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Scientific discovery in the age of artificial intelligence

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Review of Image-Guided Percutaneous Coronary Interventions

Review of Image-Guided Percutaneous Coronary Interventions

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Advances in Science and engineering Technology International Conferences

作者： Aseel Alatoom Jumana Farhat Salam Dhou Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

Image-guided surgery (IGS) involves using surgical instruments tracking system to guide the surgeon during invasive surgical procedures. The use of preoperative or intraoperative images is very essential in providing navigation for different medical applications. In this paper, a literature review of different imaging modalities that aid in the diagnosis and treatment of coronary artery disease (CAD) is proposed. The imaging modalities discussed include x-ray fluoroscopy, coronary computed tomography angiography (CCTA), fractional flow reserve (FFR), cardiac single-photon emission computed tomography (SPECT), cardiovascular magnetic resonance imaging (CMRI) and ultrasound. Additionally, pseudoaneurysms, which can result from percutaneous coronary interventions (PCI) procedure, are reviewed as well as the treatment procedure. Finally, a comparison between the different imaging modalities is carried out, based on the number of papers that have been published, in addition to the sensitivity and specificity for each modality according to PubMed and Web of Science databases.

关键词： Coronary artery disease Image-guided interventions Percutaneous coronary intervention X-ray Computed tomography Fractional flow reserve Ultrasound Single photon emission computed tomography Magnetic resonance imaging Pseudoaneurysm

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Exploiting Approximations in Supervisory Control with Distinguishers

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IFAC-PapersOnLine 2018年第7期51卷 13-18页

作者： Rosa, Marcelo Teixeira, Marcelo Malik, Robi Graduate Program in Electrical Engineering Federal University of Technology Paraná Pato Branco Brazil Department of Computer Science University of Waikato Hamilton New Zealand

Distinguishers have been used in Supervisory Control Theory (SCT) of Discrete Event Systems, as a way to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. Despite the advantages, supervisors obtained from approximated DES plants can be suboptimal, so that the modeling structure has to be reconstructed empirically, until the proper supervisor is found. In this paper we show how to construct suitable-approximations that always lead to least restrictive controllable synthesis. A manufacturing system example is provided to illustrate the approach. © 2018

关键词： Discrete event simulation Automation Control engineering Control theory Controllability Manufacture Models Supervisory personnel Controllable synthesis Distinguishers Modeling structures Modeling task Supervisory control Supervisory control theory

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Measuring hand use in the home after cervical spinal cord injury using egocentric video

arXiv

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arXiv 2022年

作者： Bandini, Andrea Dousty, Mehdy Hitzig, Sander L. Craven, B. Catharine Kalsi-Ryan, Sukhvinder Zariffa, José KITE Research Institute Toronto Rehabilitation Institute University Health Network Canada The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Institute of Biomedical Engineering University of Toronto TorontoON Canada Rehabilitation Sciences Institute Temerty Faculty of Medicine University of Toronto TorontoON Canada St. John's Rehab Research Program Sunnybrook Research Institute Sunnybrook Health Sciences Centre TorontoON Canada Department of Occupational Science & Occupational Therapy Temerty Faculty of Medicine University of Toronto TorontoON Canada Brain and Spinal Cord Rehabilitation Program Toronto Rehabilitation Institute University Health Network TorontoON Canada Division of Physical Medicine and Rehabilitation Department of Medicine Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Physical Therapy University of Toronto TorontoON Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 hours of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc);the average duration of individual interactions (Dur);the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord Independent Measure - SCIM). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use. © 2022, CC BY-NC-ND.

关键词： Video recording

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A Clinical Breathomics Dataset

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Scientific data 2024年第1期11卷 203页

作者： Ping-Hung Kuo Yue-Chen Jhong Tien-Chueh Kuo Yu-Ting Hsu Ching-Hua Kuo Yufeng Jane Tseng National Taiwan University Hospital No. 1 Changde St. Zhongzheng Dist. Taipei City 100229 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. The Metabolomics Core Laboratory Center of Genomic Medicine National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Drug Research Center College of Pharmacy College of Medicine National Taiwan University No. 33 Linsen S. Road Taipei 10055 Taiwan. Department of Pharmacy School of Pharmacy College of Medicine National Taiwan University No. 33 Linsen S. Road Taipei 10055 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.

