Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world.As a result,specific infrastructures,notab...Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world.As a result,specific infrastructures,notably bridges,would experience significant flooding for which they were not intended and would be submerged.The flow field and shear stress distribution around tandem bridge piers under pressurized flow conditions for various bridge deck widths are examined using a series of three-dimensional(3D)simulations.It is indicated that scenarios with a deck width to pier diameter(Ld/p)ratio of 3 experience the highest levels of turbulent disturbance.In addition,maximum velocity and shear stresses occur in cases with Ld/p equal to 6.Results indicate that increasing the number of piers from 1 to 2 and 3 results in the increase of bed shear stress by 24%and 20%respectively.Finally,five machine learning algorithms,including Decision Trees(DT),Feed Forward Neural Networks(FFNN),and three Ensemble models,are implemented to estimate the flow field and the turbulent structure.Results indicated that the highest accuracy for estimation of U,and W,were obtained using AdaBoost ensemble with R2=0.946 and 0.951,respectively.Besides,the Random Forest algorithm outperformed AdaBoost slightly in the estimation of V and turbulent kinetic energy(TKE)with R2=0.894 and 0.951,respectively.展开更多
Accurate characterization of the interactions between biomolecules not only provides fundamental insights into cellular processes but also paves the way for drug discovery and development. With recent increases in thr...Accurate characterization of the interactions between biomolecules not only provides fundamental insights into cellular processes but also paves the way for drug discovery and development. With recent increases in throughput and sensitivity, biophysical technologies have become prominent tools for studying biomolecular interactions. Biophysical techniques that can reduce costs, shorten detection time, simplify the complexity of the system under analysis, and simultaneously provide high-quality data content are particularly favored. Here, we summarize the qualitative and quantitative analysis of biomolecular interactions using Micro Scale Thermophoresis(MST), as well as extend the application of MST functions to explore thermodynamics, enzyme kinetics and protein folding-unfolding processes. MST has emerged as a simple and powerful biophysical approach for identifying and quantifying binding events based on the movement of molecules along microscopic temperature gradients. The advantages of MST over other competitive biophysical techniques include freedom from immobilization, rapid analysis times, lower sample consumption, and the ability to analyze binding affinities in cell lysates. This article discusses the instrumental setups, principles, experimental workflows, and examples of MST application in practice.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.52179060 and 51909024).
文摘Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world.As a result,specific infrastructures,notably bridges,would experience significant flooding for which they were not intended and would be submerged.The flow field and shear stress distribution around tandem bridge piers under pressurized flow conditions for various bridge deck widths are examined using a series of three-dimensional(3D)simulations.It is indicated that scenarios with a deck width to pier diameter(Ld/p)ratio of 3 experience the highest levels of turbulent disturbance.In addition,maximum velocity and shear stresses occur in cases with Ld/p equal to 6.Results indicate that increasing the number of piers from 1 to 2 and 3 results in the increase of bed shear stress by 24%and 20%respectively.Finally,five machine learning algorithms,including Decision Trees(DT),Feed Forward Neural Networks(FFNN),and three Ensemble models,are implemented to estimate the flow field and the turbulent structure.Results indicated that the highest accuracy for estimation of U,and W,were obtained using AdaBoost ensemble with R2=0.946 and 0.951,respectively.Besides,the Random Forest algorithm outperformed AdaBoost slightly in the estimation of V and turbulent kinetic energy(TKE)with R2=0.894 and 0.951,respectively.
基金This work was supported by State Key Laboratory of Natural and Biomimetic Drugs,Peking University。
文摘Accurate characterization of the interactions between biomolecules not only provides fundamental insights into cellular processes but also paves the way for drug discovery and development. With recent increases in throughput and sensitivity, biophysical technologies have become prominent tools for studying biomolecular interactions. Biophysical techniques that can reduce costs, shorten detection time, simplify the complexity of the system under analysis, and simultaneously provide high-quality data content are particularly favored. Here, we summarize the qualitative and quantitative analysis of biomolecular interactions using Micro Scale Thermophoresis(MST), as well as extend the application of MST functions to explore thermodynamics, enzyme kinetics and protein folding-unfolding processes. MST has emerged as a simple and powerful biophysical approach for identifying and quantifying binding events based on the movement of molecules along microscopic temperature gradients. The advantages of MST over other competitive biophysical techniques include freedom from immobilization, rapid analysis times, lower sample consumption, and the ability to analyze binding affinities in cell lysates. This article discusses the instrumental setups, principles, experimental workflows, and examples of MST application in practice.
