Genomic prediction(GP)in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines.In a GP experiment,34 elite inbred lines were selected to ma...Genomic prediction(GP)in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines.In a GP experiment,34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design.These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group.The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area(SUS)and three locations in the spring sowing area(SPS)in the main maizeproducing regions of China.Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids.By ten-fold cross-validation,the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction(GBLUP)model in SUS and SPS were 0.51 and 0.46,respectively.The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS,respectively.When additive,dominance,epistasis effects,genotype-by-environment interaction,and multi-trait effects were incorporated into the prediction model,the prediction accuracy of hybrid yield performance was improved.The ratio of training to testing population and size of training population optimal for yield prediction were determined.Multiple prediction models can improve prediction accuracy in hybrid breeding.展开更多
Based on the topological characteristics of small-world networks,a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties.To qualify the effects of uncertain paramete...Based on the topological characteristics of small-world networks,a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties.To qualify the effects of uncertain parameters in the response networks,some effective recognition rates are designed so as to achieve a steady value in the extremely fast simulation time period.Meanwhile,the Fisher-Kolmogorov and Burgers spatiotemporal chaotic systems are selected as the network nodes for constructing a drive and a response network,respectively.The simulation results confirm that the developed sliding mode could realize the effective synchronization problem between the spatiotemporal networks,and the outer synchronization is still achieved timely even when the connection probability of the small-world networks changes.展开更多
Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the p...Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.展开更多
What is the most favorite and original chemistry developed in your researchgroup?I hope it's always the next one.How do you get into this specific field?Could you please share some experiences with ourreaders?Natu...What is the most favorite and original chemistry developed in your researchgroup?I hope it's always the next one.How do you get into this specific field?Could you please share some experiences with ourreaders?Nature provides many astonishing catalytic machineries for building up molecular complexity and harnessing energy in the most efficient ways.Nature's recipe for catalysis serves as a starting point to develop new catalyst for synthetic and energy chemistry.展开更多
In this paper,we introduce economic policy uncertainty by using a stochastic discount model.Via parameter calibration and static analysis,we investigate the dynamic characteristics of stock risk on different policy un...In this paper,we introduce economic policy uncertainty by using a stochastic discount model.Via parameter calibration and static analysis,we investigate the dynamic characteristics of stock risk on different policy uncertainty.On this basis,we test the channel through policy uncertainty affects stock risk by empirical simulation,and the effect of policy uncertainty on stock risk formation by portfolio analysis,in order to verify the applicability of the theoretical model in China.Finally,the panel model is used to quantitatively analyze the relationship between policy uncertainty and stock risk.The results show that policy uncertainty can inereuse stock risk through enterprise cash flow,discounting factors and correlation coefficient.The effect is still significant after controlling traditional risk factors,corporate heterogeneity,and external environmental factors.Companies that are non-state-owned,have lower returns on invested capital and lower asset growth rate manifest greater stock risk when policy uncertainty is higher.The magnitude of the effect of policy uncertainty on stock risk is larger when the economy is weaker and the policy environment is more unstable.展开更多
Retinal degenerative diseases are a major cause of blindness.Retinal gene therapy is a trail-blazer in the human gene therapy field,leading to the frst FDA approved gene therapy product for a human genetic disease.The...Retinal degenerative diseases are a major cause of blindness.Retinal gene therapy is a trail-blazer in the human gene therapy field,leading to the frst FDA approved gene therapy product for a human genetic disease.The application of Clustered Regularly Interspaced Short Palindromic Repeat/Cas9(CRISPR/Cas9)-mediated gene editing technology is transforming the delivery of gene therapy.We review the history,present,and future pro-spects of retinal gene therapy.展开更多
基金the National Natural Science Foundation of China(32272049,32261143757)Sustainable Development International Cooperation Program from Bill&Melinda Gates Foundation(2022YFAG1002)+2 种基金the National Key Research and Development Program of China(2020YFE0202300)the Agricultural Science&Technology Innovation Program(CAASZDRW202109)the China Scholarship Council.
