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DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

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British Journal of Dermatology 2024年第SUPPLEMENT_1期191卷 i108–i108页

作者： Clarke, Emily L Magee, Derek Newton-Bishop, Julia Merchant, William Hall, Marlous Insall, Robert Maher, Nigel G Scolyer, Richard A Farnworth, Grace Ali, Anisah Saldanha, Gerald Bamford, Mark Sitcova, Eva Kujal, Petr O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds UK Division of Pathology and Data Analytics University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Leeds Institute for Medical Research University of Leeds Leeds UK Department of Clinical and Population Sciences Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Department of Cell and Developmental Biology University College London London UK Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney Sydney NSW Australia Leicester Cancer Research Centre University of Leicester Leicester UK Department of Histopathology University Hospitals of Leicester NHS Trust Leicester UK Department of Pathology Third Faculty of Medicine Charles University and Teaching Hospital Královské Vinohrady Prague Czechia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology and Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9%, respectively across, the five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34,P< 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83,P< 0.001). We have developed and performed initial evaluation of a CN

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Visual computing for archaeological artifacts with integral invariant filters in 3D

Visual computing for archaeological artifacts with integral ...

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2017 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2017

作者： Mara, H. Krmker, S. Heidelberg University IWR Interdisciplinary Center for Scientific Computing Germany FCGL Forensic Computational Geometry Laboratory VNGG - Visualization and Numerical Geometry Group Mathematikon Neuenheimer Feld 205 Heidelberg69120 Germany

ISBN: (纸本)9783038680376

3D-artifacts from ancient civilizations contain many different kinds of information in form of forensic trace evidence, e.g., tool marks from styli or fingerprints on wax sealings. These very fine structures are increasingly captured by various 3D-acquisiton techniques and stored as irregular meshes. We introduce filter algorithms for the processing of these datasets to finally extract meaningful information at predefined scales. Therefore, Multiscale-Integral Invariants (MSII) are introduced as robust filter methods with their four different variants, using volume, patch, surface and line integrals for their specific sensitivity on mean curvature, Gaussian curvature or noise detection. Smoothing as known from 2D-raster image processing cannot be applied directly. It needs adaptation to the irregular structure of the triangular grids describing 2D-manifolds in *** introduce a fast 1-ring smoothing with a skillful weighting by distance and area of the neighboring points and triangles. Finally, we apply our technique to the various motivating examples for showing the results as false color images with isolines, indicating the respective field of function values, e.g., curvature in various norms or correlations in the feature space. Smooth isolines are indicators for the successful removal of noise. We finally compare the fully automated results with a manual graphic rendering of a faded handwriting found in the tomb of the empress Gisela of Swabia. © 2017 GCH 2017 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Image processing

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Machine-learning based MRI neuro-anatomical signatures associated with cardiovascular and metabolic risk factors

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Alzheimer's & Dementia 2022年第S1期18卷

