In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,w...In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.展开更多
A simple and rapid liquid chromatography-tandem mass spectrometry （LC-MS/MS） method was de- veloped and validated for simultaneous determination of acetaminophen and oxycodone in human plasma. Acetaminophen-d4 and o...A simple and rapid liquid chromatography-tandem mass spectrometry （LC-MS/MS） method was de- veloped and validated for simultaneous determination of acetaminophen and oxycodone in human plasma. Acetaminophen-d4 and oxycodone-d3 were used as internal standards. The challenge en- countered in the method development that the high plasma concentration level of acetaminophen made the MS response saturated while the desired lower limit of quantification （LLOQ,） for oxycodone was hard to reach was well solved. The analytes were extracted by protein precipitation using acetonitrile. The matrix effect of the analytes was avoided by chromatographic separation using a hydrophilic C18 column coupled with gradient elution. Multiple reaction monitoring in positive ion mode was performed on tandem mass spectrometer employing electrospray ion source. The calibration curves were linear over the concentration ranges of 40.0-8000 ng/mL and 0.200-40.0 ng/mL for acetaminophen and oxycodone, respectively. This method, which could contribute to high throughput analysis and better clinical drug monitoring, was successfully applied to a pharmacokinetic study in healthy Chinese volunteers.展开更多
基金support from the National Science Foundation (Grant CMMI-1934300)Defense Advanced Research Projects Agency (DARPA) under the Physics of Artificial Intelligence (PAI) program (Grant HR00111890034)partial funding support by graduate fellowship from China Scholarship Council (CSC) in this effort
文摘In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.
文摘A simple and rapid liquid chromatography-tandem mass spectrometry （LC-MS/MS） method was de- veloped and validated for simultaneous determination of acetaminophen and oxycodone in human plasma. Acetaminophen-d4 and oxycodone-d3 were used as internal standards. The challenge en- countered in the method development that the high plasma concentration level of acetaminophen made the MS response saturated while the desired lower limit of quantification （LLOQ,） for oxycodone was hard to reach was well solved. The analytes were extracted by protein precipitation using acetonitrile. The matrix effect of the analytes was avoided by chromatographic separation using a hydrophilic C18 column coupled with gradient elution. Multiple reaction monitoring in positive ion mode was performed on tandem mass spectrometer employing electrospray ion source. The calibration curves were linear over the concentration ranges of 40.0-8000 ng/mL and 0.200-40.0 ng/mL for acetaminophen and oxycodone, respectively. This method, which could contribute to high throughput analysis and better clinical drug monitoring, was successfully applied to a pharmacokinetic study in healthy Chinese volunteers.
