The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET p...The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.展开更多
Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR...Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a...In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.展开更多
Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study ...Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.展开更多
基金funded by the Fujian Provincial Science and Technology Project(2021N5014,2022N5006)the Key Research Project of the Putian Science and Technology Bureau(2021ZP08,2021ZP09,2021ZP10,2021ZP11,2023GJGZ001).
文摘The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.
基金This research was funded by the Natural Science Foundation of Shandong Province of China(ZR2022MC144).
文摘Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
基金supported by the project of the National Social Science Fundation(21BJL052,20BJY020,20BJL127,19BJY090)the 2018 Fujian Social Science Planning Project(FJ2018B067)The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019(19YJA790102),The grant has been received by Aoqi Xu.
文摘In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.
基金supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education(No.2020R1I1A3068274),Received by Junho Ahn.https://www.nrf.re.kr/supported by the Korea Agency for Infrastructure Technology Advancement(KAIA)by the Ministry of Land,Infrastructure and Transport under Grant(No.22QPWO-C152223-04),Received by Chulsu Kim.https://www.kaia.re.kr/.
文摘Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.
