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Anxiety Cells in a Hippocampal-Hypothalamic Circuit

NEURON 2018年第3期97卷 670-+页

作者： Jimenez, Jessica C. Su, Katy Goldberg, Alexander R. Luna, Victor M. Biane, Jeremy S. Ordek, Gokhan Zhou, Pengcheng Ong, Samantha K. Wright, Matthew A. Zweifel, Larry Paninski, Liam Hen, Rene Kheirbek, Mazen A. Columbia Univ Dept Neurosci New York NY 10027 USA Columbia Univ Dept Psychiat New York NY 10027 USA Columbia Univ Dept Pharmacol New York NY 10027 USA New York State Psychiat Inst & Hosp Dept Psychiat Div Integrat Neurosci New York NY 10032 USA Univ Calif San Francisco Dept Psychiat San Francisco CA 94143 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Columbia Univ Dept Stat Grossman Ctr Stat Mind Ctr Theoret NeurosciKavli Inst Brain Sci New York NY USA Columbia Univ Dept Neurosci Grossman Ctr Stat Mind Ctr Theoret NeurosciKavli Inst Brain Sci New York NY USA Columbia Univ NeuroTechnol Ctr New York NY USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA Univ Washington Dept Pharmacol Seattle WA 98105 USA Univ Washington Dept Psychiat & Behav Sci Seattle WA 98105 USA Univ Calif San Francisco Neurosci Grad Program San Francisco CA 94143 USA Univ Calif San Francisco Weill Inst Neurosci San Francisco CA 94143 USA Univ Calif San Francisco Kavli Inst Fundamental Neurosci San Francisco CA 94143 USA Univ Calif San Francisco Ctr Integrat Neurosci San Francisco CA 94143 USA

The hippocampus is traditionally thought to transmit contextual information to limbic structures where it acquires valence. Using freely moving calcium imaging and optogenetics, we show that while the dorsal CA1 subregion of the hippocampus is enriched in place cells, ventral CA1 (vCA1) is enriched in anxiety cells that are activated by anxiogenic environments and required for avoidance behavior. Imaging cells defined by their projection target revealed that anxiety cells were enriched in the vCA1 population projecting to the lateral hypothalamic area (LHA) but not to the basal amygdala (BA). Consistent with this selectivity, optogenetic activation of vCA1 terminals in LHA but not BA increased anxiety and avoidance, while activation of terminals in BA but not LHA impaired contextual fear memory. Thus, the hippocampus encodes not only neutral but also valence-related contextual information, and the vCA1-LHA pathway is a direct route by which the hippocampus can rapidly influence innate anxiety behavior.

关键词： anxiety calcium imaging ventral hippocampus

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Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

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LANCET 2022年第10347期400卷 185-235页

