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arXiv 2018年

作者： Chai, Lucy R. Bassett, Danielle S. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania PhiladelphiaPA19104 United States Program in Machine Learning Speech and Language Technology Department of Engineering University of Cambridge CambridgeCB2 1ST United Kingdom Department of Physics & Astronomy College of Arts and Sciences University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical & Systems Engineering School of Engineering and Applied Sciences University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Language is hierarchically organized: words are built into phrases, sentences, and paragraphs to represent complex ideas. A similar hierarchical structure is observed across many other biological, electronic, and transportation networks supporting complex functions. Here we ask whether the organization of language in written text displays fractal hierarchical architecture. Specifically, we test two hypotheses: (i) that the structure of the exposition in scientific research articles displays the Rentian scaling principle, which marks hierarchical fractal-like structure, and (ii) that the exponent of the scaling law changes as the article is revised to maximize information transmission. Using 32 scientific manuscripts – each containing between three and 26 iterations of revision – we construct semantic networks in which nodes represented unique words in each manuscript, and in which edges connect nodes if two words appeared within the same 5-word window. We show that these semantic networks modeling the content of scientific articles display clear Rentian scaling, and that the Rent exponent varies over the publication life cycle, from the first draft to the final revision. Furthermore, we observe that manuscripts fell into three clusters in terms of how the scaling exponents changed across drafts: exponents rising over time, falling over time, and remaining relatively stable over time. This change in exponent reflects the evolution in semantic network structure over the manuscript revision process, highlighting a balance between network complexity, which increases the exponent, and network efficiency, which decreases the exponent. Lastly, the final value of the Rent exponent is negatively correlated with the number of authors. Taken together, our results suggest that semantic networks reflecting the structure of exposition in scientific research articles display striking hierarchical architecture that arbitrates trade-offs between competing constraints on network orga

关键词： Complex networks

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Transcriptional programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution

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CELL REPORTS 2020年第6期30卷 1767-+页

作者： Aynaud, Marie-Ming Mirabeau, Olivier Gruel, Nadege Grossetete, Sandrine Boeva, Valentina Durand, Simon Surdez, Didier Saulnier, Olivier Zaidi, Sakina Gribkova, Svetlana Fouche, Aziz Kairov, Ulykbek Raynal, Virginie Tirode, Franck Gruenewald, Thomas G. P. Bohec, Mylene Baulande, Sylvain Janoueix-Lerosey, Isabelle Vert, Jean-Philippe Barillot, Emmanuel Delattre, Olivier Zinovyev, Andrei Inst Curie SIREDO Oncol Ctr Quipe Labellisee LNCC INSERMU830 F-75005 Paris France PSL Res Univ Inst Curie Dept Translat Res F-75005 Paris France Univ Paris 05 CNRS Inst Cochin INSERMUMR 8104U1016UMR S1016 F-75014 Paris France Swiss Fed Inst Technol Inst Machine Learning Swiss Inst Bioinformat SIB Dept Comp Sci CH-8092 Zurich Switzerland INSERM U900 F-75005 Paris France PSL Res Univ Mines ParisTech CBIO Ctr Computat Biol F-75006 Paris France PSL Res Univ Inst Curie F-75005 Paris France Univ Paris 07 F-75013 Paris France Ecole Normale Super Paris Saclay F-94230 Cachan France Nazarbayev Univ Natl Lab Astana Ctr Life Sci Lab Bioinformat & Syst Biol Nur Sultan Kazakhstan Univ Claude Bernard Lyon 1 Univ Lyon CNRS 5286 INSERMU1052Canc Res Ctr Lyon F-69008 Lyon France Inst Curie NGS Platform F-75005 Paris France Brain Team F-75009 Paris France Lunenfeld Tanenbaum Res Inst Toronto ON M5G 1X5 Canada Hosp Sick Children Dev & Stem Cell Biol Program Toronto ON M5G 0A4 Canada Hosp Sick Children Arthur & Sonia Labatt Brain Tumour Res Ctr Toronto ON M5G 0A4 Canada Ludwig Maximilians Univ Munchen Inst Pathol Max Eder Res Grp Pediat Sarcoma Biol Fac Med Munich Germany German Canc Consortium DKTK Partner Site Munich Munich Germany German Canc Res Ctr Heidelberg Germany

EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation. However, it remains elusive how genetically homogeneous EwS cells can drive the heterogeneity of transcriptional programs. Here, we combine independent component analysis of single-cell RNA sequencing data from diverse cell types and model systems with time-resolved mapping of EWSR1-FLI1 binding sites and of open chromatin regions to characterize dynamic cellular processes associated with EWSR1-FLI1 activity. We thus define an exquisitely specific and direct enhancer-driven EWSR1-FLI1 program. In EwS tumors, cell proliferation and strong oxidative phosphorylation metabolism are associated with a well-defined range of EWSR1-FLI1 activity. In contrast, a subpopulation of cells from below and above the intermediary EWSR1-FLI1 activity is characterized by increased hypoxia. Overall, our study reveals sources of intratumoral heterogeneity within EwS tumors.

关键词： single-cell RNA-sequencing Ewing sarcoma EWSR1-FLI1 intratumor heterogeneity patient-derived xenografts Independent Component Analysis time series transcriptomics

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EMPRESS. X. Spatially resolved mass-metallicity relation in extremely metal-poor galaxies: Evidence of episodic star-formation fueled by a metal-poor gas infall

arXiv

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arXiv 2024年

作者： Nakajima, Kimihiko Ouchi, Masami Isobe, Yuki Xu, Yi Ozaki, Shinobu Nagao, Tohru Inoue, Akio K. Rauch, Michael Kusakabe, Haruka Onodera, Masato Nishigaki, Moka Ono, Yoshiaki Sugahara, Yuma Hattori, Takashi Hirai, Yutaka Hashimoto, Takuya Kim, Ji Hoon Moriya, Takashi J. Yanagisawa, Hiroto Aoyama, Shohei Fujimoto, Seiji Fukushima, Hajime Fukushima, Keita Harikane, Yuichi Hatano, Shun Hayashi, Kohei Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Motohara, Kentaro Murai, Kai Nagamine, Kentaro Nakane, Minami Saito, Tomoki Sasaki, Rin Shibuya, Takatoshi Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Yajima, Hidenobu Zhang, Yechi National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Osawa 2-21-1 Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cavendish Laboratory University of Cambridge 19 JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Observatoire de Genève Université de Genève 51 Chemin de Pégase Versoix1290 Switzerland 650 North Aohoku Place HiloHI96720 United States Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Cent

Using the Subaru/FOCAS IFU capability, we examine the spatially resolved relationships between gas-phase metallicity, stellar mass, and star-formation rate surface densities (Σ★ and ΣSFR, respectively) in extremely metal-poor galaxies (EMPGs) in the local universe. Our analysis includes 24 EMPGs, comprising 9,177 spaxels, which span a unique parameter space of local metallicity (12 + log(O/H) 6.9 to 7.9) and stellar mass surface density (Σ★ ∼ 105 to 107 Mo kpc−2), extending beyond the range of existing large integral-field spectroscopic surveys. Through spatially resolved emission line diagnostics based on the [N ii] BPT-diagram, we verify the absence of evolved active galactic nuclei in these EMPGs. Our findings reveal that, while the resolved mass-metallicity relation exhibits significant scatter in the low-mass regime, this scatter is closely correlated with local star-formation surface density. Specifically, metallicity decreases as ΣSFR increases for a given Σ★. Notably, half of the EMPGs show a distinct metal-poor horizontal branch on the resolved mass-metallicity relation. This feature typically appears at the peak clump with the highest Σ★ and ΣSFR and is surrounded by a relatively metal-enriched ambient region. These findings support a scenario in which metal-poor gas infall fuels episodic star formation in EMPGs, consistent with the kinematic properties observed in these systems. In addition, we identify four EMPGs with exceptionally low central metallicities (12 + log(O/H) 7.2), which display only a metal-poor clump without a surrounding metal-rich region. This suggests that such ultra-low metallicity EMPGs, at less than a few percent of the solar metallicity, may serve as valuable analogs for galaxies in the early stages of galaxy evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectroscopic analysis

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EMPRESS. IX. Extremely Metal-poor Galaxies are Very Gas-rich Dispersion-dominated Systems: Will the James Webb Space Telescope Witness Gaseous Turbulent High-z Primordial Galaxies?

