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arXiv 2020年

作者： Kumar, Rajat Tan, Liang Kriegstein, Alan Lithen, Andrew Polimeni, Jonathan R. Strey, Helmut H. Mujica-Parodi, Lilianne R. Department of Biomedical Engineering Stony Brook University Stony BrookNY United States ALA Scientific Instruments Inc. FarmingdaleNY United States Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Harvard Medical School Massachusetts General Hospital CharlestownMA United States Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology CambridgeMA United States Laufer Center for Physical and Quantitative Biology Stony Brook University Stony BrookNY United States

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then quantified voxel-wise noise by comparing the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and dynamic fidelity that, unlike current measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four of the "high-performance" scanners with MR-physicist-optimized acquisition protocols, still showed scanner-instability/multiplicative noise contributions of about 8-20% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 12-22% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 3- to 4-fold increase in ST-SNR and about 40-70% increase in dynamic fide

关键词： Dynamics

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Creating a Faculty Learning Committee

Creating a Faculty Learning Committee

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Frontiers in Education (FIE) Conference

作者： Veronica van Montfrans Kate Williams Cristi Bell-Huff Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA Center for Teaching and Learning Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

The goal of this special session is to describe the design and implementation of faculty learning committees focused on disseminating information about the application of inclusive pedagogical practices in engineering classrooms. Assisted by funding from the National Science Foundation via the REvolutionizing engineering and computer science departments (RED) grant program, we developed multiple approaches to create change in ways that all members of the biomedical engineering department could participate. In one approach, we established a new kind of permanent faculty committee within our department: the Faculty Learning Committee (FLC). The FLC constitutes a completely different kind of experience in which faculty can fulfill their service commitment to the department. Unlike other committees which are almost exclusively focused on completion of tasks assigned by leaders in the department's administration, the FLC engages faculty in reading, discussion, and project planning around a shared topic of interest. FLCs are designed to engage faculty, create buy-in for change within the department through participation, create a community of fellow engineers that can rely on one another for discussion and ideas, and give the facilitators a platform to address difficult but necessary topics to support wide-spread departmental change. This session will review the success and challenges experienced through two iterations of this program. Participants will leave with resources to create their own effective faculty learning committee.

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Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling

arXiv

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arXiv 2023年

作者： Ligneul, Clémence Najac, Chloé Döring, André Beaulieu, Christian Branzoli, Francesca Clarke, William T. Cudalbu, Cristina Genovese, Guglielmo Jbabdi, Saad Jelescu, Ileana Karampinos, Dimitrios Kreis, Roland Lundell, Henrik Marjańska, Malgorzata Möller, Harald E. Mosso, Jessie Mougel, Eloïse Posse, Stefan Ruschke, Stefan Simsek, Kadir Szczepankiewicz, Filip Tal, Assaf Tax, Chantal Oeltzschner, Georg Palombo, Marco Ronen, Itamar Valette, Julien Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom C.J. Gorter MRI Center Department of Radiology Leiden University Medical Center Netherlands School of Psychology Cardiff University Cardiff United Kingdom Departments of Biomedical Engineering and Radiology University of Alberta EdmontonAB Canada Paris Brain Institute - ICM Sorbonne University UMR S 1127 Inserm U 1127 CNRS UMR 7225 ParisF-75013 France CIBM Center for Biomedical Imaging Switzerland Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Center for Magnetic Resonance Research Department of Radiology University of Minnesota MinneapolisMN United States Department of Radiology Lausanne University Hospital Lausanne Switzerland Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland Department of Diagnostic and Interventional Radiology Technical University of Munich Munich Germany MR Methodology Department for Diagnostic and Interventional Neuroradiology University of Bern Switzerland Swiss Institute for Translational and Entrepreneurial Medicine Bern Switzerland Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager anf Hvidovre Denmark Department for Health Technology Technical University of Denmark Lyngby Denmark NMR Methods & Development Group Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany LIFMET EPFL Lausanne Switzerland Université Paris-Saclay CEA CNRS MIRCen Laboratoires des Maladies Neurodégénératives Fontenay-aux-Roses France Department of Neurology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Physics and Astronomy University of New Mexico School of Medicine AlbuquerqueNM87131 United States School of Computer Science and Informatics Cardiff University Cardiff United Kin

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting and modelling dMRS data and provides links to useful resources. © 2023, CC BY-NC-ND.