作者： Bryazka, Dana Reitsma, Marissa B. Griswold, Max G. Abate, Kalkidan Hassen Abbafati, Cristiana Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdoli, Amir Abdollahi, Mohammad Abdullah, Abu Yousuf Md Abhilash, E. S. Abu-Gharbieh, Eman Acuna, Juan Manuel Addolorato, Giovanni Adebayo, Oladimeji M. Adekanmbi, Victor Adhikari, Kishor Adhikari, Sangeet Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Agegnehu, Wubetu Yimam Aggarwal, Manik Ahinkorah, Bright Opoku Ahmad, Araz Ramazan Ahmad, Sajjad Ahmad, Tauseef Ahmadi, Ali Ahmadi, Sepideh Ahmed, Haroon Rashid, Tarik Ahmed Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alam, Md Zakiul Alem, Dejene Tsegaye Alene, Kefyalew Addis Alimohamadi, Yousef Alizadeh, Atiyeh Allel, Kasim Alonso, Jordi Alvand, Saba Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Ancuceanu, Robert Anderson, Jason A. Andrei, Catalina Liliana Andrei, Tudorel Arabloo, Jalal Arshad, Muhammad Artamonov, Anton A. Aryan, Zahra Asaad, Malke Asemahagn, Mulusew A. Astell-Burt, Thomas Athari, Seyyed Shamsadin Atnafu, Desta Debalkie Atorkey, Prince Atreya, Alok Ausloos, Floriane Ausloos, Marcel Ayano, Getinet Ayanore, Martin Amogre Ayinde, Olatunde O. Ayuso-Mateos, Jose L. Azadnajafabad, Sina Azanaw, Melkalem Mamuye Azangou-Khyavy, Mohammadreza Jafari, Amirhossein Azari Azzam, Ahmed Y. Badiye, Ashish D. Bagheri, Nasser Bagherieh, Sara Bairwa, Mohan Bakkannavar, Shankar M. Bakshi, Ravleen Kaur Balchut-Bilchut, Awraris Hailu Barra, Fabio Barrow, Amadou Baskaran, Pritish Belo, Luis Bennett, Derrick A. Bensenor, Isabela M. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhalla, Ashish Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Bhattacharyya, Krittika Bhojaraja, Vijayalakshmi S. Bintoro, Bagas Suryo Blokhina, Elena A. Elena Bodicha, Belay Boda Abule Boloor, Archith Bosetti, Cristina Braithwaite, Dejana Brenner, Hermann Briko, Nikolay Ivanovich Brunoni, Andre R. Butt, Zahid A. Cao, Chao Cao, Yin Cardenas, Rosario Carvalho, Andre F. Carvalho, Marcia Castaldelli-Maia, Joao Mauricio Castelpietra Univ Washington Inst Hlth Metr & Evaluat Seattle WA 98195 USA Univ Washington Sch Med Dept Hlth Metr Sci Seattle WA USA Univ Washington Div Cardiol Seattle WA 98195 USA Univ Washington Dept Global Hlth Seattle WA 98195 USA RAND Corp Social & Econ Welf Unit Santa Monica CA USA Jimma Univ Dept Nutr & Dietet Jimma Ethiopia Univ Roma La Sapienza Dept Jurid & Econ Studies Rome Italy Univ Roma La Sapienza Dept Publ Hlth & Infect Dis Rome Italy Shahid Beheshti Univ Med Sci Social Determinants Hlth Res Ctr Tehran Iran Shahid Beheshti Univ Med Sci Dept Epidemiol Tehran Iran Shahid Beheshti Univ Med Sci Sch Adv Technol Med Tehran Iran Shahid Beheshti Univ Med Sci Dept Pharmacol Tehran Iran Shahid Beheshti Univ Med Sci Obes Res Ctr Tehran Iran Shahid Beheshti Univ Med Sci Funct Neurosurg Res Ctr Tehran Iran Shahid Beheshti Univ Med Sci Dept Hlth & Community Med Tehran Iran Shahid Beheshti Univ Med Sci Sch Med Tehran Iran Shahid Beheshti Univ Med Sci Student Res Comm Tehran Iran Shahid Beheshti Univ Med Sci Med Eth & Law Res Ctr Tehran Iran Univ Tehran Med Sci Noncommunicable Dis Res Ctr NCDRC Tehran Iran Univ Tehran Med Sci Inst Pharmaceut Sci TIPS Tehran Iran Univ Tehran Med Sci Sch Pharm Tehran Iran Univ Tehran Med Sci Dept Epidemiol & Biostat Tehran Iran Univ Tehran Med Sci Liver & Pancreatobiliary Dis Res Ctr Tehran Iran Univ Tehran Med Sci Multiple Sclerosis Res Ctr Tehran Iran Univ Tehran Med Sci Dept Pathol Tehran Iran Univ Tehran Med Sci Sch Med Tehran Iran Univ Tehran Med Sci Digest Dis Res Inst Tehran Iran Univ Tehran Med Sci Dept Psychiat Tehran Iran Univ Tehran Med Sci Students Sci Res Ctr SSRC Tehran Iran Univ Tehran Med Sci Tehran Heart Ctr Tehran Iran Univ Tehran Med Sci Fac Med Tehran Iran Univ Tehran Med Sci Sina Trauma & Surg Res Ctr Tehran Iran Univ Tehran Med Sci Endocrinol & Metab Res Inst Tehran Iran Univ Tehran Med Sci Iranian Res Ctr HIV AIDS Iranian Inst Reduct High Risk Behav Tehran Iran Univ Teh

Background The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods For this analysis, we constructed burden-weighted dose-response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15-95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15-39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0-0) and 0.603 (0.400-1.00) standard drinks per day, and the NDE varied between 0.002 (0-0) and 1.75 (0.698-4.30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0.114 (0-0.403) to 1.87 (0.500-3.30) standard drinks per day and an NDE that ranged between 0.193 (0-0.900) and 6.94 (3.40-8.30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59.1% (54.3-65.4) were aged 15-39 years and 76.9% (7.0-81.3) were male. Interpretation There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable

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High-Assurance SPIRAL END-TO-END GUARANTEES FOR ROBOT AND CAR CONTROL

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第2期37卷 82-103页

作者： Franchetti, Franz Low, Tze Meng Mitsch, Stefan Mendoza, Juan Pablo Gui, Liangyan Phaosawasdi, Amarin Padua, David Kar, Soummya Moura, Jose M. F. Franusich, Michael Johnson, Jeremy Platzer, Andre Veloso, Manuela M. Carnegie Mellon Univ Dept Elect & Comp Engn Hamerschlag Hall A3125000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellons Comp Sci Dept Logical Syst Lab Pittsburgh PA USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA CMDragons Team Autonomous Soccer Robots Pittsburgh PA USA Univ Illinois Comp Sci Urbana IL 61801 USA Univ Illinois Engn Urbana IL 61801 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA IST Lisbon Portugal MIT Cambridge MA 02139 USA NYU New York NY 10003 USA Carnegie Mellon Univ CMU Portugal Program Pittsburgh PA 15213 USA SpiralGen Www Spiralgen Com Pittsburgh PA USA US Natl Acad Engn Washington DC USA US Natl Acad Inventors Washington DC USA Amer Assoc Advancemen Sci Washington DC USA Acad Sci Portugal Lisbon Portugal SpiralGen Inc Engn Washington DC USA Drexel Univ Comp Sci & Elect & Comp Engn Philadelphia PA 19104 USA Drexel Univ Dept Comp Sci Philadelphia PA 19104 USA Drexels Appl Symbol Computat Lab Philadelphia PA USA SPIRAL Project Www Spiral Net Philadelphia PA USA Carnegie Mellon Univ Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Carnegie Mellon Univ Elect & Comp Engn Dept Pittsburgh PA 15213 USA CORAL Res Lab Pittsburgh PA USA RoboCup Pittsburgh PA USA AAAI Pittsburgh PA USA

Cyber-physical systems (CPSs), ranging from critical infrastructures such as power plants, to modern (semi) autonomous vehicles, are systems that use software to control physical processes. CPSs are made up of many different computational components. Each component runs its own piece of software that implements its control algorithms, based on its model of the environment. Every component then interacts with other components through the signals and values it sends out. Collectively, these components, and the code they run, drive the complex behaviors modern society has come to expect and rely on. Due to these intricate interactions between components, managing the hundreds to millions of lines of software to ensure that the system, as a whole, performs as desired can often be unwieldy.

