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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Chai, Rong Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D’Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States Center for Global Health Perelman School of Medicine University of Pennsylvani

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Deep learning

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Observation of indirect ionization of W7+ in EBIT plasma

引用

Journal of physics: Conference Series 2020年第13期1412卷

作者： Q Lu J He H Tian M Li Y Yang K Yao C Chen J Xiao J G Li B Tu Y Zou Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai 200433 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China Max-Planck-Institute für Kernphysik Saupfercheckweg 1 69117 Heidelberg Germany

In this work, visible and EUV spectra of W7+ have been measured using the high-temperature super-conducting electron-beam ion trap at the Shanghai EBIT laboratory under extremely low-energy conditions. The relevant atomic structure has been calculated by using the flexible atomic code package and the GRASP2K code. A hypothesis for the charge-state evolution of W7+ is proposed based on our results. The occurrence of W7+ ions results from indirect ionization caused by stepwise excitation between some metastable states of lower-charge-state W ions, at the nominal electron beam energy of 59 eV.

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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

引用

Physical Review Letters 2018年第14期120卷 143001-143001页

作者： Linfeng Zhang Jiequn Han Han Wang Roberto Car Weinan E Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing 100871 People’s Republic of China

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

关键词： Classical statistical mechanics Density functional theory First-principles calculations Interatomic & molecular potentials Learning Neuronal networks Potential energy surfaces Solid-state chemistry Thermal properties Van der Waals interaction Molecular dynamics

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Volume growth, curvature, and buser-type inequalities in graphs

arXiv

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arXiv 2018年

作者： Benson, Brian Ralli, Peter Tetali, Prasad Department of Mathematics University of California RiversideCA United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States School of Mathematics Georgia Institute of Technology AtlantaGA United States

We study the volume growth of metric balls as a function of the radius in discrete spaces, and focus on the relationship between volume growth and discrete curvature. We improve volume growth bounds under a lower bound on the so-called Ollivier curvature, and discuss similar results under other types of discrete Ricci curvature. Following recent work in the continuous setting of Riemannian manifolds (by the first author), we then bound the eigenvalues of the Laplacian of a graph under bounds on the volume growth. In particular, λ2of the graph can be bounded using a weighted discrete Hardy inequality and the higher eigenvalues of the graph can be bounded by the eigenvalues of a tridiagonal matrix times a multiplicative factor, both of which only depend on the volume growth of the graph. As a direct application, we relate the eigenvalues to the Cheeger isoperimetric constant. Using these methods, we describe classes of graphs for which the Cheeger inequality is tight on the second eigenvalue. We also describe a method for proving Buser's Inequality in graphs, particularly under a lower bound assumption on curvature. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Metadata for the Flux Density Calibration of the April 2018 Event Horizon Telescope Data

arXiv

引用

arXiv 2023年

作者： Koay, J.Y. Romero-Cañizales, C. Matthews, L.D. Janssen, M. Blackburn, L. Tilanus, R.P.J. Park, J. Asada, K. Matsushita, S. Baczko, A.-K. Bella, N. La Chan, C.-K. Crew, G.B. Fish, V. Patel, N. Ramakrishnan, V. Rottmann, H. Wagner, J. Wiik, K. Friberg, P. Goddi, C. Issaoun, S. Keating, G. Kim, J. Krichbaum, T.P. Marrone, D. Narayanan, G. Roy, A. Ruíz, I. Sánchez, S. Torne, P. Weintroub, J. Academia Sinica Institute of Astronomy and Astrophysics No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States Auf dem Hügel 69 Bonn53121 Germany Center for Astrophysics Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Astronomy and Space Science Kyung Hee University 1732 Deogyeong-daero Giheung-gu Gyeonggi-do Yongin-si17104 Korea Republic of Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Finnish Centre for Astronomy with ESO University of Turku FI-20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland Tuorla Observatory Department of Physics and Astronomy University of Turku Finland East Asian Observatory 660 N. A’ohoku Place HiloHI96720 United States INAF - Osservatorio Astronomico di Cagliari Via della Scienza 5 CA Selargius09047 Italy California Institute of Technology 1200 East California Boulevard PasadenaCA91125 United States Department of Astronomy University of Massachusetts AmherstMA01003 United States Institut de Radioastronomie Millimétrique Avenida Divina Pastora 7 Local 20 GranadaE-18012 Spain

The Event Horizon Telescope (EHT) observations carried out in 2018 April at 1.3 mm wavelengths included 9 stations in the array, comprising 7 single-dish telescopes and 2 phased arrays. The metadata package for the 2018 EHT observing campaign contains calibration tables required for the a-priori amplitude calibration of the 2018 April visibility data. This memo is the official documentation accompanying the release of the 2018 EHT metadata package, providing an overview of the contents of the package. We describe how telescope sensitivities, gain curves and other relevant parameters for each station in the EHT array were collected, processed, and validated to produce the calibration tables. Copyright © 2023, The Authors. All rights reserved.

