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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Synthesis of glycerol carbonate from transesterification of glycerol with dimethyl carbonate catalyzed by CaO from natural sources as green and economical catalyst

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materials Today: Proceedings 2018年第6期5卷 13909-13915页

作者： Wuttichai Roschat Sunti Phewphong Teadkait Kaewpuang Vinich Promarak School of Chemistry Institute of Science Suranaree University of Technology Nakhon Ratchasima 30000 Thailand Program of Chemistry Faculty of Science and Technology Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Center of Excellence on Alternative Energy Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Department of Material Science and Engineering Vidyasirimedhi Institute of Science and Technology 21210 Thailand

CaO catalysts derived from natural sources were investigated in the transesterification of dimethyl carbonate with glycerol to produce glycerol carbonate. The obtained CaO catalysts were characterized by several techniques, namely TG-DTA, XRD, XRF, BET surface area, SEM, FT-IR, CO 2 -TPD, and Hammett indicator test. The transesterification was simply monitored and analyzed by NMR technique. Under the optimized reaction conditions of dimethyl carbonate to glycerol molar ratio of 3:1, catalyst loading amount of 3 wt.% and reaction temperature of 80 °C; over 92.1% yield of glycerol carbonate product could be achieved within 2 h.

关键词： Glycerol carbonate CaO Heterogeneous catalyst Transesterification NMR analysis

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Recent advances in 3D models of the nervous system for neural regeneration research and drug development

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Acta biomaterialia 2025年

作者： Hui Zhu Cong Yao Zhengqi Xu Guojin Shang Jianhua Peng Huangfan Xie Tingyu Qian Zhennan Qiu Lidia Maeso Mao Mao Yucheng Liao Yong Jiang Dichen Li Gorka Orive Aldo R Boccaccini State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China National Medical Products Administration (NMPA) Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices Xi'an Jiaotong University Xi'an 710049 P. R. China State Industry-Education Integration Center for Medical Innovations Xi'an Jiaotong University Xi'an 710049 P. R. China. Electronic address: hui.zhu@***. State Industry-Education Integration Center for Medical Innovations Xi'an Jiaotong University Xi'an 710049 P. R. China. Department of Neurosurgery The Affiliated Hospital of Southwest Medical University Luzhou Sichuan 646000 China. NanoBioCel Research Group School of Pharmacy University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain. Precision Pharmacy and Drug Development Center Department of Pharmacy Tangdu Hospital Air Force Medical University Xi' an Shaanxi China. Electronic address: liaoyucheng0423@***. Department of Neurosurgery The Affiliated Hospital of Southwest Medical University Luzhou Sichuan 646000 China. Electronic address: jiangyong@***. NanoBioCel Research Group School of Pharmacy University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Bioaraba NanoBioCel Research Group Vitoria-Gasteiz Spain Biomedical Research Networking Centre in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN) Vitoria-Gasteiz Spain University Institute for Regenerative Medicine and Oral Implantology - UIRMI (UPV/EHU-Fundación Eduardo Anitua) Vitoria 01007 Spain. Institute of Biomaterials Department of Material Science and Engineering University of Erlangen-Nuremberg 91085 Erlangen Germany.

The development of drugs for nervous diseases poses distinctive difficulties owing to the incomplete understanding of the physiology and complex pathogenesis of the multifaceted central (CNS) and peripheral (PNS) nervous systems. Conventional animal tests and in vitro two-dimensional (2D) cell cultures fail to reproduce the sophisticated structure of natural human tissues, hindering the new drug discovery process. The emerging three-dimensional (3D) neural tissue models, including organoids, organ-on-chips and 3D-printed neural scaffolds, can provide an improved reproduction of the critical features, structural complexity, biological functions, dynamic circulation micro-environment and cell-matrix/cell interactions of the nervous systems. This review examines state-of-the-art 3D models for neural physiology/pathology, emphasizing their drug development applications. Fundamental advantages of various in vitro 3D neural models for investigating the mechanisms of nerve regeneration and disorders in both the CNS and PNS are compared in terms of the different modeling techniques. In addition, the applications of 3D neural models in drug development are summarized covering a range of areas such as disease modeling for basic research, pharmacokinetic and pharmacodynamic testing for drug screening and drug safety evaluation. Furthermore, current challenges and future outlook of biomimetic models and the existing bottlenecks hindering their successful translation into clinical use are discussed. STATEMENT OF SIGNIFICANCE: This review highlights the groundbreaking potential of 3D neural models-organoids, organ-on-chips, and 3D-printed scaffolds-to revolutionize neurological research and drug development. Unlike conventional methods, these models replicate the intricate structure and function of human nervous systems, enabling precise study of diseases like Alzheimer's, spinal injuries, and brain tumors. By synthesizing recent advancements, the review compares techniques, thei