作者： Sindhuja Tirumalai Govindarajan Elizabeth Mamourian Guray Erus Ahmed Abdulkadir Randa Melhem Jimit Doshi Raymond Pomponio Duygu Tosun Yang An Aristeidis Sotiras Daniel S. Marcus Pamela J. LaMontagne Mark A. Espeland Colin L. Masters Paul Maruff Lenore J. Launer Jurgen Fripp Sterling C. Johnson John C. Morris Marilyn S. Albert R. Nick Bryan Mohamad Habes Haochang Shou David A. Wolk Ilya M. Nasrallah Christos Davatzikos Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of California San Francisco San Francisco CA USA Laboratory of Behavioral Neuroscience National Institute on Aging Intramural Research Program Baltimore MD USA Washington University in St. Louis School of Medicine St. Louis MO USA Wake Forest School of Medicine Winston-Salem NC USA Florey Institute of Neuroscience and Mental Health Parkville VIC Australia The Florey Institute of Neuroscience and Mental Health Melbourne VIC Australia Laboratory of Epidemiology and Population Sciences National Institute on Aging Baltimore MD USA CSIRO Health and Biosecurity Australian E-Health Research Centre Brisbane QLD Australia Wisconsin Alzheimer's Disease Research Center University of Wisconsin School of Medicine and Public Health Madison WI USA Knight Alzheimer Disease Research Center St. Louis MO USA Department of Neurology Division of Cognitive Neuroscience John’s Hopkins University School of Medicine Baltimore MD USA University of Pennsylvania Philadephia PA USA Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Sciences Center San Antonio TX USA Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Lower gray matter (GM) and higher white matter (WM) volumes have been observed in cognitively unimpaired people with cardiovascular and metabolic risk factors (CVMs) such as hypertension, hyperlipidemia, diabetes, obesity, and smoking [Erus 2015, Habes 2016]. However, these group-level observations do not explain the heterogeneous accumulated effects of CVMs on individual neuroanatomy. Multivariate CVM-related changes characterized at the individual level using machine learning (ML)-derived summary indices may better capture their differential roles in brain aging and dementia. In this work, we derive ML-based indices that summarize brain changes from MRI related to CVMs. Method Cognitively normal volunteers that were pooled and harmonized for the iSTAGING cohort were included in this study (N=24,902 from 8 independent studies, 54.5% female, average age=62.4, age range 45-75 years). Linear support vector classifiers were trained to discriminate between people with a specific CVM from those without the CVM using nested cross-validation for hyperparameter tuning and model selection. Input features (n=150) consisted of age, sex, intracranial volume, harmonized regional GM and WM volumes, and lobar volumes of WM hyperintensities. The resulting distance of the projection onto the vector perpendicular to the decision plane yields a continuous scalar representing ‘spatial pattern of abnormality for recognition’ (SPARE) of each CVM. SPARE-CVMs were computed for each participant and correlated with previously established imaging markers for brain age [Habes 2021] and Alzheimer’s disease (SPARE-AD)[Davatzikos 2009]. Effect sizes (Cohen’s d) were calculated to assess the utility of SPAREs with higher values indicating greater separability between the CVM+ and CVM- groups. Result Figure 1 shows the distribution, effect sizes and associated brain regions for each SPARE-CVM. SPARE-diabetes and SPARE-hypertension yielded the largest effect sizes. SPAREs for cooccurring

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI2

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Angewandte Chemie 2023年第30期135卷

作者： Dr. Daichi Kato Dr. Peng Song Dr. Hiroki Ubukata Haruki Taguro Dr. Cédric Tassel Dr. Kohei Miyazaki Prof. Takeshi Abe Dr. Kousuke Nakano Prof. Kenta Hongo Prof. Ryo Maezono Prof. Hiroshi Kageyama Department of Energy and Hydrocarbon Chemistry Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi Saitama 332-0012 Japan Contribution: Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Project administration (lead) Resources (supporting) Supervision (lead) Validation (equal) Visualization (equal) Writing - original draft (lead) Writing - review & editing (lead) School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Data curation (supporting) Formal analysis (supporting) Funding acquisition (supporting) Methodology (supporting) Software (supporting) Validation (supporting) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Writing - original draft (supporting) Contribution: Funding acquisition (supporting) Resources (supporting) Writing - original draft (supporting) Center for Basic Research on Materials (CBRM) National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan Contribution: Writing - original draft (supporting) Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (supporting)

Mixed-anion compounds have attracted growing attentions, but their synthesis is challenging, making a rational search desirable. Here, we explored LaF 3 -LaX 3 (X=Cl, Br, I) system using ab initio structure searches based on evolutionary algorithms, and predicted LaF 2 X and LaFX 2 (X=Br, I), which are respectively isostructural with LaHBr 2 and YH 2 I, consisting of layered La-F blocks with single and double ordered honeycomb lattices, separated by van der Waals gaps. We successfully synthesized these compounds: LaF 2 Br and LaFI 2 crystallize in the predicted structure, while LaF 2 I is similar to the predicted one but with different layer stacking. LaF 2 I exhibits fluoride ion conductivity comparable to that of non-doped LaF 3 , and has the potential to show better ionic conductivity upon appropriate doping, given the theoretically lower diffusion energy barrier and the presence of soft iodine anions. This study shows the structure prediction using evolutionary algorithms will accelerate the discovery of mixed-anion compounds in future, in particular those with an ordered anion arrangement.

