Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimi...Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.展开更多
Background:Melanocytic nevus is mainly treated by complete or partial removal.However,predicting the risk of malignant transformation of melanocytic nevi and which treatment patients should receive,surgical or nonsurg...Background:Melanocytic nevus is mainly treated by complete or partial removal.However,predicting the risk of malignant transformation of melanocytic nevi and which treatment patients should receive,surgical or nonsurgical management,to gain the best results and aesthetic outcomes is controversial.Methods:Global literature on melanocytic nevus treatment,published between 1997 and 2022,was scanned using the Web of Science Core Collection database.Microsoft Office Excel,CiteSpace V,VOSviewer,Scimago Graphica,Bibliometrix,and Biblioshiny packages in R were used for the bibliometric analysis to summarize the leading countries,institutions,professors,and research trends in this field.Results:This study included 1723 articles.Publications and citations exhibited positive trends over the past 20 years.The United States had the most productive organizations and publications in the comprehensive worldwide cooperation network,and China was recently one of the most active major participants.Professor Giovanni Pellacani,whose H-index,G-index,and M-index ranked first in this field,founded a virtual biopsy using reflectance confocal microscopy.In addition,Krengel and Kinsler contributed significantly to diagnosing and treating melanocytic nevi.The top 25 keywords in recent years were mostly about the mechanisms and risk factors for the malignant transformation of nevi.Conclusion:The future trend for melanocytic nevi treatment is to specify genotype-phenotype and genotypeoutcome correlations,choose proper therapy to reduce the risk of malignant transformation,and simultaneously achieve the best aesthetic outcomes.展开更多
Background:To investigate the common symptoms after Covid-19 infection,characteristics of adverse events after vaccination,changes in clinical manifestations related to Neurofibromatosis type 1(NF1),as well as the cur...Background:To investigate the common symptoms after Covid-19 infection,characteristics of adverse events after vaccination,changes in clinical manifestations related to Neurofibromatosis type 1(NF1),as well as the current vaccination status and factors related to vaccine hesitation among NF1 patients,in order to provide a basis for scientific protection and vaccine acceptance in NF1 individuals in the new phase of pandemic management.Methods:From December 29,2022,to January 10,2023,we conducted a self-assessment questionnaire survey among diagnosed NF1 patients.General data were provided including sex,age,main clinical presentations,and current treatment.This study mainly focused on the infection and vaccination status of Covid-19 among these patients with NF1.The data were statistically analyzed using SPSS26.0 software.Results:Of the 250 questionnaires distributed,226 were valid.Among the 164 patients(72.6%)with Covid-19 infection,the most common infection symptoms and incidence of patients were not significantly different from those in the normal population(P>0.05),but the incidence of symptoms such as nasal congestion,headache,myalgia,sore throat,abdominal pain,diarrhea,and eye discomfort was higher than that in the normal population(P<0.05),and no severe infection was observed;186 patients(82.3%)had completed the Covid-19 vaccination,and more than half of those who were not vaccinated had no plans for vaccination.Among the vaccinated patients,there was no significant difference in the incidence of adverse events,such as fever,pain,redness,and swelling at the injection site after vaccination,compared to the normal population(P>0.05),but the incidence of fatigue and headache was higher in NF1 patients(P<0.001).Most patients with NF1 believe that there is no significant progressive change in NF1-related clinical manifestations after Covid-19 infection and vaccination.Conclusion:Currently,some NF1 patients appear to be worried about the evolution of their disease after Covid-19 infection in the face of large fluctuations in the pandemic situation,and some patients hesitate to receive the vaccine due to their special disease condition.Thus,clinical trials should be conducted to develop a refined pandemic response and vaccination program for this special group.展开更多
Temperature profile plays an important role in the field of atmospheric research.The infrared hyperspectral vertical detector equipped with meteorological satellites can provide relatively high spatiotemporal resoluti...Temperature profile plays an important role in the field of atmospheric research.The infrared hyperspectral vertical detector equipped with meteorological satellites can provide relatively high spatiotemporal resolution data,thus being widely used for temperature profile retrieval.In recent years,deep learning,especially convolutional neural networks(CNNs),has attracted much attention in various meteorological and climate tasks,including temperature retrieval.However,it is a completely data-driven approach,which may generate results that violate physical laws.To address this issue,we propose a physical knowledge constrained CNN for temperature profile retrieval in this paper.Specifically,we take advantage of the physical knowledge from weight function and ERA5 data,and use an attention module and a loss function to guide the learning of CNN.In order to test the performance of our proposed model,we collect the geostationary interferometric infrared sounder(GIIRS)data,L-band radiosonde data,ERA5 data,and the GIIRS L2 operational product in China for experiments.It is shown that the root mean square error(RMSE)and mean bias(MB)achieved by our proposed model are 2.06 and 0.072 K,respectively,both of which are better than 2 state-of-the-art neural network-based retrieval models and the operational product.展开更多
Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and ...Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.展开更多
High-resolution relative humidity(RH)data are essential in studies of climate change and in numerical meteorological forecasting.However,because high-resolution meteorological grid data require a large number of stati...High-resolution relative humidity(RH)data are essential in studies of climate change and in numerical meteorological forecasting.However,because high-resolution meteorological grid data require a large number of stations,the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration’s Land Assimilation System(CLDAS)dataset.To retrieve high-quality and high-resolution RH data before 2008,we propose a statistical downscaling model(SDM)based on a generative adversarial network(GAN)to transform the original RH data from a resolution of0.05°to 0.01°.The GAN-based SDM(GSDM)is trained with the RH of the CLDAS(0.05°)dataset after 2008 as its input,and the RH of the high-resolution CLDAS(HRCLDAS,0.01°)dataset after 2008 as its target for training.The2-m air temperature data from the HRCLDAS dataset are also included in the input,and the station observations of RH are incorporated in the target for training.To select the optimum data combination for the model,we compared three methods:(1)incorporating without auxiliary data(GSDM),(2)incorporating air temperature as an additional input(GSDM_T),and(3)incorporating air temperature as an additional input and the RH data at stations as an additional target for training(GSDM_TO).Taking the Beijing–Tianjin–Hebei region as an example,we trained the GSDM by using data from 2018 and tested the model performance in 2019.The experimental results showed that the GSDM_TO algorithm achieved the lowest root-mean-square error(3.85%),followed by the GSDM_T(4.01%)and GSDM(4.95%)algorithms.The proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear interpolation.In general,the GSDM_TO algorithm using a combination of different sources of data(air temperature and observed RH)achieved the best results among the various deep learning approaches,indicating that more auxiliary data and more accurate observations are beneficial in downscaling.This may be helpful for the statistical downscaling of other meteorological data.展开更多
基金the National Key Research and Development Program of China(Grant No.2018YFC1506604)the National Natural Science Foundation of China(Grant No.91437220)。
文摘Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.
基金the National Natural Science Foundation of China(grant nos.82202470,82102344,and 82172228)Shanghai Rising Star Program supported by the Science and Technology Commission of Shanghai Municipality(grant no.20QA1405600)+3 种基金Natural Science Foundation of Shanghai(grant no.22ZR1422300)Innovative Research Team of High-Level Local Universities in Shanghai(grant no.SHSMU-ZDCX20210400)Clinical Research Plan of SHDC(grant no.SHDC2020CR1019B)Shanghai Clinical Research Center of Plastic and Reconstructive Surgery supported by(grant no.22MC1940300).
文摘Background:Melanocytic nevus is mainly treated by complete or partial removal.However,predicting the risk of malignant transformation of melanocytic nevi and which treatment patients should receive,surgical or nonsurgical management,to gain the best results and aesthetic outcomes is controversial.Methods:Global literature on melanocytic nevus treatment,published between 1997 and 2022,was scanned using the Web of Science Core Collection database.Microsoft Office Excel,CiteSpace V,VOSviewer,Scimago Graphica,Bibliometrix,and Biblioshiny packages in R were used for the bibliometric analysis to summarize the leading countries,institutions,professors,and research trends in this field.Results:This study included 1723 articles.Publications and citations exhibited positive trends over the past 20 years.The United States had the most productive organizations and publications in the comprehensive worldwide cooperation network,and China was recently one of the most active major participants.Professor Giovanni Pellacani,whose H-index,G-index,and M-index ranked first in this field,founded a virtual biopsy using reflectance confocal microscopy.In addition,Krengel and Kinsler contributed significantly to diagnosing and treating melanocytic nevi.The top 25 keywords in recent years were mostly about the mechanisms and risk factors for the malignant transformation of nevi.Conclusion:The future trend for melanocytic nevi treatment is to specify genotype-phenotype and genotypeoutcome correlations,choose proper therapy to reduce the risk of malignant transformation,and simultaneously achieve the best aesthetic outcomes.
