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From EEG to Eye Movements: Cross-modal Emotion Recognition Using Constrained Adversarial Network with Dual Attention

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IEEE Transactions on Affective Computing 2024年

作者： Wang, Yiting Liu, Jia-Wen Lu, Bao-Liang Zheng, Wei-Long Karolinska Institutet Department of Clinical Neuroscience Stockholm171 77 Sweden Shanghai Jiao Tong University China Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China Shanghai Jiao Tong University School of Medicine RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai200020 China Shanghai Emotionhelper Technology Co. Ltd. Shanghai China

Emotion recognition is a fundamental part of affective computing, obtaining performance gain from multimodal methods. Electroencephalography (EEG) and eye movements are extensively used as they contain complementary information. However, the inconvenient acquisition of EEG is hindering the extensive adoption of multimodal emotion recognition in daily applications while eye movements are more convenient to collect but with lower performance. To tackle this issue, we propose a Constrained Adversarial Network with Dual Attention (CANDA), exploiting the complementary information from multiple modalities during training to improve the test-time performance of single easily acquired modality, i.e., transferring knowledge from a stronger modality to a weaker modality. During training, a common joint space is learned to diminish the distribution discrepancy among different modalities and incorporate the multimodal representations. During test, single modality is converted to the common space achieving comparable performance to multiple modalities. Extensive experiments demonstrate that our model achieves the state-of-the-art performance for cross-modal emotion recognition. Specifically, the mean accuracy increases around 15% on SEED, 15% on SEED-IV, and 2% on SEED-V compared to the latest baseline for emotion recognition. Visualization of features in the joint space illustrates that the distribution of different modalities aligns together with the discriminative ability regarding various emotions. © 2010-2012 IEEE.

关键词： Electroencephalography

Current understanding of osteoarthritis pathogenesis and relevant new approaches

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Bone Research 2022年第4期10卷 627-643页

作者： Liping Tong Huan Yu Xingyun Huang Jie Shen Guozhi Xiao Lin Chen Huaiyu Wang Lianping Xing Di Chen Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518005China Faculty of Pharmaceutical Sciences Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Orthopedic Surgery School of MedicineWashington University in St.LouisSt.LouisMO 63110USA School of Medicine Southern University of Science and TechnologyShenzhen 518055China Department of Wound Repair and Rehabilitation State Key Laboratory of TraumaBurns and Combined InjuryDaping HospitalArmy Medical UniversityChongqing 400042China Research Center for Human Tissues and Organs Degeneration Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Pathology and Laboratory of Medicine Center for Musculoskeletal ResearchUniversity of Rochester Medical CenterRochesterNY 14642USA

Osteoarthritis(OA)is the most common degenerative joint disease that causes painful swelling and permanent damage to the joints in the *** molecular mechanisms of OA are currently *** is a heterogeneous disease that affects the entire joint,and multiple tissues are altered during OA *** better understand the pathological mechanisms of OA,new approaches,methods,and techniques need to be used to understand OA *** this review,we first focus on the epigenetic regulation of OA,with a particular focus on DNA methylation,histone modification,and microRNA regulation,followed by a summary of several key mediators in OA-associated *** then introduce several innovative techniques that have been and will continue to be used in the fields of OA and OA-associated pain,such as CRISPR,scRNA sequencing,and lineage ***,we discuss the timely updates concerning cell death regulation in OA pathology,including pyroptosis,ferroptosis,and autophagy,as well as their individual roles in OA and potential molecular targets in treating ***,our review highlights new directions on the role of the synovial lymphatic system in *** improved understanding of OA pathogenesis will aid in the development of more specific and effective therapeutic interventions for OA.

