[ Objective ] The aim was to study the occurrence regularity of fruit physiological disease spongy tissue in Zihua mango （Mangifera indica L. ）. [ Meth. od] Main features of disease symptoms of Zihua mango fruit spo...[ Objective ] The aim was to study the occurrence regularity of fruit physiological disease spongy tissue in Zihua mango （Mangifera indica L. ）. [ Meth. od] Main features of disease symptoms of Zihua mango fruit spongy tissue were investigated from 2002 to 2005 ,and the correlation between the incidence of Zihua mango fruit spongy tissue and its external factors （ fruit maturity, fruit size and fruit yield per plant） was analyzed comprehensively. [Result] The main features of disease symptoms appeared depressed cavity in the middle or lower part of fruit, forming spongy-like cavity. Immature fruits basically had no incidence. The dis- ease began to appear before 10 d of maturity. The disease incidence rate had extremely positive correlation with fruit weight, fruit vertical diameter or cross diame- ter. [ Conclusion] The research provides reference for field diagnoses, identification, preharvest and postharvest uninjurous test of fruit physiological disease suonaw tissue.展开更多
Obesity is a global pandemic and public health crisis over decades.Except for physical activity,dietary ad-justments,pharmacotherapy and surgical interventions,prebiotics,which are believed to relieve obesity by modul...Obesity is a global pandemic and public health crisis over decades.Except for physical activity,dietary ad-justments,pharmacotherapy and surgical interventions,prebiotics,which are believed to relieve obesity by modulating the host gut microbiota,have been a hot research area in recent years.Thus,in this study,our objective was to investigate the effects of Citrus aurantium L.’Daidai’physiological premature fruit drop(DDPD)on obesity,and further explore whether the anti-obesity effects of DDPD relies on alterations in gut microbiota.Our findings revealed that supplementation with DDPD effectively alleviated HFD-induced obesity in mice,concomitantly reducing inflammation and oxidative stress.Moreover,DDPD reversed the imbalance of gut microbiota induced by HFD,enhancing beneficial bacteria associated with improved obesity,such as Bifido-bacterium,Lachnospiraceae_NK4A136_group,Ruminococcus,and Muribaculaceae.Fecal microbiota transplantation demonstrated that DDPD-induced alterations in microbiota effectively mitigated weight gain and lipid abnor-malities induced by HFD,while reshaping the microbiota composition of recipient mice to resemble that of donor mice.Notably,beneficial lipid metabolism bacterium Lachnospiraceae_NK4A136_group identified in donor mice successfully colonized in recipient mice.Additionally,transcriptomic analysis indicated that DDPD modulated lipid metabolism through regulating the expression of genes linked to Cpt1a,Agpat1,and Pnpla3.In conclusion,DDPD demonstrated promising anti-obesity properties,offering potential solutions to the global obesity epidemic.展开更多
Metabolic processes in plant organs involving transport of water,metabolic gasses,and nutrients depend on the three-dimensional(3D)microscopic tissue morphology.However,imaging and quantifying this microstructure,incl...Metabolic processes in plant organs involving transport of water,metabolic gasses,and nutrients depend on the three-dimensional(3D)microscopic tissue morphology.However,imaging and quantifying this microstructure,including the spatial layout of parenchyma cells,pores,vascular bundles and special features such as stone cell clusters(brachysclereids),is challenging.To address this,a 3D deep learning-based panoptic segmentation model,combining semantic and instance segmentation,was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography(CT)images.In addition,various training datasets and data augmentation techniques,including synthetic data,were explored to enhance segmentation quality.The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue,respectively,outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark.The model successfully labeled vascular bundles with a Dice Similarity Coefficient(DSC)of 0.51 in apple tissue and 0.79 in pear tissue,although thin vasculature in apple remained more challenging to segment.The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue.Despite evaluating different methods to enhance seg-mentation quality,none improved test performance beyond that of the model trained on the standard dataset.The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images,without extensive sample preparation such as contrast labelling.The developed method,if not replaces,drastically accelerates conven-tional human-in-the-loop analysis of such images.展开更多
基金Supported by Natural Science Foundation of Guangxi Province(GKZ 08320338)
文摘[ Objective ] The aim was to study the occurrence regularity of fruit physiological disease spongy tissue in Zihua mango （Mangifera indica L. ）. [ Meth. od] Main features of disease symptoms of Zihua mango fruit spongy tissue were investigated from 2002 to 2005 ,and the correlation between the incidence of Zihua mango fruit spongy tissue and its external factors （ fruit maturity, fruit size and fruit yield per plant） was analyzed comprehensively. [Result] The main features of disease symptoms appeared depressed cavity in the middle or lower part of fruit, forming spongy-like cavity. Immature fruits basically had no incidence. The dis- ease began to appear before 10 d of maturity. The disease incidence rate had extremely positive correlation with fruit weight, fruit vertical diameter or cross diame- ter. [ Conclusion] The research provides reference for field diagnoses, identification, preharvest and postharvest uninjurous test of fruit physiological disease suonaw tissue.
