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iscience 2025年第6期28卷

作者： Wenjun Wang Zhenyuan Wang Qingkun Li Yizi Su Chao Zeng Bo Cheng State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing 100084 China Beijing Key Laboratory of Human–Computer Interaction Institute of Software Chinese Academy of Sciences Beijing 100190 China Automotive Software Innovation Center (Chongqing) Chongqing 401331 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Hami Vocational and Technical College Hami 839001 China

Level 3 automated driving systems require drivers to occasionally retake control, making it crucial to understand factors influencing takeover performance. This study investigates how single versus multiple non-driving-related tasks (NDRTs) affect driver behavior, perceptions, and takeover capability. Using a high-fidelity driving simulator with 32 participants, conditions varying NDRT types and task-switching frequency were examined. Results indicate that diverse NDRT engagement and frequent task switching significantly influence takeover performance. Moreover, our decision tree models highlighted the number of NDRT types and task-switching frequency as critical predictors of driver response effectiveness. These findings underscore the importance of accounting for task engagement diversity and switching behaviors in automated vehicle system design. This study provides practical implications for optimizing human-automation interactions and enhancing the safety of automated driving systems by managing drivers’ attentiveness for their in-cabin activities.

关键词： Applied sciences computer science Artificial intelligence

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

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arXiv 2023年

作者： Larsen, Kristoffer Zhao, Chen Keyak, Joyce Sha, Qiuying Paez, Diana Zhang, Xinwei Hung, Guang-Uei Zou, Jiangang Peix, Amalia Zhou, Weihua Department of Mathematical Sciences Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA United States Nuclear Medicine and Diagnostic Imaging Section Division of Human Health Department of Nuclear Sciences and Applications International Atomic Energy Agency Vienna Austria Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing China Department of Nuclear Medicine Chang Bing Show Chwan Memorial Hospital Changhua Taiwan Nuclear Medicine Department Institute of Cardiology La Habana Cuba Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Aims. Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient. Methods. 218 patients who underwent rest-gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at a 6±1 month follow-up. A multi-stage ML model was created by combining two ensemble models: Ensemble 1 was trained with clinical variables and ECG;Ensemble 2 included Ensemble 1 plus SPECT MPI features. Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary. The performance of the multi-stage model was compared with that of Ensemble models 1 and 2. Results. The response rate for CRT was 55.5% (n = 121) with overall male gender 61.0% (n = 133), an average age of 62.0±11.8, and LVEF of 27.7±11.0. The multi-stage model performed similarly to Ensemble 2 (which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy of 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65, respectively. However, the multi-stage model only required SPECT MPI data for 52.7% of the patients across all folds. Conclusions. By using rule-based logic stemming from uncertainty quantification, the multi-stage model was able to reduce the need for additional SPECT MPI data acquisition without sacrificing performance. © 2023, CC BY.

关键词： Machine learning

KinPred-RNA—kinase activity inference and cancer type classification using machine learning on RNA-seq data

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iscience 2024年第4期27卷 109333页

作者： Zhang, Yuntian Yao, Lantian Chung, Chia-Ru Huang, Yixian Li, Shangfu Zhang, Wenyang Pang, Yuxuan Lee, Tzong-Yi Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen 518172 China School of Medicine The Chinese University of Hong Kong Shenzhen 518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of Hong Kong Shenzhen 518172 China Department of Computer Science and Information Engineering National Central University Taoyuan 320953 Taiwan Division of Health Medical Intelligence Human Genome Center The Institute of Medical Science The University of Tokyo Tokyo Minato-ku Japan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan B) National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer. © 2024 The Authors

关键词： Cancer Machine learning

Cooperation among an anonymous group protected Bitcoin during failures of decentralization

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arXiv 2022年

作者： Blackburn, Alyssa Huber, Christoph Eliaz, Yossi Shamim, Muhammad S. Weisz, David Seshadri, Goutham Kim, Kevin Hang, Shengqi Aiden, Erez Lieberman The Center for Genome Architecture Baylor College of Medicine HoustonTX77030 United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Center for Theoretical Biological Physics Rice University HoustonTX77030 United States Institute for Markets and Strategy WU Vienna University of Economics and Business Vienna Austria Department of Physics University of Houston HoustonTX77204 United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Computer Science Rice University HoustonTX77030 United States

