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1,019 results on '"Lu, Zhimin"'

9. Adversarial Learning for Implicit Semantic-Aware Communications

Author

Lu, Zhimin, Xiao, Yong, Sun, Zijian, Li, Yingyu, Shi, Guangming, Chen, Xianfu, Bennis, Mehdi, and Poor, H. Vincent

Subjects
Computer Science - Machine Learning, Computer Science - Information Theory, Computer Science - Networking and Internet Architecture
Abstract

Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in this area focus on delivering explicit semantics, labels or signal features that can be directly identified from the source signals. In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user. We develop a novel adversarial learning-based implicit semantic-aware communication (iSAC) architecture in which the source user, instead of maximizing the total amount of information transmitted to the channel, aims to help the recipient learn an inference rule that can automatically generate implicit semantics based on limited clue information. We prove that by applying iSAC, the destination user can always learn an inference rule that matches the true inference rule of the source messages. Experimental results show that the proposed iSAC can offer up to a 19.69 dB improvement over existing non-inferential communication solutions, in terms of symbol error rate at the destination user.

Published
2023

15. A century of the Warburg effect

Author

Thompson, Craig B., Vousden, Karen H., Johnson, Randall S., Koppenol, Willem H., Sies, Helmut, Lu, Zhimin, Finley, Lydia W. S., Frezza, Christian, Kim, Jiyeon, Hu, Zeping, and Bartman, Caroline R.

Published
2023
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29. An improved AO algorithm optimized PID rectification model for wind speed and wind pressure regulation in video cabinet of tobacco sorting machine

Author

Lu Zhimin, Jiang Lin, Jiang Zhishan, Dong Zixin, Li Huajie, and Zhang Wei

Subjects
iao algorithm, pid controller, tobacco sorting machine system, wind speed and wind pressure regulation, 65y20, Mathematics, QA1-939
Abstract

Sorting and removing impurities is the use of raw materials and light impurities suspended speed differences through the wind to separate the hemp, paper, plastic, and other light materials to remove impurities. Article to tobacco sorting machine video cabinet wind pressure control system principle as a guide, combined with the structure of the sorting and impurity removal machine and the principle of wind speed balance designed to balance the wind speed and wind pressure of the tobacco sorting machine. The control model of wind speed and pressure in the video cabinet system of the tobacco sorting machine is established, and the improved AO algorithm is used for the design of the overall PID controller and optimization of the PID controller’s setting parameters. The IAO algorithm optimizes the parameters of the PID setting controller with a steady state error within 3%, and the introduction of the IAO-PID controller can make the wind speed of the sorting machine system control from 16.4m/s to 11.1m/s within 17ms. When the system detects that the negative pressure of wire feeding changes sharply to about 1.12MPa within 20ms and exceeds the differential pressure start value of 0.9MPa set by the sorter system, the tobacco wire feeding anti-clogging function is activated. The PID controller optimized by the IAO algorithm can achieve precise regulation and control of wind speed and pressure of tobacco sorting machines, improving the sorting efficiency of tobacco production.

Published
2024
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30. Research on automatic biomass grading and quality assessment technology for tobacco industry based on deep convolutional neural network

Author

Lu Zhimin, Zhang Wei, Jiang Quan, Dong Zixin, Li Huajie, and Li Xiaogang

Subjects
feature extraction, support vector machine, difficult samples, convolutional neural network, automatic tobacco grading, 68m12, Mathematics, QA1-939
Abstract

Automatic tobacco grading systems can promote the standardization and unification of tobacco acquisition activities, improve production efficiency, and promote the long-term stable development of this industry. In this paper, we first extract the shape and color features of tobacco leaf structure, use a support vector machine to determine the difficult samples, add the difficult samples to the training set, and use the deep convolutional neural network method to assign initial values to the model, which reduces the occurrence of a model overfitting phenomenon and improves the prediction accuracy. Constructing a convolutional neural network based on EfficientNetV2-S for an automatic tobacco grading model. The performance of the model was tested on HSV and RGB images, and finally, automatic tobacco grading was applied to two batches of high-quality and good tobacco. It is found that the accuracy of the model training set and test set of this paper is 0.077 and 0.094 higher than that of the VGG16 model on HSV and RGB images, respectively, and there is no upward trend in the loss function. The model’s training operation time for grading two batches of tobacco is 27.56 seconds, which is more efficient. The accuracy rate of the two batches of tobacco is higher than 90%. The model in this paper in the practical application performance in addition to a higher degree of grading efficiency and accuracy, which is stronger than the manual identification in terms of efficiency and accuracy recognition stability. This paper’s research offers a feasible path and useful exploration to effectively improve production efficiency and optimize the process in China’s tobacco industry.