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ASTROPHYSICAL JOURNAL 2023年第2期951卷 102-102页

作者： Isobe, Yuki Ouchi, Masami Nakajima, Kimihiko Ozaki, Shinobu Bouche, Nicolas F. Wise, John H. Xu, Yi Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Univ Tokyo Inst Cosm Ray Res 5-1-5 Kashiwanoha Kashiwa Chiba 2778582 Japan Univ Tokyo Grad Sch Sci Dept Phys 7-3-1 HongoBunkyo Tokyo 1130033 Japan Natl Astron Observ Japan 2-21-1 Osawa Mitaka Tokyo 1818588 Japan Univ Tokyo Kavli Inst Phys & Math Universe WPI Kashiwa Chiba 2778583 Japan Univ Lyon 1 Univ Lyon Ctr Rech Astrophys Lyon ENS LyonCNRSUMR 5574 F-69230 St Genis Laval France Georgia Inst Technol Ctr Relativist Astrophys Sch Phys Atlanta GA 30332 USA Univ Tokyo Grad Sch Sci Dept Astron 7-3-1 HongoBunkyo Tokyo 1130033 Japan European Southern Observ Karl Schwarzschild Str 2 D-85748 Garching Germany Univ Geneva Observ Geneve 51 Ch Maillettes CH-1290 Versoix Switzerland Natl Inst Nat Sci NINS Natl Astron Observ Japan Subaru Telescope 650 North Aohoku Pl Hilo HI 96720 USA Ehime Univ Res Ctr Space & Cosm Evolut Bunkyo Cho 2-5 Matsuyama Ehime 7908577 Japan Univ Tsukuba Ctr Computat Sci Ten Nodai 1-1-1 Tsukuba Ibaraki 3058577 Japan UCL Dept Phys & Astron Gower St London WC1E 6BT England Ichinoseki Coll Natl Inst Technol Ichinoseki 0218511 Japan Tohoku Univ Astron Inst 6-3 AobaAoba Ku Sendai Miyagi 9808578 Japan Univ Notre Dame Dept Phys & Astron 225 Nieuwland Sci Hall Notre Dame IN 46556 USA Seoul Natl Univ Dept Phys & Astron Astron Program 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ SNU Astron Res Ctr 1 Gwanak Ro Seoul 08826 South Korea Univ Wisconsin Dept Astron 475 N Charter St Madison WI 53706 USA Osaka Univ Grad Sch Sci Dept Earth & Space Sci Theoret Astrophys 1-1 Machikaneyama Toyonaka Osaka 5600043 Japan Univ Nevada Dept Phys & Astron 4505 S Maryland Pkwy Las Vegas NV 89154 USA Kitami Inst Technol 165 Koen Cho Kitami Hokkaido 0908507 Japan Waseda Univ Waseda Res Inst Sci & Engn Fac Sci & Engn 3-4-1 OkuboShinjuku Tokyo 1698555 Japan Chiba Univ Inst Management & Informat Technol 1-33 Yayoi Cho Chiba 2638522 Japan Cosm DAWN Ctr Copenhagen Denmark Univ Copenhagen Niels Bohr Inst Lyngbyvej 2

We present kinematics of six local extremely metal-poor galaxies (EMPGs) with low metallicities (0.016-0.098 Z (& ODOT;)) and low stellar masses (10(4.7)-10(7.6) M (& ODOT;)). Taking deep medium/high-resolution (R & SIM;7500) integral-field spectra with 8.2 m Subaru, we resolve the small inner velocity gradients and dispersions of the EMPGs with H & alpha;emission. Carefully masking out substructures originating by inflow and/or outflow, we fit three-dimensional disk models to the observed H & alpha;flux, velocity, and velocity dispersion maps. All the EMPGs show rotational velocities (v (rot)) of 5-23 km s(-1) smaller than the velocity dispersions (& sigma;(0)) of 17-31 km s(-1), indicating dispersion-dominated (v (rot)/& sigma;(0) = 0.29-0.80 < 1) systems affected by inflow and/or outflow. Except for two EMPGs with large uncertainties, we find that the EMPGs have very large gas-mass fractions of f (gas) & SIME;0.9-1.0. Comparing our results with other H & alpha;kinematics studies, we find that v (rot)/& sigma;(0) decreases and f (gas) increases with decreasing metallicity, decreasing stellar mass, and increasing specific star formation rate. We also find that simulated high-z (z & SIM;7) forming galaxies have gas fractions and dynamics similar to the observed EMPGs. Our EMPG observations and the simulations suggest that primordial galaxies are gas-rich dispersion-dominated systems, which would be identified by the forthcoming James Webb Space Telescope observations at z & SIM;7.

