As the junction of floral development pathways,the FLOWERING LOCUS T(FT)protein called‘florigen’plays an important role in the process of plant flowering through signal integration.We isolated four transcripts encod...As the junction of floral development pathways,the FLOWERING LOCUS T(FT)protein called‘florigen’plays an important role in the process of plant flowering through signal integration.We isolated four transcripts encoding different isoforms of a FT orthologous gene CmFTL1,from Chrysanthemum morifolium cultivar‘Jimba’.Sequence alignments suggested that the four transcripts are related to the intron 1.Expression analysis showed that four alternative splicing(AS)forms of CmFTL1 varied depending on the developmental stage of the flower.The functional complement experiment using an Arabidopsis mutant ft-10 revealed that the archetypal and AS forms of CmFTL1 had the function of complementing late flower phenotype in different levels.In addition,transgenic confirmation at transcript level showed CmFTL1 and CmFTL1ast coexist in the same tissue type at the same developmental stage,indicating a post-transcriptional modification of CmFTL1 in Arabidopsis.Moreover,ectopic expression of different AS forms in chrysanthemum resulted in the development of multiple altered phenotypes,varying degrees of early flowering.We found that an alternative splicing form(CmFTL1-astE134)without the exon 2 lacked the ability causing the earlier flower phenotype.The evidence in this study indicates that complex alternative processing of CmFTL1 transcripts in C.morifolium may be associated with flowering regulation and hold some potential for biotechnical engineering to create early-flowering phenotypes in ornamental cultivars.展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
Nomophobia,social networking site(SNS)addiction,and fear of missing out(FoMO)are increasingly recognized as interrelated digital-age phenomena that pose risks to young people's mental health.However,limited resear...Nomophobia,social networking site(SNS)addiction,and fear of missing out(FoMO)are increasingly recognized as interrelated digital-age phenomena that pose risks to young people's mental health.However,limited research has examined how specific symptoms across these domains interact and contribute to anxiety and depression.This study aims to make a novel contribution by applying network and flow analysis to uncover the symptom-level interconnections among nomophobia,SNS addiction,FoMO,and their links to mental health outcomes.A total of 3108 college students completed validated scales measuring SNS addiction,FoMO,nomophobia,anxiety,and depression.Gaussian graphical models and centrality indices were used to estimate symptom networks.Flow networks were constructed to identify pathways connecting symptoms to mental health outcomes.Strong intranetwork associations were found within all three domains.“FoMO on information”emerged as the most central and influential bridge symptom,connecting nomophobia and SNS addiction.Flow network analysis revealed that“FoMO on information”was also the strongest individual predictor of both anxiety and depression.Other symptoms,such as“fear of losing internet connection”and“SNS-related insomnia,”also showed notable associations with mental health outcomes.These findings highlight the potential of network and flow analysis to identify transdiagnostic mechanisms across digital behavioral addictions.“FoMO on information”appears to be a key symptom linking nomophobia and SNS addiction and may represent a promising target for interventions aimed at reducing comorbid anxiety and depression among adolescents.展开更多
基金This work was supported by funds from the National Natural Science Foundation of China(31372100)‘Programs of Innovation and Entrepreneurship Talents’of Jiangsu Province,the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘As the junction of floral development pathways,the FLOWERING LOCUS T(FT)protein called‘florigen’plays an important role in the process of plant flowering through signal integration.We isolated four transcripts encoding different isoforms of a FT orthologous gene CmFTL1,from Chrysanthemum morifolium cultivar‘Jimba’.Sequence alignments suggested that the four transcripts are related to the intron 1.Expression analysis showed that four alternative splicing(AS)forms of CmFTL1 varied depending on the developmental stage of the flower.The functional complement experiment using an Arabidopsis mutant ft-10 revealed that the archetypal and AS forms of CmFTL1 had the function of complementing late flower phenotype in different levels.In addition,transgenic confirmation at transcript level showed CmFTL1 and CmFTL1ast coexist in the same tissue type at the same developmental stage,indicating a post-transcriptional modification of CmFTL1 in Arabidopsis.Moreover,ectopic expression of different AS forms in chrysanthemum resulted in the development of multiple altered phenotypes,varying degrees of early flowering.We found that an alternative splicing form(CmFTL1-astE134)without the exon 2 lacked the ability causing the earlier flower phenotype.The evidence in this study indicates that complex alternative processing of CmFTL1 transcripts in C.morifolium may be associated with flowering regulation and hold some potential for biotechnical engineering to create early-flowering phenotypes in ornamental cultivars.
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
基金supported by the 2025 Annual Project of the Tao Fen Foundation:Embodied Intelligence Empowering Nationwide Reading:Integrated Pathways of Technological Innovation and Reading Promotion(TF2025170).
文摘Nomophobia,social networking site(SNS)addiction,and fear of missing out(FoMO)are increasingly recognized as interrelated digital-age phenomena that pose risks to young people's mental health.However,limited research has examined how specific symptoms across these domains interact and contribute to anxiety and depression.This study aims to make a novel contribution by applying network and flow analysis to uncover the symptom-level interconnections among nomophobia,SNS addiction,FoMO,and their links to mental health outcomes.A total of 3108 college students completed validated scales measuring SNS addiction,FoMO,nomophobia,anxiety,and depression.Gaussian graphical models and centrality indices were used to estimate symptom networks.Flow networks were constructed to identify pathways connecting symptoms to mental health outcomes.Strong intranetwork associations were found within all three domains.“FoMO on information”emerged as the most central and influential bridge symptom,connecting nomophobia and SNS addiction.Flow network analysis revealed that“FoMO on information”was also the strongest individual predictor of both anxiety and depression.Other symptoms,such as“fear of losing internet connection”and“SNS-related insomnia,”also showed notable associations with mental health outcomes.These findings highlight the potential of network and flow analysis to identify transdiagnostic mechanisms across digital behavioral addictions.“FoMO on information”appears to be a key symptom linking nomophobia and SNS addiction and may represent a promising target for interventions aimed at reducing comorbid anxiety and depression among adolescents.
