Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facil...Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.展开更多
This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations o...This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.展开更多
基金National Natural Science Foundation of China(62027812,62333012)。
文摘Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.
文摘This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.