关键词： Robot sensing systems Collision avoidance Monitoring Safety Cyber-physical systems Software development

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

arXiv

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arXiv 2025年

作者： Abe, K. Abe, S. Abhishek, A. Acero, F. Aguasca-Cabot, A. Agudo, I. Alispach, C. Crespo, N. Alvarez Ambrosino, D. Antonelli, L.A. Aramo, C. Arbet-Engels, A. Arcaro, C. Asano, K. Aubert, P. Baktash, A. Balbo, M. Bamba, A. Larriva, A. Baquero de Almeida, U. Barres Barrio, J.A. Jiménez, L. Barrios Batkovic, I. Baxter, J. González, J. Becerra 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. Caroff, S. Carosi, A. Carrasco, M.S. Cassol, F. Castrejón, 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 Bony de Lavergne, M. 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. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dore, D. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Fallah Ramazani, V. Ferrarotto, F. Fiasson, A. Foffano, L. Freixas Coromina, L. Fröse, S. Fukazawa, Y. Garcia López, 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. Günther, 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. Jimenez Martinez, I. 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. Department of Physics Tokai University 4-1-1 Kita-Kaname Kanagawa Hiratsuka259-1292 Japan Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Sezione di Fisica Via Roma 56 Siena53100 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 Cedex France FSLAC IRL 2009 CNRS/IAC Tenerife La Laguna Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 Barcelona08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Department of Astronomy University of Geneva Chemin d’Ecogia 16 VersoixCH-1290 Switzerland IPARCOS-UCM Instituto de Física de Partículas y del Cosmos EMFTEL Department Universidad Complutense de Madrid Ciudad Universitaria Plaza de Ciencias 1 Madrid28040 Spain INFN Sezione di Napoli Via Cintia ed. G Napoli80126 Italy INAF - Osservatorio Astronomico di Roma Via di Frascati 33 Monteporzio Catone00040 Italy Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany INFN Sezione di Padova Università degli Studi di Padova Via Marzolo 8 Padova35131 Italy Univ. Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Universität Hamburg Institut für Experimentalphysik Luruper Chaussee 149 Hamburg22761 Germany Graduate School of Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Faculty of Science and Technology Universidad del Azuay Cuenca Ecuador Centro Brasileiro de Pesquisas Físicas Rua Xavier Sigaud 150 RJ Rio de Janeiro22290-180 Brazil Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna C. Vía Láctea s/n Santa Cruz de Tenerife La Laguna38205 Spain CIEMAT Avda. Complutense 40 Madrid28040 Spain University of Geneva Département de physique nucléaire et corpusculaire 24 Quai Ernest Ansernet

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 6.6σ 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 cutoff 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. © 2025, CC BY.

关键词： Gamma radiography

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Automatic Thread-Level Canvas Analysis A machine-learning approach to analyzing the canvas of paintings

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IEEE SIGNAL PROCESSING MAGAZINE 2015年第4期32卷 38-45页

作者： van der Maaten, Laurens Erdmann, Robert Delft Univ Technol Comp Vis & Machine Learning NL-2600 AA Delft Netherlands Univ Calif San Diego San Diego CA 92103 USA Rijksmuseum Amsterdam Netherlands Univ Amsterdam Conservat Sci NL-1012 WX Amsterdam Netherlands Radboud Univ Nijmegen Visualizat Art Hist Nijmegen Netherlands Univ Arizona Dept Mat Sci & Engn Tucson AZ 85721 USA Univ Arizona Program Appl Math Tucson AZ 85721 USA Thread Counting Automat Project Maastricht Netherlands

Canvas analysis is an important tool in art-historical studies, as it can provide information on whether two paintings were made on canvas that originated from the same bolt. Canvas analysis algorithms analyze radiographs of paintings to identify (ir)regularities in the spacings between the canvas threads. To reduce noise, current state-of-the-art algorithms do this by averaging the signal over a number of threads, which leads to information loss in the final measurements. This article presents an algorithm capable of performing thread-level canvas analysis: the algorithm identifies each of the individual threads in the canvas radiograph and directly measures between-distances and angles of the identified threads. We present two case studies to illustrate the potential merits of our thread-level canvas analysis algorithm, viz. on a small collection of paintings ostensibly by Nicholas Poussin and on a small collection of paintings by Vincent van Gogh.

