Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilitie...Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.展开更多
A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similar...A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.展开更多
An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective f...An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.展开更多
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied....The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.展开更多
The meta search engines provide service to the users by dispensing the users' requests to the existing search engines. The existing search engines selected by meta search engine determine the searching quality. Be...The meta search engines provide service to the users by dispensing the users' requests to the existing search engines. The existing search engines selected by meta search engine determine the searching quality. Because the performance of the existing search engines and the users' requests are changed dynamically, it is not favorable for the fixed search engines to optimize the holistic performance of the meta search engine. This paper applies the genetic algorithm (GA) to realize the scheduling strategy of agent manager in our meta search engine, GSE(general search engine), which can simulate the evolution process of living things more lively and more efficiently. By using GA, the combination of search engines can be optimized and hence the holistic performance of GSE can be improved dramatically.展开更多
The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm ...The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se-quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov-ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an-tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.展开更多
Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly s...Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.展开更多
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.展开更多
The layout of a sensor network is a critical determinant of the precision and reliability of microseismic source localization.Addressing the impact of sensor network configuration on positioning accuracy,this paper in...The layout of a sensor network is a critical determinant of the precision and reliability of microseismic source localization.Addressing the impact of sensor network configuration on positioning accuracy,this paper introduces an innovative approach to sensor network optimization in underground space.It utilizes the Cramér-Rao Lower Bound principle to formulate an optimization function for the sensor network layout,followed by the deployment of an enhanced genetic encoding to solve this function and determine the optimal layout.The efficacy of proposed method is rigorously tested through simulation experiments and pencil-lead break experiments,substantiating its superiority.Its practical utility is further demonstrated through its application in a mining process within underground spaces,where the optimized sensor network solved by the proposed method achieves remarkable localization accuracy of 15 m with an accuracy rate of 4.22%in on-site blasting experiments.Moreover,the study elucidates general principles for sensor network layout that can inform the strategic placement of sensors in standard monitoring systems.展开更多
Acinetobacter baumannii is one of the most important human pathogens causing a variety of nosocomial infections. Carbapenem antibiotics have been primarily used to treat the A. baumannii infections. However, carbapene...Acinetobacter baumannii is one of the most important human pathogens causing a variety of nosocomial infections. Carbapenem antibiotics have been primarily used to treat the A. baumannii infections. However, carbapenem resistant A. baumannii producing carbapenemases causes serious treatment problems worldwide. Outbreaks of carbapenem resistant isolates have reported in some area of the United States, but their dissemination and genetic structure of the carbapenemase encoding genes are currently little known. To understand outbreaks, dissemination, and genetic structure of the carbapenemase encoding genes in Southern Texas, 32 clinical isolates collected from Austin and Houston, TX were characterized. Twenty-eight of 32 isolates were resistant to all tested β-lactam antibiotics including carbapenem (imipenem and meropenem). Three of them carried blaOXA-23 as a part of Tn2008 integrated into a known plasmid (pACICU2) and all others carried blaOXA-24 flanked by XerC/XerD-like recombinase binding sites that were adjoined by DNA sequences originated from multiple plasmids. Genotype analysis revealed that the 25 isolates carrying blaOXA-24 were all identical genotypes same as a representative isolate carrying blaOXA-24 from Chicago, IL but the 3 isolates carrying blaOXA-23 was a distinct genotype as compared with isolates carrying blaOXA-23 from Chicago, IL and Washington, D.C. Each of the blaOXA-23 and blaOXA-24 was transferred to carbapenem susceptible A. baumannii and E. coli with similar minimal inhibitory concentration (MIC) of carbapenem as that of their parental isolates but significantly lower levels of MIC in E. coli. Overall results suggest that a unique strain carrying blaOXA-23 and a similar strain carrying blaOXA-24 as seen in other geographic areas are currently disseminated in Southern Texas.展开更多
