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ConnectomeAE: Multimodal brain connectome-based dual-branch autoencoder and its application in the diagnosis of brain diseases

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computer Methods and Programs in Biomedicine 2025年 267卷 108801-108801页

作者： Zheng, Qiang Nan, Pengzhi Cui, Yongchao Li, Lin School of Computer and Control Engineering Yantai University Yantai264005 China Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Kuala Lumpur53300 Malaysia Department of Radiology Yantaishan Hospital Affiliated to Binzhou Medical University Yantai264003 China

Background and Objective: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis. Methods: A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases. Results: ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE. Conclusions: ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability t

关键词： Magnetic resonance imaging

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Photodegradation of psychotropic medications: Impact on efficacy, safety, and drug properties

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computers in Biology and Medicine 2025年 191卷 110115-110115页

作者： Udrea, Ana-Maria Buiu, Catalin Staicu, Angela Dabu, Aurelia Nicoleta Avram, Speranta Laser Department National Institute for Laser Plasma and Radiation Physics Magurele Ilfov Romania Department of Anatomy Animal Biology Animal Physiology and Biophysics Faculty of Biology University of Bucharest Bucharest Romania Earth Environmental and Life Sciences Section Research Institute of the University of Bucharest University of Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania Department of Neurosurgery The University Emergency Hospital of Bucharest Bucharest Romania

Antipsychotics and antidepressants are essential psychotropic medications used for treating various mental health conditions such as depression, schizophrenia, and bipolar disorder. However, when exposed to light, these compounds are susceptible to photodegradation, potentially changing their biological activity and safety profiles. This study evaluates the pharmacokinetic and pharmacodynamic properties of several photoproducts derived from 13 psychotropic drugs. We used computational methods to predict the biological activity, toxicity, and drug-like properties of the photoproducts. Our results indicate that photoproducts such as amisulpride_TP166, TP246, quetiapine_D4, and quetiapine_PH1 show enhanced biological affinity and ADME-Tox profiles similar to their parent compounds, suggesting possible therapeutic advantages in their interaction with targeted receptors. However, some of the photocompounds exhibit lower predicted binding affinities when interacting with those receptors compared to their parent compounds, indicating a possible loss of function. These findings emphasize the need for further investigation into the effects and safety of drug photoproducts, particularly in the context of long-term pharmacotherapy. © 2025 The Author(s)

关键词： Bioactivity

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Open and Closed Source Models for LLM-Generated Metaheuristics Solving engineering Optimization Problem 28th

Open and Closed Source Models for LLM-Generated Metaheuris...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Senkerik, Roman Viktorin, Adam Kadavy, Tomas Kovac, Jozef Janku, Peter Pekar, Libor Guzowski, Hubert Smolka, Maciej Byrski, Aleksander Pluhacek, Michal A.I.Lab Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Faculty of Computer Science AGH University of Krakow Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Krakow Poland

ISBN: (纸本)9783031900648

This paper explores the applicability of generative AI (genAI), specifically Large Language Models (LLMs), for the automatic generation and configuration of metaheuristic algorithms to address a real-world engineering problem: the optimal parameter estimation of time-delay systems in interconnected heating-cooling loops. The study introduces a pioneering workflow and iterative architecture with feedback within the emerging field of genAI-driven optimization for real optimization problems, eliminating the need for manually crafted or modified algorithms. This automated system empowers domain experts in engineering to solve complex optimization problems with minimal knowledge of optimization algorithms, lowering the barrier to entry for sophisticated algorithm use. We demonstrate how LLMs can generate effective optimizers under conditions like connstrained optimization problems where the solution lies near the boundaries of the search space. Four state-of-the-art LLMs (closed and open-sourced) have been selected for experiments. These are GPT-4o, GPT-4o mini, Claude Sonnet 3.5 and Llama 3.1. All studied LLMs generated metaheuristics that outperformed the initialization baseline optimization method (Random Search and CMA-ES). Notably, the Claude Sonnet 3.5 model generated a metaheuristic with the best mean results, almost matching the performance of the tuned state-of-the-art DISH algorithm, as an example of adaptive Differential Evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time of arrival

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Method of programming of sequential control systems using LabVIEW environment

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IFAC-PapersOnLine 2016年第25期49卷 476-481页

作者： Wcislik, Miroslaw Suchenia, Karol Laskawski, Michal S. Faculty of Electric Engineering Automatic Control and Computer Science Kielce University of Technology Poland

The paper deals with the new methodology of programming of sequential control systems in Lab VIEW language. By similarity to the SFC language and use of only a few defined blocks and connections, the proposed approach makes it easy to construct and analyze program of control system. The methodology uses only basic elements of the Lab VIEW language. That is why it may be used in wide variety of control devices including FPGA. © 2016

关键词： control systems Flip flop circuits Block diagrams decoders Graphical languages LabViEW Sequence control

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

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Knowledge-Based Systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search

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Ultrafast and accurate sequence alignment and clustering of viral genomes

