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1,981 results on '"Chen Jiong"'

1. Causal relationship between atrial fibrillation/warfarin and cutaneous melanoma: a two-sample Mendelian randomization study

Author

Wang Xiaowu, Zhou Qiang, Han Yike, Zhixuan Wu, Jin Yiheng, Chen Xuemei, Lin Sen, and Chen Jiong

Subjects
atrial fibrillation, warfarin, cutaneous melanoma, Mendelian randomization study, sensitivity analysis, Medicine (General), R5-920
Abstract

PurposeIn recent years, the relationship between malignant tumors and atrial fibrillation has attracted more and more attention. Atrial fibrillation can also cause a series of adverse events, such as the risk of thromboembolism. Also, Warfarin is often used here. But, the relationship between cutaneous melanoma and atrial fibrillation, and between cutaneous melanoma and warfarin is still unclear. Therefore, we used a two-sample Mendelian randomization to assess the causal relationship between atrial fibrillation/warfarin and cutaneous melanoma (cM).MethodsFirstly, atrial fibrillation (ukb-b-11550; nCase = 3,518, nControl = 459,415) and warfarin (ukb-b-13248; nCase = 4,623, nControl = 458,310) as exposures, with genome-wide association studies (GWAS) data from the United Kingdom Biobank. And cM (ieu-b-4969; nCase = 3,751, nControl = 372,016) as outcome, with GWAS data from the IEU Open GWAS project. Subsequently, single-nucleotide polymorphisms (SNPs) were filtered from GWAS studies using quality control measures. In addition, two-sample Mendelian randomization (MR) analysis was performed to explore the causal relationship between atrial fibrillation or warfarin and cM and used inverse variance weighting (IVW) as the primary analytical method. Finally, relevant heterogeneity and sensitivity analysis were performed to ensure the accuracy of the results.ResultsA causal relationship between atrial fibrillation and cutaneous melanoma was observed, and between warfarin and cutaneous melanoma.ConclusionThe atrial fibrillation may play a causal role in the development of cutaneous melanoma, but the mechanism and the causal relationship between warfarin and cutaneous melanoma needs to be further elucidated.

Published
2024
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2. Analysis of Salivary Bacterial Community by Direct PCR and High Resolution Melting Curve and Its Forensic Applications

Author

YANG Rui, CHEN Jiong, and ZHAO Gui-sen

Subjects
forensic genetics, high resolution melting, polymerase chain reaction （pcr）, saliva, bacterial community, Medicine
Abstract

ObjectiveTo develop a rapid test for salivary bacterial community based on direct PCR （dPCR） and high resolution melting （HRM） curve analysis, to evaluate its application value in forensic medicine.MethodsThe salivary bacteria were collected by centrifugation and then resuspended in Tris-EDTA （TE） buffer, and directly used as the template for amplification and HRM curve analysis （dPCR-HRM） of the 16S rDNA V4 region. The genotype confidence percentage （GCP） of the HRM profiles compared with the reference profile was calculated. The template DNA was extracted by traditional kit and then PCR-HRM （namely kPCR-HRM） was used as reference to validate the feasibility of dPCR-HRM. The gradient dilution templates, population samples and simulated salivary stains were analyzed by dPCR-HRM to evaluate its sensitivity, typing ability and adaptability.ResultsUsing dPCR-HRM method, the HRM profiles of salivary bacterial community were obtained within 90 minutes. The GCP between dPCR-HRM and kPCR-HRM was greater than 95.85%. For general individuals, the HRM type of bacterial community could be determined with 0.29 nL saliva by dPCR-HRM. The 61 saliva samples could be divided into 10 types. The typing of salivary stains deposited within 8 h was the same as those of fresh saliva （GCP>90.83%）.ConclusiondPCR-HRM technology can be used for rapid typing of salivary bacterial community, and has the advantage of low cost and simple operation.

Published
2022
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3. A principled framework to assess the information-theoretic fitness of brain functional sub-circuits

Author

Duong-Tran, Duy, Nguyen, Nghi, Mu, Shizhuo, Chen, Jiong, Bao, Jingxuan, Xu, Frederick, Garai, Sumita, Cadena-Pico, Jose, Kaplan, Alan David, Chen, Tianlong, Zhao, Yize, Shen, Li, and Goñi, Joaquín

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.