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Detection of RS Oph with LST-1 and modelling of its HE/VHE gamma-ray emission

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ASTRONOMY & ASTROPHYSICS 2025年 695卷

作者： Abe, K. Abe, S. Abhishek, A. Acero, F. Aguasca-Cabot, A. Agudo, I. Alispach, C. Alvarez Crespo, N. Ambrosino, D. Antonelli, L. A. Aramo, C. Arbet-Engels, A. Arcaro, C. Asano, K. Aubert, P. Baktash, A. Balbo, M. Bamba, A. Baquero Larriva, A. Barres de Almeida, U. Barrio, J. A. Barrios Jimenez, L. Batkovic, I. Baxter, J. Becerra Gonzalez, J. Bernardini, E. Bernete, J. Berti, A. Bezshyiko, I. Bhattacharjee, P. Bigongiari, C. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Borkowski, G. Brunelli, G. Bulgarelli, A. Bunse, M. Burelli, I. Burmistrov, L. Buscemi, M. Cardillo, M. Ff, S. Caro Carosi, A. Carrasco, M. S. Cassol, F. Castrejon, N. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chon, G. Chytka, L. Cicciari, G. M. Cifuentes, A. Contreras, J. L. Cortina, J. Costantini, H. Da Vela, P. Dalchenko, M. Dazzi, F. De Angelis, A. de lavergne, M. de Bony De Lotto, B. de Menezes, R. Del Burgo, R. Del Peral, L. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Di Piano, A. Di Pierro, F. Di Tria, R. Di Venere, L. Diaz, C. Dominik, R. M. Prester, D. Dominis Donini, A. Dore, D. Dorner, D. Doro, M. Eisenberger, L. Elsasser, D. Emery, G. Escudero, J. Ramazani, V. Fallah Ferrarotto, F. Fiasson, A. Foffano, L. Freixas Coromina, L. Frose, S. Fukazawa, Y. Garcia Lopez, R. Gasbarra, C. Gasparrini, D. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Green, D. Green, J. Gunji, S. Guenther, P. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Iarlori, M. Imazawa, R. Inada, T. Inome, Y. Inoue, S. Ioka, K. Iori, M. Iuliano, A. Jahanvi, J. Martinez, I. Jimenez Jimenez Quiles, J. Jurysek, J. Kagaya, M. Kalashev, O. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kohri, K. Kong, A. Kubo, H. Kushida, J. Lacave, B. Lainez, M. Lamanna, G. Lamastra, A. Lemoigne, L. Linhoff, M. Longo, F. Lopez-Coto, R. Lopez-Moya, M. Lopez-O Tokai Univ Dept Phys 4-1-1 Kita Kaname Hiratsuka Kanagawa 2591292 Japan Univ Tokyo Inst Cosm Ray Res 5-1-5 Kashiwa No Ha Kashiwa Chiba 2778582 Japan INFN Via Roma 56 I-53100 Siena Italy Univ Siena Dipartimento Sci Fis Terra & Ambiente DSFTA Sez Fis Via Roma 56 I-53100 Siena Italy Univ Paris Cite Univ Paris Saclay CEA CNRSAIM F-91191 Gif Sur Yvette France CNRS IAC FSLAC IRL 2009 Tenerife Spain Univ Barcelona IEEC UB Inst Ciencies Cosmos Dept Fis Quant & Astrofis Marti & Franques 1 E-08028 Barcelona Spain CSIC Inst Astrofis Andalucia Glorieta Astronomia S-N E-18008 Granada Spain Univ Geneva Dept Astron Chemin Ecogia 16 CH-1290 Versoix Switzerland Univ Complutense Madrid Inst Fis Particulas & Cosmos IPARCOS UCM Plaza Ciencias 1 Ciudad Univ E-28040 Madrid Spain Univ Complutense Madrid EMFTEL Dept Plaza Ciencias 1 Ciudad Univ E-28040 Madrid Spain INFN Sez Napoli Via CintiaEd G I-80126 Naples Italy INAF Osservatorio Astron Roma Via Frascati 33 I-00040 Monte Porzio Catone Italy Max Planck Inst Phys & Astrophys Fohringer Ring 6 D-80805 Munich Germany INFN Sez Padova Via Marzolo 8 I-35131 Padua Italy Univ Padua Via Marzolo 8 I-35131 Padua Italy Univ Savoie Mt Blanc CNRS Lab Annecy Phys Particules IN2P3 F-74000 Annecy France Univ Hamburg Inst Expt Phys Luruper Chaussee 149 D-22761 Hamburg Germany Univ Tokyo Grad Sch Sci 7-3-1 HongoBunkyo Ku Tokyo 1130033 Japan Univ Azuay Fac Sci & Technol Cuenca Ecuador Ctr Brasileiro Pesquisas Fis Rua Xavier Sigaud 150 BR-22290180 Rio De Janeiro RJ Brazil Univ La Laguna Inst Astrofis Canarias C Via Lactea S-N San Cristobal la Laguna 38205 Santa Cruz De T Spain Univ La Laguna Dept Astrofis C Via Lactea S-N San Cristobal la Laguna 38205 Santa Cruz De T Spain CIEMAT Avda Complutense 40 E-28040 Madrid Spain Univ Geneva Dept Phys Nucl & Corpusculaire 24 Quai Ernest Ansernet CH-1211 Geneva 4 Switzerland INFN Sez Bari Via Orabona 4 I-70124 Bari Italy Politecn Bari Via Orabona 4 I-70124 Ba

