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Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma

CNS Oncology 2024年第1期13卷 2415285页

作者： Oshima, Sonoko Yao, Jingwen Bobholz, Samuel Nagaraj, Raksha Raymond, Catalina Teraishi, Ashley Guenther, Anna-Marie Kim, Asher Sanvito, Francesco Cho, Nicholas S C Eldred, Blaine S Connelly, Jennifer M Nghiemphu, Phioanh L Lai, Albert Salamon, Noriko Cloughesy, Timothy F LaViolette, Peter S Ellingson, Benjamin M UCLA Brain Tumor Imaging Laboratory (BTIL) Center for Computer Vision & Imaging Biomarkers University of California Los Angeles Los Angeles 90024 CA United States Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Bioengineering Henry Samueli School of Engineering & Applied Science University of California Los Angeles Los Angeles 90024 CA United States Department of Radiology Medical College of Wisconsin Milwaukee 53226 WI United States Medical Scientist Training Program David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States UCLA Neuro-Oncology Program David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Neurology Medical College of Wisconsin Milwaukee 53226 WI United States Department of Neurology David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Biophysics Medical College of Wisconsin Milwaukee 53226 WI United States Department of Biomedical Engineering Medical College of Wisconsin Milwaukee 53226 WI United States Department of Psychiatry & Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Neurosurgery David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States

Aim: A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Methods: Pre- and post-contrast T₁-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans. Univariate and multivariate Cox regression analyses were used to test the influence of contrast-enhancing tumor volume, total cellularity, mean Cyt and mean ECF at baseline, immediately post-treatment and the pre- and post-treatment rate of change in volume and cellularity on overall survival (OS). Results: Smaller Cyt and larger ECF after treatment were significant predictors of OS, independent of tumor volume and other clinical prognostic factors (HR = 3.23 × 10^-6, p < 0.001 and HR = 2.39 × 10⁵, p < 0.001, respectively). Both post-treatment volumetric growth rate and the rate of change in cellularity were significantly correlated with OS (HR = 1.17, p = 0.003 and HR = 1.14, p = 0.01, respectively). Conclusion: Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： MRI rad-path radiopathomic mapping recurrent glioblastoma survival tumor growth rate

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Magnetic anisotropy reversal driven by structural symmetry-breaking in monolayer α-RuCl3

arXiv

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arXiv 2022年

作者： Yang, Bowen Goh, Yin Min Sung, Suk Hyun Ye, Gaihua Biswas, Sananda Kaib, David A.S. Dhakal, Ramesh Yan, Shaohua Li, Chenghe Jiang, Shengwei Chen, Fangchu Lei, Hechang He, Rui Valentí, Roser Winter, Stephen M. Hovden, Robert Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Strasse 1 Frankfurt am Main60438 Germany Department of Physics Center for Functional Materials Wake Forest University Winston-SalemNC27109 United States Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China Department of Physics Cornell University IthacaNY United States School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada

The Kitaev model is a celebrated spin-½ model on the two-dimensional (2D) honeycomb lattice with bond-dependent Ising interactions1, which features a highly entangled quantum spin liquid (QSL) ground state, fractionalized Majorana excitations, and a series of magnetic-field-induced quantum phase transitions1-7. The search for materials realizing the Kitaev model has been an ongoing challenge for over a decade and may potentially lead to applications in fault-tolerant topological quantum computing8. Yet, the unavoidable presence of non-Kitaev interactions (Heisenberg, off-diagonal, next-nearest neighbor, etc.) almost always drives the ground state away from the QSL phase, and a careful tuning of the exchange parameters is needed9-12. α-RuCl3 is a layered van der Waals material that is a particularly promising candidate to realize Kitaev physics13-15. Although the ground state is zigzag (ZZ) antiferromagnetic (AFM), this ordering can be suppressed with the application of an ~ 6-8T in-plane magnetic field. The presence of a half-integer thermal quantum Hall effect has been reported in this intermediate phase at low temperature16,17, while an unusual continuum of magnetic excitations can be seen even without magnetic field that persists far above the Néel temperature (TN ~ 7-8K)18,19. Both observations hint at α-RuCl3 being in proximity to a QSL, making it a current subject of intense scrutiny. Yet from a theoretical point of view, a QSL induced by in-plane field generally cannot be accounted for, as most calculations for α-RuCl3 show Kitaev phases more broadly emerging from an out-of-plane field6,20-23. Due to the strong "easy-plane" magnetic anisotropy of bulk crystals, however, prohibitively high fields above 30T are required to access such states24-26. The strong coupling between the spin, charge, and lattice degrees of freedom in α-RuCl3 presents an exciting opportunity to tune its magnetic interactions via external perturbations. Although pressure27-29, strain30,

