This paper formulates an efficient numerical method for solving the convection diffusion solute transport equations coupled to blood flow equations in vessel networks.The reduced coupled model describes the variations...This paper formulates an efficient numerical method for solving the convection diffusion solute transport equations coupled to blood flow equations in vessel networks.The reduced coupled model describes the variations of vessel cross-sectional area,radially averaged blood momentum and solute concentration in large vessel networks.For the discretization of the reduced transport equation,we combine an interior penalty discontinuous Galerkin method in space with a novel locally implicit time stepping scheme.The stability and the convergence are proved.Numerical results show the impact of the choice for the steady-state axial velocity profile on the numerical solutions in a fifty-five vessel network with physiological boundary data.展开更多
Lymphatic vessel networks have been identified in the meninges of mice,non-human primates,and humans[1].Meningeal lymphatic vessels(mLVs),composed of meningeal lymphatic endothelial cells(mLECs),are present in both ze...Lymphatic vessel networks have been identified in the meninges of mice,non-human primates,and humans[1].Meningeal lymphatic vessels(mLVs),composed of meningeal lymphatic endothelial cells(mLECs),are present in both zebrafish and mammals,although their anatomical distributions differ;they reside in the dura mater in mice,but are situated within the meninges in zebrafish[2].Moreover,the lymphatic marker genes expressed in these vessels differ between species[2].展开更多
This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation. The formation is achieved by the follower to track a virtual target defined r...This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation. The formation is achieved by the follower to track a virtual target defined relative to the leader. A robust adaptive target tracking law is proposed by using neural network and backstepping techniques. The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces, nonlinear damping, unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning. Based on Lyapunov analysis, the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the control strategy.展开更多
淡漠是脑小血管病(cerebral small vessel disease,CSVD)常见的神经精神症状,临床常表现为目标导向行为减少、认知活动降低及情感表达减少等。CSVD患者淡漠的出现可能与神经网络功能障碍有关,且与认知功能损害存在密切联系。本文旨在对C...淡漠是脑小血管病(cerebral small vessel disease,CSVD)常见的神经精神症状,临床常表现为目标导向行为减少、认知活动降低及情感表达减少等。CSVD患者淡漠的出现可能与神经网络功能障碍有关,且与认知功能损害存在密切联系。本文旨在对CSVD患者淡漠的神经网络机制进行综述。展开更多
BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural netw...BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural networks(CNNs)to the task of segmenting carotid arteries to aid in the assessment of pathology.AIM To investigate CNN’s utility as an ancillary tool for researchers who require accurate segmentation of carotid vessels.METHODS An expert reader delineated vessel wall boundaries on 4422 axial T2-weighted magnetic resonance images of bilateral carotid arteries from 189 subjects with clinically evident atherosclerotic disease.A portion of this dataset was used to train two CNNs(one to segment the vessel lumen and the other to segment the vessel wall)with the remaining portion used to test the algorithm’s efficacy by comparing CNN segmented images with those of an expert reader.Overall quantitative assessment between automated and manual segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset for each CNN applied.The average DICE coefficient for the test dataset(CNN segmentations compared to expert’s segmentations)was 0.96 for the lumen and 0.87 for the vessel wall.Pearson correlation values and the intra-class correlation coefficient(ICC)were computed for the lumen(Pearson=0.98,ICC=0.98)and vessel wall(Pearson=0.88,ICC=0.86)segmentations.Bland-Altman plots of area measurements for the CNN and expert readers indicate good agreement with a mean bias of 1%-8%.CONCLUSION Although the technique produces reasonable results that are on par with expert human assessments,our application requires human supervision and monitoring to ensure consistent results.We intend to deploy this algorithm as part of a software platform to lessen researchers’workload to more quickly obtain reliable results.展开更多
基金Puelz was supported in part by the Research Training Group in Modeling and Simulation funded by NSF via grant RTG/DMS-1646339Riviere acknowledged the support of NSF via Grant DMS 1913291.
文摘This paper formulates an efficient numerical method for solving the convection diffusion solute transport equations coupled to blood flow equations in vessel networks.The reduced coupled model describes the variations of vessel cross-sectional area,radially averaged blood momentum and solute concentration in large vessel networks.For the discretization of the reduced transport equation,we combine an interior penalty discontinuous Galerkin method in space with a novel locally implicit time stepping scheme.The stability and the convergence are proved.Numerical results show the impact of the choice for the steady-state axial velocity profile on the numerical solutions in a fifty-five vessel network with physiological boundary data.
基金supported by the National Natural Science Foundation of China(32220103006 and 82271524).
文摘Lymphatic vessel networks have been identified in the meninges of mice,non-human primates,and humans[1].Meningeal lymphatic vessels(mLVs),composed of meningeal lymphatic endothelial cells(mLECs),are present in both zebrafish and mammals,although their anatomical distributions differ;they reside in the dura mater in mice,but are situated within the meninges in zebrafish[2].Moreover,the lymphatic marker genes expressed in these vessels differ between species[2].
基金supported by the National Natural Science Foundation of China (Grant Nos. 60674037,61074017 and 61074004)the Program for New Century Excellent Talents in Universities (Grant No. NCET-09-0674)the Program for Liaoning Excellent Talents in Universities (Grant No. 2009R06)
文摘This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation. The formation is achieved by the follower to track a virtual target defined relative to the leader. A robust adaptive target tracking law is proposed by using neural network and backstepping techniques. The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces, nonlinear damping, unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning. Based on Lyapunov analysis, the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the control strategy.
文摘淡漠是脑小血管病(cerebral small vessel disease,CSVD)常见的神经精神症状,临床常表现为目标导向行为减少、认知活动降低及情感表达减少等。CSVD患者淡漠的出现可能与神经网络功能障碍有关,且与认知功能损害存在密切联系。本文旨在对CSVD患者淡漠的神经网络机制进行综述。
基金Supported by American Heart Association Grant in Aid Founders Affiliate No.17GRNT33420119(Mani V)NIH NHLBI 2R01HL070121(Fayad ZA)and NIH NHLBI 1R01HL135878(Fayad ZA)
文摘BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural networks(CNNs)to the task of segmenting carotid arteries to aid in the assessment of pathology.AIM To investigate CNN’s utility as an ancillary tool for researchers who require accurate segmentation of carotid vessels.METHODS An expert reader delineated vessel wall boundaries on 4422 axial T2-weighted magnetic resonance images of bilateral carotid arteries from 189 subjects with clinically evident atherosclerotic disease.A portion of this dataset was used to train two CNNs(one to segment the vessel lumen and the other to segment the vessel wall)with the remaining portion used to test the algorithm’s efficacy by comparing CNN segmented images with those of an expert reader.Overall quantitative assessment between automated and manual segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset for each CNN applied.The average DICE coefficient for the test dataset(CNN segmentations compared to expert’s segmentations)was 0.96 for the lumen and 0.87 for the vessel wall.Pearson correlation values and the intra-class correlation coefficient(ICC)were computed for the lumen(Pearson=0.98,ICC=0.98)and vessel wall(Pearson=0.88,ICC=0.86)segmentations.Bland-Altman plots of area measurements for the CNN and expert readers indicate good agreement with a mean bias of 1%-8%.CONCLUSION Although the technique produces reasonable results that are on par with expert human assessments,our application requires human supervision and monitoring to ensure consistent results.We intend to deploy this algorithm as part of a software platform to lessen researchers’workload to more quickly obtain reliable results.
