Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR d...Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.展开更多
Approximately 30%–40%of growth hormone–secreting pituitary adenomas(GHPAs)harbor somatic activating mutations in GNAS(αsubunit of stimulatory G protein).Mutations in GNAS are associated with clinical features of sm...Approximately 30%–40%of growth hormone–secreting pituitary adenomas(GHPAs)harbor somatic activating mutations in GNAS(αsubunit of stimulatory G protein).Mutations in GNAS are associated with clinical features of smaller and less invasive tumors.However,the role of GNAS mutations in the invasiveness of GHPAs is unclear.GNAS mutations were detected in GHPAs using a standard polymerase chain reaction(PCR)sequencing procedure.The expression of mutation-associated maternally expressed gene 3(MEG3)was evaluated with RT-qPCR.MEG3 was manipulated in GH3 cells using a lentiviral expression system.Cell invasion ability was measured using a Transwell assay,and epithelial–mesenchymal transition(EMT)-associated proteins were quantified by immunofluorescence and western blotting.Finally,a tumor cell xenograft mouse model was used to verify the effect of MEG3 on tumor growth and invasiveness.The invasiveness of GHPAs was significantly decreased in mice with mutated GNAS compared with that in mice with wild-type GNAS.Consistently,the invasiveness of mutant GNASexpressing GH3 cells decreased.MEG3 is uniquely expressed at high levels in GHPAs harboring mutated GNAS.Accordingly,MEG3 upregulation inhibited tumor cell invasion,and conversely,MEG3 downregulation increased tumor cell invasion.Mechanistically,GNAS mutations inhibit EMT in GHPAs.MEG3 in mutated GNAS cells prevented cell invasion through the inactivation of the Wnt/β-catenin signaling pathway,which was further validated in vivo.Our data suggest that GNAS mutations may suppress cell invasion in GHPAs by regulating EMT through the activation of the MEG3/Wnt/β-catenin signaling pathway.展开更多
The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of o...The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization.In this paper,the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object.Fuzzy control was used to optimize the loading path,and the fuzzy rule base was established based on FEM.The minimum wall thickness and wall thickness reduction rate were determined as input membership functions,and the axial feeds variable value of the next step was used as output membership functions.The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path.The round corner lamination rate of the tube is 91.2%under the fuzzy control optimized loading path,which was increased by 47.1%and 22.6%compared with linear loading Path 1 and Path 2,respectively.Based on the optimized loading path in the experiment,the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%.The experimental results were consistent with the simulation results,which verified the accuracy of fuzzy control.The research results provide a reference for improving the forming quality of thin-walled tubes and plates.展开更多
Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial ...Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.展开更多
Glucose is the primary fuel source of the brain,and therefore glucose levels need to be tightly regulated and maintained within a small physiological range.Certainly,the body necessitates a stable supply of energy mai...Glucose is the primary fuel source of the brain,and therefore glucose levels need to be tightly regulated and maintained within a small physiological range.Certainly,the body necessitates a stable supply of energy mainly provided by glucose for various bodily functions.High or low blood glucose levels would impair the physiological functions of various organs of the body.展开更多
Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,...Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.展开更多
As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not...As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.展开更多
Foreground detection methods can be applied to efficiently distinguish foreground objects including moving or static objects from back- ground which is very important in the application of video analysis, especially v...Foreground detection methods can be applied to efficiently distinguish foreground objects including moving or static objects from back- ground which is very important in the application of video analysis, especially video surveillance. An excellent background model can obtain a good foreground detection results. A lot of background modeling methods had been proposed, but few comprehensive evaluations of them are available. These methods suffer from various challenges such as illumination changes and dynamic background. This paper first analyzed advantages and disadvantages of various background modeling methods in video analysis applications and then compared their performance in terms of quality and the computational cost. The Change detection.Net （CDnet2014） dataset and another video dataset with different envi- ronmental conditions （indoor, outdoor, snow） were used to test each method. The experimental results sufficiently demonstrated the strengths and drawbacks of traditional and recently proposed state-of-the-art background modeling methods. This work is helpful for both researchers and engineering practitioners. Codes of background modeling methods evaluated in this paper are available atwww.yongxu.org/lunwen.html.展开更多