关键词： Painting Instruction sets Radiography Algorithm design and analysis Signal processing algorithms Art Feature extraction Information analysis

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The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

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GENOME BIOLOGY 2019年第1期20卷 1-23页

作者： Zhou, Naihui Jiang, Yuxiang Bergquist, Timothy R. Lee, Alexandra J. Kacsoh, Balint Z. Crocker, Alex W. Lewis, Kimberley A. Georghiou, George Nguyen, Huy N. Hamid, Md Nafiz Davis, Larry Dogan, Tunca Atalay, Volkan Rifaioglu, Ahmet S. Dalkiran, Alperen Atalay, Rengul Cetin Zhang, Chengxin Hurto, Rebecca L. Freddolino, Peter L. Zhang, Yang Bhat, Prajwal Supek, Fran Fernandez, Jose M. Gemovic, Branislava Perovic, Vladimir R. Davidovic, Radoslav S. Sumonja, Neven Veljkovic, Nevena Asgari, Ehsaneddin Mofrad, Mohammad R. K. Profiti, Giuseppe Savojardo, Castrense Martelli, Pier Luigi Casadio, Rita Boecker, Florian Schoof, Heiko Kahanda, Indika Thurlby, Natalie McHardy, Alice C. Renaux, Alexandre Saidi, Rabie Gough, Julian Freitas, Alex A. Antczak, Magdalena Fabris, Fabio Wass, Mark N. Hou, Jie Cheng, Jianlin Wang, Zheng Romero, Alfonso E. Paccanaro, Alberto Yang, Haixuan Goldberg, Tatyana Zhao, Chenguang Holm, Liisa Toronen, Petri Medlar, Alan J. Zosa, Elaine Borukhov, Itamar Novikov, Ilya Wilkins, Angela Lichtarge, Olivier Chi, Po-Han Tseng, Wei-Cheng Linial, Michal Rose, Peter W. Dessimoz, Christophe Vidulin, Vedrana Dzeroski, Saso Sillitoe, Ian Das, Sayoni Lees, Jonathan Gill Jones, David T. Wan, Cen Cozzetto, Domenico Fa, Rui Torres, Mateo Vesztrocy, Alex Warwick Rodriguez, Jose Manuel Tress, Michael L. Frasca, Marco Notaro, Marco Grossi, Giuliano Petrini, Alessandro Re, Matteo Valentini, Giorgio Mesiti, Marco Roche, Daniel B. Reeb, Jonas Ritchie, David W. Aridhi, Sabeur Alborzi, Seyed Ziaeddin Devignes, Marie-Dominique Koo, Da Chen Emily Bonneau, Richard Gligorijevic, Vladimir Barot, Meet Fang, Hai Toppo, Stefano Lavezzo, Enrico Falda, Marco Berselli, Michele Tosatto, Silvio C. E. Carraro, Marco Piovesan, Damiano Rehman, Hafeez Ur Mao, Qizhong Zhang, Shanshan Vucetic, Slobodan Black, Gage S. Jo, Dane Suh, Erica Dayton, Jonathan B. Larsen, Dallas J. Omdahl, Ashton R. McGuffin, Liam J. Brackenridge, Danielle A. Babbitt, Patricia C. Yunes, Jeffrey M. Fontana, Paolo Zhang, Feng Zhu, Shanfeng You, Ronghui Zhang, Zihan Da Iowa State Univ Vet Microbiol & Prevent Med Ames IA USA Program Bioinformat & Computat Biol Ames IA USA Indiana Univ Bloomington Bloomington IN USA Univ Washington Dept Biomed Informat & Med Educ Seattle WA USA Univ Pennsylvania Dept Syst Pharmacol & Translat Therapeut Philadelphia PA USA Geisel Sch Med Hanover NH USA Dept Mol & Syst Biol Hanover NH USA Geisel Sch Med Dept Microbiol & Immunol Hanover NH USA European Bioinformat Inst EMBL EBI European Mol Biol Lab Hinxton England Program Comp Sci Ames IA USA Hacettepe Univ Dept Comp Engn Ankara Turkey Middle East Tech Univ METU Dept Comp Engn Ankara Turkey Iskenderun Tech Univ Dept Comp Engn Antakya Turkey Middle East Tech Univ Grad Sch Informat CanSyL Ankara Turkey Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI USA Univ Michigan Dept Biol Chem Ann Arbor MI 48109 USA Achira Labs Bangalore Karnataka India Inst Res Biomedicine IRB Barcelona Barcelona Spain Inst Catalana Recerca Estudis Avancats ICREA Barcelona Spain Barcelona Supercomput Ctr Dept Life Sci INB Coordinat Unit Barcelona Spain Spanish Natl Canc Res Ctr INB GN2 Struct & Computat Biol Programme Barcelona Spain Univ Belgrade Inst Nucl Sci VINCA Lab Bioinformat & Computat Chem Belgrade Serbia Univ Calif Berkeley Dept Bioengineering Mol Cell Biomechan Lab Berkeley CA USA Helmholtz Ctr Infect Res Computat Biol Infect Res Berkeley CA USA Dept Bioengn Berkeley CA USA Dept Mech Engn Berkeley CA USA Univ Bologna Dept Pharm & Biotechnol Bologna Biocomputing Grp Bologna Italy IBIOM Natl Res Council Bologna Italy Univ Bonn INRES Crop Bioinformat Bonn Germany Univ Bonn INRES Crop Bioinformat Bonn Germany Montana State Univ Gianforte Sch Comp Bozeman MT USA Univ Bristol Comp Sci Bristol Avon England Helmholtz Ctr Infect Res Computat Biol Infect Res Braunschweig ME Germany RESIST DFG Cluster Excellence 2155 Braunschweig ME Germany Univ Iibre Bruxelles Vrije Univ Brussel Interunivers Inst Bioinformat Brusse