文摘Genomic prediction(GP)in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines.In a GP experiment,34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design.These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group.The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area(SUS)and three locations in the spring sowing area(SPS)in the main maizeproducing regions of China.Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids.By ten-fold cross-validation,the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction(GBLUP)model in SUS and SPS were 0.51 and 0.46,respectively.The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS,respectively.When additive,dominance,epistasis effects,genotype-by-environment interaction,and multi-trait effects were incorporated into the prediction model,the prediction accuracy of hybrid yield performance was improved.The ratio of training to testing population and size of training population optimal for yield prediction were determined.Multiple prediction models can improve prediction accuracy in hybrid breeding.
基金Project supported by the National Natural Science Foundation of China(Nos.11602146,11872304,and 11962019)the Science Foundation of Shanghai(No.18ZR1438200)and the Chen Guang Project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation(No.16CG65)。
文摘Based on the topological characteristics of small-world networks,a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties.To qualify the effects of uncertain parameters in the response networks,some effective recognition rates are designed so as to achieve a steady value in the extremely fast simulation time period.Meanwhile,the Fisher-Kolmogorov and Burgers spatiotemporal chaotic systems are selected as the network nodes for constructing a drive and a response network,respectively.The simulation results confirm that the developed sliding mode could realize the effective synchronization problem between the spatiotemporal networks,and the outer synchronization is still achieved timely even when the connection probability of the small-world networks changes.
基金supported by the National Natural Science Foundation of China(T2225002,82273855 to Mingyue Zheng)Lingang Laboratory(LG202102-01-02 to Mingyue Zheng)+1 种基金the National Key Research and Development Program of China(2022YFC3400504 to Mingyue Zheng)the open fund of state key laboratory of Pharmaceutical Biotechnology,Nanjing University,China(KF-202301 to Mingyue Zheng).
文摘Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.
基金references,for their significant contributions to this project and the Natural Science Foundation of China(21861132003,91956000 and 22031006)Tsinghua University Initiative Scientific Research Program for financial support.
文摘What is the most favorite and original chemistry developed in your researchgroup?I hope it's always the next one.How do you get into this specific field?Could you please share some experiences with ourreaders?Nature provides many astonishing catalytic machineries for building up molecular complexity and harnessing energy in the most efficient ways.Nature's recipe for catalysis serves as a starting point to develop new catalyst for synthetic and energy chemistry.
基金This study is supported by the National Social Sciences Fund of China(No.16BJL028)the National Natural Science Fund of China(No.71771193,No.71471154)China Postdoctoral Science Foundation funded project(No.2017M622671).
文摘In this paper,we introduce economic policy uncertainty by using a stochastic discount model.Via parameter calibration and static analysis,we investigate the dynamic characteristics of stock risk on different policy uncertainty.On this basis,we test the channel through policy uncertainty affects stock risk by empirical simulation,and the effect of policy uncertainty on stock risk formation by portfolio analysis,in order to verify the applicability of the theoretical model in China.Finally,the panel model is used to quantitatively analyze the relationship between policy uncertainty and stock risk.The results show that policy uncertainty can inereuse stock risk through enterprise cash flow,discounting factors and correlation coefficient.The effect is still significant after controlling traditional risk factors,corporate heterogeneity,and external environmental factors.Companies that are non-state-owned,have lower returns on invested capital and lower asset growth rate manifest greater stock risk when policy uncertainty is higher.The magnitude of the effect of policy uncertainty on stock risk is larger when the economy is weaker and the policy environment is more unstable.
文摘Retinal degenerative diseases are a major cause of blindness.Retinal gene therapy is a trail-blazer in the human gene therapy field,leading to the frst FDA approved gene therapy product for a human genetic disease.The application of Clustered Regularly Interspaced Short Palindromic Repeat/Cas9(CRISPR/Cas9)-mediated gene editing technology is transforming the delivery of gene therapy.We review the history,present,and future pro-spects of retinal gene therapy.