Objective： To study the mechanisms in gambogic acid （GA） -induced JeKo-1 human Mantle Cell Lymphoma cell apoptosis in vitro. Methods： The proliferation of GA-treated JeKo-1 cells was measured by CCK-8 assay and Ki...Objective： To study the mechanisms in gambogic acid （GA） -induced JeKo-1 human Mantle Cell Lymphoma cell apoptosis in vitro. Methods： The proliferation of GA-treated JeKo-1 cells was measured by CCK-8 assay and Ki-67 immunocytochemical detection. Apopt0sis, cell cycle and mitochondrial membrane potential were measured by flow cytometric analysis. Caspase-3, -8 and -9 were detected by colorimetric assay. Bcl-2 and Bax were analyzed by Western blotting. Results： GA inhibited cell growth in a time- and dose- dependent manner. GA induces apoptosis in JeKo- 1 cells but not in normal bone marrow cells, which was involved in reducing the membrane potential of mitochondria, activating caspases-3, -8 and -9 and decreasing the ratio of Bd-2 and Bax without cell cycle arresting. Conclusions： GA induced apoptosis in human MCL JeKo-1 cells by regulating Bcl-2/Bax and activating caspase-3, -8 and -9 via mitochondrial pathway without affecting cell cycle.展开更多
An intrinsic magnetic topological insulator(TI) is a stoichiometric magnetic compound possessing both inherent magnetic order and topological electronic states. Such a material can provide a shortcut to various novel ...An intrinsic magnetic topological insulator(TI) is a stoichiometric magnetic compound possessing both inherent magnetic order and topological electronic states. Such a material can provide a shortcut to various novel topological quantum effects but remained elusive experimentally for a long time. Here we report the experimental realization of thin films of an intrinsic magnetic TI, MnBi2Te4, by alternate growth of a Bi2Te3 quintuple layer and a MnTe bilayer with molecular beam epitaxy. The material shows the archetypical Dirac surface states in angle-resolved photoemission spectroscopy and is demonstrated to be an antiferromagnetic topological insulator with ferromagnetic surfaces by magnetic and transport measurements as well as first-principles calculations. The unique magnetic and topological electronic structures and their interplays enable the material to embody rich quantum phases such as quantum anomalous Hall insulators and axion insulators at higher temperature and in a well-controlled way.展开更多
MADS box proteins play an important role in floral development. To find genes involved in the floral transition of Prunus species, cDNAs for two MADS box genes, PpMADS1 and PpMADSIO, were cloned using degenerate prime...MADS box proteins play an important role in floral development. To find genes involved in the floral transition of Prunus species, cDNAs for two MADS box genes, PpMADS1 and PpMADSIO, were cloned using degenerate primers and 5＇- and T-RACE based on the sequence database of P. persiea and P. duleis. The full length of PpMADS1 cDNA is 1,071 bp containing an open reading frame （ORF） of 717 bp and coding for a polypeptide of 238 amino acid residues. The full length of PpMADSIO cDNA is 937 bp containing an ORF of 633 bp and coding for a polypeptide of 210 amino acid residues. Sequence comparison revealed that PpMADS1 and PpMADSIO were highly homologous to genes API and PI in Arabidopsis, respectively. Phylogenetic analysis indicated that PpMADS1 belongs to the euAP1 clade of class A, and PpMADSIO is a member of GLO/PI clade of class B. RT-PCR analysis showed that PpMADS1 was expressed in sepal, petal, carpel, and fruit, which was slightly different from the expression pattern ofAPl; PpMADS10 was expressed in petal and stamen, which shared the same expression pattern as PI. Using selective mapping strategy, PpMADSI was assigned onto the Binl：50 on the G1 linkage group between the markers MCO44 and TSA2, and PpMADSIO onto the Bin1：73 on the same linkage group between the markers Lap- 1 and FGA8. Our results provided the basis for further dissection of the two MADS box gene function.展开更多
基金National Natural Science Foundation of China,Grant/Award Numbers:62001141,62272319Science,Technology and Innovation Commission of Shenzhen Municipality,Grant/Award Numbers:GJHZ20210705141812038,JCYJ20210324094413037,JCYJ20210324131800002,RCBS20210609103820029Stable Support Projects for Shenzhen Higher Education Institutions,Grant/Award Number:20220715183602001。
文摘Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.
基金supported by the Applied Basic Research Programs of Science and Technology Commission Foundation of Jiangsu Province(No.BE2015684).
