Objective:To investigate whether cardiac mast cells(MCs)participate in pressure overload-induced myocardial hypertrophy through the regulation of transient receptor potential vanilloid 4(TRPV4).Methods:Pressure overlo...Objective:To investigate whether cardiac mast cells(MCs)participate in pressure overload-induced myocardial hypertrophy through the regulation of transient receptor potential vanilloid 4(TRPV4).Methods:Pressure overload-induced myocardial hypertrophy was induced via abdominal aortic constriction(AAC).Myocardial hypertrophy was evaluated by measuring the heart weight index(HW/BW),lung weight index(LW/BW),ratio of heart weight to tibia length(HW/TL),ratio of lung weight to tibia length(LW/TL),and cross-sectional area of myocardial cells.qRT-PCR was used to detect the mRNA expression of TRPV4.Western blotting was used to detect the protein expression of TRPV4,mast cell tryptase,myosin heavy chain beta(β-MHC),calcineurin A(CnA),and nuclear factor of activated T-cell c3(NFATc3).ELISA was used to measure the levels of brain natriuretic peptide(BNP)and histamine.Fluo4 AM was used to detect the calcium signal in H9c2 myocardial cells.Results:Compared with those of the sham rats,the myocardial mast cells,tryptase,HW/BW,LW/BW,HW/TL,and LW/TL,the cross-sectional area of the myocardial cells,and the expression ofβ-MHC,TRPV4,CnA,and NFATc3 in the myocardial tissue and the serum BNP of the AAC-treated rats increased significantly,whereas the MC stabilizer cromolyn sodium(CS)reversed these indicators.In H9c2 cardiomyocytes,treatment with histamine and the TRPV4 agonist GSK1016790A upregulated the expression of TRPV4,β-MHC,BNP,CnA and NFATc3 and increased calcium ion influx,whereas these effects were inhibited by the H2 receptor inhibitor famotidine and the TRPV4 inhibitor HC067047.Conclusion:Cardiac MCs participate in pressure overload-induced myocardial hypertrophy through the upregulation of TRPV4 via its mediator histamine,and the Ca^(2+)/CnA/NFATc3 signaling pathway is involved in this process.展开更多
Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological info...Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.展开更多
This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data.Travel delays caused by traffic crashes are quantified according to the diff...This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data.Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments.Based on multiple sources of data in Hong Kong,we also develop a generalized linear model with explanatory variables including crash characteristics,temporal attributes,road network features,traffic indicators,and built environment features,to unveil the relationship between travel delays and these factors.The findings show that crash characteristics alone inadequately explain variations in delays.The model performance improves after factors about the built environment and the dynamic road conditions are incorporated.This underscores the importance of urban factors in traffic delay analysis.Furthermore,we estimate the total travel delays caused by traffic crashes in the city.It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021.The associated economic loss amounts to US$11.02 million.This study contributes to methodological advances in estimating crash-induced travel delays.The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future.In addition,the findings highlight the significance and magnitude of another negative externality of traffic crashes-traffic delays-in a complex urban road network.展开更多
基金supported by grants from the National Natural Science Foundation of China(No.30872716)the Natural Science Foundation of Hubei Province(No.2015CFB288).
文摘Objective:To investigate whether cardiac mast cells(MCs)participate in pressure overload-induced myocardial hypertrophy through the regulation of transient receptor potential vanilloid 4(TRPV4).Methods:Pressure overload-induced myocardial hypertrophy was induced via abdominal aortic constriction(AAC).Myocardial hypertrophy was evaluated by measuring the heart weight index(HW/BW),lung weight index(LW/BW),ratio of heart weight to tibia length(HW/TL),ratio of lung weight to tibia length(LW/TL),and cross-sectional area of myocardial cells.qRT-PCR was used to detect the mRNA expression of TRPV4.Western blotting was used to detect the protein expression of TRPV4,mast cell tryptase,myosin heavy chain beta(β-MHC),calcineurin A(CnA),and nuclear factor of activated T-cell c3(NFATc3).ELISA was used to measure the levels of brain natriuretic peptide(BNP)and histamine.Fluo4 AM was used to detect the calcium signal in H9c2 myocardial cells.Results:Compared with those of the sham rats,the myocardial mast cells,tryptase,HW/BW,LW/BW,HW/TL,and LW/TL,the cross-sectional area of the myocardial cells,and the expression ofβ-MHC,TRPV4,CnA,and NFATc3 in the myocardial tissue and the serum BNP of the AAC-treated rats increased significantly,whereas the MC stabilizer cromolyn sodium(CS)reversed these indicators.In H9c2 cardiomyocytes,treatment with histamine and the TRPV4 agonist GSK1016790A upregulated the expression of TRPV4,β-MHC,BNP,CnA and NFATc3 and increased calcium ion influx,whereas these effects were inhibited by the H2 receptor inhibitor famotidine and the TRPV4 inhibitor HC067047.Conclusion:Cardiac MCs participate in pressure overload-induced myocardial hypertrophy through the upregulation of TRPV4 via its mediator histamine,and the Ca^(2+)/CnA/NFATc3 signaling pathway is involved in this process.
基金supported by the National Natural Science Foundation of China(grant numbers 41871331,41801343,and 42001357).
文摘Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.
基金General Research Fund(GRF)project on“Improving bus safety in Hong Kong:From advanced spatial analysis to artificial intelligence”(No.17616221)Guangdong-Hong Kong-Macao Joint Laboratory on Smart Cities initiatives funded by the Guangdong Science and Technology Department(No.2020B1212030009).
文摘This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data.Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments.Based on multiple sources of data in Hong Kong,we also develop a generalized linear model with explanatory variables including crash characteristics,temporal attributes,road network features,traffic indicators,and built environment features,to unveil the relationship between travel delays and these factors.The findings show that crash characteristics alone inadequately explain variations in delays.The model performance improves after factors about the built environment and the dynamic road conditions are incorporated.This underscores the importance of urban factors in traffic delay analysis.Furthermore,we estimate the total travel delays caused by traffic crashes in the city.It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021.The associated economic loss amounts to US$11.02 million.This study contributes to methodological advances in estimating crash-induced travel delays.The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future.In addition,the findings highlight the significance and magnitude of another negative externality of traffic crashes-traffic delays-in a complex urban road network.
