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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs

arXiv

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arXiv 2022年

作者： Li, Alex J. Sundar, Vikram Grigoryan, Gevorg Keating, Amy E. Department of Chemistry Massachusetts Institute of Technology United States Computational and Systems Biology Program Massachusetts Institute of Technology United States Department of Computer Science Dartmouth College United States Departments of Biology and Biological Engineering Massachusetts Institute of Technology United States

computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in neural protein design frameworks. Our graph-based architecture, TERMinator, incorporates TERM-based and coordinate-based information and outputs a Potts model over sequence space. TERMinator outperforms state-of-the-art models on native sequence recovery tasks, suggesting that utilizing TERM-based and coordinate-based features together is beneficial for protein design. © 2022, CC BY.

关键词： Potts model

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Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction: Preliminary studies

arXiv

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arXiv 2023年

作者： da Silva, Davi Guimarães de Moura Meneses, Anderson Alvarenga Federal University of Western Pará – Graduate Program in Society Nature and Development R. Vera Paz s/n Salé PA SantarémCEP 68.035-110 Brazil Federal Institute of Education Science and Technology of Pará Brazil Federal University of Western Pará Institute of Geosciences and Engineering Laboratory of Computational Intelligence Brazil

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models’ robustness. Four DSs were used, related to the power consumption of: (a) a household in France;(b) a university building in Santarém, Brazil;(c) the Tétouan city zones, in Morocco;and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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Enrollment of underserved racial and ethnic populations in pediatric asthma clinical trials

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Journal of Allergy and Clinical Immunology: Global 2024年第4期3卷 100315页

作者： Geanacopoulos, Alexandra T. Wu, Ann Chen Bourgeois, Florence T. Peltz, Alon Walsh, Ryan Han, Amy Ong, Mei-Sing Division of Emergency Medicine Boston Children's Hospital Pediatric Therapeutics and Regulatory Science Initiative Computational Health Informatics Program Boston Children's Hospital Division of Child Health Research and Policy Harvard Medical School & Harvard Pilgrim Health Care Institute Department of Population Medicine Harvard Medical School & Harvard Pilgrim Health Care Institute Department of Pediatrics Harvard Medical School

Background: The existing data on enrollment trends of historically underserved racial and ethnic children in clinical trials are limited. Objective: We sought to evaluate documentation and representation of race and ethnicity in pediatric asthma clinical trials in the United States. Methods: This is a cross-sectional study of United States–based interventional trials studying pediatric asthma that were completed between 2008 and 2022 and registered on ***. Enrollment disparities were assessed by using the measure enrollment prevalence difference (EPD) (defined as the median difference between the proportion of participants enrolled and asthma prevalence in the US population by race and ethnicity). Results: Of the 67 trials reviewed, 53 (79.2%) and 36 (53.7%) reported on race and ethnicity at ***, respectively. Most participants were White (39.1%), Black (37.1%), or non-Hispanic (66.1%). Black, Hispanic, multiracial, and White children were enrolled in the expected proportions based on their contribution to asthma burden. However, American Indian or Alaska Native (AI/AN) (EPD = –1 [95% CI = –1 to –1]) and Asian children (EPD = –3 [95% CI = –3 to –3]) were underrepresented relative to disease burden in these respective groups. Fewer Black children were enrolled in drug or device trials (β = –0.80 [95% CI = –1.60 to –0.01]) than in other trials. Fewer Hispanic children were enrolled in early-phase than late-phase trials (β = –2.42 [95% CI = –3.66 to –1.19]). Conclusions: Enrollment in pediatric asthma trials conducted in the United States was commensurate with the demographics of children affected by asthma for most racial and ethnic groups, but American Indian or Alaska Native and Asian children were underrepresented. Concerted efforts are needed to promote inclusion of these underserved groups in future trials. © 2024 The Author(s)

