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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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PB-09Alpha-synuclein Binds Unconventional Microtubules That Have a Unique Function

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Microscopy 2017年第SUPPL_1期66卷 i37-i37页

作者： Toba, Shiori Jin, Mingyue Yamada, Masami Matsumoto, Sakiko Yasunaga, Takuo Fukunaga, Yuko Miyazawa, Atsuo Kojima, Hiroaki Arai, Yoshiyuki Nagai, Takeharu Hirotsune, Shinji Osaka City Univ. Grad. Sch. of Medicine Present address: Dept. of Emergency Medical Technology Hirosaki University of Health and Welfare Junior College Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan JST-SENTAN JST-CREST Grad. Sch. of Life Science Univ. of Hyogo RSC-Univ. of Hyogo Leading Program Center RIKEN SPring-8 Center Advanced ICT Research Inst. National Inst. of Information and Communications Technology and Inst. of Scientific and Industrial Research Osaka Univ. Japan.

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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

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Data science in science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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Corrigendum to "A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer"

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Computational and structural biotechnology journal 2018年 16卷 196页

作者： M Q Yang D Li W Yang Y Zhang J Liu W Tong Joint Bioinformatics Graduate Program Department of Information Science George W. Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock USA. University of Arkansas for Medical Sciences 2801 S. University Ave Little Rock AR 72204 USA. School of Computer Science Carnegie Mellon University 5000 Forbes Ave Pittsburgh PA 15213 USA. Department of Statistics Harvard University Cambridge MA 02138 USA. Divisions of Bioinformatics and Biostatistics National Center for Toxicological Research US Food and Drug Administration 3900 NCTR Road Jefferson AR 72079 USA.

[This corrects the article DOI: 10.1016/***.2017.09.003.].

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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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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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Euclid: Early Release Observations – A preview of the Euclid era through a galaxy cluster magnifying lens

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Astronomy and Astrophysics 2025年 697卷

作者： Atek, H. Gavazzi, R. Weaver, J.R. Diego, J.M. Schrabback, T. Hatch, N.A. Aghanim, N. Dole, H. Hartley, W.G. Taamoli, S. Congedo, G. Jimenez-Teja, Y. Cuillandre, J.-C. Bañados, E. Belladitta, S. Bowler, R.A.A. Franco, M. Jauzac, M. Mahler, G. Richard, J. Rocci, P.-F. Serjeant, S. Toft, S. Abriola, D. Bergamini, P. Biviano, A. Dimauro, P. Ezziati, M. Golden-Marx, J.B. Grillo, C. Hughes, A.C.N. Kang, Y. Kneib, J.-P. Lombardi, M. Mamon, G.A. McPartland, C.J.R. Meneghetti, M. Miyatake, H. Montes, M. Mortlock, D.J. Oesch, P.A. Okabe, N. Rosati, P. Taylor, A.N. Tarsitano, F. Weller, J. Kluge, M. Laureijs, R. Paltani, S. Saifollahi, T. Schirmer, M. Stone, C. Mora, A. Altieri, B. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Bender, R. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Candini, G.P. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Douspis, M. Dubath, F. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Fotopoulou, S. Frailis, M. Franceschi, E. Galeotta, S. Gillard, W. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Hailey, M. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holliman, M.S. Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Aix-Marseille Université CNRS CNES LAM Marseille France Department of Astronomy University of Massachusetts AmherstMA01003 United States Instituto de Física de Cantabria Edificio Juan Jordá Avenida de los Castros Santander39005 Spain Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Switzerland Physics and Astronomy Department University of California 900 University Ave. RiversideCA92521 United States Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía Granada18080 Spain Observatorio Nacional Rua General Jose Cristino 77-Bairro Imperial de Sao Cristovao Rio de Janeiro20921-400 Brazil Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom The University of Texas at Austin AustinTX78712 United States Department of Physics Centre for Extragalactic Astronomy Durham University South Road DH1 3LE United Kingdom Department of Physics Institute for Computational Cosmology Durham University South Road DH1 3LE United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of

