ABSTRACT Plant lineage has traditionally constrained grafting compatibility, with monocots generally considered incompatible because of their dispersed vascular bundles and limited secondary growth. Recent studies have shown that embryonic grafting can establish successful graft unions in selected monocot systems by exploiting early developmental plasticity before anatomical constraints become fully established. Experimental evidence from cereals and orchids has demonstrated callus adhesion, vascular reconnection, and early tissue integration under controlled conditions, indicating that embryonic grafting represents a promising developmental approach distinct from conventional grafting. However, successful graft union formation does not necessarily ensure long‐term functional integration. Current evidence indicates that distant grafts may exhibit developmental desynchronization, endoplasmic reticulum stress‐associated reproductive defects, genomic dosage imbalance, and sterility, while reproducibility across taxa and translation beyond controlled environments remains poorly understood. Genome‐editing studies have identified sterility‐associated genes (ORF3/4/5, Ms1, and S‐RNase), flowering regulators (Hd1 and FT homologs), and meristem‐vascular identity genes (WUS, CUC/LOB, and MADS‐box family members) as candidate targets for investigating mechanisms underlying compatibility and reproductive stability. However, the application of these molecular approaches to embryonic graft‐derived systems remains largely unexplored. This review synthesizes current advances in embryonic grafting, discusses the molecular basis of graft compatibility and reproductive stability, and highlights key challenges and future research directions for integrating developmental biology with genome editing to improve graft success across wider taxonomic boundaries.
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Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.