关键词： Telescopes

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Regularized matrix data clustering and its application to image analysis

arXiv

引用

arXiv 2018年

作者： Gao, Xu Shen, Weining Ombao, Hernando Department of Statistics University of California IrvineCA United States Program on Applied Mathematics & Computational Science King Abdullah University of Science and Technology Saudi Arabia

In this paper, we propose a regularized mixture probabilistic model to cluster matrix data and apply it to brain signals. The approach is able to capture the sparsity (low rank, small/zero values) of the original signals by introducing regularization terms into the likelihood function. Through a modified EM algorithm, our method achieves the optimal solution with low computational cost. Theoretical results are also provided to establish the consistency of the proposed estimators. Simulations show the advantages of the proposed method over other existing methods. We also apply the approach to two real datasets from different experiments. Promising results imply that the proposed method successfully characterizes signals with different patterns while yielding insightful scientific interpretation. Copyright © 2018, The Authors. All rights reserved.

关键词： Matrix algebra

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Role of XUV Photons in Atomic High-Order Above-Threshold Ionization Processes in IR+XUV Two-Color Laser Fields

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Chinese physics Letters 2017年第11期34卷 15-19页

作者：张奎刘敏王兵兵郭迎春严宗朝陈京柳晓军 Laboratory of Optical Physics Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing 100190 Department of Physics East China Normal UniversityShanghai 200241 Department of Physics University of New BrunswickFrederictonNew BrunswickCanada State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and MathematicsChinese Academy of SciencesWuhan 430071 Center for Cold Atom Physics Chinese Academy of SciencesWuhan 430071 HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100871 Institute of Applied Physics and Computational Mathematics Beijing 100088

We investigate the above-threshold ionization of an atom in a combined infrared （IR） and extreme ultraviolet （XUV） two-color laser field and focus on the role of XUV field in the high-order above-threshold ionization （HATI） process. It is demonstrated that, in stark contrast to previous studies, the XUV laser may play a significant role in atomic HATI process, and in particular, the XUV laser can accelerate the ionized electron in a quantized way during the collision between the electron and its parent ion. This process cannot be explained by the elassical three-step model Our results indicate that the previously well-established concept that HATI is an elastic recollision process is broken down.

关键词： ATI Role of XUV Photons in Atomic High-Order Above-Threshold Ionization Processes in IR+XUV Two-Color Laser Fields IR

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Design, Synthesis, and In Vitro Characterization of a Tryptamine-Based Visible-Light Photoswitchable 5-HT2AR Ligand Showing Efficacy Preference for β-Arrestin over Mini-Gq

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Journal of medicinal chemistry 2025年

作者： Alexandra Sink Eline Pottie Samuel J Carter Robert J Tombari Verena Weber Paolo Carloni Giulia Rossetti David E Olson Christophe P Stove Michael Decker Institut für Pharmazie und Lebensmittelchemie Pharmazeutische und Medizinische Chemie Julius-Maximilians-Universität Würzburg (JMU) Am Hubland Würzburg 97074 Germany. Laboratory of Toxicology Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent B-9000 Belgium. Department of Chemistry University of California Davis One Shields Avenue Davis California 95616 United States. Institute for Psychedelics and Neurotherapeutics University of California Davis Davis California 95616 United States. Chemistry and Chemical Biology Graduate Program University of California Davis Davis California 95616 United States. Institute for Neuroscience and Medicine (INM-9) Computational Biomedicine Forschungszentrum Jülich Wilhelm-Johnen-Straße Jülich 52425 Germany. Department of Chemistry Faculty of Mathematics Computer Science and Natural Sciences RWTH Aachen University institution Aachen 52062 Germany. Department of Physics Faculty of Mathematics Computer Science and Natural Sciences RWTH Aachen University Aachen 52062 Germany. Jülich Supercomputing Center Forschungszentrum Jülich Wilhelm-Johnen-Straße Jülich 52425 Germany. Department of Neurology University Hospital Aachen RWTH Aachen University Pauwelsstraße 30 Aachen 52074 Germany. Department of Biochemistry & Molecular Medicine School of Medicine University of California Davis 2700 Stockton Blvd Suite 2102 Sacramento California 95817 United States. Center for Neuroscience University of California Davis 1544 Newton Ct Davis California 95618 United States.

The serotonin 2A receptor (5-HTR) modulates various neurotransmitter systems and is implicated in psychiatric disorders, including depression and schizophrenia. Despite progress, the detailed mechanisms of signaling at the 5-HTR and its therapeutic implications remain unclear, warranting further exploration. Overcoming the limitations of conventional pharmacology, photopharmacology addresses issues such as spatial selectivity and spatiotemporal resolution by incorporating light as an additional external control element. To study the roles of G protein- and β-arrestin2-dependent signaling pathways independently, we designed a photoswitchable, pathway-selective 5-HTR ligand. In radioligand binding studies, the -photoisomer has a greater affinity than the isomer at the 5-HTR and binds at nanomolar concentrations. In two highly analogous functional assays, the photoswitchable ligand showed a preference for β-arrestin2 recruitment over mini-Gα recruitment relative to LSD, providing a compelling tool for investigating the role of β-arrestin2 recruitment in 5-HTR signaling and elucidating its potential role in psychedelic effects.

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