Aerosol liquid water content(ALWC)plays an important role in secondary aerosol formation.In this study,a whole year field campaign was conducted at Shanxi in north Zhejiang Province during 2021.ALWC estimated by ISORR...Aerosol liquid water content(ALWC)plays an important role in secondary aerosol formation.In this study,a whole year field campaign was conducted at Shanxi in north Zhejiang Province during 2021.ALWC estimated by ISORROPIA-Ⅱ was then investigated to explore its characteristics and relationship with secondary aerosols.ALWC exhibited a highest value in spring(66.38μg/m^(3)),followed by winter(45.08μg/m^(3)),summer(41.64μg/m^(3)),and autumn(35.01μg/m^(3)),respectively.It was supposed that the secondary inorganic aerosols(SIA)were facilitated under higher ALWC conditions(RH>80%),while the secondary organic species tended to form under lower ALWC levels.Higher RH(>80%)promoted the NO_(3)^(-)formation via gas-particle partitioning,while SO_(4)^(2-)was generated at a relative lower RH(>50%).The ALWC was more sensitive to NO_(3)^(-)(R=0.94)than SO_(4)^(2-)(R=0.90).Thus,the self-amplifying processes between the ALWC and SIA enhanced the particle mass growth.The sensitivity of ALWC and OX(NO_(2)+O_(3))to secondary organic carbon(SOC)varied in different seasons at Shanxi,more sensitive to aqueous-phase reactions(daytime R=0.84;nighttime R=0.54)than photochemical oxidation(daytime R=0.23;nighttime R=0.41)in wintertime with a high level of OX(daytime:130-140μg/m^(3);nighttime:100-140μg/m^(3)).The self-amplifying process of ALWC and SIA and the aqueous-phase formation of SOC will enhance aerosol formation,contributing to air pollution and reduction of visibility.展开更多
Deep learning has shown potential in domains with large-scale annotated datasets.However,manual annotation is expensive,time-consuming,and tedious.Pixel-level annotations are particularly costly for semantic segmentat...Deep learning has shown potential in domains with large-scale annotated datasets.However,manual annotation is expensive,time-consuming,and tedious.Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances,such as in plant images.In this work,we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation.As a use case,we focus on wheat head segmentation.We synthesize a computationally annotated dataset—using a few annotated images,a short unannotated video clip of a wheat field,and several video clips with no wheat—to train a customized U-Net model.Considering the distribution shift between the synthesized and real images,we apply three domain adaptation steps to gradually bridge the domain gap.Only using two annotated images,we achieved a Dice score of 0.89 on the internal test set.When further evaluated on a diverse external dataset collected from 18 different domains across five countries,this model achieved a Dice score of 0.73.To expose the model to images from different growth stages and environmental conditions,we incorporated two annotated images from each of the 18 domains to further fine-tune the model.This increased the Dice score to 0.91.The result highlights the utility of the proposed approach in the absence of large-annotated datasets.Although our use case is wheat head segmentation,the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.展开更多
Surface ozone(O_(3))poses significant threats to public health,agricultural crops,and plants in natural ecosystems.Global warming is likely to increase future O_(3)mainly by altering atmospheric photochemical reaction...Surface ozone(O_(3))poses significant threats to public health,agricultural crops,and plants in natural ecosystems.Global warming is likely to increase future O_(3)mainly by altering atmospheric photochemical reactions and enhancing biogenic volatile organic compound(BVOC)emissions.To assess the impacts of the future 1.5 K climate target on O_(3)concentrations and ecological O_(3)exposure in China,numerical simulations were conducted using the CMAQ(Community Multiscale Air Quality)model during April-October 2018.Ecological O_(3)exposure was estimated using six indices(i.e.,M7,M24,N100,SUM60,W126,and AOT40f).The results show that the temperature rise increases the MDA8 O_(3)(maximum daily eight-hour average O_(3))concentrations by∼3 ppb and the number of O_(3)exceedance days by 10-20 days in the North China Plain(NCP),Yangtze River Delta(YRD),and Sichuan Basin(SCB)regions.All O_(3)exposure indices show substantial increases.M24 and M7 in eastern and southern China will rise by 1-3 ppb and 2-4 ppb,respectively.N100 increases by more than 120 h in the surrounding regions of Beijing.SUM60 increases by greater than 9 ppm h^(−1),W126 increases by greater than 15 ppm h^(−1)in Shaanxi and SCB,and AOT40f increases by 6 ppm h^(−1)in NCP and SCB.The temperature increase also promotes atmospheric oxidation capacity(AOC)levels,with the higher AOC contributed by OH radicals in southern China but by NO_(3)radicals in northern China.The change in the reaction rate caused by the temperature increase has a greater influence on O_(3)exposure and AOC than the change in BVOC emissions.展开更多
Uridine diphosphate(UDP)-glucuronosyltransferases(UGTs)are enzymes involved in the biotransformation of important endogenous compounds such as steroids,bile acids,and hormones as well as exogenous substances including...Uridine diphosphate(UDP)-glucuronosyltransferases(UGTs)are enzymes involved in the biotransformation of important endogenous compounds such as steroids,bile acids,and hormones as well as exogenous substances including drugs,environmental toxicants,and carcinogens.Here,a novel fluorescent probe BDMP was developed based on boron-dipyrromethene(BODIPY)with high sensitivity for the detection of UGT1A8.The glucuronidation of BDMP not only exhibited a redemission wavelength(lex/lem=500/580 nm),but also displayed an excellent UGT1A8-dependent fluorescence signal with a good linear relationship with UGT1A8 concentration.Based on this perfect biocompatibility and cell permeability,BDMP was successfully used to image endogenous UGT1A8 in human cancer cell lines(LoVo and HCT15)in real time.In addition,BDMP could also be used to visualize UGT1A8 in tumor tissues.These results suggested that BDMP is a promising molecular tool for the investigation of UGT1A8-mediated physiological function in humans.展开更多