关键词： Ground state

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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Back Cover: Robust, Ultrafast and Reversible Photoswitching in Bulk Polymers Enabled by Octupolar Molecule Design (Angew. Chem. Int. Ed. 16/2024)

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Angewandte Chemie International Edition 2024年第16期63卷

作者： Long Fang Ziwei Lin Yang Zhang Bin Ye Jing Li Qishan Ran Xiaotong Wang Meijia Yang Prof. Zhongke Yuan Prof. Xiaofeng Lin Prof. Dingshan Yu Prof. Xudong Chen Prof. Quan Li Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education Key Laboratory of High Performance Polymer-based Composites of Guangdong Province GBRCE for Functional Molecular Engineering School of Chemistry Sun Yat-sen University Guangzhou 510006 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510275 China Institute of Applied Physics and Materials Engineering University of Macau Taipa Macao SAR 999078 China School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou 510006 China Guangdong Laboratory of Chemistry and Fine Chemical Industry Jieyang Center Jieyang 515200 China Institute of Advanced Materials and School of Chemistry and Chemical Engineering Southeast University Nanjing 211189 China Materials Science Graduate Program Kent State University Kent OH 44242 USA

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Rücktitelbild: Robust, Ultrafast and Reversible Photoswitching in Bulk Polymers Enabled by Octupolar Molecule Design (Angew. Chem. 16/2024)

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Angewandte Chemie 2024年第16期136卷

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Robust, Ultrafast and Reversible Photoswitching in Bulk Polymers Enabled by Octupolar Molecule Design

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Angewandte Chemie 2024年第16期136卷

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Author Correction: Generation of accurate, expandable phylogenomic trees with uDance

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Nature biotechnology 2024年第5期42卷 814页

作者： Metin Balaban Yueyu Jiang Qiyun Zhu Daniel McDonald Rob Knight Siavash Mirarab Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. School of Life Sciences Arizona State University Tempe AZ USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu. Department of Computer Science and Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu.

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AIR-CUSHION LANDING CRAFT NAVIGATION

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NAVAL ENGINEERS JOURNAL 1985年第4期97卷 248-260页

作者： GRAHAM, HR KIM, JC BAND, EGU FOWLER, AW Herbert R. Graham:received his degrees of B.S. in 1951 and M.S. in 1958 in aeronautical engineering from the Massachusetts Institute of Technology and the California Institute of Technology respectively. He also attended the Harvard Graduate School of Business Administration. He is presently a task manager at TRW Inc. McLean Virginia responsible for landing craft air cushion (LCAC) engineering support. Since joining TRW in 1967 he has had several technical project management and system engineering responsibilities in amphibious ships transportation and energy. He was responsible for the preliminary engineering design and cost estimates for tracked air cushion vehicles (TACVs). He has been active in several professional societies including ASNE and served as vice-chairman Los Angeles Section American Institute of Aeronautics and Astronautics. John C. Kim:received his degrees of B.S. in electrical engineering Tri-State University 1959 M.S. in electrical engineering Michigan State University 1960 and Ph.D. in electrical engineering Michigan State University. He is presently a senior staff engineer with TRW Inc. McLean Virginia where his technical experience has included communications system engineering and navigation system analysis. Since joining TRW in 1969 he has held numerous positions including section head project manager and department manager. His previous employment includes E-Systems/Melpar Division and Honeywell. Dr. Kim has been active in the IEEE Washington Chapter activities which included secretary vice-chairman and chairman of Systems Science and Cybernetics Group. Edward G.U. Band:received a B.S. degree in mechnical engineering in 1946 and a D.I.C in aeronautical engineering in 1947 at the City and Guilds College of London University. In 1951 he received an M.S. degree from Stevens Institute of Technology in fluid dynamics. After a career in the aircraft industry in England Canada and the U.S.A. he spent several years teaching in Chile and at Webb Institute of Naval Archi

Air cushion vehicles (ACVs) have operated successfully on commercial routes for about twenty years. The routes are normally quite short; the craft are equipped with radar and radio navigation aids and maintain continuous contact with their terminals. Navigation of these craft, therefore, does not present any unusual difficulty. The introduction of air cushion vehicles into military service, however, can present a very different picture, especially when external navigation aids are not available and the craft must navigate by dead reckoning. This paper considers the problems involved when navigating a high-speed air cushion vehicle by dead reckoning in conditions of poor visibility. A method is presented to assess the ACV's navigational capability under these circumstances. A figure of merit is used to determine the sensitivity of factors which affect navigation such as the range of visibility, point-to-point distance, speed, turning radius and accuracy of onboard equipment. The method provides simplistic but adequate answers and can be used effectively to compare the-capability and cost of alternative navigation concepts.

关键词： AIR CUSHION VEHICLES

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