Published
2024
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31. The 150 most important questions in cancer research and clinical oncology series: questions 102–104

Author

Lu Zhimin

Subjects
imaging, pet/ct, targeted therapy, cancer metabolism, isocitrate dehydrogenase, pyruvate, choline, Medicine
Abstract

To accelerate our endeavors to overcome cancer, Visualized Cancer Medicine continues the previously launched program of publishing the 150 most important questions in cancer research and clinical oncology. In this article, three more questions are presented as follows: Question 102: Can non-glucose-based tumor imaging techniques facilitate tumor detection and metabolic classification? Question 103: how can we develop medicines to inhibit the cancer-specific metabolic functions of metabolic enzymes in tumors expressing wild-type IDH, without interfering with their canonical catalytic activities? Question 104: Can dietary-based cancer therapy be proven effective in treating cancer?

Published
2024
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40. Methionine Adenosyltransferase α1 Is Targeted to the Mitochondrial Matrix and Interacts with Cytochrome P450 2E1 to Lower Its Expression

Author

Murray, Ben, Peng, Hui, Barbier‐Torres, Lucia, Robinson, Aaron E, Li, Tony WH, Fan, Wei, Tomasi, Maria Lauda, Gottlieb, Roberta A, Van Eyk, Jenny, Lu, Zhimin, Martínez‐Chantar, Maria L, Liangpunsakul, Suthat, Skill, Nicholas J, Mato, José M, and Lu, Shelly C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Substance Misuse, Digestive Diseases, Alcoholism, Alcohol Use and Health, Chronic Liver Disease and Cirrhosis, Liver Disease, Underpinning research, Aetiology, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Animals, Cytochrome P-450 CYP2E1, Female, HSP70 Heat-Shock Proteins, Humans, Liver Diseases, Alcoholic, Male, Membrane Potential, Mitochondrial, Methionine Adenosyltransferase, Methylation, Mice, Mitochondria, Liver, Mitochondrial Proteins, Reactive Oxygen Species, Medical Biochemistry and Metabolomics, Gastroenterology & Hepatology, Clinical sciences
Abstract

Methionine adenosyltransferase α1 (MATα1, encoded by MAT1A) is responsible for hepatic biosynthesis of S-adenosyl methionine, the principal methyl donor. MATα1 also act as a transcriptional cofactor by interacting and influencing the activity of several transcription factors. Mat1a knockout (KO) mice have increased levels of cytochrome P450 2E1 (CYP2E1), but the underlying mechanisms are unknown. The aims of the current study were to identify binding partners of MATα1 and elucidate how MATα1 regulates CYP2E1 expression. We identified binding partners of MATα1 by coimmunoprecipitation (co-IP) and mass spectrometry. Interacting proteins were confirmed using co-IP using recombinant proteins, liver lysates, and mitochondria. Alcoholic liver disease (ALD) samples were used to confirm relevance of our findings. We found that MATα1 negatively regulates CYP2E1 at mRNA and protein levels, with the latter being the dominant mechanism. MATα1 interacts with many proteins but with a predominance of mitochondrial proteins including CYP2E1. We found that MATα1 is present in the mitochondrial matrix of hepatocytes using immunogold electron microscopy. Mat1a KO hepatocytes had reduced mitochondrial membrane potential and higher mitochondrial reactive oxygen species, both of which were normalized when MAT1A was overexpressed. In addition, KO hepatocytes were sensitized to ethanol and tumor necrosis factor α-induced mitochondrial dysfunction. Interaction of MATα1 with CYP2E1 was direct, and this facilitated CYP2E1 methylation at R379, leading to its degradation through the proteasomal pathway. Mat1a KO livers have a reduced methylated/total CYP2E1 ratio. MATα1's influence on mitochondrial function is largely mediated by its effect on CYP2E1 expression. Patients with ALD have reduced MATα1 levels and a decrease in methylated/total CYP2E1 ratio. Conclusion: Our findings highlight a critical role of MATα1 in regulating mitochondrial function by suppressing CYP2E1 expression at multiple levels.

Published
2019

41. Fructose-1,6-bisphosphatase 1 functions as a protein phosphatase to dephosphorylate histone H3 and suppresses PPARα-regulated gene transcription and tumour growth

Author

Wang, Zheng, Li, Min, Jiang, Hongfei, Luo, Shudi, Shao, Fei, Xia, Yan, Yang, Mengke, Ren, Xiangle, Liu, Tong, Yan, Meisi, Qian, Xu, He, Haiyan, Guo, Dong, Duan, Yuran, Wu, Ke, Wang, Lei, Ji, Guimei, Shen, Yuli, Li, Lin, Zheng, Peixiang, Dong, Bofei, Fang, Jing, Zheng, Min, Liang, Tingbo, Li, Haitao, Yu, Rilei, Xu, Daqian, and Lu, Zhimin

Published
2022
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