized resistance management frameworks. Sustainable maize pest management therefore depends on transnational cooperation and innovative, context-specific strategies to safeguard global food security in a changing climate.

The continuous increase in maize yield has become increasingly dependent on high-density planting, rendering plant height (PH) and plant architecture core traits that balance light interception efficiency and lodging resistance. Although many PH regulators have been well characterized, we are still confused on how to handle these regulators for maize breeding. At the same time, biotechnological advances have provided us with tools to address this issue. This review presents perspectives on integrating molecular information underlying maize PH regulation with these biotechnological tools to improve high-density tolerance in maize. We first compile an inventory of genes associated with maize PH and classify them into hormone-related and non-hormonal pathways, and reveal that loss-of-function mutations in most of these genes exhibit prominent allelic characteristics, including extreme dwarfism and pleiotropy. We discuss the underlying mechanisms of pleiotropy, which is important for successful editing aimed at achieving moderate dwarfism. Then, we discuss current strategies for high-density tolerance improvement, including genome editing strategies, introgression of dominance genes, multi-gene stacking, and phenotypic prediction, and offer key insights for their practical implementation. We offer insights and perspectives for future high-density tolerance improvement, which requires cell-resolution gene regulatory networks, AI-enabled precision genome editing, and phenotypic prediction models incorporating all environmental factors influencing plant growth. This review aims to provide guidance for researchers and breeders in translating molecular knowledge into improved high-density tolerance, lodging resistance, and yield gain.

Stay-green (SG) breeding has been widely adopted to improve maize grain yield, but its impact on nitrogen use efficiency (NUE) remains poorly understood. We evaluated the impacts of SG on yield and NUE in Chinese maize hybrids over six decades with multi-location field trials, combined with physiological and transcriptome analysis. Hybrids were classified by SG level into normal senescence (SE, <35%), optimal SG (opt-SG, 35%–55%), and over SG (ov-SG, >55%). Across 138 hybrids, grain yield increased linearly with SG up to ~45% and then plateaued, while N remobilization efficiency (NRE) declined linearly. Ov-SG impaired NRE and grain N concentration without further yield gain, whereas opt-SG achieved both high yield and efficient N remobilization. The opt-SG hybrid XY335 had higher leaf NRE and greater post-silking N uptake than ov-SG hybrid ZD958. The superior NRE in XY335 involved early N remobilization from middle and upper leaves during silking to 30 days after silking (DAS), preferential degradation of soluble proteins and cell wall-associated N while maintaining photosynthesis. Transcriptome analysis and WGCNA (Weighted Gene Co-expression Network Analysis) identified a module highly expressed in XY335 at 30 DAS, which is enriched in many N-remobilization genes, including cysteine/serine proteases and amino acid transporters. These findings define opt-SG as an ideotype that synergistically enhances yield and N remobilization, which offers targets for breeding N-efficient maize for sustainable agriculture.