A relatively well-preserved rodent fossil,including its incisors,cheek teeth,and postcranial skeleton,was collected from the Baiyin Obo in Siziwang Banner,Nei Mongol.A multifaceted research approach was undertaken in ...A relatively well-preserved rodent fossil,including its incisors,cheek teeth,and postcranial skeleton,was collected from the Baiyin Obo in Siziwang Banner,Nei Mongol.A multifaceted research approach was undertaken in this study to conduct a comprehensive analysis on the newly discovered specimen.Based on a morphological comparison,the new specimen was identified as Hulgana cf.H.ertnia within the Ischyromyidae family.Incisive enamel microstructure analysis revealed the typical pauciserial enamel structure of Hulgana.Bone histological analysis indicates that the specimen represents a juvenile individual,which is consistent with the ontogenetic stage indicated by dental developmental stage and wear pattern.The application of geometric morphometrics to the calcaneus and bone histology of the femur and phalanx further substantiates the taxonomic classification of Hulgana as a terrestrial and cursorial rodent,exhibiting a degree of fossorial ability.This classification is analogous to that of certain extant cricetids and ground squirrels.展开更多
Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
保存部分后肢的标本 IVPP V 10597 最初被描述为蒙古蜥鸟龙(Saurornithoides mongoliensis) 的幼年个体,但存在一些疑问。近年来有关伤齿龙科(Troodontidae) 的研究,尤其是有关伤齿龙类分类学研究取得了重要进展,因此有必要对该标本的...保存部分后肢的标本 IVPP V 10597 最初被描述为蒙古蜥鸟龙(Saurornithoides mongoliensis) 的幼年个体,但存在一些疑问。近年来有关伤齿龙科(Troodontidae) 的研究,尤其是有关伤齿龙类分类学研究取得了重要进展,因此有必要对该标本的分类学重新进行评估。通过细致的形态比较和数值化的系统发育分析,确认相对于蒙古蜥鸟龙,V 10597 更加接近于同域的谭氏临河猎龙(Linhevenator tani) ,指示其有可能代表谭氏临河猎龙的幼年个体。但 V 10597 的许多后肢特征,包括许多涉及后肢骨骼间比例的特征,显示出与包括谭氏临河猎龙在内的其他伤齿龙类的明显区别。这些形态差异可能具有分类学意义,表明 V 10597 代表一个新种。通过骨组织学分析,确认该标本不可能代表谭氏临河猎龙或者其他大型伤齿龙类的幼年个体。基于已有的形态学和骨组织学信息,将 V 10597 定为一新属、新种,命名为柯瑞氏菲利猎龙(Philovenator curriei gen. et sp. nov. ) 。这一发现增加了白垩纪晚期伤齿龙类的种群分异度和形态差异度。展开更多
文摘A relatively well-preserved rodent fossil,including its incisors,cheek teeth,and postcranial skeleton,was collected from the Baiyin Obo in Siziwang Banner,Nei Mongol.A multifaceted research approach was undertaken in this study to conduct a comprehensive analysis on the newly discovered specimen.Based on a morphological comparison,the new specimen was identified as Hulgana cf.H.ertnia within the Ischyromyidae family.Incisive enamel microstructure analysis revealed the typical pauciserial enamel structure of Hulgana.Bone histological analysis indicates that the specimen represents a juvenile individual,which is consistent with the ontogenetic stage indicated by dental developmental stage and wear pattern.The application of geometric morphometrics to the calcaneus and bone histology of the femur and phalanx further substantiates the taxonomic classification of Hulgana as a terrestrial and cursorial rodent,exhibiting a degree of fossorial ability.This classification is analogous to that of certain extant cricetids and ground squirrels.
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
基金supported by the National Natural Science Foundation of China 40830210the Department of Land and Resources,Nei Mongol,China重大国际合作项目(批准号:41120124002)资助~~
文摘保存部分后肢的标本 IVPP V 10597 最初被描述为蒙古蜥鸟龙(Saurornithoides mongoliensis) 的幼年个体,但存在一些疑问。近年来有关伤齿龙科(Troodontidae) 的研究,尤其是有关伤齿龙类分类学研究取得了重要进展,因此有必要对该标本的分类学重新进行评估。通过细致的形态比较和数值化的系统发育分析,确认相对于蒙古蜥鸟龙,V 10597 更加接近于同域的谭氏临河猎龙(Linhevenator tani) ,指示其有可能代表谭氏临河猎龙的幼年个体。但 V 10597 的许多后肢特征,包括许多涉及后肢骨骼间比例的特征,显示出与包括谭氏临河猎龙在内的其他伤齿龙类的明显区别。这些形态差异可能具有分类学意义,表明 V 10597 代表一个新种。通过骨组织学分析,确认该标本不可能代表谭氏临河猎龙或者其他大型伤齿龙类的幼年个体。基于已有的形态学和骨组织学信息,将 V 10597 定为一新属、新种,命名为柯瑞氏菲利猎龙(Philovenator curriei gen. et sp. nov. ) 。这一发现增加了白垩纪晚期伤齿龙类的种群分异度和形态差异度。
