Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modula...Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified.This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper(SMAp)in modulating MSL across different complexity levels and for both hands,as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation.Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL,which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions,particularly in interhemispheric connections.These findings may have important clinical implications,particularly for motor rehabilitation in populations such as post-stroke patients.展开更多
In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complem...In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.展开更多
Stabilizing metal nanoparticle catalysts typically requires supports with strong interactions,yet this poses a significant challenge for relatively inert supports(e.g.,silica).In this study,we address this issue by st...Stabilizing metal nanoparticle catalysts typically requires supports with strong interactions,yet this poses a significant challenge for relatively inert supports(e.g.,silica).In this study,we address this issue by strategically introducing methyl groups adjacent to copper nanoparticles on the silica surface.These methyl groups do not directly interact with the copper nanoparticles but effectively stabilize them against sintering during catalytic reactions.This is due to the significantly elevated energy barrier for copper nanoparticles to migrate through the noninteracting methyl groups,thereby preventing the sintering via migration-coalescence or Ostwald ripening routes.Consequently,this strategy leads to exceptional durability of silica-supported copper catalysts in the hydrogenation of dimethyl oxalate,a reaction that suffers from deactivation from copper sintering,and outperforms generally supported copper nanoparticle catalysts.展开更多
Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the co...Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the commercial Raney nickel catalyst can be greatly tuned by modulation of the nickel surface by silica.Using quinoline hydrogenation as a model,we show that the silica-modified Raney nickel catalysts exhibit good activity,excellent selectivity,and long stability,whereas the undesired over-hydrogenation reactions are effectively hindered.In contrast,the pristine Raney nickel catalyst shows inferior selectivity for the targeted product.Mechanistic studies confirm a positive role of silica to facilitate the desorption of 1,2,3,4-tetrahydroquinoline from the catalyst surface,thus enhancing the product selectivity.展开更多
Face processing is known to decline in older adults;however,a clear understanding of the brain networks behind this cognitive decline is still lacking.In this study,we investigated the neural correlates of the decline...Face processing is known to decline in older adults;however,a clear understanding of the brain networks behind this cognitive decline is still lacking.In this study,we investigated the neural correlates of the declined face processing with aging from a resting-state brain network perspective.Nineteen healthy old adults and 22 young adults were recruited and underwent two functional magnetic resonance imaging(fMRI)scanning sessions(i.e.,resting-state and localizer task)and two behavioral tests(face matching and symbolform matching).We examined age-related alterations in resting-state functional connectivity(FC)within face network as well as between face network and other networks,and tested their associations with behavioral performance of face and symbol-form processing.We found that(a)compared with young adults,old adults exhibited decreased FC between face-selective regions(fusiform face area and occipital face area),but increased FC between face-selective regions and non-face-selective regions;(b)these age-related FC alterations were correlated with individuals’behavioral performance of face and symbolform processing.Collectively,these findings suggest the declines of face processing are associated with a mixture of decreased integration within the face network and segregation beyond the face network in the aging brain,and provide evidence for a neural basis of cognitive aging in face processing from an intrinsic brain network perspective.展开更多
基金supported by grants from the Zhejiang Provincial Natural Science Foundation(LGJ22H180001)Zhejiang Medical and Health Science and Technology Project(2021KY249)the National Key R&D Program of China(2017YFC1310000).
文摘Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified.This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper(SMAp)in modulating MSL across different complexity levels and for both hands,as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation.Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL,which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions,particularly in interhemispheric connections.These findings may have important clinical implications,particularly for motor rehabilitation in populations such as post-stroke patients.
基金supported by the National Natural Science Foundation of China(No.61976023)。
文摘In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.
基金supported by the National Key Research and Development Program of China(grant nos.2022YFA1503502 and 2023YFA1507601)the National Natural Science Foundation of China(grant nos.22288101,22241801,and 22202176)+1 种基金the China Postdoctoral Science Foundation(CPSF)(grant no.2024M752801)the Postdoctoral Fellowship Program of CPSF(grant no.GZB20240650).
文摘Stabilizing metal nanoparticle catalysts typically requires supports with strong interactions,yet this poses a significant challenge for relatively inert supports(e.g.,silica).In this study,we address this issue by strategically introducing methyl groups adjacent to copper nanoparticles on the silica surface.These methyl groups do not directly interact with the copper nanoparticles but effectively stabilize them against sintering during catalytic reactions.This is due to the significantly elevated energy barrier for copper nanoparticles to migrate through the noninteracting methyl groups,thereby preventing the sintering via migration-coalescence or Ostwald ripening routes.Consequently,this strategy leads to exceptional durability of silica-supported copper catalysts in the hydrogenation of dimethyl oxalate,a reaction that suffers from deactivation from copper sintering,and outperforms generally supported copper nanoparticle catalysts.
基金the National Key Research and Development Program of China(2022YFA1503502)National Natural Science Foundation of China(U21B20101,21932006,and 22202175)China Postdoctoral Science Foundation(2021M700119).
文摘Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the commercial Raney nickel catalyst can be greatly tuned by modulation of the nickel surface by silica.Using quinoline hydrogenation as a model,we show that the silica-modified Raney nickel catalysts exhibit good activity,excellent selectivity,and long stability,whereas the undesired over-hydrogenation reactions are effectively hindered.In contrast,the pristine Raney nickel catalyst shows inferior selectivity for the targeted product.Mechanistic studies confirm a positive role of silica to facilitate the desorption of 1,2,3,4-tetrahydroquinoline from the catalyst surface,thus enhancing the product selectivity.
基金National Key R&D Program of China,Grant/Award Number:2017YFC1310000National Natural Science Foundation of China(NSFC),Grant/Award Numbers:32171063,81771911the Cultivation Project of the Province Leveled Pre-pronderant Characteristic Discipline in the College of Education of Hangzhou Normal University,Grant/Award Numbers:19JYXK001,20JYXK026。
文摘Face processing is known to decline in older adults;however,a clear understanding of the brain networks behind this cognitive decline is still lacking.In this study,we investigated the neural correlates of the declined face processing with aging from a resting-state brain network perspective.Nineteen healthy old adults and 22 young adults were recruited and underwent two functional magnetic resonance imaging(fMRI)scanning sessions(i.e.,resting-state and localizer task)and two behavioral tests(face matching and symbolform matching).We examined age-related alterations in resting-state functional connectivity(FC)within face network as well as between face network and other networks,and tested their associations with behavioral performance of face and symbol-form processing.We found that(a)compared with young adults,old adults exhibited decreased FC between face-selective regions(fusiform face area and occipital face area),but increased FC between face-selective regions and non-face-selective regions;(b)these age-related FC alterations were correlated with individuals’behavioral performance of face and symbolform processing.Collectively,these findings suggest the declines of face processing are associated with a mixture of decreased integration within the face network and segregation beyond the face network in the aging brain,and provide evidence for a neural basis of cognitive aging in face processing from an intrinsic brain network perspective.