Genetically encoded biosensors are powerful tools for monitoring plant proteins,which could offer high spatial and temporal resolution and help reveal the molecular mechanisms underlying plant growth and stress respon...Genetically encoded biosensors are powerful tools for monitoring plant proteins,which could offer high spatial and temporal resolution and help reveal the molecular mechanisms underlying plant growth and stress responses.However,a comprehensive review focused on the spatiotemporal monitoring of plant proteins using these biosensors is still lacking.This review highlights key advancements in the field,evaluates the strengths and limitations of current biosensors,and discusses their applications for tracking plant protein dynamics.We aim to provide a thorough understanding of genetically encoded biosensors for plant proteins,promote the development of these technologies,and foster deeper insights into molecular mechanisms in plant cells.Future research should prioritize overcoming challenges such as interference from plant autofluorescence and enhancing the sensitivity of biosensors,particularly in complex cellular compartments like chloroplasts and cell walls,to further improve spatial and temporal resolution.展开更多
The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism under- lying how the brain metabolizes substances to support neur...The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism under- lying how the brain metabolizes substances to support neural activity is a fundamental issue for neuroscience studies. A well-known model in the brain, the astrocyte- neuron lactate shuttle, postulates that glucose uptake and glycolytic activity are enhanced in astrocytes upon neu- ronal activation and that astrocytes transport lactate into neurons to fulfill their energy requirements. Current evidence for this hypothesis has yet to reach a clear consensus, and new concepts beyond the shuttle hypothesis are emerging. The discrepancy is largely attributed to the lack of a critical method for real-time monitoring of metabolic dynamics at cellular resolution. Recent advances in fluorescent protein-based sensors allow the generation of a sensitive, specific, real-time readout of subcellular metabolites and fill the current technological gap. Here,we summarize the development of genetically encoded metabolite sensors and their applications in assessing cell metabolism in living cells and in vivo, and we believe that these tools will help to address the issue of elucidating neural energy metabolism.展开更多
Synthesis of macromolecular systems with precise structural and functional control constitutes a fundamental challenge for materials science and engineering. Development of the ability to construct complex bio-macromo...Synthesis of macromolecular systems with precise structural and functional control constitutes a fundamental challenge for materials science and engineering. Development of the ability to construct complex bio-macromolecular architectures provides a solution to this challenge. The past few years have witnessed the emergence of a new category of peptide-protein chemistry which can covalently stitch together protein]peptide molecules with high specificity under mild physiological conditions. It has thus inspired the concept of genetically encoded click chemistry （GECC）. As a prototype of GECC, SpyTag/ SpyCatcher chemistry has enabled the precise synthesis ofmacromolecules both in vitro and in vivo, exerting precise control over the fundamental properties of these macromolecules including length, sequence, stereochemistry and topology and leading to the creation of diverse biomaterials for a variety of applications. We thus anticipate a potential toolbox of GECC comprising multiple mutually orthogonal, covalent-bond forming peptide-protein reactive pairs with diverse features, which shall bridge synthetic biology and materials science and open up enormous opportunities for biomaterialsin the future.展开更多
Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm f...Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm for optimizing pilot assignment in a multi-user massive multiple input multiple output(MIMO)system.Results show improvement on existing results especially in the case of strong user estimation rates.展开更多
The NSF Cotton Genome Centers EST projecthas released】36000 cotton fiber EST sequencesfrom Gossypium arboreum,an A-genome diploidspecies.Of the approximately 10000 genesexpressed in rapidly elongating cotton fibers,5...The NSF Cotton Genome Centers EST projecthas released】36000 cotton fiber EST sequencesfrom Gossypium arboreum,an A-genome diploidspecies.Of the approximately 10000 genesexpressed in rapidly elongating cotton fibers,50% or more encode unknown gene functions.The next challenge facing cotton researchers isdetermining the function of the fiber genes,andwhat role each plays in determiningagronomically important fiber traits.展开更多
Mitochondrial membrane potential(MMP)plays a crucial role in the function of cells and organelles,involving various cellular physiological processes,including energy production,formation of reactive oxygen species(ROS...Mitochondrial membrane potential(MMP)plays a crucial role in the function of cells and organelles,involving various cellular physiological processes,including energy production,formation of reactive oxygen species(ROS),unfolded protein stress,and cell survival.Currently,there is a lack of genetically encoded fluorescence indicators(GEVIs)for MMP.In our screening of various GEVIs for their potential monitoring MMP,the Accelerated Sensor of Action Potentials(ASAP)demonstrated optimal performance in targeting mitochondria and sensitivity to depolarization in multiple cell types.However,mitochondrial ASAPs also displayed sensitivity to ROS in cardiomyocytes.Therefore,two ASAP mutants resistant to ROS were generated.A double mutant ASAP3-ST exhibited the highest voltage sensitivity but weaker fluorescence.Overall,four GEVIs capable of targeting mitochondria were obtained and named mitochondrial potential indicators 1-4(MPI-1-4).In vivo,fiber photometry experiments utilizing MPI-2 revealed a mitochondrial depolarization during isoflurane-induced narcosis in the M2 cortex.展开更多