基金supported by the National Natural Science Foundation of China (grant nos. 82102344 and 82172228)Shanghai Rising Star Program supported by Science and Technology Commission of Shanghai Municipality (grant no. 20QA1405600)+4 种基金Science and Technology Commission of Shanghai Municipality (grant no. 19JC1413)Natural Science Foundation of Shanghai (grant no. 22ZR1422300)“Chenguang Program” supported by Shanghai Education Development Foundation (grant no. 19CG18)Shanghai Municipal Key Clinical Specialty (grant no.shslczdzk00901)Innovative Research Team of High-level Local Universities in Shanghai (grant no. SSMUZDCX20180700)
文摘Background:To investigate the common symptoms after Covid-19 infection,characteristics of adverse events after vaccination,changes in clinical manifestations related to Neurofibromatosis type 1(NF1),as well as the current vaccination status and factors related to vaccine hesitation among NF1 patients,in order to provide a basis for scientific protection and vaccine acceptance in NF1 individuals in the new phase of pandemic management.Methods:From December 29,2022,to January 10,2023,we conducted a self-assessment questionnaire survey among diagnosed NF1 patients.General data were provided including sex,age,main clinical presentations,and current treatment.This study mainly focused on the infection and vaccination status of Covid-19 among these patients with NF1.The data were statistically analyzed using SPSS26.0 software.Results:Of the 250 questionnaires distributed,226 were valid.Among the 164 patients(72.6%)with Covid-19 infection,the most common infection symptoms and incidence of patients were not significantly different from those in the normal population(P>0.05),but the incidence of symptoms such as nasal congestion,headache,myalgia,sore throat,abdominal pain,diarrhea,and eye discomfort was higher than that in the normal population(P<0.05),and no severe infection was observed;186 patients(82.3%)had completed the Covid-19 vaccination,and more than half of those who were not vaccinated had no plans for vaccination.Among the vaccinated patients,there was no significant difference in the incidence of adverse events,such as fever,pain,redness,and swelling at the injection site after vaccination,compared to the normal population(P>0.05),but the incidence of fatigue and headache was higher in NF1 patients(P<0.001).Most patients with NF1 believe that there is no significant progressive change in NF1-related clinical manifestations after Covid-19 infection and vaccination.Conclusion:Currently,some NF1 patients appear to be worried about the evolution of their disease after Covid-19 infection in the face of large fluctuations in the pandemic situation,and some patients hesitate to receive the vaccine due to their special disease condition.Thus,clinical trials should be conducted to develop a refined pandemic response and vaccination program for this special group.
基金supported by the National Natural Science Foundation of China(grant numbers U21B2049 and 62472230).
文摘Temperature profile plays an important role in the field of atmospheric research.The infrared hyperspectral vertical detector equipped with meteorological satellites can provide relatively high spatiotemporal resolution data,thus being widely used for temperature profile retrieval.In recent years,deep learning,especially convolutional neural networks(CNNs),has attracted much attention in various meteorological and climate tasks,including temperature retrieval.However,it is a completely data-driven approach,which may generate results that violate physical laws.To address this issue,we propose a physical knowledge constrained CNN for temperature profile retrieval in this paper.Specifically,we take advantage of the physical knowledge from weight function and ERA5 data,and use an attention module and a loss function to guide the learning of CNN.In order to test the performance of our proposed model,we collect the geostationary interferometric infrared sounder(GIIRS)data,L-band radiosonde data,ERA5 data,and the GIIRS L2 operational product in China for experiments.It is shown that the root mean square error(RMSE)and mean bias(MB)achieved by our proposed model are 2.06 and 0.072 K,respectively,both of which are better than 2 state-of-the-art neural network-based retrieval models and the operational product.
基金Supported by the Project of“Advanced Research on Civil Space Technology during the 14th Five-Year Plan”of National Meteorological Information Centre of China Meteorological Administration(NMICJY202305)National Natural Science Foundation of China(42205153,42430602,and 92037000)。
文摘Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.
基金Supported by the National Natural Science Foundation of China(92037000)National Key Research and Development Program of China(2018YFC1506601 and NMICJY202106)。
文摘High-resolution relative humidity(RH)data are essential in studies of climate change and in numerical meteorological forecasting.However,because high-resolution meteorological grid data require a large number of stations,the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration’s Land Assimilation System(CLDAS)dataset.To retrieve high-quality and high-resolution RH data before 2008,we propose a statistical downscaling model(SDM)based on a generative adversarial network(GAN)to transform the original RH data from a resolution of0.05°to 0.01°.The GAN-based SDM(GSDM)is trained with the RH of the CLDAS(0.05°)dataset after 2008 as its input,and the RH of the high-resolution CLDAS(HRCLDAS,0.01°)dataset after 2008 as its target for training.The2-m air temperature data from the HRCLDAS dataset are also included in the input,and the station observations of RH are incorporated in the target for training.To select the optimum data combination for the model,we compared three methods:(1)incorporating without auxiliary data(GSDM),(2)incorporating air temperature as an additional input(GSDM_T),and(3)incorporating air temperature as an additional input and the RH data at stations as an additional target for training(GSDM_TO).Taking the Beijing–Tianjin–Hebei region as an example,we trained the GSDM by using data from 2018 and tested the model performance in 2019.The experimental results showed that the GSDM_TO algorithm achieved the lowest root-mean-square error(3.85%),followed by the GSDM_T(4.01%)and GSDM(4.95%)algorithms.The proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear interpolation.In general,the GSDM_TO algorithm using a combination of different sources of data(air temperature and observed RH)achieved the best results among the various deep learning approaches,indicating that more auxiliary data and more accurate observations are beneficial in downscaling.This may be helpful for the statistical downscaling of other meteorological data.