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging;although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

关键词： Protein function prediction Long-term memory Biofilm Critical assessment Community challenge

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High-order neural networks and kernel methods for peptide-MHC binding prediction

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BIOINFORMATICS 2015年第22期31卷 3600-3607页

作者： Kuksa, Pavel P. Min, Martin Renqiang Dugar, Rishabh Gerstein, Mark Univ Penn Sch Med Inst Biomed Informat Philadelphia PA 19104 USA Univ Penn Sch Med Dept Pathol & Lab Med Philadelphia PA 19104 USA NEC Labs Amer Dept Machine Learning Princeton NJ 08540 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT 06511 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06511 USA Yale Univ Dept Comp Sci New Haven CT 06511 USA

Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding. Results: The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25-40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding.

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Federated learning enables big data for rare cancer boundary detection

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NATURE COMMUNICATIONS 2022年第1期13卷 7346-7346页

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michalek, Jan Matula, Petr Kerkovsky, Milos Koprivova, Tereza Dostal, Marek Vybihal, Vaclav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcao, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C. A. Sprenger, Flavia Menotti, David Lucio, Diego R. La Montagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Murcia, Derrick Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, T Univ Penn Ctr Biomed Image Comp & Analyt CBICA Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Radiol Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Pathol & Lab Med Philadelphia PA 19104 USA Tech Univ Munich Dept Informat Munich Germany Intel Corp Santa Clara CA USA Univ Heidelberg Hosp Dept Neuroradiol Heidelberg Germany German Canc Res Ctr German Canc Consortium DKTK Cooperat Unit Neuropathol Heidelberg Germany Univ Heidelberg Hosp Dept Neuropathol Heidelberg Germany German Canc Res Ctr Div Med Image Computing Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Heidelberg Univ Hosp Neurol Clin Heidelberg Germany Univ Calif San Francisco Dept Radiol & Biomed Imaging San Francisco CA USA Symbiosis Int Univ Symbiosis Ctr forMed Image Anal Pune Maharashtra India Natl Inst Mental Hlth & Neurosci Dept Neuroimaging & Intervent Radiol Bangalore Karnataka India Univ Wisconsin Sch Med & Publ Hlth Dept Radiol Madison WI USA Univ Wisconsin Sch Med & Publ Hlth Dept Med Phys Madison WI USA Leeds Teaching Hosp Trust Dept Radiol Leeds W Yorkshire England Harvard Med Sch Brigham & Womens Hosp Dept Radiol Boston MA USA Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Catalan Inst Oncol Badalona Spain Consorci MAR Parc Salut Barcelona Barcelona Catalonia Spain Josep Trueta Univ Hosp Girona Biomed Res Inst IdIBGi Dept Radiol IDI Girona Spain Kepler Univ Hosp Linz Neuromed Campus NMC Inst Neuroradiol Linz Austria Kepler Univ Hosp Linz Neuromed Campus NMC Dept Neurooncol Linz Austria RKH Klinikum Ludwigsburg Inst Diagnost & Intervent Neuroradiol Ludwigsburg Germany Christiana Care Hlth Syst Dept Radiat Oncol Philadelphia PA USA Thomas Jefferson Univ Sidney Kimmel Med Coll Philadelphia PA USA Univ Maryland Dept Rad Oncol Baltimore MD USA Thomas Jefferson Univ Sidney Kimmel Canc Ctr Dept Radiat Oncol Philadelphia PA USA Ohio State

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numericalmodel updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multisite collaborations, alleviating the need for data-sharing.

关键词： CNS cancer Computer science Medical imaging Medical research Biomedical engineering

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Global burden of chronic respiratory diseases and risk factors, 1990-2019: an update from the Global Burden of Disease Study 2019

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ECLINICALMEDICINE 2023年 59卷 101936-101936页