Context. The recurrent nova RS Ophiuchi (RS Oph) underwent a thermonuclear eruption in August 2021. In this event, RS Oph was detected by the High Energy Stereoscopic System (H.E.S.S.), the Major Atmospheric Gamma Imaging Cherenkov (MAGIC), and the first Large-Sized Telescope (LST-1) of the future Cherenkov Telescope Array Observatory (CTAO) at very-high gamma-ray energies above 100 GeV. This means that novae are a new class of very-high-energy (VHE) gamma-ray emitters. Aims. We report the analysis of the RS Oph observations with LST-1. We constrain the particle population that causes the observed emission in hadronic and leptonic scenarios. Additionally, we study the prospects of detecting further novae using LST-1 and the upcoming LST array of CTAO-North. Methods. We conducted target-of-opportunity observations with LST-1 from the first day of this nova event. The data were analysed in the framework of cta-lstchain and Gammapy, the official CTAO-LST reconstruction and analysis packages. One-zone hadronic and leptonic models were considered to model the gamma-ray emission of RS Oph using the spectral information from Fermi-LAT and LST-1, together with public data from the MAGIC and H.E.S.S. telescopes. Results. RS Oph was detected at 6.6 sigma with LST-1 in the first 6.35 hours of observations following the eruption. The hadronic scenario is preferred over the leptonic scenario considering a proton energy spectrum with a power-law model with an exponential cuto ff whose position increases from (0.26 +/- 0.08) TeV on day 1 up to (1.6 +/- 0.6) TeV on day 4 after the eruption. The deep sensitivity and low energy threshold of the LST-1 /LST array will allow us to detect faint novae and increase their discovery rate.

关键词： radiation mechanisms: non-thermal binaries: close binaries: symbiotic novae, cataclysmic variables stars: individual: RS Ophiuchi gamma rays: stars

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Prediction of potent shRNAs with a sequential classification algorithm

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NATURE BIOTECHNOLOGY 2017年第4期35卷 350-+页