关键词： 3D neural tissue models CNS/PNS Drug development Nervous system-on-chips Neural organoids

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Anti-Reflective Coating for Flexible Devices Using Plasma Enhanced Chemical Vapor Deposition Technique

Anti-Reflective Coating for Flexible Devices Using Plasma En...

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IEEE/LEOS International Conference on Optical MEMS

作者： Mahmuda Akter Monne Almusaied Zaid Dalim Mia Jagadish Khanal Alexender Zakhidov Maggie Yihong Chen Material Science Engineering and Commercialization Program Texas State University San Marcos TX USA Ingram School of Engineering Texas State University San Marcos TX USA Department of Physics Texas State University San Marcos TX USA

In this work, high performance anti-reflective coating (ARC) is achieved using plasma enhanced chemical vapor deposition (PECVD) on the flexible substrate at 100°C, which is the lowest growth temperature reported so far. PECVD is used to deposit double-layer ARC (DLARC) of SiOx and SiNx (wavelengths between 600 nm and 700 nm) on nylon-6. Optical parameters of different deposited layers are controlled by varying gas flow ratio, growth temperature, and chamber pressure. It has been observed that the bare nylon-6 has a reflection of about 4% itself, and with the DLARC the reflection of light is reduced by about 50%.

关键词： Artificial intelligence Three-dimensional displays

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

引用

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Adaptive in vivo Device for Theranostics of Inflammation by Monitoring of Cytokine Interferon-γ and Its Triggered Aspirin Release

SSRN

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SSRN 2019年

作者： Cao, Chaomin Jin, Ronghua Wei, Hui Liu, Zhongning Ni, Shengnan Chen, Xin Young, Howard A. Liu, Guojun Liu, Guozhen International Joint Research Center for Intelligent Biosensor Technology and Health Central China Normal University Wuhan430079 China School of Chemical Engineering and Technology Shanxi Key Laboratory of Energy Chemical Process Intensification Institute of Polymer Science in Chemical Engineering Xi’an Jiao Tong University Xi’an710049 China Department of Prosthodontics Peking University School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Engineering Laboratory for Digital and Material Technology of Stomatology Beijing Key Laboratory of Digital Stomatology Beijing100081 China Australian Nuclear Science and Technology Organization New Illuwarra Road Lucas HeightsNSW2234 Australia Graduate School of Biomedical Engineering ARC Centre of Excellent for Nanoscale Bipphotonics Faculty of Engineering University of New South Wales SydneyNSW2052 Australia Cancer and Inflammation Program Center for Cancer Research National Cancer Institute at Frederick FrederickMD21702 United States

Cytokines mediate and control immune and inflammatory responses. Complex interactions exist between cytokines, inflammation and the adaptive responses in maintaining homeostasis, health, and well being. On-demand, local delivery of anti-inflammatory drug molecules to target tissues provides a means for effective drug dosing while reducing the adverse effects of systemic drug delivery. This work demonstrates a proof-of-concept theranostic approach for inflammation based on analyte-kissing induced signaling, whereby a drug (in this report, aspirin) can be released upon detection of a target level of a proinfammatory cytokine(i.e. Interferon-γ (IFN-γ)) in real time. The structure-switching aptamer based biosensor described here is capable of quantitatively and dynamically detecting IFN-γ both in vitro and in vivo with a sensitivity of 10 pg mL-1. Moreover, the released aspirin triggered by the inflammatory cytokine IFN-γ is able to inhibit inflammation in the inflammatory rat model, and the releasing of aspirin can be quantitatively controlled. Herein research provides a novel and promising strategy for in vivo detection of proinflammatory cytokines and the subsequent therapeutic delivery of anti-inflammatory molecules. This universal theranostic platform is expected to achieve great potentials on personalized medicine towards treatment of various of diseases and health conditions. © 2019, The Authors. All rights reserved.