Bitcoin is a digital currency designed to rely on a decentralized, trustless network of anonymous agents. Using a pseudonymous-address-linking procedure that achieves >99% sensitivity and >99% specificity, we reveal that between launch (1/3/2009), and when the price reached $1 (2/9/2011), most bitcoin was mined by only sixty-four agents. This was due to the rapid emergence of Pareto distributions in bitcoin income, producing such extensive resource centralization that almost all contemporary bitcoin addresses can be connected to these top agents by a chain of six transactions. Centralization created a social dilemma. Attackers could routinely exploit bitcoin via a "51% attack", making it possible for them to repeatedly spend the same bitcoins. Yet doing so would harm the community. Strikingly, we find that potential attackers always chose to cooperate instead. We model this dilemma using an N-player Centipede game in which anonymous players can choose to exploit, and thereby undermine, an appreciating good. Combining theory and economic experiments, we show that, even when individual payoffs are unchanged, cooperation is more frequent when the game is played by an anonymous group. Although bitcoin was designed to rely on a decentralized, trustless network of anonymous agents, its early success rested instead on cooperation among a small group of altruistic founders. © 2022, CC BY.

关键词： Pareto principle

Adaptive optics two-photon microscopy for in vivo imaging of mouse retina 6

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Adaptive optics two-photon microscopy for in vivo imaging of...

Adaptive Optics and Wavefront Control for Biological Systems VI 2020

作者： Qin, Zhongya He, Sicong Chen, Congping Yang, Chao Yung, Jasmine Wu, Wanjie Leung, Christopher K. Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health School of Science and Institute for Advanced Study Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781510632592

Non-invasive retinal imaging has greatly facilitated the research of eye disease and neurodegenerative disorders in the central nervous system (CNS). Two-photon microscopy is a powerful tool for in vivo imaging of mouse retina because it provides intrinsic optical sectioning capability and the infrared laser is less likely to excite the photoreceptors. However, the dilated mouse eye has large optical aberrations, which must be corrected to achieve high-resolution or even diffraction-limited imaging. Here, we developed an adaptive optics (AO) two-photon microscope for in vivo imaging of retinal neurons through the eyeball of living mouse. We used the two-photon excited fluorescence signal of retina as the guide star to measure and correct the aberration of mouse eye. After AO correction, the fluorescence signal was increased by at least fivefold and the fine structures such as axons of retinal ganglion cells (RGC) were clearly resolved. To take advantage of the non-invasive high-resolution imaging, we demonstrated functional calcium imaging of RGC responding to the light stimulations. © 2020 SPIE.

关键词： Adaptive optics

A novel computational predictive biological approach distinguishes Integrin β1 as a salient biomarker for breast cancer chemoresistance

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Biochimica et Biophysica Acta - Molecular Basis of Disease 2023年第6期1869卷 166702页

作者： Das, Subhayan Kundu, Moumita Hassan, Atif Parekh, Aditya Jena, Bikash Ch. Mundre, Swati Banerjee, Indranil Yetirajam, Rajesh Das, Chandan K. Pradhan, Anjan K. Das, Swadesh K. Emdad, Luni Mitra, Pralay Fisher, Paul B. Mandal, Mahitosh School of Medical Science & Technology Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science & Engineering Indian Institute of Technology Kharagpur Kharagpur India Anant National University Gujarat Ahmedabad India School of Pharmacy Sister Nivedita University (Techno India Group) West Bengal Kolkata India Department of Human and Molecular Genetics Virginia Commonwealth University School of Medicine Richmond VA United States VCU Institute of Molecular Medicine Virginia Commonwealth University School of Medicine Richmond VA United States VCU Massey Cancer Center Virginia Commonwealth University School of Medicine Richmond VA United States

Chemoresistance is a primary cause of breast cancer treatment failure, and protein-protein interactions significantly contribute to chemoresistance during different stages of breast cancer progression. In pursuit of novel biomarkers and relevant protein-protein interactions occurring during the emergence of breast cancer chemoresistance, we used a computational predictive biological (CPB) approach. CPB identified associations of adhesion molecules with proteins connected with different breast cancer proteins associated with chemoresistance. This approach identified an association of Integrin β1 (ITGB1) with chemoresistance and breast cancer stem cell markers. ITGB1 activated the Focal Adhesion Kinase (FAK) pathway promoting invasion, migration, and chemoresistance in breast cancer by upregulating Erk phosphorylation. FAK also activated Wnt/Sox2 signaling, which enhanced self-renewal in breast cancer. Activation of the FAK pathway by ITGB1 represents a novel mechanism linked to breast cancer chemoresistance, which may lead to novel therapies capable of blocking breast cancer progression by intervening in ITGB1-regulated signaling pathways. © 2023 Elsevier B.V.

