Plant height(PH)is associated with lodging resistance and planting density,which is regulated by a complicated gene network.In this study,we identified a spontaneous dwarfing mutation in maize,m30,with decreased inter...Plant height(PH)is associated with lodging resistance and planting density,which is regulated by a complicated gene network.In this study,we identified a spontaneous dwarfing mutation in maize,m30,with decreased internode number and length but increased internode diameter.A candidate gene,ZmCYP90D1,which encodes a member of the cytochrome P450 family,was isolated by map-based cloning.ZmCYP90D1 was constitutively expressed and showed highest expression in basal internodes,and its protein was targeted to the nucleus.A G-to-A substitution was identified to be the causal mutation,which resulted in a truncated protein in m30.Loss of function of ZmCYP90D1 changed expression of hormoneresponsive genes,in particular brassinosteroid(BR)-responsive genes which is mainly involved in cell cycle regulation and cell wall extension and modification in plants.The concentration of typhasterol(TY),a downstream intermediate of ZmCYP90D1 in the BR pathway,was reduced.A haplotype conferring dwarfing without reducing yield was identified.ZmCYP90D1 was inferred to influence plant height and stalk diameter via hormone-mediated cell division and cell growth via the BR pathway.展开更多
Brassinosteroids(BRs),a class of steroid phytohormones,play a critical role in plant growth and development.The DWF4 gene encodes a cytochrome P450 enzyme(CYP90B1),which is considered a rate-limiting enzyme in BR bios...Brassinosteroids(BRs),a class of steroid phytohormones,play a critical role in plant growth and development.The DWF4 gene encodes a cytochrome P450 enzyme(CYP90B1),which is considered a rate-limiting enzyme in BR biosynthesis.Here,we identified a homologous gene of DWF4 in chrysanthemum,CmDWF4.This gene was predicted to encode 491 amino acid residues with a molecular weight of 56.2 kDa and an isoelectric point(pI)of 9.10.Overexpression of CmDWF4 in chrysanthemum was found to significantly increase growth rate,number,and length of lateral buds.Transcriptome analysis showed that multiple xyloglucan endotransglycosylase/hydrolase(XTH)family encoding genes associated with cell wall modification were up-regulated in CmDWF4-overexpressing lines.qRT-PCR assay confirmed the up-regulation of CmXTH6,CmXTH23,and CmXTH28 in CmDWF4-overexpression line.Overall,this work establishes a mechanism by which BR biosynthetic gene CmDWF4 promotes lateral bud outgrowth in chrysanthemum,possibly through regulating cell elongation and expansion.展开更多
Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiolo...Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.展开更多
基金This work was supported by the National Natural Science Foundation of China(U2004144,31971893,32101743)the Key Technologies R&D Program of Henan Province(232102111080)Yunnan Academician Expert Workstation(202305AF150082).
文摘Plant height(PH)is associated with lodging resistance and planting density,which is regulated by a complicated gene network.In this study,we identified a spontaneous dwarfing mutation in maize,m30,with decreased internode number and length but increased internode diameter.A candidate gene,ZmCYP90D1,which encodes a member of the cytochrome P450 family,was isolated by map-based cloning.ZmCYP90D1 was constitutively expressed and showed highest expression in basal internodes,and its protein was targeted to the nucleus.A G-to-A substitution was identified to be the causal mutation,which resulted in a truncated protein in m30.Loss of function of ZmCYP90D1 changed expression of hormoneresponsive genes,in particular brassinosteroid(BR)-responsive genes which is mainly involved in cell cycle regulation and cell wall extension and modification in plants.The concentration of typhasterol(TY),a downstream intermediate of ZmCYP90D1 in the BR pathway,was reduced.A haplotype conferring dwarfing without reducing yield was identified.ZmCYP90D1 was inferred to influence plant height and stalk diameter via hormone-mediated cell division and cell growth via the BR pathway.
基金This research was funded by the National Natural Science Foundation of China(31872149,32172609)China Agriculture Research System(CARS-23-A18),the“JBGS”Project of Seed Industry Revitalization in Jiangsu Province(JBGS[2021]020)the earmarked fund for Jiangsu Agricultural Industry Technology System,and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Brassinosteroids(BRs),a class of steroid phytohormones,play a critical role in plant growth and development.The DWF4 gene encodes a cytochrome P450 enzyme(CYP90B1),which is considered a rate-limiting enzyme in BR biosynthesis.Here,we identified a homologous gene of DWF4 in chrysanthemum,CmDWF4.This gene was predicted to encode 491 amino acid residues with a molecular weight of 56.2 kDa and an isoelectric point(pI)of 9.10.Overexpression of CmDWF4 in chrysanthemum was found to significantly increase growth rate,number,and length of lateral buds.Transcriptome analysis showed that multiple xyloglucan endotransglycosylase/hydrolase(XTH)family encoding genes associated with cell wall modification were up-regulated in CmDWF4-overexpressing lines.qRT-PCR assay confirmed the up-regulation of CmXTH6,CmXTH23,and CmXTH28 in CmDWF4-overexpression line.Overall,this work establishes a mechanism by which BR biosynthetic gene CmDWF4 promotes lateral bud outgrowth in chrysanthemum,possibly through regulating cell elongation and expansion.
基金This research was funded by Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0190.
文摘Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.
基金financially supported by the National Natural Science Foundation of China(No.22008167,21978187,21978196)the Natural Science Foundation for Young Scientists of Shanxi Province,China(Nos.201901D211100,201901D211058,201901D211027)。