The paper offers an overview of quantum and macro gravity, two of the three pillars of the Grand Unified Theory (GUT), the other thermodynamics, developed in a series of papers since the solution of the gravitational ...The paper offers an overview of quantum and macro gravity, two of the three pillars of the Grand Unified Theory (GUT), the other thermodynamics, developed in a series of papers since the solution of the gravitational n-body problem in 1997 (J. Nonlinear Analysis, A-Series: Theory, Methods and Applications, Vol. 30, No. 8, 1997, pp. 5021 - 5032) and consolidated in the paper, The Grand Unified Theory (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 69, No. 3, 2008, pp. 823 - 831). GUT is further advanced by the paper, The Mathematics of GUT (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 71, 2009, pp. e420 - e431) and the discovery of more natural laws in the course of analyzing and explaining the disastrous final flight of the Columbia Space Shuttle in 2004 (J. Nonlinear Studies, Vol. 14, No. 3, 2007, pp. 241 - 260). Qualitative modeling was the key to the development of GUT and its theoretical and practical applications. The relevant natural laws of GUT that provide the foundations of the Unified Theory of Evolution are stated. GUT provides the basis for the development of the electromagnetic engine and the Unified Theory of Evolution, its theoretical application, for the development of appropriate technology for electromagnetic treatment of genetic diseases such as cancer, systemic lupos erythematosus, diabetes, muscular dystrophy and mental disorder, the central focus of this paper.展开更多
基金New Century Program for Excellent Talents of Minis-try of Education of China (NECT-06-0166)The Eleventh Five-year Scientific and Technological Development Plan of National Defense Pre-study Foundation (A2120060006)
文摘Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.
文摘A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.
基金This project is supported by Provincial Science Foundation of Hebei (No.01213553).
文摘An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.
基金supported by the National Key Research and Development Program of China (No.2020YFB1710500)the National Natural Science Foundation of China(No.51805253)the Fundamental Research Funds for the Central Universities(No. NP2020304)
文摘The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.
基金Supported in part by the National Natural Science F oundation of China(NSFC) (6 0 0 730 12 )
文摘The meta search engines provide service to the users by dispensing the users' requests to the existing search engines. The existing search engines selected by meta search engine determine the searching quality. Because the performance of the existing search engines and the users' requests are changed dynamically, it is not favorable for the fixed search engines to optimize the holistic performance of the meta search engine. This paper applies the genetic algorithm (GA) to realize the scheduling strategy of agent manager in our meta search engine, GSE(general search engine), which can simulate the evolution process of living things more lively and more efficiently. By using GA, the combination of search engines can be optimized and hence the holistic performance of GSE can be improved dramatically.
文摘The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se-quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov-ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an-tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.
基金the National Natural Science Foundation of China(Nos.61004088 and 61374160)
文摘Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.
文摘Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
基金support provided by the National Natural Science Foundation of China(Grant No.52304123)Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZB20230914)+3 种基金the 10th Young Talent Lifting Project of the China Association for Science and Technology(No.2024QNRC001)China Postdoctoral Science Foundation(Grant No.2023M730412)Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project(No.CSTB2024TIAD-CYKJCXX0016)National Key Research and Development Program for Young Scientists(Grant No.2021YFC2900400).
文摘The layout of a sensor network is a critical determinant of the precision and reliability of microseismic source localization.Addressing the impact of sensor network configuration on positioning accuracy,this paper introduces an innovative approach to sensor network optimization in underground space.It utilizes the Cramér-Rao Lower Bound principle to formulate an optimization function for the sensor network layout,followed by the deployment of an enhanced genetic encoding to solve this function and determine the optimal layout.The efficacy of proposed method is rigorously tested through simulation experiments and pencil-lead break experiments,substantiating its superiority.Its practical utility is further demonstrated through its application in a mining process within underground spaces,where the optimized sensor network solved by the proposed method achieves remarkable localization accuracy of 15 m with an accuracy rate of 4.22%in on-site blasting experiments.Moreover,the study elucidates general principles for sensor network layout that can inform the strategic placement of sensors in standard monitoring systems.
