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Background Paeonia section Moutan DC. is a significant perennial subshrub, the ornamental value of which heavily depends on the type of flower it possesses. MADS-box transcription factors have a particular impact on the intricate process of floral organ development and differentiation. The release of the whole-genome data from Paeonia ostii now allows us to conduct a thorough investigation of the tree peony MADS-box gene family. Results In this study, we identified 110 MADS-box genes in Paeonia ostii that were classified into 5 subgroups. Gene structure, domain and motif analyses revealed the conservation of the structure of these subgroups. Analysis of the cis -acting elements revealed that the 110 PoMADS genes contained different kinds of hormones and stress-related cis -acting elements in their promoter regions. Quantitative real-time PCR analysis was employed to validate the expression patterns of some PoMADS genes related to floral organ development. Genome collinearity analysis with Arabidopsis and grape revealed the conservation of PoMADS genes during evolution. A total of 857 SSRs were identified by analysing the genome sequences of identified genes. We additionally created protein‒protein interaction networks for PoMADS proteins and analysed proteins that could interact among PoMADSs in Arabidopsis thaliana and grape. Conclusion These findings offer fundamental insights for understanding the function of the MADS-box gene family, which can aid in the selection and breeding of tree peony varieties with high ornamental value in addition to supporting the understanding of the process of tree peony floral organogenesis.

Tree peony ( Paeonia section Moutan DC.) produce strikingly large, vibrant blooms that exhibit remarkable morphological diversity, ranging from single petals to complex double forms, making them highly prized for ornamental horticulture. While auxin response factors (ARFs) are well-established as crucial regulators of organogenesis in model plants, the functional characterization of PoARF genes in tree peony remains largely unexplored. Genome-wide analysis identified 17 PoARF genes ( PoARF1 - PoARF17) in tree peony ( Paeonia ostii ). Their encoded proteins vary in length (420–993 aa) and molecular weight (47.2–109.8 kDa), with isoelectric points of 5.19–9.48 (mostly weakly acidic) and all predicted to localize to the nucleus. Phylogenetic analysis clustered the PoARFs into three clades (Groups I-III). All members contained eight conserved motifs (Motif1-7,9), indicating structural and functional conservation within this gene family. Gene architecture analysis revealed that most PoARF genes feature untranslated regions (UTRs) flanking both termini, while subgroup members exhibited remarkably similar exon–intron organizational patterns. Conserved sequence analysis result showed that the motif arrangement is relatively conserved within each subfamily. The promoter region of PoARF genes in tree peony contains four types of cis-regulatory elements, suggesting that it may be involved in biological processes such as light response, hormone regulation, and tissue-specific expression. The results of the instantaneous transformation test of tree peony showed that PoARF7 promotes petal development while inhibiting stamens development, whereas PoARF10 exhibits opposing functions. These results elucidate ARF-mediated control of floral organ ratios and provide a foundation for studying ARF genes family functions in woody angiosperms.

The spraying time of nitrogen fertilizer is a key factor to consider when fertilizing with an intelligent micro-sprinkler irrigation system. This study aims to investigate the impact of nitrogen fertilizer spraying time on the seed oil quality of tree peony, with the expectation of providing theoretical support for the application of intelligent micro-sprinkler irrigation systems in the production of tree peony. In 2022 and 2023, foliar nitrogen application was conducted on Paeonia ostii ‘Fengdan’ utilizing an intelligent micro-spray irrigation system, with four distinct nitrogen fertilizer spraying times (3:00–4:00, 7:00–8:00, 14:00–15:00, and 19:00–20:00). Based on this, the study assessed nitrogen metabolism indicators in leaves and seeds at various growth stages and the fatty acid composition of seed oil in Paeonia ostii ‘Fengdan’. The results revealed that foliar nitrogen application between 14:00 and 15:00 significantly enhanced the levels of free amino acids (FAA), nitrate reductase (NR), glutamine synthetase (GS), and glutamate synthase (GOGAT) activity in both leaves and seeds. Furthermore, the ratio of α-linolenic acid in the seed oil was significantly increased. Correlation analysis demonstrated a positive or highly significant positive correlation between the levels of nitrogen metabolism indicators and the ratio of unsaturated fatty acids. In conclusion, foliar nitrogen application between 14:00 and 15:00 significantly enhances the FAA content and the activity of nitrogen metabolism enzymes within the leaves and seeds and promotes the synthesis of unsaturated fatty acids in seed oil. This study contributes to the efficient and high-quality cultivation of tree peony.

Food not only fulfills basic human survival needs but also significantly impacts health and culture. Research on food-related topics holds substantial theoretical and practical significance, with food image recognition being a core task in fine-grained image recognition. This field has broad applications and promising prospects in smart dining, intelligent healthcare, and smart retail. With the rapid advancement of artificial intelligence, deep learning has emerged as a key technology that enhances recognition efficiency and accuracy, enabling more practical applications. This paper comprehensively reviews the techniques and challenges of deep learning in food image recognition. First, we outline the historical development of food image recognition technologies, categorizing the primary methods into manual feature extraction-based and deep learning-based approaches. Next, we systematically organize existing food image datasets and summarize the characteristics of several representative datasets. Additionally, we analyze typical deep learning models and their performance on different datasets. Finally, we discuss the practical applications of food image recognition in calorie estimation and food safety, identify current research challenges, and propose future research directions.

Tumor angiogenesis is one of the hallmarks of cancer as well as an attractive target for cancer therapy. Characterization of novel pathways that act in parallel with the VEGF/VEGFR axis to promote tumor angiogenesis may provide insights into novel anti‐angiogenic therapeutic targets. We found that the expression level of Nogo‐B is positively correlated with tumor vessel density in hepatocellular carcinoma (HCC). While Nogo‐B depletion inhibited tumor angiogenesis, Nogo‐B overexpression promoted tumor angiogenesis in a tumor xenograft subcutaneous model of the human HCC cell line. Mechanically, Nogo‐B regulates tumor angiogenesis based on its association with integrin αvβ3 and activation of focal adhesion kinase. Moreover, Nogo‐B antibody successfully abolished the function of Nogo‐B in tumor angiogenesis in vitro and in vivo. Collectively, our results strongly suggest that Nogo‐B is an important tumor angiogenic factor and blocking Nogo‐B selectively inhibits tumor angiogenesis. We found that the expression level of Nogo‐B is positively correlated with tumor vessel density in hepatocellular carcinoma and promotes tumor angiogenesis in vitro and in vivo, while Nogo‐B antibody inhibited tumor angiogenesis in vitro and in vivo. Collectively, our results strongly suggest that Nogo‐B is an important tumor angiogenic factor and blocking Nogo‐B selectively inhibits tumor angiogenesis.