关键词： pathogenesis swelling concerning

Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor

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arXiv 2022年

作者： Arshad, Muhammad Zeeshan Jamsrandorj, Ankhzaya Kim, Jinwook Mun, Kyung-Ryoul The Center for Artificial Intelligence KIST Seoul Korea Republic of The Department of Human Computer Interface & Robotics Engineering University of Science & Technology Daejon Korea Republic of KHU-KIST Department of Converging Science and Technology KHU Seoul Korea Republic of

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to explore a way of improving the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data was gathered from elderly subjects equipped with three IMU sensors while they walked. The input was taken only from the waist sensor was used to train 16 deep-learning models including CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. Fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6TS (±6ms) and ±1TS (±1ms) respectively. Advancing from the previous studies exploring gait event detection, the model showed a great improvement in terms of its prediction error having an MAE of 6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of ±1TS. The results showed that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon. Copyright © 2022, The Authors. All rights reserved.

关键词： Gait analysis

Polarized Online Discourse on Abortion: Frames and Hostile Expressions among Liberals and Conservatives

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arXiv 2023年

作者： Rao, Ashwin Chang, Rong-Ching Zhong, Qiankun Lerman, Kristina Wojcieszak, Magdalena University of Southern California Information Sciences Institute Marina del ReyCA United States Department of Computer Science University of California Davis United States Max Planck Institute for Human Development Germany Department of Communication University of California Davis United States Center for Excellence in Social Science University of Warsaw Poland

Abortion has been one of the most divisive issues in the United States. Yet, missing is comprehensive longitudinal evidence on how political divides on abortion are reflected in public discourse over time, on a national scale, and in response to key events before and after the overturn of Roe v Wade. We analyze a corpus of over 3.5M tweets related to abortion over the span of one year (January 2022 to January 2023) from over 1.1M users. We estimate users’ ideology and rely on state-of-the-art transformer-based classifiers to identify expressions of hostility and extract five prominent frames surrounding abortion. We use those data to examine (a) how prevalent were expressions of hostility (i.e., anger, toxic speech, insults, obscenities, and hate speech), (b) what frames liberals and conservatives used to articulate their positions on abortion, and (c) the prevalence of hostile expressions in liberals and conservative discussions of these frames. We show that liberals and conservatives largely mirrored each other’s use of hostile expressions: as liberals used more hostile rhetoric, so did conservatives, especially in response to key events. In addition, the two groups used distinct frames and discussed them in vastly distinct contexts, suggesting that liberals and conservatives have differing perspectives on abortion. Lastly, frames favored by one side provoked hostile reactions from the other: liberals use more hostile expressions when addressing religion, fetal personhood, and exceptions to abortion bans, whereas conservatives use more hostile language when addressing bodily autonomy and women’s health. This signals disrespect and derogation, which may further preclude understanding and exacerbate polarization. © 2023, CC BY.

关键词： Polarization

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

Impairments in Spatial Representations of Place Cells and Neural rhythms In APP knock-in rat model

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Impairments in Spatial Representations of Place Cells and Ne...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xueling Wang Yimeng Wang Keliang Pang Bai Lu Yutao Tian Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China Aging and Longevity Institute & Institute of Biological Science Zhongshan Hospital Fudan University Shanghai China School of Pharmaceutical Sciences IDG/McGovern Institute for Brain Research Tsinghua University-Peking University Joint Center for Life Sciences Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Alzheimer's disease (AD) is a neurodegenerative disease that severely impacts spatial memory. Place cells in the hippocampus play an important role in spatial memory. However, neuronal activity in the hippocampus is difficult to monitor and study in human AD patients. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed APP NL-G-F rat model, which is a knock-in rat line of the amyloid precursor protein (APP) with Swedish-Beyreuther/Iberian-Arctic mutations, presents a unique opportunity. In this study, we investigated how hippocampal place cells and neural rhythms were impaired in APP rats. Place cells of APP rats showed a less defined spatial tunning and disrupted remapping. In addition, APP rats exhibited decreased power of theta rhythm and slow gamma rhythm. These results suggest that the impairment of spatial memory in APP rats may be caused by the damage of place cells' ability to represent spatial location and abnormal neural rhythm in the hippocampus.

关键词： Proteins Rats Alzheimer's disease Engineering in medicine and biology Biomedical monitoring Hippocampus Monitoring

Federated semi-supervised medical image classification via inter-client relation matching

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arXiv 2021年

作者： Liu, Quande Yang, Hongzheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Beihang University China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named Federated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks4 © 2021, CC BY-NC-ND.

