| 1 | CAS FGOALS-f3-L Model Datasets for CMIP6 GMMIP Tier-1 and Tier-3 Experiments显示文摘The Chinese Academy of Sciences(CAS)Flexible Global Ocean Atmosphere Land System(FGOALS-f3-L)model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project(CMIP6)Global Monsoons Model Intercomparison Project(GMMIP)Tier-1 and Tier-3 experiments are introduced in this paper,and the model descriptions,experimental design and model outputs are demonstrated.There are three simulations in Tier-1,with different initial states,and five simulations in Tier-3,with different topographies or surface thermal status.Specifically,Tier-3 contains four orographic perturbation experiments that remove the Tibetan Iranian Plateau,East African and Arabian Peninsula highlands,Sierra Madre,and Andes,and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan Iranian Plateau and surrounding regions at altitudes above 500 m.These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation,and to a better understanding of the orographic impact on the global monsoon system over highlands. | Bian HE Yimin LIU Guoxiong WU Qing BAO Tianjun ZHOU Xiaofei WU Lei WANG Jiandong LI Xiaocong WANG Jinxiao LI Wenting HU Xiaoqi ZHANG Chen SHENG and Yiqiong TANG | 2020 | Advances in Atmospheric Sciences2020，37，1： | 4 |
| 2 | Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas显示文摘This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss. | Wenting Rui Shengjie Zhang Huidong Shi Yaru Sheng Fengping Zhu YiDi Yao Xiang Chen Haixia Cheng Yong Zhang Ababikere Aili Zhenwei Yao Xiao‑Yong Zhang Yan Ren | 2023 | Phenomics2023，3，3： | 0 |
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