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Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors

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Journal of neuro-oncology 2025年 1-12页

作者： Gianluca Nocera Francesco Sanvito Jingwen Yao Sonoko Oshima Samuel A Bobholz Ashley Teraishi Catalina Raymond Kunal Patel Richard G Everson Linda M Liau Jennifer Connelly Antonella Castellano Pietro Mortini Noriko Salamon Timothy F Cloughesy Peter S LaViolette Benjamin M Ellingson UCLA Brain Tumor Imaging Laboratory (BTIL) Center for Computer Vision and Imaging Biomarkers University of California Los Angeles Los Angeles CA USA. Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. University Vita-Salute San Raffaele Milan Italy. Neuroradiology Unit and CERMAC IRCCS Ospedale San Raffaele Milan Italy. Department of Neurosurgery and Gamma Knife Radiosurgery IRCCS Ospedale San Raffaele Milan Italy. Department of Diagnostic Imaging and Nuclear Medicine Graduate School of Medicine Kyoto University Kyoto Japan. Department of Radiology Medical College of Wisconsin Milwaukee WI USA. Department of Neurosurgery David Geffen School of Medicine University of California Los Angeles CA USA. Department of Neurology Medical College of Wisconsin Milwaukee WI USA. Department of Neurology Ronald Reagan UCLA Medical Center University of California Los Angeles CA USA. David Geffen School of Medicine UCLA Neuro-Oncology Program University of California Los Angeles CA USA. Department of Biomedical Engineering Medical College of Wisconsin Milwaukee WI USA. UCLA Brain Tumor Imaging Laboratory (BTIL) Center for Computer Vision and Imaging Biomarkers University of California Los Angeles Los Angeles CA USA. bellingson@mednet.ucla.edu. Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. bellingson@mednet.ucla.edu. Department of Neurosurgery David Geffen School of Medicine University of California Los Angeles CA USA. bellingson@mednet.ucla.edu. Department of Bioengineering Henry Samueli School of Engineering and Applied Science University of California Los Angeles Los Angeles CA USA. bellingson@mednet.ucla.edu. Department of Psychiatry and Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles CA USA. bellingson@mednet.ucla.edu. UCLA Radiology UCLA Brain Tumor Imaging

PURPOSE:In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity. METHODS:A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations. RESULTS:Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations. CONCLUSION:MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this co

关键词： Artificial intelligence Diffusion imaging Glioma Histological validation Imaging biomarker

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Breast phantom design for X-ray phase-contrast imaging

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Research on biomedical engineering 2019年第1期35卷 21-26页

作者： Badelli, Juliana do Carmo Ribeiro-Junior, Sebastião Antoniassi, Marcelo Conceição, Andre Luiz Coelho Graduate Program in Electrical and Computer Engineering - UTFPR Av. Sete de Setembro 3165 CuritibaParaná80230-901 Brazil Electrical Engineering Department Federal University of Paraná Rodovia BR-116 Km 98 no. 8813 Jardim das Américas CuritibaParaná81531-980 Brazil Physics Department Federal University of Technology - Paraná Av. Sete de Setembro 3165 CuritibaParaná80230-901 Brazil Deutsches Elektronen-Synchrotron DESY Notkestraße 85 Hamburg22607 Germany

Purpose: Breast cancer is one of the most diffuse diseases and its incidence rate is increasing. Mammography is the gold standard exam for screening breast cancer. Nevertheless, to provide better conditions for visualization and detection of tumors, in particular to young women, techniques exploiting the X-ray phase contrast to generate images have been studied and proposed for clinical use. As every imaging modality clinically implemented, the capabilities and limitations of an X-ray phase-contrast system dedicated to breast imaging should be evaluated by a phantom. Although for mammography, there are several commercial phantoms, for tomographic X-ray phase-contrast imaging systems dedicated to breast screening, they are absent. Therefore, this study aimed to design a breast phantom for application in phase-contrast computed tomography (PC-CT) imaging. Methods: A breast phantom dedicated to X-ray phase-contrast imaging was designed. The phantom has a cylindrical shape and is composed by PMMA, to mimic the breast of a young woman, with some inserts filled with tissue substitutes to normal and pathological breast tissues, such as ethanol and glycerol. These materials were chosen due to the similarity in the attenuation and scattering properties of normal and pathological human breast tissues. Results: A comparison between tomographic X-ray absorption imaging and tomographic X-ray phase-contrast imaging showed a significant increase in edges definition, even with materials with similar attenuation properties. Conclusion: The results of this work reinforce the need for dedicated phantoms to exploit the features of each imaging modality more realistic. In particular, the breast phantom–designed breast screening by PC-CT allows exploiting the features of this new imaging modality. © 2019, Sociedade Brasileira de Engenharia Biomedica.

关键词： Phantoms

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Nano functional neural interfaces

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Nano Research 2018年第10期11卷 5065-5106页

作者： Yongchen Wang Hanlin Zhu Huiran Yang Aaron D. Argall Lan Luan Chong Xie Liang Guo Department of Biomedical Engineering The Ohio State University Columbus 43210 USA Department of Biomedical Engineering The University of Texas at Austin Austin 78712 USA Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing 211816 China Biomedical Sciences Graduate Program The Ohio State University Columbus 43210 USA Department of Neuroscience The Ohio State University Columbus 43210 USA

Engineered functional neural interfaces （fNIs） serve as essential abiotic-biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms, fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.

关键词： neural interface neurotechnology nanoelectrode nanomaterial neural recording neural stimulation

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