关键词： Evolutionary Algorithm Fluoride Ion Conductor Mixed Anion

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Surrogate model of hybridized numerical relativity binary black hole waveforms

arXiv

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arXiv 2018年

作者： Varma, Vijay Field, Scott E. Scheel, Mark A. Blackman, Jonathan Kidder, Lawrence E. Pfeiffer, Harald P. Theoretical Astrophysics California Institute of Technology PasadenaCA91125 United States Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Center for Radiophysics and Space Research Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms’ length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at 20 Hz) for stellar mass binaries with total masses as low as 2.25 M. We include the ` ≤ 4 and (5, 5) spin-weighted spherical harmonic modes but not the (4, 1) or (4, 0) modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios q ≤ 8, and |χ1z|, |χ2z| ≤ 0.8, where χ1z (χ2z) is the spin of the heavier (lighter) BH in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches . 3 ×10−4 over the mass range 2.25M ≤ M ≤ 300M. At high masses (M & 40M), where the merger and ringdown are more prominent, we show roughly two orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal. Copyright © 2018, The Authors. All rights reserved.

关键词： Merging

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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IDEAS: Immersive dome experiences for accelerating science

arXiv

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arXiv 2019年

作者： Faherty, Jacqueline K. SubbaRao, Mark Wyatt, Ryan Ynnerman, Anders deGrasse Tyson, Neil Geller, Aaron Weber, Maria Rosenfield, Philip Stoeckle, Gabriel Weiskopf, Daniel Magnor, Marcus Williams, Peter K.G. Abbott, Brian Marchetti, Lucia Jarrrett, Thomas Fay, Jonathan Peek, Joshua Graur, Or Durrell, Patrick Homeier, Derek Preston, Heather Müller, Thomas Vos, Johanna M. Brown, David Godfrey, Paige Giorla Rice, Emily Gagliuffi, Daniella Bardalez Bock, Alexander American Museum of Natural History Hayden Planetarium Adler Planetarium International Planetarium Society California Academy of Sciences Linköping University Adler Planetarium University of Chicago AAS WorldWide Telescope Instituto de Astronomiá UNAM Natural History Museum Vienna Austria Visualization Research Center University of Stuttgart Computer Graphics Lab TU Braunschweig Center for Astrophysics | Harvard & Smithsonian American Astronomical Society American Museum of Natural History Hayden Planetarium University of Cape Town Iziko Planetarium & Digital Dome Space Telescope Science Institute Johns Hopkins University Youngstown State University Ward Beecher Planetarium Förderkreis Planetarium Göttingen Germany Calusa Nature Center & Planetarium Haus der Astronomie MPIA Microsoft Research Slooh CUNY Macaulay Honors College Scientific Computing and Imaging Institute University of Utah

Astrophysics lies at the crossroads of big datasets (such as the Large Synoptic Survey Telescope and Gaia), open source software to visualize and interpret high dimensional datasets (such as Glue, WorldWide Telescope, and OpenSpace), and uniquely skilled software engineers who bridge data science and research fields. At the same time, more than 4,000 planetariums across the globe immerse millions of visitors in scientific data. We have identified the potential for critical synergy across data, software, hardware, locations, and content that-if prioritized over the next decade-will drive discovery in astronomical research. Planetariums can and should be used for the advancement of scientific research. Current facilities such as the Hayden Planetarium in New York City, Adler Planetarium in Chicago, Morrison Planetarium in San Francisco, the Iziko Planetarium & Digital Dome Research Consortium in Cape Town, and visualization center C in Norrköping are already developing software which ingests catalogs of astronomical and multi-disciplinary data critical for exploration research primarily for the purpose of creating scientific storylines for the general public. We propose a transformative model whereby scientists become the audience and explorers in planetariums, utilizing software for their own investigative purposes. In this manner, research benefits from the authentic and unique experience of data immersion contained in an environment bathed in context and equipped for collaboration. Consequently, in this white paper we argue that over the next decade the research astronomy community should partner with planetariums to create visualization-based research opportunities for the field. Realizing this vision will require new investments in software and human capital. Copyright © 2019, The Authors. All rights reserved.