文摘Approximately 30%–40%of growth hormone–secreting pituitary adenomas(GHPAs)harbor somatic activating mutations in GNAS(αsubunit of stimulatory G protein).Mutations in GNAS are associated with clinical features of smaller and less invasive tumors.However,the role of GNAS mutations in the invasiveness of GHPAs is unclear.GNAS mutations were detected in GHPAs using a standard polymerase chain reaction(PCR)sequencing procedure.The expression of mutation-associated maternally expressed gene 3(MEG3)was evaluated with RT-qPCR.MEG3 was manipulated in GH3 cells using a lentiviral expression system.Cell invasion ability was measured using a Transwell assay,and epithelial–mesenchymal transition(EMT)-associated proteins were quantified by immunofluorescence and western blotting.Finally,a tumor cell xenograft mouse model was used to verify the effect of MEG3 on tumor growth and invasiveness.The invasiveness of GHPAs was significantly decreased in mice with mutated GNAS compared with that in mice with wild-type GNAS.Consistently,the invasiveness of mutant GNASexpressing GH3 cells decreased.MEG3 is uniquely expressed at high levels in GHPAs harboring mutated GNAS.Accordingly,MEG3 upregulation inhibited tumor cell invasion,and conversely,MEG3 downregulation increased tumor cell invasion.Mechanistically,GNAS mutations inhibit EMT in GHPAs.MEG3 in mutated GNAS cells prevented cell invasion through the inactivation of the Wnt/β-catenin signaling pathway,which was further validated in vivo.Our data suggest that GNAS mutations may suppress cell invasion in GHPAs by regulating EMT through the activation of the MEG3/Wnt/β-catenin signaling pathway.
基金supported by the Shenyang Science and Technology Program(grant number 22-301-1-10).
文摘The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization.In this paper,the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object.Fuzzy control was used to optimize the loading path,and the fuzzy rule base was established based on FEM.The minimum wall thickness and wall thickness reduction rate were determined as input membership functions,and the axial feeds variable value of the next step was used as output membership functions.The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path.The round corner lamination rate of the tube is 91.2%under the fuzzy control optimized loading path,which was increased by 47.1%and 22.6%compared with linear loading Path 1 and Path 2,respectively.Based on the optimized loading path in the experiment,the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%.The experimental results were consistent with the simulation results,which verified the accuracy of fuzzy control.The research results provide a reference for improving the forming quality of thin-walled tubes and plates.
基金Shenzhen Science and Technology Program,Grant/Award Number:ZDSYS20211021111415025Shenzhen Institute of Artificial Intelligence and Robotics for SocietyYouth Science and Technology Talents Development Project of Guizhou Education Department,Grant/Award Number:QianJiaoheKYZi[2018]459。
文摘Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.
基金supported by grants from the NIH(P01DK113954,R01DK115761,R01DK117281,R01DK125480 and R01DK120858 to YXR01DK129548 to YH)+1 种基金USDA/CRIS(51000-064-01S to YX)Postdoctoral Fellowship(2020AHA000POST000204188)to LT。
文摘Glucose is the primary fuel source of the brain,and therefore glucose levels need to be tightly regulated and maintained within a small physiological range.Certainly,the body necessitates a stable supply of energy mainly provided by glucose for various bodily functions.High or low blood glucose levels would impair the physiological functions of various organs of the body.
基金the National Natural Science Foundation of China(No.62063006)the Natural Science Foundation of Guangxi Province(No.2023GXNS-FAA026025)+3 种基金the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005)the Research Project for Young andMiddle-Aged Teachers in Guangxi Universi-ties(ID:2020KY15013)the Special Research Project of Hechi University(ID:2021GCC028)financially supported by the Project of Outstanding Thousand Young Teachers’Training in Higher Education Institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region.
文摘Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.
文摘As AI, starting with ChatGPT has become increasingly prevalent in academic discussions, school especially, colleges have become hotspots of AI activities and debates. Colleges have the responsibility of addressing not only the academic, integrity-based concerns of students using AI for their homework, but also as the forebearers of new learning and technology, how AI will change their students’ futures and careers. In this study, we will explore the different factors, such as Computer Science Score and location, that might affect how much a college discusses AI, ChatGPT specifically. To demonstrate the validity of our research, we used self-collected data with our methods detailed below.