关键词： Asthma disparities pediatric clinical trials

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Surface Oxygen Vacancy and Hydride Species on Ceria Are Detrimental to Acetylene Semihydrogenation Reaction

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ACS Catalysis 2023年第8期13卷 5213-5224页

作者： Li, Zhaorui Chen, Lu Wu, Zongfang Jia, AiPing Shi, Shucheng Zhang, Hui Wang, Jia Liu, Zhi Shao, Wei-Peng Yang, Fan Wu, Xin-Ping Gong, Xue-Qing Huang, Weixin Key Laboratory of Precise and Intelligent Chemistry iChEM Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes Department of Chemical Physics University of Science and Technology of China Hefei 230026 China Key Laboratory for Advanced Materials and Joint International Research Laboratory for Precision Chemistry and Molecular Engineering Feringa Nobel Prize Scientist Joint Research Center Centre for Computational Chemistry and Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai 200237 China Hefei National Research Center for Physical Science at the Microscale Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230026 China Key Laboratory of the Ministry of Education for Advanced Catalysis Materials Institute of Physical Chemistry Zhejiang Normal University Jinhua 321004 China School of Physical Science and Technology Shanghai Tech University Shanghai 201210 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China Dalian National Laboratory for Clean Energy Chinese Academy of Sciences Dalian 116023 China

Reactivity of OH and hydride species in oxide-catalyzed hydrogenation reactions has attracted great interest. Herein, we report a combined in situ spectroscopic characterization and density functional theory (DFT) calculation study of ceria-catalyzed acetylene semihydrogenation reaction. The ceria surface is fully hydroxylated during the adopted reaction condition. C₂H₂ adsorbs molecularly on the stoichiometric CeO₂ surface and hydrogenates with OH groups selectively to produce C₂H₄. Semihydrogenation of C₂H₂ to C₂H₄ with either OH groups or hydride species on ceria surfaces with surface oxygen vacancies proceeds more facilely than on a stoichiometric CeO₂ surface, but C₂H₄ adsorbs more strongly and further hydrogenates to C₂H₆ more facilely;moreover, dissociative adsorption of C₂H₂ to C₂H species occurs, which facilely hydrogenates with the hydride species eventually to form C₂H₆ and react with each other to produce oligomers, decreasing the catalytic selectivity and stability, respectively. These results demonstrate that the ceria catalyst with a stoichiometric surface is extremely selective in catalyzing C₂H₂ semihydrogenation reaction to C₂H₄, whereas surface oxygen vacancies or hydride species on ceria are harmful to the catalytic performance. © 2023 American Chemical Society.

关键词： active species DFT calculations heterogeneous catalysis in situ characterization reaction mechanism

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Urban Transportation Planning, Sustainable Cities, Big Data, Intelligent Transport Systems

SSRN

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SSRN 2024年

作者： Vieira, Túlio Moita, Gray Ribeiro, Renato Meireles, Magali Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Transport Engineering Department CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Institute of Mathematical Sciences and Informatics PUC-Minas Walter Ianni 255 Minas Gerais Belo Horizonte31980-110 Brazil

This paper presents a comprehensive study on the critical role of efficient transportation systems in urban settings, particularly in developing countries. It underscores the socio-economic and environmental impacts of inadequate urban planning and highlights the disproportionate effects on the most vulnerable populations. The study proposes a novel, low-cost framework that leverages advancements in artificial intelligence, information systems, and hardware to improve the efficiency and accessibility of public transportation systems. The framework enables the construction of automatic origin-destination matrices and continuous monitoring of key transportation indicators, facilitating more accurate and timely decision-making. The paper also presents a comparative analysis of the proposed methodology with existing ones in the literature, validating its effectiveness with field-collected data. The study concludes by emphasizing thepotential of technology in addressing urban transportation challenges, contributing to the broader goal of creating sustainable and inclusive cities. This work serves as a valuable tool for urban planners in resource-constrained settings, bridging the gap between advanced technology and urban planning. © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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