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Nature Methods 2025年 1-4页

作者： Zielezinski, Andrzej Gudyś, Adam Barylski, Jakub Siminski, Krzysztof Rozwalak, Piotr Dutilh, Bas E. Deorowicz, Sebastian Department of Computational Biology Faculty of Biology Adam Mickiewicz University Poznan Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Molecular Virology Faculty of Biology Adam Mickiewicz University Poznan Poland Institute of Biodiversity Faculty of Biological Sciences Cluster of Excellence Balance of the Microverse Friedrich Schiller University Jena Jena Germany Theoretical Biology and Bioinformatics Science4Life Utrecht University Utrecht Netherlands

Viromics produces millions of viral genomes and fragments annually, overwhelming traditional sequence comparison methods. Here we introduce Vclust, an approach that determines average nucleotide identity by Lempel–Ziv parsing and clusters viral genomes with thresholds endorsed by authoritative viral genomics and taxonomy consortia. Vclust demonstrates superior accuracy and efficiency compared to existing tools, clustering millions of genomes in a few hours on a mid-range workstation. © The Author(s) 2025.

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Systematic Review and Meta-Analysis of the Association Between Clinical Severity and Co-Infection of Human Adenovirus With Other Respiratory Pathogens in Children

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Journal of Medical Virology 2025年第5期97卷 e70370页

作者： Niu, Dandan Gao, Yanxiao Zhang, Yingluan Lv, Qiuying Jiang, Yiwen Jia, Yuanxi Chen, Zhigao Wang, Honglin Cheng, Yanpeng Sha, Feng Ren, Meng Chen, Yixiong Zhang, Xindong Zhang, Zhen Tang, Jinling Feng, Tiejian Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Communicable Diseases Control and Prevention Shenzhen Center for Disease Control and Prevention Shenzhen China Shenzhen Research Center for Communicable Disease Control and Prevention Chinese Academy of Medical Sciences Shenzhen China Department of Communicable Diseases Control and Prevention Baoan District Center for Disease Control and Prevention Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

The correlation between the co-infection of human respiratory adenovirus (HAdV) and clinical severity has not been firmly established yet. We carried out a systematic review and meta-analysis. We scoured six databases for studies published up to 16 May 2024. A total of 66 cohort studies, which involved 16 251 participants, were incorporated. When compared with patients suffering from HAdV single infection, those with co-infection of viruses (risk ratios [RRs] = 1.40, 95% confidence interval [CI]: 1.05–1.86), bacteria (RR = 1.50, 95% CI: 1.05–2.16), or fungi (RR = 2.86, 95% CI: 2.17–3.76) were more prone to experience severe clinical outcomes. Co-infection with Mycoplasma pneumoniae had a tendency to elevate the risk of common pneumonia (RR = 1.81, 95% CI: 1.66–1.97), and bacterial co-infection was likely to extend the hospital stay (mean differences = 2.23 days, 95% CI: 0.44–4.03). In summary, the co-infection of HAdV with other viral, bacterial, fungal respiratory pathogens or Mycoplasma pneumoniae heightened the risk of severe clinical outcomes in pediatric patients, leading to an increased utilization of medical resources. This implied that the ecological and biological mechanisms underlying the potential interactions between HAdV and other microorganisms merited further investigation. © 2025 The Author(s). Journal of Medical Virology published by Wiley Periodicals LLC.

关键词： clinical outcomes co-infection human respiratory adenovirus infections

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IoT and Edge Computing: Applications and Reliability Implications

IoT and Edge Computing: Applications and Reliability Implica...

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Latin American Test Workshop, LATW

作者： Edward-W. Caro-Anzola Giuseppe Esposito Juan-David Guerrero-Balaguera Andrés-F. Jiménez-López Fabián-R. Jiménez-López Robert Limas-Sierra Gustavo Ramirez-Espinosa Edoardo Giusto Bartolomeo Montrucchio Oscar-F. Vera-Cely Josie E. Rodriguez Condia Universidad Pedagógica y Tecnológica de Colombia Faculty of Engineering Tunja Colombia Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Universidad de los Llanos Villavicencio Colombia Department of Electronics Pontificia Universidad Javeriana Bogotá Colombia University of Naples Federico II DIETI Naples Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Nowadays, the Internet of Things (IoT) and edge computing paradigms drive innovation in several fields, including autonomous systems, environmental monitoring, smart farming, and surveillance. The wide adoption of IoT and edge computing through commercial-off-the-shelf (COTS) provides adequate computational resources under limited energy consumption and affordable prices, which allow the development of versatile, scalable, and adaptable systems for several domains. Both features (versatility and scalability) have brought attention to domains with strict dependability requirements. However, ensuring effective, reliable, fault-tolerant IoT systems is challenging and mostly neglected in some domains. This special session briefly overviews strategies for the reliable design of IoT and edge computing systems for environmental monitoring in smart farming and urban scenarios. Then, we analyze an emerging paradigm (Split computing) and its reliability features. Further-more, the session discusses and focuses on design challenges, learned lessons, reliability implications, and open questions.

关键词： Smart agriculture Data integrity Reliability engineering Real-time systems Hardware Calibration Reliability Internet of Things Environmental monitoring Edge computing

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