作者： Momtazmanesh, Sara Moghaddam, Sahar Saeedi Ghamari, Seyyed-Hadi Rad, Elaheh Malakan Rezaei, Negar Shobeiri, Parnian Aali, Amirali Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdoun, Meriem Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Aidin Abolhassani, Hassan Abrehdari-Tafreshi, Zahra Achappa, Basavaprabhu Adane, Denberu Eshetie Adane Adane, Tigist Demssew Addo, Isaac Yeboah Adnan, Mohammad Adnani, Qorinah Estiningtyas Sakilah Ahmad, Sajjad Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Ayman Rashid, Tarik Ahmed Al Hamad, Hanadi Alahdab, Fares Alemayehu, Astawus Alif, Sheikh Mohammad Aljunid, Syed Mohamed Almustanyir, Sami Altirkawi, Khalid A. Alvis-Guzman, Nelson Dehkordi, Javad Aminian Amir-Behghadami, Mehrdad Ancuceanu, Robert Andrei, Catalina Liliana Andrei, Tudorel Antony, Catherine M. Anyasodor, Anayochukwu Edward Arabloo, Jalal Arulappan, Judie Ashraf, Tahira Athari, Seyyed Shamsadin Attia, Engi F. Ayele, Meshesha Tsegazeab Azadnajafabad, Sina Babu, Abraham Samuel Bagherieh, Sara Baltatu, Ovidiu Constantin Banach, Maciej Bardhan, Mainak Barone-Adesi, Francesco Barrow, Amadou Basu, Saurav Bayileyegn, Nebiyou Simegnew Bensenor, Isabela M. Bhardwaj, Nikha Bhardwaj, Pankaj Bhat, Ajay Nagesh Bhattacharyya, Krittika Bouaoud, Souad Braithwaite, Dejana Brauer, Michael Butt, Muhammad Hammad Butt, Zahid A. Calina, Daniela Camera, Luis Alberto Chanie, Gashaw Sisay Charalampous, Periklis Chattu, Vijay Kumar Chimed-Ochir, Odgerel Chu, Dinh-Toi Cohen, Aaron J. Cruz-Martins, Natalia Dadras, Omid Darwesh, Aso Mohammad Das, Saswati Debela, Sisay Abebe Delgado-Ortiz, Daniel Dereje, Diriba Dianatinasab, Mostafa Diao, Nancy Diaz, Daniel Digesa, Lankamo Ena Dirirsa, Gebisa Doku, Paul Narh Dongarwar, Deepa Douiri, Abdel Dsouza, Haneil Larson Eini, Ebrahim Ekholuenetale, Michael Ekundayo, Temitope Cyrus Elagali, Ahmed Elabbas Mustafa Elhadi, Muhammed Enyew, Daniel Berhanie Erkhembayar, Ryenchindorj Etaee, Farshid Fagbamigbe, Adeniyi Francis Faro, Andre Fatehizadeh, Ali Fekadu, Ginenus Filip, Irin Univ Tehran Med Sci Noncommunicable Dis Res Ctr Endocrinol & Metab Populat Sci Inst 10 Al E Ahmad & Chamran Highway Intersect Tehran *** Iran Univ Tehran Med Sci Endocrinol & Metab Res Ctr Endocrinol & Metab Clin Sci Inst Tehran Iran Univ Tehran Med Sci Sch Med Tehran Iran Univ Tehran Med Sci Dept