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13. Pan‐cancer analysis of genome‐wide methylation profiling discover type‐specific markers targeting circulating free DNA for the detection of colorectal cancer

Author

Zhang, Lei, primary, Li, Dapeng, additional, Gao, Lijing, additional, Zhang, Ding, additional, Fu, Qingzhen, additional, Sun, Hongru, additional, Tan, Shiheng, additional, Huang, Hao, additional, Zheng, Ting, additional, Tian, Tian, additional, Jia, Chenyang, additional, Zhou, Haibo, additional, Li, Zinan, additional, Zhu, Lin, additional, Zhang, Xianyu, additional, Pang, Da, additional, Xu, Shidong, additional, Hu, Lihong, additional, Bao, Weiwei, additional, Zhao, Ning, additional, Zhang, Depei, additional, Cheng, Zesong, additional, Liu, Yanlong, additional, Wang, Fan, additional, Cui, Binbin, additional, and Zhao, Yashuang, additional

Published
2023
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15. Microscopic experiment of consolidating tailings by Linglong cementitious material

Author

Wang Bingwen, Gao Lijing, Zhao Wenhua, Li Yanan, Ding Wei, and Li Zhao

Subjects
unclassified tailing backfill, uniaxial compressive strength, hydration products, micro-morphology, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, TA703-712, Mining engineering. Metallurgy, TN1-997
Abstract

In order to investigate the influence of Linglong cementitious material on the consolidation of tailings under different conditions,a series of backfill blocks at different ratios,different densities and different curing ages are designed with ordinary Portland cement control and their uniaxial compressive strength is measured through strength test.Then the phase composition and the micro-morphology of the tailings backfill are discussed by such measuring and testing techniques as XRD and SEM.The results show that the hydration reaction of the cementitious material produces ettringite and C-S-H gel,and promotes the consolidation of the tailings backfill;the age and the ratio of cementitious material to tailings will affect the development of the content and morphology of ettringite and C-S-H gel in the hydration products,and the content of hydration products will increase with the increase of age and the ratio of cementitious material to tailings.The content of the products increases and overlaps to form a dense network structure,which is macroscopically expressed as an increase in strength;the mass concentration affects the pores in the test block,thereby affecting the strength.

Published
2019
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17. Prognostic scoring system based on eosinophil- and basophil-related markers for predicting the prognosis of patients with stage II and stage III colorectal cancer: a retrospective cohort study

Author

Gao, Lijing, primary, Yuan, Chao, additional, Fu, Jinming, additional, Tian, Tian, additional, Huang, Hao, additional, Zhang, Lei, additional, Li, Dapeng, additional, Liu, Yupeng, additional, Meng, Shuhan, additional, Liu, Ying, additional, Zhang, Yuanyuan, additional, Xu, Jing, additional, Jia, Chenyang, additional, Zhang, Ding, additional, Zheng, Ting, additional, Fu, Qingzhen, additional, Tan, Shiheng, additional, Lan, Li, additional, Yang, Chao, additional, Zhao, Yashuang, additional, and Liu, Yanlong, additional

Published
2023
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21. HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene Classification.

Author

Guo, Ningbo, Jiang, Mingyong, Gao, Lijing, Li, Kaitao, Zheng, Fengjie, Chen, Xiangning, and Wang, Mingdong

Subjects
CONVOLUTIONAL neural networks, DATA integrity, REMOTE sensing, CLASSIFICATION
Abstract

Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Additional file 1 of Development and validation of prognostic nomograms based on De Ritis ratio and clinicopathological features for patients with stage II/III colorectal cancer

Author

Fu, Jinming, Du, Fenqi, Tian, Tian, Huang, Hao, Zhang, Lei, Li, Dapeng, Liu, Yupeng, Zhang, Ding, Gao, Lijing, Zheng, Ting, Liu, Yanlong, and Zhao, Yashuang