文摘Acinetobacter baumannii is one of the most important human pathogens causing a variety of nosocomial infections. Carbapenem antibiotics have been primarily used to treat the A. baumannii infections. However, carbapenem resistant A. baumannii producing carbapenemases causes serious treatment problems worldwide. Outbreaks of carbapenem resistant isolates have reported in some area of the United States, but their dissemination and genetic structure of the carbapenemase encoding genes are currently little known. To understand outbreaks, dissemination, and genetic structure of the carbapenemase encoding genes in Southern Texas, 32 clinical isolates collected from Austin and Houston, TX were characterized. Twenty-eight of 32 isolates were resistant to all tested β-lactam antibiotics including carbapenem (imipenem and meropenem). Three of them carried blaOXA-23 as a part of Tn2008 integrated into a known plasmid (pACICU2) and all others carried blaOXA-24 flanked by XerC/XerD-like recombinase binding sites that were adjoined by DNA sequences originated from multiple plasmids. Genotype analysis revealed that the 25 isolates carrying blaOXA-24 were all identical genotypes same as a representative isolate carrying blaOXA-24 from Chicago, IL but the 3 isolates carrying blaOXA-23 was a distinct genotype as compared with isolates carrying blaOXA-23 from Chicago, IL and Washington, D.C. Each of the blaOXA-23 and blaOXA-24 was transferred to carbapenem susceptible A. baumannii and E. coli with similar minimal inhibitory concentration (MIC) of carbapenem as that of their parental isolates but significantly lower levels of MIC in E. coli. Overall results suggest that a unique strain carrying blaOXA-23 and a similar strain carrying blaOXA-24 as seen in other geographic areas are currently disseminated in Southern Texas.
基金the National Key Research and Development Program of China(2021YFD1700102)the National Science Fund for Distinguished Young Scholars(22422702)+1 种基金Knowledge Innovation Program of Wuhan-Basic Research(No.2022013301015174)Prof.Alexander Jones at Cambridge University for his guidance and contribution.
文摘Genetically encoded biosensors are powerful tools for monitoring plant proteins,which could offer high spatial and temporal resolution and help reveal the molecular mechanisms underlying plant growth and stress responses.However,a comprehensive review focused on the spatiotemporal monitoring of plant proteins using these biosensors is still lacking.This review highlights key advancements in the field,evaluates the strengths and limitations of current biosensors,and discusses their applications for tracking plant protein dynamics.We aim to provide a thorough understanding of genetically encoded biosensors for plant proteins,promote the development of these technologies,and foster deeper insights into molecular mechanisms in plant cells.Future research should prioritize overcoming challenges such as interference from plant autofluorescence and enhancing the sensitivity of biosensors,particularly in complex cellular compartments like chloroplasts and cell walls,to further improve spatial and temporal resolution.
基金supported by the National Key Research and Development Program of China(2017YFA050400 and2017YFC0906900)the National Natural Science Foundation of China(31722033,91649123,31671484,31225008,and 31470833)+4 种基金the Shanghai Science and Technology Commission(14XD1401400,16430723100,and 15YF1402600)Young Elite Scientists Sponsorship Program by China Association for Science and Technology(to YZ)Shanghai Young Top-notch Talent(to YZ)the State Key Laboratory of Bioreactor Engineering(to YY)Fundamental Research Funds for the Central Universities(to YY and YZ)
文摘The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism under- lying how the brain metabolizes substances to support neural activity is a fundamental issue for neuroscience studies. A well-known model in the brain, the astrocyte- neuron lactate shuttle, postulates that glucose uptake and glycolytic activity are enhanced in astrocytes upon neu- ronal activation and that astrocytes transport lactate into neurons to fulfill their energy requirements. Current evidence for this hypothesis has yet to reach a clear consensus, and new concepts beyond the shuttle hypothesis are emerging. The discrepancy is largely attributed to the lack of a critical method for real-time monitoring of metabolic dynamics at cellular resolution. Recent advances in fluorescent protein-based sensors allow the generation of a sensitive, specific, real-time readout of subcellular metabolites and fill the current technological gap. Here,we summarize the development of genetically encoded metabolite sensors and their applications in assessing cell metabolism in living cells and in vivo, and we believe that these tools will help to address the issue of elucidating neural energy metabolism.