Pediat Cardiol Tehran Iran Univ Tehran Med Sci Res Ctr Immunodeficiencies Tehran Iran Univ Tehran Med Sci Inst Environm Res Tehran Iran Univ Tehran Med Sci Students Sci Res Ctr Tehran Iran Univ Tehran Med Sci Childrens Med Ctr Tehran Iran Univ Tehran Med Sci Dept Cardiol Tehran Iran Univ Tehran Med Sci Tehran Heart Ctr Tehran Iran Univ Tehran Med Sci Natl Inst Hlth Res Tehran Iran Univ Tehran Med Sci Dept Med Tehran Iran Kiel Inst World Econ Kiel Germany Mashhad Univ Med Sci Fac Med Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci Appl Biomed Res Ctr Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci Biotechnol Res Ctr Mashhad Razavi Khorasan Iran Shahid Beheshti Univ Med Sci Social Determinants Hlth Res Ctr Tehran Iran Shahid Beheshti Univ Med Sci Dept Epidemiol Tehran Iran Shahid Beheshti Univ Med Sci Funct Neurosurg Ctr Tehran Iran Shahid Beheshti Univ Med Sci Sch Med Tehran Iran Shahid Beheshti Univ Med Sci Urol & Nephrol Res Ctr Tehran Iran Marshall Univ Dept Surg Huntington WV USA Univ Setif Algeria Fac Med Setif Algeria Univ Duhok Community & Matern Nursing Unit Duhok Iraq Univ Waterloo Sch Planning Fac Environm Waterloo ON Canada Univ Waterloo Sch Publ Hlth & Hlth Syst Waterloo ON Canada Univ Southern Calif Dept Neurosurg Los Angeles CA USA Univ Southern Calif Keck Sch Med Los Angeles CA USA Univ Southern Calif Mark & Mary Stevens Neuroimaging & Informat Inst Los Angeles CA USA Karolinska Univ Hosp Dept Biosci & Nutr Huddinge Sweden Univ Tehran Cellular & Mol Biol Dept Tehran Iran Manipal Acad Higher Educ Dept Internal Med Mangalore India Manipal Acad Higher Educ Dept Gen Med Mangalore India Manipal Acad Hi

Background Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6-4.3) with a prevalence of 454.6 million cases (417.4-499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4-225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9-3.6) deaths. With 262.4 million (224.1-309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Lowand low-middle SDI count

关键词： Asthma Chronic obstructive pulmonary disease Epidemiology Interstitial lung disease Lung disease Morbidity Mortality Pneumoconiosis Pulmonary emphysema

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of machine learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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