关键词： Diffusion

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MODELING VISUAL MOTION PROCESSING DURING SMOOTH PURSUIT EYE MOVEMENTS USING RECURRENT NEURAL NETWORKS

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IBRO Neuroscience Reports 2023年 15卷 S799-S799页

作者： Min Ki Kim Joonyeol Lee Sung-Phil Kim Jeong-Woo Sohn Catholic Kwandong University The Institute Of Healthcare Convergence College Of Medicine Gangneung-si Korea Republic of Institute for Basic Science (IBS) Center For Neuroscience Imaging Research Suwon Korea Republic of Ulsan National Institute of Science and Technology Biomedical Engineering Ulsan Korea Republic of Catholic Kwandong University Department Of Medical Science Gangneung-si Korea Republic of

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Corrigendum to 'Injectable and high-strength PLGA/CPC loaded ALN/MgO bone cement for bone regeneration by facilitating osteogenesis and inhibiting osteoclastogenesis in osteoporotic bone defects' [Mater. Today Bio 26, June 2024, 101092]

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Materials today. Bio 2024年 28卷 101171页

作者： Lei Huang Peihao Cai Mengxuan Bian Jieqin Yu Lan Xiao Shunyi Lu Jiayi Wang Weisin Chen Guanjie Han Xingdong Xiang Xin Liu Libo Jiang Yulin Li Jian Zhang Department of Orthopedic Surgery Zhongshan Hospital Fudan University Shanghai 200032 China. The Key Laboratory for Ultrafine Materials of Ministry of Education Frontiers Science Center for Materiobiology and Dynamic Chemistry Engineering Research Center for Biomedical Materials of Ministry of Education School of Materials Science and Engineering East China University of Science and Technology Shanghai 200237 China. Department of Orthopedic Surgery Zhejiang Provincial Hospital of Chinese Medicine Hangzhou Zhejiang Province 310003 China. School of Medicine and Dentistry Griffith University Gold COast QLD 4222 Australia.

[This corrects the article DOI: 10.1016/***.2024.101092.].

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Photometric characterization of dopamine nucleus accumbens and calcium ventro tegmental area dynamics with behavioral outcomes - Impact of different medial forebrain bundle Deep Brain Stimulation parameters

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Brain Stimulation 2023年第1期16卷 264-264页

作者： Telega, Lidia Miguel Vajari, Danesh Ashouri Stieglitz, Thomas Coenen, Volker Arnold Döbrössy, Máté Daniel Laboratory of Stereotaxy and Interventional Neurosciences (SIN) Department of Stereotactic and Functional Neurosurgery University Freiburg Medical Center Germany Department of Stereotactic and Functional Neurosurgery University Freiburg Medical Center Germany Faculty of Biology University of Freiburg Germany BrainLinks-BrainTools IMBIT (Institute for Machine-Brain Interfacing Technology) University of Freiburg Germany Laboratory for Biomedical Microtechnology Department of Microsystems Engineering (IMTEK) University of Freiburg Germany Bernstein Center Freiburg University of Freiburg Germany Faculty of Medicine University of Freiburg Germany Center for Basics in Neuromodulation University of Freiburg Germany

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Learned end-to-end high-resolution lensless fiber imaging toward intraoperative real-time cancer diagnosis

arXiv

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arXiv 2022年

作者： Wu, Jiachen Wang, Tijue Uckermann, Ortrud Galli, Roberta Schackert, Gabriele Cao, Liangcai Czarske, Jürgen Kuschmierz, Robert Laboratory of Measurement and Sensor System Technique TU Dresden Helmholtzstrasse 18 Dresden01069 Germany State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instruments Tsinghua University Beijing100084 China Department of Neurosurgery University Hospital Carl Gustav Carus TU Dresden Dresden Germany Division of Medical Biology Department of Psychiatry Faculty of Medicine University Hospital Carl Gustav Carus TU Dresden Germany Department of Medical Physics and Biomedical Engineering Faculty of Medicine Carl Gustav Carus TU Dresden Germany Competence Center BIOLAS TU Dresden Dresden Germany Excellence Cluster Physics of Life TU Dresden Dresden Germany Faculty of Physics School of Science TU Dresden Dresden Germany

Endomicroscopy is indispensable for minimally invasive diagnostics in clinical practice. For optical keyhole monitoring of surgical interventions, high-resolution fiber endoscopic imaging is considered to be very promising, especially in combination with label-free imaging techniques to realize in vivo diagnosis. However, the inherent honeycomb-artifacts of coherent fiber bundles (CFB) reduce the resolution and limit the clinical applications. We propose an end-to-end lensless fiber imaging scheme toward intraoperative real-time cancer diagnosis. The framework includes resolution enhancement and classification networks that use single-shot fiber bundle images to provide both high-resolution images and tumor diagnosis result. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8% to 95.6%. The novel technique can enable histological real-time imaging through lensless fiber endoscopy and is promising for rapid and minimal-invasive intraoperative diagnosis in clinics. Copyright © 2022, The Authors. All rights reserved.

