Shikimic acid(SA) is the key synthetic material for the chemical synthesis of Oseltamivir, which is prescribed as the front-line treatment for serious cases of influenza. Multi-gene expression vector can be used for e...Shikimic acid(SA) is the key synthetic material for the chemical synthesis of Oseltamivir, which is prescribed as the front-line treatment for serious cases of influenza. Multi-gene expression vector can be used for expressing the plurality of the genes in one plasmid, so it is widely applied to increase the yield of metabolites. In the present study, on the basis of a shikimate kinase genetic defect strain Escherichia coli BL21(?aro L/aro K, DE3), the key enzyme genes aro G, aro B, tkt A and aro E of SA pathway were co-expressed and compared systematically by constructing a series of multi-gene expression vectors. The results showed that different gene co-expression combinations(two, three or four genes) or gene orders had different effects on the production of SA. SA production of the recombinant BL21-GBAE reached to 886.38 mg·L^(-1), which was 17-fold(P < 0.05) of the parent strain BL21(?aro L/aro K, DE3).展开更多
More accurate and reliable estimation of residual strength friction angle(/r)of clay is crucial in many geotechnical engineering applications,including riverbank stability analysis,design,and assessment of earthen dam...More accurate and reliable estimation of residual strength friction angle(/r)of clay is crucial in many geotechnical engineering applications,including riverbank stability analysis,design,and assessment of earthen dam slope stabilities.However,a general predictive equation for/r,with applicability in a wide range of effective parameters,remains an important research gap.The goal of this study is to develop a more accurate equation for/r using the Pareto Optimal Multi-gene Genetic Programming(POMGGP)approach by evaluating a comprehensive dataset of 290 experiments compiled from published literature databases worldwide.A new framework for integrated equation derivation proposed that hybridizes the Subset Selection of Maximum Dissimilarity Method(SSMD)with Multi-gene Genetic Programming(MGP)and Pareto-optimality(PO)to find an accurate equation for/r with wide range applicability.The final predictive equation resulted from POMGGP modeling was assessed in comparison with some previously published machine learning-based equations using statistical error analysis criteria,Taylor diagram,revised discrepancy ratio(RDR),and scatter plots.Base on the results,the POMGGP has the lowest uncertainty with U95=2.25,when compared with Artificial Neural Network(ANN)(U95=2.3),Bayesian Regularization Neural Network(BRNN)(U95=2.94),Levenberg-Marquardt Neural Network(LMNN)(U95=3.3),and Differential Evolution Neural Network(DENN)(U95=2.37).The more reliable results in estimation of/r derived by POMGGP with reliability 59.3%,and resiliency 60%in comparison with ANN(reliability=30.23%,resiliency=28.33%),BRNN(reliability=10.47%,resiliency=10.39%),LMNN(reliability=19.77%,resiliency=20.29%)and DENN(reliability=27.91%,resiliency=24.19%).Besides the simplicity and ease of application of the new POMGGP equation to a broad range of conditions,using the uncertainty,reliability,and resilience analysis confirmed that the derived equation for/r significantly outperformed other existing machine learning methods,including the ANN,BRNN,LMNN,and DENN equations。展开更多
The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavement...The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavementscompared to conventional design guidelines. It is achieved through optimizing pavement structural andthickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizingeconomic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volumeconcrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative.This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4 mm (6 in.). Thispaper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concretepavements (JPCPs) without modification of Pavement ME. It utilizes multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performanceanalyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. Thispermits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axlespectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigidpavements with conventional concrete slab thicknesses and enable rational extrapolation of performance predictionfor thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable lowvolumepavement design using optimized ME solutions for Pittsburgh, PA, conditions.展开更多
Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted so...Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted source-sink balance,causing trade-offs among photosynthesis,yield,and stress response.To address these limitations,we present an integrated synthetic biological framework that synergistically enhances photosynthetic efficiency(source capacity),sink optimization,and abiotic stress tolerance.We developed an editing-overexpression coupling(EOC)vector system enabling simultaneous overexpression of four photosynthesis-enhancing genes(Cyt c6,PsbA,FBPase,OsMGT3),knockout of three yield-limiting genes(GS3,Gn1a,OsAAP5),and self-excision of selection markers,gene-editing modules,and fragment deletion cassettes.Field evaluations of CFMP-gga transgenic lines revealed significant physiological improvements,including 13%–17%increase in photosynthetic rates,improved chlorophyll fluorescence parameters,and increased stomatal conductance.These enhancements translated into remarkable agronomic gains,including 18.7%–22.3%higher grain yield,23.1%–26.1%increased biomass,and improved panicle architecture(increased grain size and grain number per panicle).The engineered lines maintained superior thermotolerance(under 42°C stress)and alkali tolerance(at pH 10)compared to wild-type controls.This study provides a strategy for enhancing crop yield by demonstrating that coordinated multi-gene regulation of source-sink dynamics,coupled with stress resilience engineering,achieves concurrent improvements.展开更多
基金supported by National Important Special Foundation of the New Drug Development(2012ZX 09301002-003 and 2014ZX09201001-012)Shanghai Innovation Action Plan of Science and Technology(14431905900)
文摘Shikimic acid(SA) is the key synthetic material for the chemical synthesis of Oseltamivir, which is prescribed as the front-line treatment for serious cases of influenza. Multi-gene expression vector can be used for expressing the plurality of the genes in one plasmid, so it is widely applied to increase the yield of metabolites. In the present study, on the basis of a shikimate kinase genetic defect strain Escherichia coli BL21(?aro L/aro K, DE3), the key enzyme genes aro G, aro B, tkt A and aro E of SA pathway were co-expressed and compared systematically by constructing a series of multi-gene expression vectors. The results showed that different gene co-expression combinations(two, three or four genes) or gene orders had different effects on the production of SA. SA production of the recombinant BL21-GBAE reached to 886.38 mg·L^(-1), which was 17-fold(P < 0.05) of the parent strain BL21(?aro L/aro K, DE3).