关键词： Hospitals

Biomolecular condensates and disease pathogenesis

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science China(Life sciences) 2024年第9期67卷 1792-1832页

作者： Ke Ruan Ge Bai Yanshan Fang Dan Li Tingting Li Xingguo Liu Boxun Lu Qing Lu Zhou Songyang Shuguo Sun Zheng Wang Xin Zhang Wen Zhou Hong Zhang The First Affiliated Hospital&School of Life Sciences Ministry of Education Key Laboratory for Membrane-less Organelles&Cellular DynamicsHefei National Research Center for Interdisciplinary Sciences at the MicroscaleCenter for Advanced Interdisciplinary Science and Biomedicine of IHMDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China Nanhu Brain-computer Interface Institute Hangzhou 311100China Department of Neurology of Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou 310058China Interdisciplinary Research Center on Biology and Chemistry Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghai 201210China University of Chinese Academy of Sciences Beijing 100049China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)Shanghai Jiao Tong UniversityShanghai 200030China Department of Biomedical Informatics School of Basic Medical SciencesPeking University Health Science CenterBeijing 100191China CAS Key Laboratory of Regenerative Biology Joint School of Life SciencesGuangzhou Institutes of Biomedicine and HealthChinese Academy of SciencesGuangzhou 510530China Joint School of Life Sciences Guangzhou Medical UniversityGuangzhou 510000China Neurology Department at Huashan Hospital State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceSchool of Life SciencesFudan UniversityShanghai 200433 China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders Ministry of EducationBio-X InstitutesShanghai Jiao Tong UniversityShanghai 200030China State Key Laboratory of Biocontrol MOE Key Laboratory of Gene Function and Regulation and Guangzhou Key Laboratory of Healthy Aging ResearchSchool of Life SciencesSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510275China Department of Human Anatomy Hist

Biomolecular condensates or membraneless organelles(MLOs)formed by liquid-liquid phase separation(LLPS)divide intracellular spaces into discrete compartments for specific *** of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration,tumorigenesis,and many other pathological ***,we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase *** then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders,hearing loss,cancers,and immunological ***,we describe the emerging discovery of chemical modulators of phase separation.

关键词： liquid-liquid phase separation biomolecular condensate membraneless organelle

An Explainable Deep Neural Network Model Based on Multi-Granularity Co-Attention to Improve Prediction of Drug-Target Binding Affinity

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SSRN

SSRN 2024年

作者： Lin, Yanmei Cao, Aiqing Deng, Lei Liao, Mingzhi Zhang, Ru Zhang, Hao Peng, Yuzhong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Guangxi Academy of Sciences Nanning530007 China School of Computer Science and Engineering Central South University Changsha410083 China Center of Bioinformatics College of Life Sciences Northwest A&F University Shaanxi Yangling712100 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Center for Applied Mathematics of Guangxi Nanning Normal University Nanning530100 China

The prediction of drug-target interactions is pivotal in modern new drug development and drug repositioning. Nonetheless, current methods for predicting drug-target interactions often lose essential molecular structure information during the modeling process, leading to low prediction accuracy. This work proposes rMolGIN, a novel graph neural network with residual connectivity for molecular feature learning. rMolGIN not only takes into account the differences in chemical bonding properties and atomic neighborhood interactions, but also captures global and local structural information of drug molecules. Furthermore, we propose a novel drug-target affinity model based on rMolGIN and deep neural networks, coupled with a multi-granularity co-attention mechanism called HMCoDTA to capture comprehensive structural insights from both drugs and proteins. The proposed innovative model allows for comprehensive insight into the structural relationships between drugs and proteins, facilitating a more accurate prediction of binding affinities. To validate the effectiveness of our proposed methods, we conducted extensive experiments on three benchmark datasets of drug-target binding affinity, comparing HMCoDTA against 19 state-of-the-art models. Notably, the results show that HMCoDTA outperforms existing methods, demonstrating the effectiveness and advantages of our model in predicting drug-target binding affinity. © 2024, The Authors. All rights reserved.

关键词： Prediction models