Long-term exposure to ambient fine particulate matter(PM2.5)may increase the risk of neurotoxicity in human populations.However,research studies on the underlying mechanisms of chronic PM2.5-induced depression-like be...Long-term exposure to ambient fine particulate matter(PM2.5)may increase the risk of neurotoxicity in human populations.However,research studies on the underlying mechanisms of chronic PM2.5-induced depression-like behaviors,and potential therapeutical strategies,remain scarce.In the present study,after long-term exposure to real-world PM2.5 for 15 weeks,male mice displayed depression-like behaviors,which were revealed using the open field and sucrose preference tests.Mechanistically,chronic PM2.5 exposure promoted astrocytic A1 polarization and disrupted reduction-oxidation balance in the mouse hippocampus.Furthermore,PM2.5-exposed mice displayed pathological damage to hippocampal neurons as well as the inhibition of nuclear factor erythroid 2-related factor 2 signaling.Astrocytic ablation of nuclear factor erythroid 2-related factor 2 exacerbated PM2.5-induced hippocampal neuronal injury in mice via the disruption of astrocyte-to-microglia communication;this finding was confirmed in mice with bilateral and unilateral hippocampal astrocytic Nfe2l2 knockdown.Importantly,the upregulation of nuclear factor erythroid 2-related factor 2 activation by procyanidin significantly ameliorated PM2.5-induced depression-like behaviors through the remodeling of astrocyte-to-microglia communication.Together,our findings shed light on the important role of hippocampal astrocytic nuclear factor erythroid 2-related factor 2 activation for maintaining astrocyte-to-microglia communication,and indicate potential research avenues for therapeutic strategies against PM2.5-induced depresson-like behaviors.展开更多
Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clus...Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.展开更多
Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an au...Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.展开更多
基金National Basic Research Plan Project of China,No.2023YFC3708303the National Natural Science Foundation of China,No.82241084the High-level Talent in Public Health of Beijing,No.Discipline Leaders-03-29(all to XL).
文摘Long-term exposure to ambient fine particulate matter(PM2.5)may increase the risk of neurotoxicity in human populations.However,research studies on the underlying mechanisms of chronic PM2.5-induced depression-like behaviors,and potential therapeutical strategies,remain scarce.In the present study,after long-term exposure to real-world PM2.5 for 15 weeks,male mice displayed depression-like behaviors,which were revealed using the open field and sucrose preference tests.Mechanistically,chronic PM2.5 exposure promoted astrocytic A1 polarization and disrupted reduction-oxidation balance in the mouse hippocampus.Furthermore,PM2.5-exposed mice displayed pathological damage to hippocampal neurons as well as the inhibition of nuclear factor erythroid 2-related factor 2 signaling.Astrocytic ablation of nuclear factor erythroid 2-related factor 2 exacerbated PM2.5-induced hippocampal neuronal injury in mice via the disruption of astrocyte-to-microglia communication;this finding was confirmed in mice with bilateral and unilateral hippocampal astrocytic Nfe2l2 knockdown.Importantly,the upregulation of nuclear factor erythroid 2-related factor 2 activation by procyanidin significantly ameliorated PM2.5-induced depression-like behaviors through the remodeling of astrocyte-to-microglia communication.Together,our findings shed light on the important role of hippocampal astrocytic nuclear factor erythroid 2-related factor 2 activation for maintaining astrocyte-to-microglia communication,and indicate potential research avenues for therapeutic strategies against PM2.5-induced depresson-like behaviors.
基金The study is fully supported by the National Natural Science Foundation of China(61873283)the Changsha Science&Technology Project(KQ1707017)the Innovation Driven Project of the Central South University(2019CX005).
文摘Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.
基金funded by grant number 14-INF1015-10 from the National ScienceTechnology,and Innovation Plan(MAARIFAH)+1 种基金the King Abdul-Aziz City for Science and Technology(KACST)Kingdom of Saudi Arabia.We thank the Science and Technology Unit at Umm Al-Qura University for their continued logistics support.
文摘Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.
