Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the...Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.展开更多
Auxin and auxin-mediated signaling pathways are known to regulate lateral root development.Although exocytic vesicle trafficking plays an important role in recycling the PIN-FORMED(PIN)auxin efflux carriers and in pol...Auxin and auxin-mediated signaling pathways are known to regulate lateral root development.Although exocytic vesicle trafficking plays an important role in recycling the PIN-FORMED(PIN)auxin efflux carriers and in polar auxin transport during lateral root formation,the mechanistic details of these processes are not well understood.Here,we demonstrate that BYPASS1-LIKE(B1 L)regulates lateral root initiation via exocytic vesicular trafficking-mediated PIN recycling in Arabidopsis thaliana.b1 l mutants contained significantly more lateral roots than the wild type,primarily due to increased lateral root primordium initiation.Furthermore,the auxin signal was stronger in stage I lateral root primordia of b1 l than in those of the wild type.Treatment with exogenous auxin and an auxin transport inhibitor indicated that the lateral root phenotype of b1 l could be attributed to higher auxin levels and that B1 L regulates auxin efflux.Indeed,compared to the wild type,C-terminally green fluorescent protein-tagged PIN1 and PIN3 accumulated at higher levels in b1 l lateral root primordia.B1 L interacted with the exocyst,and b1 l showed defective PIN exocytosis.These observations indicate that B1 L interacts with the exocyst to regulate PIN-mediated polar auxin transport and lateral root initiation in Arabidopsis.展开更多
基金This research was supported by National Natural Science Foundation of China(Grant Nos.41661144039,91337102,41401481)and Natural Science Foundation of Jiangsu Province of China(Grant No.BK20140997).
文摘Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.
基金supported by the Key Program National Natural Science Foundation of China(41830321)the National Natural Science Foundation of China(31770432,32071482)the Fundamental Research Funds for the Central Universities(lzujbky-2020-31)。
文摘Auxin and auxin-mediated signaling pathways are known to regulate lateral root development.Although exocytic vesicle trafficking plays an important role in recycling the PIN-FORMED(PIN)auxin efflux carriers and in polar auxin transport during lateral root formation,the mechanistic details of these processes are not well understood.Here,we demonstrate that BYPASS1-LIKE(B1 L)regulates lateral root initiation via exocytic vesicular trafficking-mediated PIN recycling in Arabidopsis thaliana.b1 l mutants contained significantly more lateral roots than the wild type,primarily due to increased lateral root primordium initiation.Furthermore,the auxin signal was stronger in stage I lateral root primordia of b1 l than in those of the wild type.Treatment with exogenous auxin and an auxin transport inhibitor indicated that the lateral root phenotype of b1 l could be attributed to higher auxin levels and that B1 L regulates auxin efflux.Indeed,compared to the wild type,C-terminally green fluorescent protein-tagged PIN1 and PIN3 accumulated at higher levels in b1 l lateral root primordia.B1 L interacted with the exocyst,and b1 l showed defective PIN exocytosis.These observations indicate that B1 L interacts with the exocyst to regulate PIN-mediated polar auxin transport and lateral root initiation in Arabidopsis.