关键词： Fibers

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Controlled Deformation Mode and Amplitude of Liquid Crystal Actuators Through Orthogonal Light and Heat-Induced Exchanges

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Angewandte Chemie 2025年

作者： Jian Ding Tuan Liu Jinwen Zhang Yuzhan Li Xuepei Miao Caicai Li Wanqi Chen Baihang Chen Xinyi Huang Liangdong Zhang Kun Wang Zhixiang Dong Bingkun Bao Linyong Zhu Qiuning Lin Department School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200240 P.R. China School of Mechanical and Materials Engineering Composite Materials and Engineering Center Washington State University Pullman WA 99164 USA School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China School of Chemical Engineering and Materials Changzhou Institute of Technology Changzhou 213032 P.R. China SINOPEC Beijing Research Institute of Chemical Industry Beijing 100013 P.R. China College of Chemistry and Materials Science Shanghai Normal University Shanghai 200234 P.R. China

Liquid crystal elastomers (LCEs) are versatile soft actuators known for their flexible texture, low density, and ability to undergo reversible deformations that mimic the behavior of skeletal muscles. These properties make them highly attractive for applications in exoskeletons, soft robotics, and medical devices. However, their functionality is typically limited to simple and discontinuous deformations. This study introduces a novel structural design that enables precise control of both the mode and amplitude of deformation. This design integrates photo-reactive o -nitrobenzyl moieties and temperature-dependent hydrogen bonds into the LCE structure. The o -nitrobenzyl moieties enable irreversible reconfiguration of the LCE crosslinked network through photoreactions, allowing for easy alignment and reshaping of the material. Meanwhile, the hydrogen bonds act as “temperature-dependent locks”, regulating the mobility of polymer chains during thermal deformation. By adjusting the heating temperature, the deformation amplitude can be finely tuned across a wide range (0%–103%). The synergy of these two mechanisms—light-induced irreversible reconfiguration and temperature-induced reversible H-bond exchanges—empowers LCEs to achieve customizable and continuous deformations. This represents a significant advancement in bridging the gap between synthetic actuators and biological motion systems.

关键词： Hydrogen bond Liquid crystal elastomer Photocage Polyurethane Supramolecular polymer

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Assembly and breakage of head-to-head double hexamer reveals mpox virus E5-catalyzed DNA unwinding initiation

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Nature communications 2025年第1期16卷 5176页

作者： Yingxian Cheng Pu Han Qi Peng Ruili Liu Hao Liu Bin Yuan Yunfei Zhao Lu Kuai Jianxun Qi Kai Miao Yi Shi George Fu Gao Han Wang College of Veterinary Medicine Henan Agricultural University Zhengzhou Henan China. Beijing Life Science Academy Beijing China. CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of Microbiology Chinese Academy of Sciences Beijing China. Department of Infectious Diseases Shenzhen Children's Hospital Shenzhen 518038 China. Cancer Center Faculty of Health Sciences University of Macau Taipa Macau SAR China. Department of Biomedical Engineering College of Future Technology Peking University Beijing China. CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of Microbiology Chinese Academy of Sciences Beijing China. shiyi@***. CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of Microbiology Chinese Academy of Sciences Beijing China. gaof@***. Department of Biomedical Engineering College of Future Technology Peking University Beijing China. hanwang@***.

The replicative helicase-catalyzed unwinding of the DNA double helix is the initiation of DNA replication. Helicases and primases are functionally related enzymes that have even been expressed as fusion proteins in some organisms and viruses. However, the mechanism underlying DNA unwinding initiation by these helicase-primase fusion enzymes and the functional association between domains have not been elucidated. Herein, we report the cryo-EM structures of mpox virus E5, the founding member of these helicase-primase enzymes, in various enzymatic stages. Notably, E5 forms a head-to-head double hexamer encircling dsDNA, disrupted by the conformational rearrangement of primase domains upon nucleotide incorporation. Five E5-ssDNA-ATP structures further support an ATP cycle-driven non-classical escort model for E5 translocation. Finally, the helicase domain is found to enhance the primase function as a DNA scaffold. Together, our data shed light on the E5-mediated DNA unwinding model including dsDNA loading, DNA melting, ssDNA translocation, and provide a reasonable interpretation for evolutionary preservation of helicase-primase fusion from a functional perspective.

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Multi-Layer Pseudo-Supervision for histopathology Tissue semantic segmentation using patch-level classification labels

arXiv

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arXiv 2021年

作者： Han, Chu Lin, Jiatai Mai, Jinhai Wang, Yi Zhang, Qingling Zhao, Bingchao Chen, Xin Pan, Xipeng Shi, Zhenwei Xu, Xiaowei Yao, Su Yan, Lixu Lin, Huan Xu, Zeyan Huang, Xiaomei Han, Guoqiang Liang, Changhong Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Cardiovascular Institute Guangzhou China The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangdong Guangzhou510080 China The School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Cardiovascular Institute Guangdong Provincial Key Laboratory of South China Structural Heart Disease Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangdong Guangzhou510080 China The National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China The Department of Radiology Guangzhou First People's Hospital The Second Affiliated Hospital of South China University of Technology Guangzhou510180 China

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We proposed a two-step model including a classification and a segmentation phases. In the classification phase, we proposed a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms two state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling, our model can greatly save the annotation time from hours to minutes. The source code is available at: https://***/ChuHan89/*** Codes 68U10 © 2021, CC BY.

关键词： Semantic Segmentation

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