关键词： Pathology

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Resolving spin, valley, and moiré quasi-angular momentum of interlayer excitons in WSe2/WS2 heterostructures

arXiv

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arXiv 2019年

作者： Jin, Chenhao Regan, Emma C. Wang, Danqing Iqbal Bakti Utama, M. Yang, Chan-Shan Cain, Jeffrey Qin, Ying Shen, Yuxia Zheng, Zhiren Watanabe, Kenji Taniguchi, Takashi Tongay, Sefaattin Zettl, Alex Wang, Feng Department of Physics University of California at Berkeley BerkeleyCA94720 United States Graduate Group in Applied Science and Technology University of California at Berkeley BerkeleyCA94720 United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA94720 United States Institute of Electro-Optical Science and Technology Undergraduate Program of Electro-Optical Engineering National Taiwan Normal University Taipei11677 Taiwan Material Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kavli Energy NanoSciences Institute University of California Berkeley Lawrence Berkeley National Laboratory BerkeleyCA94720 United States School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan

Moiré superlattices provide a powerful way to engineer properties of electrons and excitons in two-dimensional van der Waals heterostructures1-11. The moiré effect can be especially strong for interlayer excitons, where electrons and holes reside in different layers and can be addressed separately. In particular, it was recently proposed that the moiré superlattice potential not only localizes interlayer exciton states at different superlattice positions, but also hosts an emerging moiré quasi-angular momentum (QAM) that periodically switches the optical selection rules for interlayer excitons at different moiré sites12,13. Here we report the observation of multiple interlayer exciton states coexisting in a WSe2/WS2 moiré superlattice and unambiguously determine their spin, valley, and moiré QAM through novel resonant optical pump-probe spectroscopy and photoluminescence excitation spectroscopy. We demonstrate that interlayer excitons localized at different moiré sites can exhibit opposite optical selection rules due to the spatially-varying moiré QAM. Our observation reveals new opportunities to engineer interlayer exciton states and valley physics with moiré superlattices for optoelectronic and valleytronic applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Angular momentum

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Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

npj metabolic health and disease

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npj metabolic health and disease 2024年第1期2卷 15页

作者： Annalise Schweickart Richa Batra Bryan J Neth Cameron Martino Liat Shenhav Anru R Zhang Pixu Shi Naama Karu Kevin Huynh Peter J Meikle Leyla Schimmel Amanda Hazel Dilmore Kaj Blennow Henrik Zetterberg Colette Blach Pieter C Dorrestein Rob Knight Suzanne Craft Rima Kaddurah-Daouk Jan Krumsiek Tri-Institutional Program in Computational Biology & Medicine Weill Cornell Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine Institute for Computational Biomedicine Englander Institute for Precision Medicine New York NY USA. Department of Neurology Mayo Clinic Rochester MN USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Microbiology New York University Grossman School of Medicine New York NY USA. Department of Biostatistics and Bioinformatics Duke University Durham NC USA. Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS) Hobart TAS Australia. Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC Australia. Baker Department of Cardiovascular Research Translation and Implementation La Trobe University Bundoora VIC Australia. Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA. Department of Psychiatry and Neurochemistry University of Gothenburg Gothenburg Sweden. Duke Molecular Physiology Institute Duke University Durham NC USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Departments of Pediatrics Computer Science and Engineering Bioengineering University of California San Diego La Jolla CA USA. Department of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston Salem NC USA. Duke Institute of Brain Sciences Duke University Durham NC USA. Department of Medicine Duke University Durham NC USA.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

关键词： Diseases Metabolomics

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Erratum: Extended Drude Model for Intraband-Transition-Induced Optical Nonlinearity [Phys. Rev. Applied 11, 064062 (2019)]

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Physical Review Applied 2020年第3期14卷 039901-039901页

作者： Heng Wang Kang Du Chuhao Jiang Zhiqiang Yang Lixia Ren Wending Zhang Soo Jin Chua Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University Xi’an 710129 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 LEES Program Singapore-MIT Alliance for Research & Technology (SMART) 1 CREATE Way #10-01 CREATE Tower Singapore 138602

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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