作者： Pelossof, Raphael Fairchild, Lauren Huang, Chun-Hao Widmer, Christian Sreedharan, Vipin T. Sinha, Nishi Lai, Dan-Yu Guan, Yuanzhe Premsrirut, Prem K. Tschaharganeh, Darjus F. Hoffmann, Thomas Thapar, Vishal Xiang, Qing Garippa, Ralph J. Ratsch, Gunnar Zuber, Johannes Lowe, Scott W. Leslie, Christina S. Fellmann, Christof Mem Sloan Kettering Canc Ctr Computat Biol Program 1275 York Ave New York NY 10021 USA Triinst Training Program Computat Biol & Med New York NY USA Mem Sloan Kettering Canc Ctr Dept Canc Biol & Genet 1275 York Ave New York NY 10021 USA Cornell Univ Weill Grad Sch Med Sci Cell & Dev Biol Program New York NY 10021 USA Berlin Inst Technol Dept Comp Sci Machine Learning Grp Berlin Germany Mirimus Inc Woodbury NY 11797 USA Vienna Bioctr Res Inst Mol Pathol Vienna Austria Mem Sloan Kettering Canc Ctr RNAi Core 1275 York Ave New York NY 10021 USA ETH Dept Comp Sci Zurich Switzerland Howard Hughes Med Inst New York NY USA Mem Sloan Kettering Canc Ctr 1275 York Ave New York NY 10021 USA Univ Calif Berkeley Dept Mol & Cell Biol 229 Stanley Hall Berkeley CA 94720 USA

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.

关键词： Forecasting

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

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arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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EMPRESS. XII. Statistics on the Dynamics and Gas Mass Fraction of Extremely-Metal Poor Galaxies

arXiv

引用

arXiv 2023年

作者： Xu, Yi Ouchi, Masami Isobe, Yuki Nakajima, Kimihiko Ozaki, Shinobu Bouché, Nicolas F. Wise, John H. Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Univ Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology AtlantaGA30332 United States European Southern Observatory Karl-Schwarzschild-Straße 2 Garching85748 Germany Observatoire de Genéve Université de Genéve 51 Ch. des Maillettes Versoix1290 Switzerland 650 North A’ohoku Place HiloHI96720 United States Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Center for Computational Sciences University of Tsukuba Ten-nodai 1-1-1 Tsukuba Ibaraki305-8577 Japan Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom National Institute of Technology Ichinoseki College Hagisho Ichinoseki021-8511 Japan Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Astronomy University of Wisconsin-Madison 475 N. Charter Street MadisonWI53706 United States Theoretical Astrophysics Department of Earth & Space S

We present demography of the dynamics and gas-mass fraction of 33 extremely metal-poor galaxies (EMPGs) with metallicities of 0.015 − 0.195 Zo and low stellar masses of 104 − 108 Mo in the local universe. We conduct deep optical integral-field spectroscopy (IFS) for the low-mass EMPGs with the medium high resolution (R = 7500) grism of the 8m-Subaru FOCAS IFU instrument by the EMPRESS 3D survey, and investigate Hα emission of the EMPGs. Exploiting the resolution high enough for the low-mass galaxies, we derive gas dynamics with the Hα lines by the fitting of 3-dimensional disk models. We obtain an average maximum rotation velocity (vrot) of 15 ± 3 km s−1 and an average intrinsic velocity dispersion (σ0) of 27 ± 10 km s−1 for 15 spatially resolved EMPGs out of the 33 EMPGs, and find that all of the 15 EMPGs have vrot/σ0 rot/σ0 with the decreasing stellar mass and metallicity. We derive the gas mass fraction (fgas) for all of the 33 EMPGs, and find no clear dependence on stellar mass and metallicity. These vrot/σ0 and fgas trends should be compared with young high-z galaxies observed by the forthcoming JWST IFS programs to understand the physical origins of the EMPGs in the local universe. © 2023, CC BY.

关键词： Galaxies

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machine learning in Cardiac Health Monitoring and Decision Support

引用

COMPUTER 2016年第11期49卷 38-48页

作者： Hijazi, Shurouq Page, Alex Kantarci, Burak Soyata, Tolga Univ Rochester Machine Learning ML Lab Rochester NY 14627 USA Univ Rochester Heart Res Follow Up Program Med Ctr Rochester NY 14627 USA Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada SUNY Albany Dept Elect & Comp Engn Albany NY 12222 USA

Portable medical devices generate volumes of data that could be useful in identifying health risks. The proposed method filters patients' electrocardiograms (ECGs) and applies machine-learning classifiers to identify cardiac health risks and estimate severity. The authors present the results of applying their method in a case study.

关键词： Electrocardiography Medical Diagnostic Imaging Biomedical Monitoring Data Mining Diseases Data Visualization Decision Support Systems Smart Devices machine learning Smart Health Personalized Healthcare Medical Decision Support machine learning Data Mining Decision Support Systems

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