基金supported by the National Natural Science Foundation of China(32201960,32073020,32330084)the Science and Technology Innovation Program of Hunan Province(2022RC1150)+3 种基金Hunan Provincial Natural Science Foundation of China(2023JJ40364)the Agricultural Science and Technology Innovation Fund of Hunan(2023CX49,2023CX30)National Key Research and Development Program of China(2022YFD2100804)Science and Technology Innovation&Entrepreneur Team of Hunan Kanglu Bio-medicine.
文摘Obesity is a global pandemic and public health crisis over decades.Except for physical activity,dietary ad-justments,pharmacotherapy and surgical interventions,prebiotics,which are believed to relieve obesity by modulating the host gut microbiota,have been a hot research area in recent years.Thus,in this study,our objective was to investigate the effects of Citrus aurantium L.’Daidai’physiological premature fruit drop(DDPD)on obesity,and further explore whether the anti-obesity effects of DDPD relies on alterations in gut microbiota.Our findings revealed that supplementation with DDPD effectively alleviated HFD-induced obesity in mice,concomitantly reducing inflammation and oxidative stress.Moreover,DDPD reversed the imbalance of gut microbiota induced by HFD,enhancing beneficial bacteria associated with improved obesity,such as Bifido-bacterium,Lachnospiraceae_NK4A136_group,Ruminococcus,and Muribaculaceae.Fecal microbiota transplantation demonstrated that DDPD-induced alterations in microbiota effectively mitigated weight gain and lipid abnor-malities induced by HFD,while reshaping the microbiota composition of recipient mice to resemble that of donor mice.Notably,beneficial lipid metabolism bacterium Lachnospiraceae_NK4A136_group identified in donor mice successfully colonized in recipient mice.Additionally,transcriptomic analysis indicated that DDPD modulated lipid metabolism through regulating the expression of genes linked to Cpt1a,Agpat1,and Pnpla3.In conclusion,DDPD demonstrated promising anti-obesity properties,offering potential solutions to the global obesity epidemic.
基金This research was funded by the Research Foundation–Flanders(FWO,grant number S003421N,SBO project FoodPhase)KU Leuven(project C1 C14/22/076).
文摘Metabolic processes in plant organs involving transport of water,metabolic gasses,and nutrients depend on the three-dimensional(3D)microscopic tissue morphology.However,imaging and quantifying this microstructure,including the spatial layout of parenchyma cells,pores,vascular bundles and special features such as stone cell clusters(brachysclereids),is challenging.To address this,a 3D deep learning-based panoptic segmentation model,combining semantic and instance segmentation,was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography(CT)images.In addition,various training datasets and data augmentation techniques,including synthetic data,were explored to enhance segmentation quality.The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue,respectively,outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark.The model successfully labeled vascular bundles with a Dice Similarity Coefficient(DSC)of 0.51 in apple tissue and 0.79 in pear tissue,although thin vasculature in apple remained more challenging to segment.The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue.Despite evaluating different methods to enhance seg-mentation quality,none improved test performance beyond that of the model trained on the standard dataset.The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images,without extensive sample preparation such as contrast labelling.The developed method,if not replaces,drastically accelerates conven-tional human-in-the-loop analysis of such images.