基金supported by the National Natural Science Foundation of China(Nos.91844301 and 42005087)the support from State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex,Shanghai Academy of Environment Sciences(No.CX2020080581)。
文摘Aerosol liquid water content(ALWC)plays an important role in secondary aerosol formation.In this study,a whole year field campaign was conducted at Shanxi in north Zhejiang Province during 2021.ALWC estimated by ISORROPIA-Ⅱ was then investigated to explore its characteristics and relationship with secondary aerosols.ALWC exhibited a highest value in spring(66.38μg/m^(3)),followed by winter(45.08μg/m^(3)),summer(41.64μg/m^(3)),and autumn(35.01μg/m^(3)),respectively.It was supposed that the secondary inorganic aerosols(SIA)were facilitated under higher ALWC conditions(RH>80%),while the secondary organic species tended to form under lower ALWC levels.Higher RH(>80%)promoted the NO_(3)^(-)formation via gas-particle partitioning,while SO_(4)^(2-)was generated at a relative lower RH(>50%).The ALWC was more sensitive to NO_(3)^(-)(R=0.94)than SO_(4)^(2-)(R=0.90).Thus,the self-amplifying processes between the ALWC and SIA enhanced the particle mass growth.The sensitivity of ALWC and OX(NO_(2)+O_(3))to secondary organic carbon(SOC)varied in different seasons at Shanxi,more sensitive to aqueous-phase reactions(daytime R=0.84;nighttime R=0.54)than photochemical oxidation(daytime R=0.23;nighttime R=0.41)in wintertime with a high level of OX(daytime:130-140μg/m^(3);nighttime:100-140μg/m^(3)).The self-amplifying process of ALWC and SIA and the aqueous-phase formation of SOC will enhance aerosol formation,contributing to air pollution and reduction of visibility.
文摘Deep learning has shown potential in domains with large-scale annotated datasets.However,manual annotation is expensive,time-consuming,and tedious.Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances,such as in plant images.In this work,we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation.As a use case,we focus on wheat head segmentation.We synthesize a computationally annotated dataset—using a few annotated images,a short unannotated video clip of a wheat field,and several video clips with no wheat—to train a customized U-Net model.Considering the distribution shift between the synthesized and real images,we apply three domain adaptation steps to gradually bridge the domain gap.Only using two annotated images,we achieved a Dice score of 0.89 on the internal test set.When further evaluated on a diverse external dataset collected from 18 different domains across five countries,this model achieved a Dice score of 0.73.To expose the model to images from different growth stages and environmental conditions,we incorporated two annotated images from each of the 18 domains to further fine-tune the model.This increased the Dice score to 0.91.The result highlights the utility of the proposed approach in the absence of large-annotated datasets.Although our use case is wheat head segmentation,the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.
基金supported by the National Natural Science Foundation of China[grant numbers 42277095 and 42021004].
文摘Surface ozone(O_(3))poses significant threats to public health,agricultural crops,and plants in natural ecosystems.Global warming is likely to increase future O_(3)mainly by altering atmospheric photochemical reactions and enhancing biogenic volatile organic compound(BVOC)emissions.To assess the impacts of the future 1.5 K climate target on O_(3)concentrations and ecological O_(3)exposure in China,numerical simulations were conducted using the CMAQ(Community Multiscale Air Quality)model during April-October 2018.Ecological O_(3)exposure was estimated using six indices(i.e.,M7,M24,N100,SUM60,W126,and AOT40f).The results show that the temperature rise increases the MDA8 O_(3)(maximum daily eight-hour average O_(3))concentrations by∼3 ppb and the number of O_(3)exceedance days by 10-20 days in the North China Plain(NCP),Yangtze River Delta(YRD),and Sichuan Basin(SCB)regions.All O_(3)exposure indices show substantial increases.M24 and M7 in eastern and southern China will rise by 1-3 ppb and 2-4 ppb,respectively.N100 increases by more than 120 h in the surrounding regions of Beijing.SUM60 increases by greater than 9 ppm h^(−1),W126 increases by greater than 15 ppm h^(−1)in Shaanxi and SCB,and AOT40f increases by 6 ppm h^(−1)in NCP and SCB.The temperature increase also promotes atmospheric oxidation capacity(AOC)levels,with the higher AOC contributed by OH radicals in southern China but by NO_(3)radicals in northern China.The change in the reaction rate caused by the temperature increase has a greater influence on O_(3)exposure and AOC than the change in BVOC emissions.
基金the Natural Science Foundation of Liaoning Province 2020-MS-252the National Key R&D Program of China(Grant No.2018YFC1603001).
文摘Uridine diphosphate(UDP)-glucuronosyltransferases(UGTs)are enzymes involved in the biotransformation of important endogenous compounds such as steroids,bile acids,and hormones as well as exogenous substances including drugs,environmental toxicants,and carcinogens.Here,a novel fluorescent probe BDMP was developed based on boron-dipyrromethene(BODIPY)with high sensitivity for the detection of UGT1A8.The glucuronidation of BDMP not only exhibited a redemission wavelength(lex/lem=500/580 nm),but also displayed an excellent UGT1A8-dependent fluorescence signal with a good linear relationship with UGT1A8 concentration.Based on this perfect biocompatibility and cell permeability,BDMP was successfully used to image endogenous UGT1A8 in human cancer cell lines(LoVo and HCT15)in real time.In addition,BDMP could also be used to visualize UGT1A8 in tumor tissues.These results suggested that BDMP is a promising molecular tool for the investigation of UGT1A8-mediated physiological function in humans.