基金financial supports from the Research Grants Council of Hong Kong SAR Government to F. Sun (RGC-ECS Nos. #26103915 and Ao E/M-09/12)the 863 Program (No. 2015AA020941)+2 种基金the National Natural Science Foundation of China (Nos. 21474003, 91427304)"1000 Plan (Youth)"the Department of Chemical and Biological Engineering, HKUST for the faculty start-up fund
文摘Synthesis of macromolecular systems with precise structural and functional control constitutes a fundamental challenge for materials science and engineering. Development of the ability to construct complex bio-macromolecular architectures provides a solution to this challenge. The past few years have witnessed the emergence of a new category of peptide-protein chemistry which can covalently stitch together protein]peptide molecules with high specificity under mild physiological conditions. It has thus inspired the concept of genetically encoded click chemistry （GECC）. As a prototype of GECC, SpyTag/ SpyCatcher chemistry has enabled the precise synthesis ofmacromolecules both in vitro and in vivo, exerting precise control over the fundamental properties of these macromolecules including length, sequence, stereochemistry and topology and leading to the creation of diverse biomaterials for a variety of applications. We thus anticipate a potential toolbox of GECC comprising multiple mutually orthogonal, covalent-bond forming peptide-protein reactive pairs with diverse features, which shall bridge synthetic biology and materials science and open up enormous opportunities for biomaterialsin the future.
文摘Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm for optimizing pilot assignment in a multi-user massive multiple input multiple output(MIMO)system.Results show improvement on existing results especially in the case of strong user estimation rates.
文摘The NSF Cotton Genome Centers EST projecthas released】36000 cotton fiber EST sequencesfrom Gossypium arboreum,an A-genome diploidspecies.Of the approximately 10000 genesexpressed in rapidly elongating cotton fibers,50% or more encode unknown gene functions.The next challenge facing cotton researchers isdetermining the function of the fiber genes,andwhat role each plays in determiningagronomically important fiber traits.
基金supported by the National Natural Science Foundation (NSF)of China:JSK (32071137 and 92054103)Funding for Scientific Research and Innovation Team of The First Affliated Hospital of Zhengzhou University:JSK (ZYCXTD2023014)。
文摘Mitochondrial membrane potential(MMP)plays a crucial role in the function of cells and organelles,involving various cellular physiological processes,including energy production,formation of reactive oxygen species(ROS),unfolded protein stress,and cell survival.Currently,there is a lack of genetically encoded fluorescence indicators(GEVIs)for MMP.In our screening of various GEVIs for their potential monitoring MMP,the Accelerated Sensor of Action Potentials(ASAP)demonstrated optimal performance in targeting mitochondria and sensitivity to depolarization in multiple cell types.However,mitochondrial ASAPs also displayed sensitivity to ROS in cardiomyocytes.Therefore,two ASAP mutants resistant to ROS were generated.A double mutant ASAP3-ST exhibited the highest voltage sensitivity but weaker fluorescence.Overall,four GEVIs capable of targeting mitochondria were obtained and named mitochondrial potential indicators 1-4(MPI-1-4).In vivo,fiber photometry experiments utilizing MPI-2 revealed a mitochondrial depolarization during isoflurane-induced narcosis in the M2 cortex.
文摘The paper offers an overview of quantum and macro gravity, two of the three pillars of the Grand Unified Theory (GUT), the other thermodynamics, developed in a series of papers since the solution of the gravitational n-body problem in 1997 (J. Nonlinear Analysis, A-Series: Theory, Methods and Applications, Vol. 30, No. 8, 1997, pp. 5021 - 5032) and consolidated in the paper, The Grand Unified Theory (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 69, No. 3, 2008, pp. 823 - 831). GUT is further advanced by the paper, The Mathematics of GUT (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 71, 2009, pp. e420 - e431) and the discovery of more natural laws in the course of analyzing and explaining the disastrous final flight of the Columbia Space Shuttle in 2004 (J. Nonlinear Studies, Vol. 14, No. 3, 2007, pp. 241 - 260). Qualitative modeling was the key to the development of GUT and its theoretical and practical applications. The relevant natural laws of GUT that provide the foundations of the Unified Theory of Evolution are stated. GUT provides the basis for the development of the electromagnetic engine and the Unified Theory of Evolution, its theoretical application, for the development of appropriate technology for electromagnetic treatment of genetic diseases such as cancer, systemic lupos erythematosus, diabetes, muscular dystrophy and mental disorder, the central focus of this paper.