文摘Foreground detection methods can be applied to efficiently distinguish foreground objects including moving or static objects from back- ground which is very important in the application of video analysis, especially video surveillance. An excellent background model can obtain a good foreground detection results. A lot of background modeling methods had been proposed, but few comprehensive evaluations of them are available. These methods suffer from various challenges such as illumination changes and dynamic background. This paper first analyzed advantages and disadvantages of various background modeling methods in video analysis applications and then compared their performance in terms of quality and the computational cost. The Change detection.Net （CDnet2014） dataset and another video dataset with different envi- ronmental conditions （indoor, outdoor, snow） were used to test each method. The experimental results sufficiently demonstrated the strengths and drawbacks of traditional and recently proposed state-of-the-art background modeling methods. This work is helpful for both researchers and engineering practitioners. Codes of background modeling methods evaluated in this paper are available atwww.yongxu.org/lunwen.html.
基金supported by a grant from the Key Project supported by medical science and technology development Foundation of Nanjing Department of Health (No. ZKX09016)
文摘Objective： To study the mechanisms in gambogic acid （GA） -induced JeKo-1 human Mantle Cell Lymphoma cell apoptosis in vitro. Methods： The proliferation of GA-treated JeKo-1 cells was measured by CCK-8 assay and Ki-67 immunocytochemical detection. Apopt0sis, cell cycle and mitochondrial membrane potential were measured by flow cytometric analysis. Caspase-3, -8 and -9 were detected by colorimetric assay. Bcl-2 and Bax were analyzed by Western blotting. Results： GA inhibited cell growth in a time- and dose- dependent manner. GA induces apoptosis in JeKo- 1 cells but not in normal bone marrow cells, which was involved in reducing the membrane potential of mitochondria, activating caspases-3, -8 and -9 and decreasing the ratio of Bd-2 and Bax without cell cycle arresting. Conclusions： GA induced apoptosis in human MCL JeKo-1 cells by regulating Bcl-2/Bax and activating caspase-3, -8 and -9 via mitochondrial pathway without affecting cell cycle.
基金Supported by the Ministry of Science and Technology of Chinathe National Science Foundation of Chinathe Beijing Advanced Innovation Center for Future Chip(ICFC)
文摘An intrinsic magnetic topological insulator(TI) is a stoichiometric magnetic compound possessing both inherent magnetic order and topological electronic states. Such a material can provide a shortcut to various novel topological quantum effects but remained elusive experimentally for a long time. Here we report the experimental realization of thin films of an intrinsic magnetic TI, MnBi2Te4, by alternate growth of a Bi2Te3 quintuple layer and a MnTe bilayer with molecular beam epitaxy. The material shows the archetypical Dirac surface states in angle-resolved photoemission spectroscopy and is demonstrated to be an antiferromagnetic topological insulator with ferromagnetic surfaces by magnetic and transport measurements as well as first-principles calculations. The unique magnetic and topological electronic structures and their interplays enable the material to embody rich quantum phases such as quantum anomalous Hall insulators and axion insulators at higher temperature and in a well-controlled way.
基金supported by the National Natural Science Foundation of China(No.30500395)the National High Technology Research and Development Program(863 Projects)of China(No.2006AA10Z130 and 2006AA100108-3-7).
文摘MADS box proteins play an important role in floral development. To find genes involved in the floral transition of Prunus species, cDNAs for two MADS box genes, PpMADS1 and PpMADSIO, were cloned using degenerate primers and 5＇- and T-RACE based on the sequence database of P. persiea and P. duleis. The full length of PpMADS1 cDNA is 1,071 bp containing an open reading frame （ORF） of 717 bp and coding for a polypeptide of 238 amino acid residues. The full length of PpMADSIO cDNA is 937 bp containing an ORF of 633 bp and coding for a polypeptide of 210 amino acid residues. Sequence comparison revealed that PpMADS1 and PpMADSIO were highly homologous to genes API and PI in Arabidopsis, respectively. Phylogenetic analysis indicated that PpMADS1 belongs to the euAP1 clade of class A, and PpMADSIO is a member of GLO/PI clade of class B. RT-PCR analysis showed that PpMADS1 was expressed in sepal, petal, carpel, and fruit, which was slightly different from the expression pattern ofAPl; PpMADS10 was expressed in petal and stamen, which shared the same expression pattern as PI. Using selective mapping strategy, PpMADSI was assigned onto the Binl：50 on the G1 linkage group between the markers MCO44 and TSA2, and PpMADSIO onto the Bin1：73 on the same linkage group between the markers Lap- 1 and FGA8. Our results provided the basis for further dissection of the two MADS box gene function.
