An observational campaign was conducted in the street canyon of Zhujiang Road in Nanjing city in 2007. Hourly mean concentrations of PM10 were measured at street and roof levels. The Operational Street Pollution Model...An observational campaign was conducted in the street canyon of Zhujiang Road in Nanjing city in 2007. Hourly mean concentrations of PM10 were measured at street and roof levels. The Operational Street Pollution Model （OSPM） street canyon dispersion model was used to calculate the street concentrations and the results were compared with the measurements. The results show that there is good agreement between measured and predicted concentrations. The correlation coefficient R2 values （R2 is a measure of the correlation of the predicted and measured time series of concentrations） are 0.5319, 0.8044, and 0.6630 for the scatter plots of PM10 corresponding to light wind speed conditions, higher wind speed conditions, and all wind speed conditions, respectively. PM10 concentrations tend to be smaller for the higher wind speed cases and decrease rapidly with increasing wind speed. The presentations of measured and modelled concentration dependence on wind direction show fairly good agreement. PM10 concentrations measured on the windward side are relatively smaller, compared with the corresponding results for the leeward side. This study demonstrates that it is possible to use the OSPM to model PM10 dispersion rules for an urban street canyon.展开更多
Malignant and premalignant ocular surface tumors(OSTs)can be sight-threatening or even life-threatening if not diagnosed and treated promptly.Artificial intelligence holds great promise for the early detection of thes...Malignant and premalignant ocular surface tumors(OSTs)can be sight-threatening or even life-threatening if not diagnosed and treated promptly.Artificial intelligence holds great promise for the early detection of these diseases.However,training traditional convolutional neural networks(CNNs)for this task presents challenges due to the lack of large,well-annotated datasets containing OST images labeled according to histopathological results.Here,we introduce the ocular surface pretrained model(OSPM),a domain-specific pretrained model designed to address the scarcity of labeled data.OSPM is constructed utilizing self-supervised learning on approximately 0.76 million unlabeled ocular surface images from 10 clinical centers across China and can be readily adapted to the OST classification task.We then develop and evaluate an OSPM-enhanced classification model(OECM)using 1,455 OST images labeled with histopathological diagnoses to differentiate between malignant,premalignant,and benign OSTs.OECM achieves excellent performance with AUROCs ranging from 0.891 to 0.993 on internal,external,and prospective test datasets,significantly outperforming the traditional CNN models.OECM demonstrated performance comparable to that of senior ophthalmologists and increased the diagnostic accuracy of junior ophthalmologists.Greater label efficiency was observed in OECM compared to CNN models.Our proposed model has high potential to enhance the early detection and treatment of malignant and premalignant OSTs,thereby reducing cancer-related mortality and optimizing functional outcomes.展开更多
基金supported by the Natural Science Foundation of Jiangsu Province(No.BK2004216)
文摘An observational campaign was conducted in the street canyon of Zhujiang Road in Nanjing city in 2007. Hourly mean concentrations of PM10 were measured at street and roof levels. The Operational Street Pollution Model （OSPM） street canyon dispersion model was used to calculate the street concentrations and the results were compared with the measurements. The results show that there is good agreement between measured and predicted concentrations. The correlation coefficient R2 values （R2 is a measure of the correlation of the predicted and measured time series of concentrations） are 0.5319, 0.8044, and 0.6630 for the scatter plots of PM10 corresponding to light wind speed conditions, higher wind speed conditions, and all wind speed conditions, respectively. PM10 concentrations tend to be smaller for the higher wind speed cases and decrease rapidly with increasing wind speed. The presentations of measured and modelled concentration dependence on wind direction show fairly good agreement. PM10 concentrations measured on the windward side are relatively smaller, compared with the corresponding results for the leeward side. This study demonstrates that it is possible to use the OSPM to model PM10 dispersion rules for an urban street canyon.
基金funding from the National Natural Science Foundation of China(grant nos.82201148 and 62276210)the Natural Science Foundation of Zhejiang Province(grant no.LQ22H120002)+2 种基金the Medical Health Science and Technology Project of Zhejiang Province(grant no.2023KY1140)the Natural Science Foundation of Ningbo(grant no.2023J390)the Ningbo Top Medical and Health Research Program(grant no.2023030716).
文摘Malignant and premalignant ocular surface tumors(OSTs)can be sight-threatening or even life-threatening if not diagnosed and treated promptly.Artificial intelligence holds great promise for the early detection of these diseases.However,training traditional convolutional neural networks(CNNs)for this task presents challenges due to the lack of large,well-annotated datasets containing OST images labeled according to histopathological results.Here,we introduce the ocular surface pretrained model(OSPM),a domain-specific pretrained model designed to address the scarcity of labeled data.OSPM is constructed utilizing self-supervised learning on approximately 0.76 million unlabeled ocular surface images from 10 clinical centers across China and can be readily adapted to the OST classification task.We then develop and evaluate an OSPM-enhanced classification model(OECM)using 1,455 OST images labeled with histopathological diagnoses to differentiate between malignant,premalignant,and benign OSTs.OECM achieves excellent performance with AUROCs ranging from 0.891 to 0.993 on internal,external,and prospective test datasets,significantly outperforming the traditional CNN models.OECM demonstrated performance comparable to that of senior ophthalmologists and increased the diagnostic accuracy of junior ophthalmologists.Greater label efficiency was observed in OECM compared to CNN models.Our proposed model has high potential to enhance the early detection and treatment of malignant and premalignant OSTs,thereby reducing cancer-related mortality and optimizing functional outcomes.