Published
2024

4. Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development

Author

Ma, Longfei, Cheng, Nan, Wang, Xiucheng, Chen, Jiong, Gao, Yinjun, Zhang, Dongxiao, and Zhang, Jun-Jie

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning
Abstract

The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict the dynamics of real-world systems remains substantial. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems. Moreover, the Mean STate Error (MSTE) is proposed as a robust metric for evaluating the performance of algorithms within these digital space. The efficacy of our framework is demonstrated through extensive simulations that show our DT not only accurately mirrors the physical reality but also provides a reliable platform for algorithm evaluation. This work lays a foundation for future research into DT technologies, highlighting pathways for both theoretical enhancements and practical implementations in various industries.

Published
2024

6. A RESEARCH ON LOAD SPECTRUM STATISTICAL ANALYSIS OF T100C TRAIN BOGIE BASED ON KERNEL DENSITY ESTIMATION ALGORITHM

Author

GAO YanJie, YANG YongFa, CHEN Jiong, and YANG CanYu

Subjects
Bogie, Load spectrum, Rain-flow counting, Kernel density estimation, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Traditional train load spectrum statistics are all based on actual measurements,using Weibull distribution to fit test results and calculate load at different levels. But the required testing mileage is long and the entire process is timeconsuming. It is also difficult to fit Weibull parameters. More importantly,the final results are not necessarily reflect the real characteristic of a sample due to the assuming of load distribution beforehand. In order to overcome the disadvantages of traditional load spectrum statistical methods,in this paper,we use kernel density estimation techniques in statistical work of side frame load spectrum for SRM80 full ballast cleaning machine’s T100 C new bogie,which is based on the use of rain-flow counting method in time-load history statistics. Results show that the kernel density estimation method can restore the original load spectrum,remedy the problems of shortage of small sample data and save manpower and resources for train load spectrum statistical work.

Published
2016
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7. Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Author

Wen, Junhao, Yang, Zhijian, Nasrallah, Ilya M., Cui, Yuhan, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Mamourian, Elizabeth, Hwang, Gyujoon, Singh, Ashish, Bergman, Mark, Bao, Jingxuan, Varol, Erdem, Zhou, Zhen, Boquet-Pujadas, Aleix, Chen, Jiong, Toga, Arthur W., Saykin, Andrew J., Hohman, Timothy J., Thompson, Paul M., Villeneuve, Sylvia, Gollub, Randy, Sotiras, Aristeidis, Wittfeld, Katharina, Grabe, Hans J., Tosun, Duygu, Bilgel, Murat, An, Yang, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L., Heckbert, Susan R., Austin, Thomas R., Launer, Lenore J., Espeland, Mark, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Bryan, R. Nick, Resnick, Susan M., Ferrucci, Luigi, Fan, Yong, Habes, Mohamad, Wolk, David, Shen, Li, Shou, Haochang, and Davatzikos, Christos

Published
2024
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12. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Published
2024
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16. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Published
2023

17. Fast GPU-Based Two-Way Continuous Collision Handling

Author

Wang, Tianyu, Chen, Jiong, Li, Dongping, Liu, Xiaowei, Wang, Huamin, and Zhou, Kun

Subjects
Computer Science - Graphics
Abstract

Step-and-project is a popular way to simulate non-penetrated deformable bodies in physically-based animation. First integrating the system in time regardless of contacts and post resolving potential intersections practically strike a good balance between plausibility and efficiency. However, existing methods could be defective and unsafe when the time step is large, taking risks of failures or demands of repetitive collision testing and resolving that severely degrade performance. In this paper, we propose a novel two-way method for fast and reliable continuous collision handling. Our method launches the optimization at both ends of the intermediate time-integrated state and the previous intersection-free state, progressively generating a piecewise-linear path and finally reaching a feasible solution for the next time step. Technically, our method interleaves between a forward step and a backward step at a low cost, until the result is conditionally converged. Due to a set of unified volume-based contact constraints, our method can flexibly and reliably handle a variety of codimensional deformable bodies, including volumetric bodies, cloth, hair and sand. The experiments show that our method is safe, robust, physically faithful and numerically efficient, especially suitable for large deformations or large time steps.

Published
2022

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