关键词： Breast cancer Cancer stem cell Chemoresistance Computational biology FAK signaling Integrin β1

Bayesian Multivariate Sparse Functional Principal Components Analysis with application to longitudinal microbiome multi-omics data

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arXiv 2021年

作者： Jiang, Lingjing Elord, Chris Kim, Jane J. Swafford, Austin D. Knight, Rob Thompson, Wesley K. Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego United States Julia Computing Department of Pediatrics University of California San Diego United States Center for Microbiome Innovation University of California San Diego United States Department of Computer Science and Engineering University of California San Diego United States Department of Bioengineering University of California San Diego United States

Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate Sparse Functional Principal Components Analysis (mSFPCA), extending existing SFPCA methods to simultaneously characterize multiple temporal trajectories and their inter-relationships. As with existing SFPCA methods, the mSFPCA algorithm characterizes each trajectory as a smooth mean plus a weighted combination of the smooth major modes of variation about the mean, where the weights are given by the component scores for each subject. Unlike existing SFPCA methods, the mSFPCA algorithm allows estimation of multiple trajectories simultaneously, such that the component scores, which are constrained to be independent within a particular outcome for identifiability, may be arbitrarily correlated with component scores for other outcomes. A Cholesky decomposition is used to estimate the component score covariance matrix efficiently and guarantee positive semi-definiteness given these constraints. Mutual information is used to assess the strength of marginal and conditional temporal associations across outcome trajectories. Importantly, we implement mSFPCA as a Bayesian algorithm using R and stan, enabling easy use of packages such as PSIS-LOO for model selection and graphical posterior predictive checks to assess the validity of mSFPCA models. Although we focus on application of mSFPCA to microbiome data in this paper, the mSFPCA model is of general utility and can be used in a wide range of real-world applications. © 2021, CC BY.

关键词： Trajectories

Comparing Large Language Model AI and human‑Generated Coaching Messages for Behavioral Weight Loss

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arXiv 2023年

作者： Huang, Zhuoran Berry, Michael P. Chwyl, Christina Hsieh, Gary Wei, Jing Forman, Evan M. Khoury College of Computer Sciences Northeastern University 440 Huntington Ave BostonMA02115 United States VA Connecticut Healthcare System West HavenCT06516 United States Portland Psychotherapy Clinic Research and Training Center 3700 N Williams Ave. PortlandOR97227 United States Department of Human Centered Design & Engineering University of Washington SeattleWA98195 United States School of Computing and Information Systems The University of Melbourne ParkvilleVIC3052 Australia Center for Weight Eating and Lifestyle Science Drexel University 3141 Chestnut Street Stratton Hall PhiladelphiaPA19104 United States Department of Psychological and Brain Sciences Drexel University 3141 Chestnut Street Stratton Hall PhiladelphiaPA19104 United States

Automated coaching messages for weight control can save time and costs, but their repetitive, generic nature may limit their effectiveness compared to human coaching. Large language model (LLM) based artificial intelligence (AI) chatbots, like ChatGPT, could offer more personalized and novel messages to address repetition with their data-processing abilities. While LLM AI demonstrates promise to encourage healthier lifestyles, studies have yet to examine the feasibility and acceptability of LLM-based BWL coaching. Eighty-seven adults in a weight-loss trial (BMI ≥ 27 kg/m2) rated ten coaching messages’ helpfulness (five human-written, five ChatGPT-generated) using a 5-point Likert scale, providing additional open-ended feedback to justify their ratings. Participants also identified which messages they believed were AI-generated. The evaluation occurred in two phases: messages in Phase 1 were perceived as impersonal and negative, prompting revisions for messages in Phase 2. In Phase 1, AI-generated messages were rated less helpful than human-written ones, with 66% receiving a helpfulness rating of 3 or higher. However, in Phase 2, the AI messages matched the human-written ones regarding helpfulness, with 82% scoring three or above. Additionally, 50% were misidentified as human-written, suggesting AI’s sophistication in mimicking human-generated content. A thematic analysis of open-ended feedback revealed that participants appreciated AI’s empathy and personalized suggestions but found them more formulaic, less authentic, and too data-focused. This study reveals the preliminary feasibility and perceived helpfulness of LLM AIs, like ChatGPT, in crafting potentially effective weight control coaching messages. Our findings also underscore areas for future enhancement. © 2023, CC BY.

关键词： Weight control

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

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arXiv 2022年

作者： Zheng, Kaizhong Yu, Shujian Li, Baojuan Jenssen, Robert Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam and the Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The School of Biomedical Engineering Fourth Military Medical University Xi’an China

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learning-based diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 3 baselines and 7 state-of-the-art brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at GitHub repository (https://***/SJYuCNEL/brain-and-Information-Bottleneck/). Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks