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Molecular architecture of mammalian pyruvate dehydrogenase complex

Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Association Rule Mining in Distributed Environment: A Survey 4th

Association Rule Mining in Distributed Environment: A Survey

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Baishya, Bhaswati Ahmed, Shafiul Alom Nath, Bhabesh Department of Computer Science and Engineering Tezpur University Assam India School of Computing Science and Engineering VIT-Bhopal University Sehore Bhopal India

ISBN: (纸本)9789819942831

Due to the increasing popularity of digital mediums and easy access to Internet technology, society has become dependent on digital resources. This generates a huge amount of data and identifying relationships or patterns among these data has become a predominant field of research. Traditional rule-mining techniques are primarily centralized, which makes these techniques ineffective when dealing with distributed databases. Distributed Association Rule Mining (D-ARM) is being developed for finding association rules from distributed databases stored in different locations. We have explored various D-ARM algorithms based on FP-Growth and Apriori, and we also examined the advantages and limitations of these algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Association rules

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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Santhakumar, D. Soundararajan, Gopalakrishnan Chintala, Suman Bharath, E. Murthy Inumula, Krishna Chouhan, Kuldeep Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai602105 India Muscat College Ruwi Oman Business Intelligence Architect United States C K College of Engineering & Technology Anna University Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India Maharashtra Pune411057 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality. © 2025 IEEE.

关键词： Simulated annealing

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Market Segmentation Using Machine Learning and data Analysis: A Comprehensive Study 15

Market Segmentation Using Machine Learning and Data Analysis...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Ajay Dhanda, Namrata Tiwari, Sharda Verma, Rajat Amity University Uttar Pradesh Amity School of Engineering and Technology Lucknow India Babu Banarasi Das Northern India Institute of Technology Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350370249

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning's potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact. © 2024 IEEE.

关键词： consumer behavior customer insights machine learning market segmentation predictive analytics targeted marketing

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Intelligent Decision-Based Edge Server Sleep for Green Computing in MEC-Enabled IoV Networks

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第2期9卷 3687-3703页

作者： Hou, Peng Huang, Yi Zhu, Hongbin Lu, Zhihui Huang, Shin-Chia Chai, Hongfeng Fudan University School of Computer Science Shanghai200438 China Fudan University Institute of Fintech Shanghai200438 China National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan

With the advent of the 6G era, addressing the increasing demand for mobile computing has become crucial. In the next-generation Internet of Vehicles (IoVs), widespread deployment of edge servers ensures low delay and high reliability for vehicular applications. However, densely deployed edge servers are designed to accommodate peak traffic. This results in unnecessary resource wastage, high maintenance costs, and energy consumption during off-peak hours. Enabling the sleep function of edge servers and dynamically controlling their operating mode is crucial for reducing energy consumption and enabling green IoVs. In this paper, we study the dynamic sleep problem of edge servers in the IoV network and propose traffic-aware intelligent sleep decision-making algorithms based on Deep Reinforcement Learning (DRL). We propose a Centralized DRL-based Dynamic Sleep (CDDS) algorithm, which leverages the deep deterministic policy gradient algorithm to learn the optimal decision policy through environmental interaction. To enhance the stability of agent learning and mitigate the impact of environmental changes, we propose a baseline transformation strategy based on the greedy algorithm. Additionally, to overcome the limitations of CDDS, we combine federated learning with DRL and propose a Federated DRL-based Dynamic Sleep (FDDS) algorithm. This approach speeds up model training and improves the model's generalization ability. Furthermore, we conduct extensive experimental verification using real-world datasets. The experimental results demonstrate that both CDDS and FDDS successfully learn the optimal sleep control policy, leading to system cost reduction of 11.06% to 45.36% and 8.71% to 43.77%, respectively, compared to the baselines. © 2016 IEEE.

关键词： Energy utilization

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A Framework for Generating Informative Benchmark Instances 28

A Framework for Generating Informative Benchmark Instances

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28th International Conference on Principles and Practice of Constraint programming, CP 2022

作者： Dang, Nguyen Akgün, Özgür Espasa, Joan Miguel, Ian Nightingale, Peter School of Computer Science University of St Andrews United Kingdom Department of Computer Science University of York United Kingdom

ISBN: (纸本)9783959772402

Benchmarking is an important tool for assessing the relative performance of alternative solving approaches. However, the utility of benchmarking is limited by the quantity and quality of the available problem instances. Modern constraint programming languages typically allow the specification of a class-level model that is parameterised over instance data. This separation presents an opportunity for automated approaches to generate instance data that define instances that are graded (solvable at a certain difficulty level for a solver) or can discriminate between two solving approaches. In this paper, we introduce a framework that combines these two properties to generate a large number of benchmark instances, purposely generated for effective and informative benchmarking. We use five problems that were used in the MiniZinc competition to demonstrate the usage of our framework. In addition to producing a ranking among solvers, our framework gives a broader understanding of the behaviour of each solver for the whole instance space;for example by finding subsets of instances where the solver performance significantly varies from its average performance. © Nguyen Dang, Özgür Akgün, Joan Espasa, Ian Miguel, and Peter Nightingale.

关键词： Benchmarking

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EEG-Based Emotion Detection: A Machine Learning Approach with Multiple Classifiers

EEG-Based Emotion Detection: A Machine Learning Approach wit...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Senthil, K. Thejasree, J. Rushitha, Bathini Venkata Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

The present work sought to evaluate the ability of three classification methodologies Decision Tree, Support Vector Machine (SVM), and Naive Bayes in classifying emotional states using EEG data. The models were trained and tested in a dataset that covers a broad range of emotional labels. The performance of both models was measured using quantitative measures such as accuracy, precision, recall, and F1-score. A Decision Tree Classifier that achieved 95.78% accuracy with a good set of precision and recall in all emotion categories, where particularly the Negative emotions are detected better than other classifiers. The SVM model presented improved classification ability and overall robustness regarding categorizing whether an emotion was a Neutral, Negative, or Positive category compared with the Decision Tree model doing so with 96.02% accuracy (Table 1). However, the Naive Bayes Classifier demonstrated lower overall performance, achieving an accuracy of 69.79%. It tended to over-allocate to non-positive categories, which impacted its precision in correctly predicting the exact category. Upon evaluating the confusion matrix, it is found that Decision Tree and SVM models managed to significantly reduce misclassifications. According to the result, Ensemble models are more effective in EEG-based emotion classification tasks compared with SVM and Decision Tree. On the other hand, Naive Bayes might not be an ideal choice for these kinds of tasks because it assumes a very simple model. The paper emphasizes the demand for careful selection of machine learning models to be used in recognizing emotions from EEG data. It also points to future possible directions, such as optimization of models and rich feature engineering improvements using deep learning approaches. Overall, Support Vector Machines (SVM) and Decision Trees show strong potential for practical application in emotional processing to offer fresh insights into improving the accuracy/ reliability of classification

关键词： Support vector machines

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Investigating Labeled Cyberbullying Incidents on the Weibo Social Network 21

Investigating Labeled Cyberbullying Incidents on the Weibo S...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Xu, Jun Yu, Songnan Lu, Xiaoyu Sean Wu, Di Kang, Qi School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210000 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9798350365221

This study aims to investigate cyberbullying incidents on Weibo by constructing a comprehensive dataset of labeled conversations. We collect 89K social media sessions from 10K user profiles and manually annotated the data to identify instances of cyberbullying. We analyze cyberbullying based on three fundamental characteristics, examining its distribution across different topics and the relationship between posting times and cyberbullying incidents. Additionally, we explore the influence of user popularity on the similarity of receiving bullying comments. Our study provides valuable insights into the characteristics of cyberbullying on Chinese social media and highlights the need for culturally sensitive detection models. © 2024 IEEE.

关键词： Phishing

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Arrhythmia Classification using Supervised Machine Learning Algorithms

Arrhythmia Classification using Supervised Machine Learning ...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sriram, Suthir Vyshnavi, P. Nivethitha, V. Thangavel, M. Ravikumar, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India United Arab Emirates University College of Information and Technology Al Ain United Arab Emirates Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Heart related disorders remain as one of the most chronic health issues in the world;generally, cardiovascular diseases are rated as a prime factor for morbidity and mortality worldwide. The early diagnosis and detection of such conditions may lead to imperative treatment measures and prevent unpleasant complications. This would considerably reduce the risk of serious health complications. One of the diagnostic tools for cardiac abnormalities is an electrocardiogram, a noninvasive, more generally adopted clinical investigative tool that captures over a period of time the heart's electrical function. Due to the fact that it is very accessible and easy to use, an ECG would normally be done first in diagnosing many cardiac conditions, including arrhythmias. The paper represents the detailed classification of arrhythmia, which is an irregularity in heart rhythm and can be a symptom of severe underlying cardiac problems based on ECG data. We have explored the efficacy of machine learning algorithms and Ensemble Method that combined the former for better predictions. Our results indicate that this ensemble learning approach performs better compared to all of the other individual algorithms used in this study, with an accuracy of 72.15%. The present study not only endorses the effort being made in better cardiac diagnostic tools but also calls for further attention toward advanced computational methods in health care to resolve some of the pivotal, complex challenges in medicine. © 2024 IEEE.

关键词： Diseases

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