文摘More accurate and reliable estimation of residual strength friction angle(/r)of clay is crucial in many geotechnical engineering applications,including riverbank stability analysis,design,and assessment of earthen dam slope stabilities.However,a general predictive equation for/r,with applicability in a wide range of effective parameters,remains an important research gap.The goal of this study is to develop a more accurate equation for/r using the Pareto Optimal Multi-gene Genetic Programming(POMGGP)approach by evaluating a comprehensive dataset of 290 experiments compiled from published literature databases worldwide.A new framework for integrated equation derivation proposed that hybridizes the Subset Selection of Maximum Dissimilarity Method(SSMD)with Multi-gene Genetic Programming(MGP)and Pareto-optimality(PO)to find an accurate equation for/r with wide range applicability.The final predictive equation resulted from POMGGP modeling was assessed in comparison with some previously published machine learning-based equations using statistical error analysis criteria,Taylor diagram,revised discrepancy ratio(RDR),and scatter plots.Base on the results,the POMGGP has the lowest uncertainty with U95=2.25,when compared with Artificial Neural Network(ANN)(U95=2.3),Bayesian Regularization Neural Network(BRNN)(U95=2.94),Levenberg-Marquardt Neural Network(LMNN)(U95=3.3),and Differential Evolution Neural Network(DENN)(U95=2.37).The more reliable results in estimation of/r derived by POMGGP with reliability 59.3%,and resiliency 60%in comparison with ANN(reliability=30.23%,resiliency=28.33%),BRNN(reliability=10.47%,resiliency=10.39%),LMNN(reliability=19.77%,resiliency=20.29%)and DENN(reliability=27.91%,resiliency=24.19%).Besides the simplicity and ease of application of the new POMGGP equation to a broad range of conditions,using the uncertainty,reliability,and resilience analysis confirmed that the derived equation for/r significantly outperformed other existing machine learning methods,including the ANN,BRNN,LMNN,and DENN equations。
基金the financial support from the University of Pittsburgh Anthony Gill Chair and the Impactful Resilient Infrastructure Science and Engineering Consortium(IRISE)at University of Pittsburgh.
文摘The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavementscompared to conventional design guidelines. It is achieved through optimizing pavement structural andthickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizingeconomic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volumeconcrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative.This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4 mm (6 in.). Thispaper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concretepavements (JPCPs) without modification of Pavement ME. It utilizes multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performanceanalyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. Thispermits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axlespectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigidpavements with conventional concrete slab thicknesses and enable rational extrapolation of performance predictionfor thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable lowvolumepavement design using optimized ME solutions for Pittsburgh, PA, conditions.
基金the National Key Research and Development Program of China(2020YFA0907600)National Natural Science Foundation of China(31100869)+1 种基金Central Public-interest Scientific Institutions Basal Research Fund for Zhang Zhiguo(Y2025YY06)the Fundamental Research Funds for Central Nonprofit Scientific Institutions for Lu Tiegang,and Cui Xuean.
文摘Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted source-sink balance,causing trade-offs among photosynthesis,yield,and stress response.To address these limitations,we present an integrated synthetic biological framework that synergistically enhances photosynthetic efficiency(source capacity),sink optimization,and abiotic stress tolerance.We developed an editing-overexpression coupling(EOC)vector system enabling simultaneous overexpression of four photosynthesis-enhancing genes(Cyt c6,PsbA,FBPase,OsMGT3),knockout of three yield-limiting genes(GS3,Gn1a,OsAAP5),and self-excision of selection markers,gene-editing modules,and fragment deletion cassettes.Field evaluations of CFMP-gga transgenic lines revealed significant physiological improvements,including 13%–17%increase in photosynthetic rates,improved chlorophyll fluorescence parameters,and increased stomatal conductance.These enhancements translated into remarkable agronomic gains,including 18.7%–22.3%higher grain yield,23.1%–26.1%increased biomass,and improved panicle architecture(increased grain size and grain number per panicle).The engineered lines maintained superior thermotolerance(under 42°C stress)and alkali tolerance(at pH 10)compared to wild-type controls.This study provides a strategy for enhancing crop yield by demonstrating that coordinated multi-gene regulation of source-sink dynamics,coupled with stress resilience engineering,achieves concurrent improvements.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(Brain Science and Brain-Like Intelligence Technology,No.2025ZD0214902)the National Natural Science Foundation of China(No.32271047,32471054)。
