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An investigation and forecast on CO₂ emission of China
Lei Wen,Zeyang Ma,Yue Li,Qiao Li 대한환경공학회 2017 Environmental Engineering Research Vol.22 No.4
CO₂ emission is increasingly focused by public. Beijing and Tianjin are conceived to be a new economic point of growth in China. However, both of them are suffering serious environmental stress. In order to seek for the effect of socioeconomic factors on the CO₂ emission of this region, a novel methodology –symbolic regression– is adopted to investigate the relationship between CO₂ emission and influential factors of Beijing and Tianjin. Based on this method, CO₂ emission models of Beijing and Tianjin are built respectively. The models results manifested that Beijing and Tianjin own different CO₂ emission indicators. The RMSE of models in Beijing and Tianjin are 255.39 and 603.99, respectively. Further analysis on indicators and forecast trend shows that CO₂ emission of Beijing expresses an inverted-U shaped curve, whilst Tianjin owns a monotonically increasing trend. From analytical results, it could be argued that the diversity rooted in different development orientation and the mixture of different natural and industrial environment. This research further expands the investigation on CO₂ emission of Beijing and Tianjin region, and can be used for reference in the study of carbon emissions in similar regions. Based on the investigation, several policy suggestions are presented.
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Yang Jiejin,Chen Zeyang,Liu Weipeng,Wang Xiangpeng,Ma Shuai,Jin Feifei,Wang Xiaoying 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.3
Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.
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KETCH1 imports HYL1 to nucleus for miRNA biogenesis in <i>Arabidopsis</i>
Zhang, Zhonghui,Guo, Xinwei,Ge, Chunxiao,Ma, Zeyang,Jiang, Mengqiu,Li, Tianhong,Koiwa, Hisashi,Yang, Seong Wook,Zhang, Xiuren National Academy of Sciences 2017 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.114 No.15
<P>MicroRNA (miRNA) is processed from primary transcripts with hairpin structures (pri-miRNAs) by microprocessors in the nucleus. How cytoplasmic-borne microprocessor components are transported into the nucleus to fulfill their functions remains poorly understood. Here, we report KETCH1 (karyopherin enabling the transport of the cytoplasmic HYL1) as a partner of hyponastic leaves 1 (HYL1) protein, a core component of microprocessor in Arabidopsis and functional counterpart of DGCR8/Pasha in animals. Null mutation of ketch1 is embryonic-lethal, whereas knockdown mutation of ketch1 caused morphological defects, reminiscent of mutants in the miRNA pathway. ketch1 knockdown mutation also substantially reduced miRNA accumulation, but did not alter nuclear-cytoplasmic shuttling of miRNAs. Rather, the mutation significantly reduced nuclear portion of HYL1 protein and correspondingly compromised the pri-miRNA processing in the nucleus. We propose that KETCH1 transports HYL1 from the cytoplasm to the nucleus to constitute functional microprocessor in Arabidopsis. This study provides insight into the largely unknown nuclear-cytoplasmic trafficking process of miRNA biogenesis components through eukaryotes.</P>
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