Vision depends on accurate signal conduction from the retina to the brain through the optic nerve,an important part of the central nervous system that consists of bundles of axons originating from retinal ganglion cel...Vision depends on accurate signal conduction from the retina to the brain through the optic nerve,an important part of the central nervous system that consists of bundles of axons originating from retinal ganglion cells.The mammalian optic nerve,an important part of the central nervous system,cannot regenerate once it is injured,leading to permanent vision loss.To date,there is no clinical treatment that can regenerate the optic nerve and restore vision.Our previous study found that the mobile zinc(Zn^(2+))level increased rapidly after optic nerve injury in the retina,specifically in the vesicles of the inner plexiform layer.Furthermore,chelating Zn^(2+)significantly promoted axonal regeneration with a long-term effect.In this study,we conditionally knocked out zinc transporter 3(ZnT3)in amacrine cells or retinal ganglion cells to construct two transgenic mouse lines(VGAT^(Cre)ZnT3^(fl/fl)and VGLUT2^(Cre)ZnT3^(fl/fl),respectively).We obtained direct evidence that the rapidly increased mobile Zn^(2+)in response to injury was from amacrine cells.We also found that selective deletion of ZnT3 in amacrine cells promoted retinal ganglion cell survival and axonal regeneration after optic nerve crush injury,improved retinal ganglion cell function,and promoted vision recovery.Sequencing analysis of reginal ganglion cells revealed that inhibiting the release of presynaptic Zn^(2+)affected the transcription of key genes related to the survival of retinal ganglion cells in postsynaptic neurons,regulated the synaptic connection between amacrine cells and retinal ganglion cells,and affected the fate of retinal ganglion cells.These results suggest that amacrine cells release Zn^(2+)to trigger transcriptomic changes related to neuronal growth and survival in reginal ganglion cells,thereby influencing the synaptic plasticity of retinal networks.These results make the theory of zinc-dependent retinal ganglion cell death more accurate and complete and provide new insights into the complex interactions between retinal cell networks.展开更多
In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only de...In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.展开更多
In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there ar...In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.展开更多
基金the National Key R&D Project of China,No.2020YFA0112701(to YZ)the National Natural Science Foundation of China,Nos.82171057(to YZ),81870657(to YL)+1 种基金Science and Technology Program of Guangzhou of China,No.202206080005(to YZ)the Natural Science Foundation of Guangdong Province of China,No.2022A1515012168(to YL)。
文摘Vision depends on accurate signal conduction from the retina to the brain through the optic nerve,an important part of the central nervous system that consists of bundles of axons originating from retinal ganglion cells.The mammalian optic nerve,an important part of the central nervous system,cannot regenerate once it is injured,leading to permanent vision loss.To date,there is no clinical treatment that can regenerate the optic nerve and restore vision.Our previous study found that the mobile zinc(Zn^(2+))level increased rapidly after optic nerve injury in the retina,specifically in the vesicles of the inner plexiform layer.Furthermore,chelating Zn^(2+)significantly promoted axonal regeneration with a long-term effect.In this study,we conditionally knocked out zinc transporter 3(ZnT3)in amacrine cells or retinal ganglion cells to construct two transgenic mouse lines(VGAT^(Cre)ZnT3^(fl/fl)and VGLUT2^(Cre)ZnT3^(fl/fl),respectively).We obtained direct evidence that the rapidly increased mobile Zn^(2+)in response to injury was from amacrine cells.We also found that selective deletion of ZnT3 in amacrine cells promoted retinal ganglion cell survival and axonal regeneration after optic nerve crush injury,improved retinal ganglion cell function,and promoted vision recovery.Sequencing analysis of reginal ganglion cells revealed that inhibiting the release of presynaptic Zn^(2+)affected the transcription of key genes related to the survival of retinal ganglion cells in postsynaptic neurons,regulated the synaptic connection between amacrine cells and retinal ganglion cells,and affected the fate of retinal ganglion cells.These results suggest that amacrine cells release Zn^(2+)to trigger transcriptomic changes related to neuronal growth and survival in reginal ganglion cells,thereby influencing the synaptic plasticity of retinal networks.These results make the theory of zinc-dependent retinal ganglion cell death more accurate and complete and provide new insights into the complex interactions between retinal cell networks.
基金This work was supported by the Shanxi Province Applied Basic Research Project,China(Grant No.201901D111100).Xiaoli Hao received the grant,and the URL of the sponsors’website is http://kjt.shanxi.gov.cn/.
文摘In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.
基金supported by the Fundamental Research Funds for the Central Universities (Grant No.2020JBM265)the Beijing Natural Science Foundation (Grant No.3222016)+2 种基金the National Natural Science Foundation of China (Grant No.62103035)the China Postdoctoral Science Foundation(Grant No.2021M690337)the Beijing Laboratory for Urban Mass Transit (Grant No.353203535)。
文摘In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.