We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use imaging data from the Visible instrument (VIS) and the Near-Infrared Spectrometer and Photometer (NISP) to produce photometric catalogs for a total of ∼500 000 objects. The imaging data reach a typical depth of 5 σ in the range 25.1–25.4 AB in the NISP bands and 27.1–27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we searched for high-redshift galaxies. We identified 30 Lyman-break galaxy (LBG) candidates at z > 6 and 139 extremely red sources (ERSs), most of which likely lie at lower redshift. The VIS imaging is deeper than the NISP imaging, which means that we can routinely identify high-redshift Lyman-break galaxies at about a magnitude of 3, which reduces contamination by brown dwarf stars and low-redshift galaxies. The difficulty of spatially resolving most of these sources in 0′′ . 3 pix−1 imaging means that it is difficult to distinguish between galaxies and quasars. Spectroscopic follow-up campaigns of these bright sources will help us to constrain the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at z > 6, and it will constrain the physical nature of these objects. Additionally, we performed a combined strong- and weak-lensing analysis of A2390, and we show that Euclid will contribute to constraining the virial mass of galaxy clusters better. We also identify optical and near-infrared counterparts of known z > 0.6 clusters in these data. These counterparts exhibit strong-lensing features. This establishes that Euclid can characterize high-redshift clusters. Finally, we provide a glimpse of the ability of Euclid to map the intracluster light out to larger radii than current facilities, which enables us to understand the cluster assembly history better and to map the dark matter distribution. This ini

关键词： Moon

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Zeeman polaritons as a platform for probing Dicke physics in condensed matter

arXiv

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arXiv 2024年

作者： Kritzell, T. Elijah Doumani, Jacques Asano, Tobias Yamada, Sota Tay, Fuyang Xu, Hongjing Yan, Han Katayama, Ikufumi Takeda, Jun Nevidomskyy, Andriy Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Physics Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Miyagi Sendai980-8577 Japan Rice Advanced Materials Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd3Ga5O12. The electron paramagnetic resonance of Gd3+ ions is tuned by a magnetic field into resonance with a Fabry–Pérot cavity mode, resulting in the formation of spin–photon hybrid states, or Zeeman polaritons. We observe that the light–matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin–boson system is more compatible with the Dicke model and has advantages over boson–boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light–matter hybrids. Copyright © 2024, The Authors. All rights reserved.

关键词： Phonons

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Observation of the Magnonic Dicke Superradiant Phase Transition

arXiv

引用

arXiv 2024年

作者： Kim, Dasom Dasgupta, Sohail Ma, Xiaoxuan Park, Joong-Mok Wei, Hao-Tian Luo, Liang Doumani, Jacques Li, Xinwei Yang, Wanting Cheng, Di Kim, Richard H.J. Everitt, Henry O. Kimura, Shojiro Nojiri, Hiroyuki Wang, Jigang Cao, Shixun Bamba, Motoaki Hazzard, Kaden R.A. Kono, Junichiro Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Ames National Laboratory AmesIA50011 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics National University of Singapore 117551 Singapore Department of Physics and Astronomy Iowa State University AmesIA50011 United States Smalley–Curl Institute Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory-South HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Physics Yokohama National University Yokohama240-8501 Japan Department of Physics and Astronomy University of California DavisCA95616 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Two-level atoms coupled with single-mode cavity photons are predicted to exhibit a quantum phase transition when the coupling strength exceeds a critical value, entering a phase in which atomic polarization and photonic field are finite even at zero temperature and without external driving. However, this phenomenon, the superradiant phase transition (SRPT), is forbidden by a no-go theorem due to the existence of the diamagnetic term in the Hamiltonian. Here, we present spectroscopic evidence for a magnonic SRPT in ErFeO3, where the role of the photonic mode (two-level atoms) in the photonic SRPT is played by an Fe3+ magnon mode (Er3+ spins). The absence of the diamagnetic term in the Fe3+–Er3+ exchange coupling ensures that the no-go theorem does not apply. Terahertz and gigahertz magnetospectroscopy experiments revealed the signatures of the SRPT – a kink and a softening, respectively, of two spin–magnon hybridized modes at the critical point. Copyright © 2024, The Authors. All rights reserved.

关键词： Phase transitions

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Biomimetics: Biomimetic Microfingerprints for Anti‐Counterfeiting Strategies (Adv. Mater. 12/2015)

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Advanced Materials 2015年第12期27卷

作者： Hyung Jong Bae Sangwook Bae Cheolheon Park Sangkwon Han Junhoi Kim Lily Nari Kim Kibeom Kim Suk‐Heung Song Wook Park Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Center for Nanoparticle Research Institute for Basic Science (IBS) Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Institute of Entrepreneurial Bio Convergence Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Interdisciplinary Program for Bioengineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Department of Electronics and Radio Engineering Institute for Laser Engineering Kyung Hee University Deongyeong‐daero Giheung‐gu Yongin‐si Gyeonggi‐do 446‐701 South Korea

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