Charcot-Marie-Tooth disease type 1A（CMT1A） is caused by duplication of the peripheral myelin protein 22（PMP22） gene on chromosome 17. It is the most common inherited demyelinating neuropathy. Type 2 diabetes melli...Charcot-Marie-Tooth disease type 1A（CMT1A） is caused by duplication of the peripheral myelin protein 22（PMP22） gene on chromosome 17. It is the most common inherited demyelinating neuropathy. Type 2 diabetes mellitus is a common metabolic disorder that frequently causes predominantly sensory neuropathy. In this study, we report the occurrence of CMT1 A in a Chinese family affected by type 2 diabetes mellitus. In this family, seven individuals had duplication of the PMP22 gene, although only four had clinical features of polyneuropathy. All CMT1 A patients with a clinical phenotype also presented with type 2 diabetes mellitus. The other three individuals had no signs of CMT1 A or type 2 diabetes mellitus. We believe that there may be a genetic link between these two diseases.展开更多
Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based ...Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.展开更多
文摘Charcot-Marie-Tooth disease type 1A（CMT1A） is caused by duplication of the peripheral myelin protein 22（PMP22） gene on chromosome 17. It is the most common inherited demyelinating neuropathy. Type 2 diabetes mellitus is a common metabolic disorder that frequently causes predominantly sensory neuropathy. In this study, we report the occurrence of CMT1 A in a Chinese family affected by type 2 diabetes mellitus. In this family, seven individuals had duplication of the PMP22 gene, although only four had clinical features of polyneuropathy. All CMT1 A patients with a clinical phenotype also presented with type 2 diabetes mellitus. The other three individuals had no signs of CMT1 A or type 2 diabetes mellitus. We believe that there may be a genetic link between these two diseases.
基金funded by Fundamental Research Funds of CAF(CAFYBB2023PA003)Science and Technology Innovation 2030-Major Projects(2023ZD0406103)National Natural Science Foundation of China(32271877).
文摘Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.