Abstract

Additional file 1: Supplementary Figure 1. X-tile analyses of overall survival in thetraining set. Supplementary Figure 2. Kaplan-Meier curves and log-rank testing of overall survival in relation tolactate dehydrogenase (A), alkaline phosphatase (B), APRI (C), ALRI (D), ANRI(E), and APPRI (F) in the training set. SupplementaryFigure 3. Kaplan-Meier curves and log-rank testing of disease-free survivalin relation to lactate dehydrogenase (A), alkaline phosphatase (B), APRI (C),ALRI (D), ANRI (E), and APPRI (F) in the training set. Supplementary Figure 4. Kaplan-Meier curves and log-rank testing ofoverall survival in relation to lactate dehydrogenase (A), alkaline phosphatase(B), APRI (C), ALRI (D), ANRI (E), and APPRI (F) in the testing set. Supplementary Figure 5. Kaplan-Meiercurves and log-rank testing of disease-free survival in relation to lactatedehydrogenase (A), alkaline phosphatase (B), APRI (C), ALRI (D), ANRI (E), andAPPRI (F) in the testing set. SupplementaryFigure 6. Prognostic values of De Ritis ratio in different subgroups. Subgroupanalyses were performed in colorectal cancer patients stratified by age,gender, tumor location, tumor diameter, CEA, and CA19-9. All the analyses wereadjusted for the significant clinicopathological factors in relation to overallsurvival (A) and disease-free survival (B). Supplementary Table 1. Association between lactate dehydrogenase andclinicopathological features in the training set. Supplementary Table 2. Association between alkaline phosphatase andclinicopathological features in the training set. Supplementary Table 3. Association between De Ritis ratio andclinicopathological features in the training set. Supplementary Table 4. Association between APRI andclinicopathological features in the training set. Supplementary Table 5. Association between ALRI andclinicopathological features in the training set. Supplementary Table 6. Association between ANRI andclinicopathological features in the training set. Supplementary Table 7. Association between APPRI andclinicopathological features in the training set. Supplementary Table 8. The overall survival rates at 1-, 3-, and 5-year of patients stratified by serum liver enzyme markers in the training set. Supplementary Table 9. The disease-free survival rates at 1-, 3-, and 5-year of patients stratified by serum liver enzyme markers in the training set. Supplementary Table 10. The overall survival rates at 1-, 3-, and 5-year of patients stratified by serum liver enzyme markers in the testing set. Supplementary Table 11. The disease-free survival rates at 1-, 3-, and 5-year of patients stratified by serum liver enzyme markers in the testing set. Supplementary Table 12. The C-index of TNM staging, AJCC staging and nomograms. SupplementaryTable 13. Time-dependent ROC analyses for predicting overall survival and disease-free survival of patients with colorectal cancer in the training set. Supplementary Table 14. Time-dependent ROC analyses for predicting overall survival and disease-free survival of patients with colorectal cancer in the testing set.

Published
2023
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24. Biodiesel Preparation without a Cosolvent in an Opposite-Side Micro-Fixed-Bed Reactor.

Author

Yue, Qianqian, Gao, Lijing, Xiao, Guomin, and Xu, Wei

Subjects
*COMPUTATIONAL fluid dynamics, *SOY oil, *BIODIESEL fuels, *TRANSESTERIFICATION, *METHANOL as fuel
Abstract

In the process of preparing biodiesel using microreactors, cosolvents and homogeneous catalysts are generally used, which adds to the separation cost. Hence, this study explored the use of an opposite-side feed micro-fixed-bed reactor with an inner diameter of 2 mm to produce biodiesel by the transesterification of soybean oil and methanol without any cosolvent, in which a fixed solid base acted as catalyst and obstacles. A biodiesel yield of 99.4% was achieved when the methanol-to-oil molar ratio was 12, the temperature was 70 °C, and the residence time was 8.05 min. A mixing study was conducted for this microreactor through computational fluid dynamics (CFD) simulations. The results showed that the best mixing index could reach 0.94 within 25 mm (length). Then, the transesterification kinetics in this reactor were found to be consistent with the proposed pseudo-homogeneous secondary reaction model. With the kinetics data and mixed-model, the biodiesel yield could be predicted. The simulated results were consistent with the experimental data. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Corrigendum: Polymeric immunoglobulin receptor suppresses colorectal cancer through the AKT-FOXO3/4 axis by downregulating LAMB3 expression

Author

Zhang, Ding, primary, Huang, Hao, additional, Zheng, Ting, additional, Zhang, Lei, additional, Cui, Binbin, additional, Liu, Yanlong, additional, Tan, Shiheng, additional, Zhao, Liyuan, additional, Tian, Tian, additional, Gao, Lijing, additional, Fu, Qingzhen, additional, Cheng, Zesong, additional, and Zhao, Yashuang, additional

Published
2022
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28. Polymeric immunoglobulin receptor suppresses colorectal cancer through the AKT-FOXO3/4 axis by downregulating LAMB3 expression

Author

Zhang, Ding, primary, Huang, Hao, additional, Zheng, Ting, additional, Zhang, Lei, additional, Cui, Binbin, additional, Liu, Yanlong, additional, Tan, Shiheng, additional, Zhao, Liyuan, additional, Tian, Tian, additional, Gao, Lijing, additional, Fu, Qingzhen, additional, Cheng, Zesong, additional, and Zhao, Yashuang, additional

Published
2022
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44. Prognostic Inflammatory Index Based on Preoperative Peripheral Blood for Predicting the Prognosis of Colorectal Cancer Patients

Author

Fu, Jinming, primary, Zhu, Ji, additional, Du, Fenqi, additional, Zhang, Lijie, additional, Li, Dapeng, additional, Huang, Hao, additional, Tian, Tian, additional, Liu, Yupeng, additional, Zhang, Lei, additional, Liu, Ying, additional, Zhang, Yuanyuan, additional, Xu, Jing, additional, Meng, Shuhan, additional, Jia, Chenyang, additional, Sun, Simin, additional, Li, Xue, additional, Zhao, Liyuan, additional, Zhang, Ding, additional, Kang, Lixin, additional, Gao, Lijing, additional, Zheng, Ting, additional, Cai, Sanjun, additional, Liu, Yanlong, additional, and Zhao, Yashuang, additional

Published
2020
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