Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.展开更多
Gingival adenoid cystic carcinoma (ACC) is a rare malignancy. We describe the diagnosis and treatment of a 43 year- old woman who presented with a persistent oral ulcer for approximately 1 year, and subsequent pain in...Gingival adenoid cystic carcinoma (ACC) is a rare malignancy. We describe the diagnosis and treatment of a 43 year- old woman who presented with a persistent oral ulcer for approximately 1 year, and subsequent pain in the left posterior maxillary region. Clinical examination revealed an ulcer in the left upper molar gingiva, with swelling in the region from the second premolar to the third molar. X-ray images demonstrated the involvement of the maxillary alveolar bone. The histopathological and immunohistochemical features were diagnostic of ACC. ACC is often presented as a gingival lesion; thus, it may easily be neglected by patients. The identification of this tumor using specific pathological analyses prevents misdiagnosis and enables clinicians to determine the appropriate treatment. In this case, no recurrence or distant metastasis was observed after 2 years of follow-up.展开更多
To the Editor:Hearing loss is the most common sensory deficit in humans,affecting daily communication and the overall quality of life.Oxidative stress plays a critical role in the development and progression of age-re...To the Editor:Hearing loss is the most common sensory deficit in humans,affecting daily communication and the overall quality of life.Oxidative stress plays a critical role in the development and progression of age-related hearing loss.Studies show that endogenous antioxidant deficiency or insufficient dietary antioxidant nutrient intake increased hearing loss risk,while antioxidant supplementation could prevent age-related hearing loss development.展开更多
基金supported by the projects of the China Geological Survey(DD20221729,DD20190291)Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
文摘Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
文摘Gingival adenoid cystic carcinoma (ACC) is a rare malignancy. We describe the diagnosis and treatment of a 43 year- old woman who presented with a persistent oral ulcer for approximately 1 year, and subsequent pain in the left posterior maxillary region. Clinical examination revealed an ulcer in the left upper molar gingiva, with swelling in the region from the second premolar to the third molar. X-ray images demonstrated the involvement of the maxillary alveolar bone. The histopathological and immunohistochemical features were diagnostic of ACC. ACC is often presented as a gingival lesion; thus, it may easily be neglected by patients. The identification of this tumor using specific pathological analyses prevents misdiagnosis and enables clinicians to determine the appropriate treatment. In this case, no recurrence or distant metastasis was observed after 2 years of follow-up.
基金supported by a grant from the Zhejiang Key Research and Development Program(No.2015C03050)。
文摘To the Editor:Hearing loss is the most common sensory deficit in humans,affecting daily communication and the overall quality of life.Oxidative stress plays a critical role in the development and progression of age-related hearing loss.Studies show that endogenous antioxidant deficiency or insufficient dietary antioxidant nutrient intake increased hearing loss risk,while antioxidant supplementation could prevent age-related hearing loss development.
