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1,605 results on '"Zhang, Tiejun"'

11. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas

Author

Ren, Yanming, Huang, Zongyao, Zhou, Lingling, Xiao, Peng, Song, Junwei, He, Ping, Xie, Chuanxing, Zhou, Ran, Li, Menghan, Dong, Xiangqun, Mao, Qing, You, Chao, Xu, Jianguo, Liu, Yanhui, Lan, Zhigang, Zhang, Tiejun, Gan, Qi, Yang, Yuan, Chen, Tengyun, Huang, Bowen, Yang, Xiang, Xiao, Anqi, Ou, Yun, Su, Zhengzheng, Chen, Lu, Zhang, Yan, Ju, Yan, Zhang, Yuekang, and Wang, Yuan

Published
2023
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13. Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

Author

Alfarisi, Omar, Ouzzane, Djamel, Sassi, Mohamed, and Zhang, Tiejun

Subjects
Computer Science - Machine Learning, Astrophysics - Earth and Planetary Astrophysics, Computer Science - Computer Vision and Pattern Recognition, Physics - Geophysics
Abstract

Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture., Comment: 1-Acknowledgement section is updated. 2- References section is update with one additional reference

Published
2021

14. The Understanding of Intertwined Physics: Discovering Capillary Pressure and Permeability Co-Determination

Author

Alfarisi, Omar, Ouzzane, Djamel, Sassi, Mohamed, and Zhang, TieJun

Subjects
Physics - Fluid Dynamics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Geophysics
Abstract

Although capillary and permeability are the two most important physical properties controlling fluid distribution and flow in nature, the interconnectivity function between them was a pressing challenge. Because knowing permeability leads to determining capillary pressure. Geodynamics (e.g., subsurface water, CO2 sequestration) and organs (e.g., plants, blood vessels) depend on capillary pressure and permeability. The first determines how far the fluid can reach, while the second determines how fast the fluid can flow in porous media. They are also vital to designing synthetic materials and micro-objects like membranes and micro-robotics. Here, we reveal the capillary and permeability intertwined behavior function. And demonstrate the unique physical connectors: pore throat size and network, linking capillary pressure and permeability. Our discovery quantifies the inverse relationship between capillary pressure and permeability for the first time, which we analytically derived and experimentally proved., Comment: 1- Added the Acknowledgement section

Published
2021

23. Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions

Author

Alfarisi, Omar, Raza, Aikifa, Ouzzane, Djamel, Li, Hongxia, Sassi, Mohamed, and Zhang, Tiejun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Mathematics - Geometric Topology, Physics - Geophysics
Abstract

Permeability has a dominant influence on the flow properties of a natural fluid. Lattice Boltzmann simulator determines permeability from the nano and micropore network. The simulator holds millions of flow dynamics calculations with its accumulated errors and high consumption of computing power. To efficiently and consistently predict permeability, we propose a morphology decoder, a parallel and serial flow reconstruction of machine learning segmented heterogeneous Cretaceous texture from 3D micro computerized tomography and nuclear magnetic resonance images. For 3D vision, we introduce controllable-measurable-volume as new supervised segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies boundaries in a novel way to produce permeability. Morphology decoder method consists of five novel processes, which describes in this paper, these novel processes are: (1) Geometrical 3D Permeability, (2) Machine Learning guided 3D Properties Recognition of Rock Morphology, (3) 3D Image Properties Integration Model for Permeability, (4) MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes)., Comment: 1- Added Affiliation section. 2- Added Remark section. 3- Applied grammar corrections

Published
2021

24. Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

Author

Alfarisi, Omar, Raza, Aikifa, Zhang, Hongtao, Ozzane, Djamel, Sassi, Mohamed, and Zhang, Tiejun

Subjects
Physics - Geophysics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists face difficulties in setting the direction of the optimum method for determining petrophysical properties from rock images, Micro-Computed Tomography (uCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D uCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF). We have built reference 3D micro models and collected images for calibration of IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D uCT and MRI images of natural heterogeneous carbonate rock. We measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and Pyrite) volumes with 97.7% accuracy., Comment: 1- Added Affiliation section. 2- Updated the Acknowledgement section

Published
2021

27. Investigating the Theoretical Structure of the Survey of Reading Strategies

Author

Zhang, Tiejun and Zheng, Jiali

Abstract

The purpose of this study is to investigate the theoretical structure of the Survey of Reading Strategies (SORS) developed for assessing ESL students' use of metacognitive reading strategies. The data for this study were collected from 1,431 Chinese college students whose second language (L2) was English. Both explanatory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the SORS. The EFA failed to recover the three hypothesized subscales but supported a unidimensional construct. The CFA analysis suggested that all alternative factor models fitted the sample data poorly, but comparative fit indices indicated the construct of the SORS was unidimensional. Therefore, the factor structure of the SORS needs to be further explored. Suggestions and recommendations for the use of the survey were provided.

Published
2020

47. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test.

Author

Chen, Xingdong, Gole, Jeffrey, Gore, Athurva, He, Qiye, Lu, Ming, Min, Jun, Yuan, Ziyu, Yang, Xiaorong, Jiang, Yanfeng, Zhang, Tiejun, Suo, Chen, Li, Xiaojie, Cheng, Lei, Zhang, Zhenhua, Niu, Hongyu, Li, Zhe, Xie, Zhen, Shi, Han, Zhang, Xiang, Fan, Min, Wang, Xiaofeng, Yang, Yajun, Dang, Justin, McConnell, Catie, Zhang, Juan, Wang, Jiucun, Yu, Shunzhang, Ye, Weimin, Gao, Yuan, Zhang, Kun, Liu, Rui, and Jin, Li

Subjects
Humans, Neoplasms, Genetic Markers, Sensitivity and Specificity, Longitudinal Studies, Reproducibility of Results, DNA Methylation, Adult, Aged, Aged, 80 and over, Middle Aged, China, Female, Male, Early Detection of Cancer, Young Adult, Epigenomics, High-Throughput Nucleotide Sequencing, Healthy Volunteers, Biomarkers, Tumor, Circulating Tumor DNA
Abstract

Early detection has the potential to reduce cancer mortality, but an effective screening test must demonstrate asymptomatic cancer detection years before conventional diagnosis in a longitudinal study. In the Taizhou Longitudinal Study (TZL), 123,115 healthy subjects provided plasma samples for long-term storage and were then monitored for cancer occurrence. Here we report the preliminary results of PanSeer, a noninvasive blood test based on circulating tumor DNA methylation, on TZL plasma samples from 605 asymptomatic individuals, 191 of whom were later diagnosed with stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. We also assay plasma samples from an additional 223 cancer patients, plus 200 primary tumor and normal tissues. We show that PanSeer detects five common types of cancer in 88% (95% CI: 80-93%) of post-diagnosis patients with a specificity of 96% (95% CI: 93-98%), We also demonstrate that PanSeer detects cancer in 95% (95% CI: 89-98%) of asymptomatic individuals who were later diagnosed, though future longitudinal studies are required to confirm this result. These results demonstrate that cancer can be non-invasively detected up to four years before current standard of care.

Published
2020

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