Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class...Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion...Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion of CAG repeats disrupts the genetic stability of animal models,which is detrimental to disease research.Methods:In this study,we established a mouse model in which CAG repeats do not undergo microsatellite instability(MSI)across generations.A humanized ATXN2 cDNA with four CAA interruptions within 73 CAG expansions was inserted into the Rosa26 locus of C57BL/6J mice.A 23 CAG control mouse model was also generated to verify ATXN2 integration and expression.Results:In our model,the number of CAG repeats remained stable during transmission,with no CAG repeat expansion observed in 64 parent-to-offspring transmissions.Compared with SCA2-Q23 mice,SCA2-Q73 mice exhibited progressive motor impairment,reduced Purkinje cell count and volume(indicative of cell atrophy),and muscle atrophy.These observations in the mice suggest that the behavioral and neuropathological phenotypes may reflect the features of SCA2 patients.RNA-seq analysis of the gastrocnemius muscle in SCA2-Q73 mice showed significant changes in muscle differentiation and development gene expression at 56 weeks,with no significant differences at 16 weeks compared to SCA2-Q23 mice.The expression level of the Myf6 gene significantly changed in the muscles of aged mice.Conclusion:In summary,the establishment of this model not only provides a stable animal model for studying CAG transmission in SCA2 but also indicates that the lack of long-term neural stimulation leads to muscle atrophy.展开更多
Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune press...Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit mode...Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金funded by grants from the National Key Research and Development Program of China(Grant Nos.:2022YFE0205600 and 2022YFC3400504)the National Natural Science Foundation of China(Grant Nos.:82373792 and 82273857)the Fundamental Research Funds for the Central Universities,China,and the East China Normal University Medicine and Health Joint Fund,China(Grant No.:2022JKXYD07001).
文摘Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金CAMS Innovation Fund for Medical Sciences,Grant/Award Number:CIFMS,2021-I2M-1-024The Joint Fund for the Department of Science and Technology of Yunnan Province-Kunming Medical University,Grant/Award Number:202201AY070001-007+1 种基金Open Research Fund Project of Yunnan Provincial Key Laboratory of Pharmacology of Natural Medicines,Grant/Award Number:YKLPNP-G2403The Science and Technology Leading Talent Program of Yunnan Province,Grant/Award Number:202405AB350002。
文摘Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion of CAG repeats disrupts the genetic stability of animal models,which is detrimental to disease research.Methods:In this study,we established a mouse model in which CAG repeats do not undergo microsatellite instability(MSI)across generations.A humanized ATXN2 cDNA with four CAA interruptions within 73 CAG expansions was inserted into the Rosa26 locus of C57BL/6J mice.A 23 CAG control mouse model was also generated to verify ATXN2 integration and expression.Results:In our model,the number of CAG repeats remained stable during transmission,with no CAG repeat expansion observed in 64 parent-to-offspring transmissions.Compared with SCA2-Q23 mice,SCA2-Q73 mice exhibited progressive motor impairment,reduced Purkinje cell count and volume(indicative of cell atrophy),and muscle atrophy.These observations in the mice suggest that the behavioral and neuropathological phenotypes may reflect the features of SCA2 patients.RNA-seq analysis of the gastrocnemius muscle in SCA2-Q73 mice showed significant changes in muscle differentiation and development gene expression at 56 weeks,with no significant differences at 16 weeks compared to SCA2-Q23 mice.The expression level of the Myf6 gene significantly changed in the muscles of aged mice.Conclusion:In summary,the establishment of this model not only provides a stable animal model for studying CAG transmission in SCA2 but also indicates that the lack of long-term neural stimulation leads to muscle atrophy.
基金National Science and Technology Infrastructure of China,Grant/Award Number:National Pathogen Resource Center-NPRC-32National Key Research and Development Program of China,Grant/Award Number:2023YFF0724800CAMS Innovation Fund for Medical Sciences,Grant/Award Number:2021-I2M-1-035。
文摘Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the National Natural Science Foundation of China(Grant Nos.12020101005,12475202,12347131,and 12405289).
文摘Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.
