【目的】揭示根蘖繁殖与嫁接繁殖对灵武长枣果实可溶性糖代谢变化的差异,为提高果实品质提供科学依据。【方法】以根蘖与嫁接灵武长枣在白熟期(NB vs JB)、着色期(NZ vs JZ)和成熟期(NC vs JC)的果实为材料,测定果实葡萄糖、果糖、蔗糖...【目的】揭示根蘖繁殖与嫁接繁殖对灵武长枣果实可溶性糖代谢变化的差异,为提高果实品质提供科学依据。【方法】以根蘖与嫁接灵武长枣在白熟期(NB vs JB)、着色期(NZ vs JZ)和成熟期(NC vs JC)的果实为材料,测定果实葡萄糖、果糖、蔗糖及可溶性糖含量;基于转录组数据,从糖酵解/糖异生(ko00010)、淀粉与蔗糖代谢(ko00500)、果糖与甘露糖代谢(ko00051)以及氨基糖和核苷酸糖代谢(ko000520)4个关键糖代谢通路中,筛选与可溶性糖合成代谢相关的差异基因;将表型数据与转录组数据结合,进行WGCNA分析;利用qRT-PCR验证以确保结果的可靠性。【结果】灵武长枣在白熟期和着色期的糖分积累形式主要为果糖,而在成熟期则以蔗糖为主要积累形式。不同繁殖方式下灵武长枣果实糖类积累呈现显著的发育阶段特异性。着色期根蘖繁殖的果糖含量比嫁接繁殖高20.25%;成熟期根蘖繁殖的蔗糖含量比嫁接繁殖高49.32%。从上述4条通路中筛选获得了24个显著差异表达基因(DEGs)。通过GO功能富集分析发现,这些差异基因主要富集在碳水化合物代谢过程相关功能条目中。此外,加权基因共表达网络分析结果表明,黑色、棕色和绿色模块与可溶性糖代谢显著关联,其中ncbi_112492650、HERC2(ncbi_107420452)、PGMP(ncbi_107414395)基因的连通性最强。qRT-PCR分析结果表明,所测定的差异表达基因在不同时期的表达模式存在差异,与转录组数据中的表达趋势一致。【结论】基因ncbi_112492650、HERC2(ncbi_107420452)、PGMP(ncbi_107414395)在调控灵武长枣可溶性糖代谢方面发挥关键作用,可作为后续关键验证基因。本研究为优化灵武长枣的繁殖方式及提高果实品质提供了重要的理论依据。展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
文摘【目的】揭示根蘖繁殖与嫁接繁殖对灵武长枣果实可溶性糖代谢变化的差异,为提高果实品质提供科学依据。【方法】以根蘖与嫁接灵武长枣在白熟期(NB vs JB)、着色期(NZ vs JZ)和成熟期(NC vs JC)的果实为材料,测定果实葡萄糖、果糖、蔗糖及可溶性糖含量;基于转录组数据,从糖酵解/糖异生(ko00010)、淀粉与蔗糖代谢(ko00500)、果糖与甘露糖代谢(ko00051)以及氨基糖和核苷酸糖代谢(ko000520)4个关键糖代谢通路中,筛选与可溶性糖合成代谢相关的差异基因;将表型数据与转录组数据结合,进行WGCNA分析;利用qRT-PCR验证以确保结果的可靠性。【结果】灵武长枣在白熟期和着色期的糖分积累形式主要为果糖,而在成熟期则以蔗糖为主要积累形式。不同繁殖方式下灵武长枣果实糖类积累呈现显著的发育阶段特异性。着色期根蘖繁殖的果糖含量比嫁接繁殖高20.25%;成熟期根蘖繁殖的蔗糖含量比嫁接繁殖高49.32%。从上述4条通路中筛选获得了24个显著差异表达基因(DEGs)。通过GO功能富集分析发现,这些差异基因主要富集在碳水化合物代谢过程相关功能条目中。此外,加权基因共表达网络分析结果表明,黑色、棕色和绿色模块与可溶性糖代谢显著关联,其中ncbi_112492650、HERC2(ncbi_107420452)、PGMP(ncbi_107414395)基因的连通性最强。qRT-PCR分析结果表明,所测定的差异表达基因在不同时期的表达模式存在差异,与转录组数据中的表达趋势一致。【结论】基因ncbi_112492650、HERC2(ncbi_107420452)、PGMP(ncbi_107414395)在调控灵武长枣可溶性糖代谢方面发挥关键作用,可作为后续关键验证基因。本研究为优化灵武长枣的繁殖方式及提高果实品质提供了重要的理论依据。
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