关键词： Planetariums

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Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Nature Mental Health

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Nature Mental Health 2024年 1120页

作者： Cynthia H. Y. Fu Danilo Arnone Roland Zahn Allan H. Young Mathilde Antoniades Guray Erus Jose A. Garcia Yong Fan Haochang Shou Ashish Singh Christos Davatzikos Stephen R. Arnott Stephen C. Strother Taolin Chen Kun Qin Qiyong Gong Ki Sueng Choi Helen S. Mayberg Cherise Chin Fatt Madhukar H. Trivedi Benicio N. Frey Vibe G. Frokjaer Melanie Ganz Gitte M. Knudsen Beata R. Godlewska Catherine J. Harmer Stefanie Hassel Keith Ho Susan Rotzinger Sidney H. Kennedy Andrew M. McIntosh Aleks Stolicyn Heather C. Whalley Matthew D. Sacchet Jonathan Savitz Teresa A. Victor Martin P. Paulus Irina Strigo Duygu Tosun Dongtao Wei Jiang Qiu Toby Wise Ian M. Anderson J. F. William Deakin Rebecca Elliott W. Edward Craighead Boadie W. Dunlop Ian H. Gotlib Chao-Gan Yan School of Psychology University of East London London UK Centre for Affective Disorders Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King’s College London London UK South London and Maudsley NHS Foundation Trust Bethlem Royal Hospital London UK Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA USA Rotman Research Institute Baycrest Centre Toronto Ontario Canada Department of Medical Biophysics University of Toronto Toronto Ontario Canada Huaxi MR Research Center Department of Radiology West China Hospital Sichuan University Chengdu China Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China Department of Radiology Taihe Hospital Hubei University of Medicine Shiyan China Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Center for Depression Research and Clinical Care University of Texas Southwestern Medical Center Dallas TX USA Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton Ontario Canada Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic St Joseph’s Healthcare Hamilton Hamilton Ontario Canada Neurobiology Research Unit University Hospital Rigshospitalet Copenhagen Denmark Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Psychiatry Psychiatric Centre Copenhagen Copenhagen Denmark Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Psychiatry University of Oxford Oxford UK Oxford Health NHS Foundation Trust Warneford Hospital Oxford UK Mathison Centre for Me

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A large-scale ENIGMA multisite replication study of brain age in depression

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Neuroimage: Reports 2022年第4期2卷 100149-100149页

作者： Han, Laura K.M. Dinga, Richard Leenings, Ramona Hahn, Tim Cole, James H. Aftanas, Lyubomir I. Amod, Alyssa R. Besteher, Bianca Colle, Romain Corruble, Emmanuelle Couvy-Duchesne, Baptiste Danilenko, Konstantin V. Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Goya-Maldonado, Roberto Groenewold, Nynke A. Hamilton, Paul Ichikawa, Naho Ipser, Jonathan C. Itai, Eri Koopowitz, Sheri-Michelle Li, Meng Okada, Go Okamoto, Yasumasa Churikova, Olga S. Osipov, Evgeny A. Penninx, Brenda W.J.H. Pomarol-Clotet, Edith Rodríguez-Cano, Elena Sacchet, Matthew D. Shinzato, Hotaka Sim, Kang Stein, Dan J. Uyar-Demir, Aslihan Veltman, Dick J. Schmaal, Lianne Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia Orygen Parkville VIC Australia Department of Cognitive Science & Artificial Intelligence Tilburg University Tilburg Netherlands Institute for Translational Psychiatry University of Münster Münster Germany Faculty of Mathematics and Computer Science University of Münster Münster Germany Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London United Kingdom Scientific Research Institute of Neuroscience and Medicine Novosibirsk Russian Federation Novosibirsk State University Novosibirsk Russian Federation Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town South Africa Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Service Hospitalo-Universitaire de Psychiatrie de Bicêtre Mood Center Paris Saclay Assistance Publique-Hôpitaux de Paris Hôpitaux Universitaires Paris-Saclay Hôpital de Bicêtre Le Kremlin Bicêtre F-94275 France MOODS Team INSERM 1018 CESP (Centre de Recherche en Epidémiologie et Santé des Populations) Université Paris-Saclay Faculté de Médecine Paris-Saclay Le Kremlin Bicêtre F-94275 France Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de La Pitié Salpêtrière Paris F-75013 France Institute for Molecular Bioscience The University of Queensland St Lucia 4072 QLD Australia FIDMAG Hermanas Hospitalarias Research Foundation Barcelona Spain CIBERSAM Barcelona Spain SoCAT Lab Department of Psychiatry School of Medicine Ege University Izmir Turkey Department of Psychology Stanford University Stanford CA United States Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab) University Medical Center Göttingen Göttingen Germany Center for Social and Affective Neuroscience Department o

Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases;+1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (***/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R² = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-s

关键词： Biological aging Brain age Depression ENIGMA consortium Replication study

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