In the Research Article“Pt–Se hybrid nanozymes with potent catalytic activities to scavenge ROS/RONS and regulate macrophage polarization for osteoarthritis therapy”[1],the authors made an inadvertent error where 2...In the Research Article“Pt–Se hybrid nanozymes with potent catalytic activities to scavenge ROS/RONS and regulate macrophage polarization for osteoarthritis therapy”[1],the authors made an inadvertent error where 2 images was mistakenly included in Fig.9Opens in image viewerF and Fig.S5A.Specifically,during the figure assembly process,the images of“CD206”in 4W+osteoarthritis(OA)+PtSe nanoparticle(NP)group were misused as images of“CD206”in 8W+OA+Se NP group in Fig.9Opens in image viewerF.Moreover,in Fig.S5A,another image of“0 d”in the“free Cy5.5”group was inadvertently chosen as the“0 d”image in the“PtSe/DSPE NP”(PtSe coated with DSPE-NH2)group,so they appear to be remarkably similar.The authors want to assure readers that this issue has been promptly addressed,and the corrected image of the Fig.9Opens in image viewerF and Fig.S5A is below.Importantly,it should be noted that this error does not affect the scientific conclusions drawn in the study.The authors sincerely apologize for any inconvenience caused by this oversight.展开更多
In the Research Article“Inhibition of ALOX12–12-HETE Alleviates Lung Ischemia–Reperfusion Injury by Reducing Endothelial Ferroptosis-Mediated Neutrophil Extracellular Trap Formation”,the authors have identified 2 ...In the Research Article“Inhibition of ALOX12–12-HETE Alleviates Lung Ischemia–Reperfusion Injury by Reducing Endothelial Ferroptosis-Mediated Neutrophil Extracellular Trap Formation”,the authors have identified 2 inadvertent errors in their figures[1].In the upper right panel of Fig.2A,the H&E image of Alox12-KO+Sham group was incorrect.Additionally,during the figure assembly process,the incorrect image of ACTB was used in Fig.6F and H.展开更多
In the version of article originally published in the volume 67,2024 of Sci China Mater(pages 3733,3735,3736,https://doi.org/10.1007/s40843-024-3002-0),three minor errors appeared in the original article,all of which ...In the version of article originally published in the volume 67,2024 of Sci China Mater(pages 3733,3735,3736,https://doi.org/10.1007/s40843-024-3002-0),three minor errors appeared in the original article,all of which were caused by incorrect placement of images during the layout process.The corrected Fig.1g,Fig.3b,and Fig.4a are as follows.展开更多
Compressed ultrafast photography(CUP)is a computational imaging technique that can simultaneously achieve an imaging speed of 10^(13)frames per second and a sequence depth of hundreds of frames.It is a powerful tool f...Compressed ultrafast photography(CUP)is a computational imaging technique that can simultaneously achieve an imaging speed of 10^(13)frames per second and a sequence depth of hundreds of frames.It is a powerful tool for observing unrepeatable ultrafast physical processes.However,since the forward model of CUP is a data compression process,the reconstruction process is an ill-posed problem.This causes inconvenience in the practical application of CUP,especially in those scenes with complex temporal behavior,high noise level and compression ratio.In this paper,the CUP system model based on spatial-intensity-temporal constraints is proposed by adding an additional charge-coupled device(CCD)camera to constrain the spatial and intensity behaviors of the dynamic scene and an additional narrow-slit streak camera to constrain the temporal behavior of the dynamic scene.Additionally,the unsupervised deep learning CUP reconstruction algorithm with low-rank tensor embedding is also proposed.The algorithm enhances the low-rankness of the reconstructed image by maintaining the low-rank structure of the dynamic scene and effectively utilizes the implicit prior information of the neural network and the hardware physical model.The proposed joint learning model enables high-quality reconstruction of complex dynamic scenes without training datasets.The simulation and experimental results demonstrate the application prospect of the proposed joint learning model in complex ultrafast physical phenomena imaging.展开更多
文摘In the Research Article“Pt–Se hybrid nanozymes with potent catalytic activities to scavenge ROS/RONS and regulate macrophage polarization for osteoarthritis therapy”[1],the authors made an inadvertent error where 2 images was mistakenly included in Fig.9Opens in image viewerF and Fig.S5A.Specifically,during the figure assembly process,the images of“CD206”in 4W+osteoarthritis(OA)+PtSe nanoparticle(NP)group were misused as images of“CD206”in 8W+OA+Se NP group in Fig.9Opens in image viewerF.Moreover,in Fig.S5A,another image of“0 d”in the“free Cy5.5”group was inadvertently chosen as the“0 d”image in the“PtSe/DSPE NP”(PtSe coated with DSPE-NH2)group,so they appear to be remarkably similar.The authors want to assure readers that this issue has been promptly addressed,and the corrected image of the Fig.9Opens in image viewerF and Fig.S5A is below.Importantly,it should be noted that this error does not affect the scientific conclusions drawn in the study.The authors sincerely apologize for any inconvenience caused by this oversight.
文摘In the Research Article“Inhibition of ALOX12–12-HETE Alleviates Lung Ischemia–Reperfusion Injury by Reducing Endothelial Ferroptosis-Mediated Neutrophil Extracellular Trap Formation”,the authors have identified 2 inadvertent errors in their figures[1].In the upper right panel of Fig.2A,the H&E image of Alox12-KO+Sham group was incorrect.Additionally,during the figure assembly process,the incorrect image of ACTB was used in Fig.6F and H.
文摘In the version of article originally published in the volume 67,2024 of Sci China Mater(pages 3733,3735,3736,https://doi.org/10.1007/s40843-024-3002-0),three minor errors appeared in the original article,all of which were caused by incorrect placement of images during the layout process.The corrected Fig.1g,Fig.3b,and Fig.4a are as follows.
基金National Natural Science Foundation of China(11975184)。
文摘Compressed ultrafast photography(CUP)is a computational imaging technique that can simultaneously achieve an imaging speed of 10^(13)frames per second and a sequence depth of hundreds of frames.It is a powerful tool for observing unrepeatable ultrafast physical processes.However,since the forward model of CUP is a data compression process,the reconstruction process is an ill-posed problem.This causes inconvenience in the practical application of CUP,especially in those scenes with complex temporal behavior,high noise level and compression ratio.In this paper,the CUP system model based on spatial-intensity-temporal constraints is proposed by adding an additional charge-coupled device(CCD)camera to constrain the spatial and intensity behaviors of the dynamic scene and an additional narrow-slit streak camera to constrain the temporal behavior of the dynamic scene.Additionally,the unsupervised deep learning CUP reconstruction algorithm with low-rank tensor embedding is also proposed.The algorithm enhances the low-rankness of the reconstructed image by maintaining the low-rank structure of the dynamic scene and effectively utilizes the implicit prior information of the neural network and the hardware physical model.The proposed joint learning model enables high-quality reconstruction of complex dynamic scenes without training datasets.The simulation and experimental results demonstrate the application prospect of the proposed joint learning model in complex ultrafast physical phenomena imaging.
