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Current Research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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Erratum to: Search for squarks and gluinos in final states with one isolated lepton, jets, and missing transverse momentum at s = 13 TeV with the ATLAS detector

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The European Physical Journal C 2021年第10期81卷 1-15页

作者： Aad, G. Abbott, B. Abbott, D. C. Abed Abud, A. Abeling, K. Abhayasinghe, D. K. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B. S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A. A. Afik, Y. Agapopoulou, C. Agaras, M. N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahmad, A. Ahmadov, F. Ahmed, W. S. Ai, X. Aielli, G. Akatsuka, S. Akbiyik, M. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Al Khoury, K. Alberghi, G. L. Albert, J. Alconada Verzini, M. J. Alderweireldt, S. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M. G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amrouche, C. S. An, F. Anastopoulos, C. Andari, N. Andeen, T. Anders, J. K. Andrean, S. Y. Andreazza, A. Andrei, V. Anelli, C. R. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Anthony, M. T. Antipov, E. Antonelli, M. Antrim, D.J. A. Anulli, F. Aoki, M. Aparisi Pozo, J. A. Aparo, M. A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Araujo Pereira, R. Arcangeletti, C. Arce, A.T. H. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A. J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z. P. Artoni, G. Asada, H. Asai, K. Asai, S. Asawatavonvanich, T. Asbah, N. Asimakopoulou, E. M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R. J. Atkinson, M. Atlay, N. B. Atmani, H. Atmasiddha, P. A. Augsten, K. Austrup, V. A. Avolio, G. Ayoub, M. K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Backman, F. Bagnaia, P. Bahmani, M. Bahrasemani, H. Bailey, A. J. Bailey, V. R. Baines, J. T. Bakalis, C. Baker, O. K. Bakker, P. J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E. M. Balek, P. Balli, F. Balunas CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Department of Physics University of Massachusetts Amherst USA CERN Geneva Switzerland II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham UK Department of Physics University of Toronto Toronto Canada Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Physics and Astronomy University of Sussex Brighton UK Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel High Energy Physics Division Argonne National Laboratory Argonne USA INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK Institut für Physik Universität Mainz Mainz Germany LAPP Université Grenoble Alpes Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Department of Physics Northern Illinois University DeKalb USA Department of Physics Bogazici University Istanbul Turkey Dept. of Physics Istanbul University Istanbul Turkey Département de Physique Nucléaire et Corpusculaire Université de Genève Geneva Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot UK Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz USA IJCLab CNRS/IN2P3 Université Paris-Saclay Orsay France LPC Université Clermont Auvergne CNRS/IN2P3 Clermont-Ferrand France Institute for Mathematics Astrophysics and Particle Physics Radboud University/Nikhef Nijmegen The Netherlands Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física E

A correction to this paper has been published: 10.1140/epjc/s10052-021-09344-w

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Experimental demonstration of high-rate measurement-device-independent quantum key distribution over asymmetric channels

arXiv

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arXiv 2018年

作者： Liu, Hui Wang, Wenyuan Wei, Kejin Fang, Xiao-Tian Li, Li Liu, Nai-Le Liang, Hao Zhang, Si-Jie Zhang, Weijun Li, Hao You, Lixing Wang, Zhen Lo, Hoi-Kwong Chen, Teng-Yun Xu, Feihu Pan, Jian-Wei Shanghai Branch Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Shanghai201315 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Dept. of Electrical & Computer Engineering Dept. of Physics University of Toronto TorontoONM5S 3G4 Canada State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate all detector side channels and it is practical with current technology. Previous implementations of MDI-QKD all use two symmetric channels with similar losses. However, the secret key rate is severely limited when different channels have different losses. Here we report the results of the first high-rate MDI-QKD experiment over asymmetric channels. By using the recent 7-intensity optimization approach, we demonstrate >10x higher key rate than previous best-known protocols for MDI-QKD in the situation of large channel asymmetry, and extend the secure transmission distance by more than 20-50 km in standard telecom fiber. The results have moved MDI-QKD towards widespread applications in practical network settings, where the channel losses are asymmetric and user nodes could be dynamically added or deleted. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

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REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

arXiv

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arXiv 2019年

作者： Orlando, Jose Ignacio Fu, Huazhu Breda, Jo-Ao Barbossa Keer, Karelvan Bathula, Deepti R. Diaz-Pinto, Andres Fang, Ruogu Heng, Pheng-Ann Kim, Jeyoung Lee, JoonHo Lee, Joonseok Li, Xiaoxiao Liu, Peng Lu, Shuai Murugesan, Balamurali Naranjo, Valery Phaye, Sai Samarth R. Shankaranarayana, Sharath M. Sikka, Apoorva Son, Jaemin Hengel, Antonvanden Wang, Shujun Wu, Junyan Wu, Zifeng Xu, Guanghui Xu, Yongli Yin, Pengshuai Li, Fei Zhang, Xiulan Xu, Yanwu Bogunovic, Hrvoje Department of Ophthalmology and Optometry Medical University of Vienna Spitalgasse 23 Vienna1090 Australia Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Surgery and Physiology Department Ophthalmology Unit Faculty of Medicine University of Porto Porto Research Group Ophthalmology KU Leuven Leuven Belgium Ropar Rupnagar140001 India Instituto de Investigacione Innovacion en Bioingeniera Universitat Politecnica de Valencia Valencia46022 Spain J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida 32611 United States Department of Computer Science and Engineering Chinese University of Hong Kong 999077 Hong Kong Gachon University Gyeonggi-Do461-701 Korea Republic of Samsung SDS AI Research Center Seoul06765 Korea Republic of Yale University New HavenCT06510 United States Faculty of Science Beijing University of Chemical Technology Beijing100029 China Healthcare Technology Innovation Centre IIT Madras India Department of Electrical Engineering IIT Madras India VUNO Inc Seoul137-810 Korea Republic of Australian Institute for Machine Learning Australia Cleerly Inc New YorkNY10022 United States South China University of Technology Guangzhou510006 China Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou Shi China Articial Intelligence Innovation Business Baidu Inc China and Cixi Institute of Bio Medical Engineering Chinese Academy of Sciences China

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes dificult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (http://***), held in conjunction with MICCAI 2018. The challenge consistedof two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results. Copyright © 2019, arXiv, All rights reserved.

关键词： Ophthalmology

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Mixed-sensitivity design of a dynamic controller for systems pre-compensated by input shapers

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IFAC-PapersOnLine 2017年第1期50卷 1304-1309页

作者： Pilbauer D. Michiels W. Vyhlídal T. Hromčík M. Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A Heverlee B-3001 Belgium CIIRC - Czech Institute of Informatics Robotics and Cybernetics Dept. of Instrumentation and Control Eng. Faculty of Mechanical Engineering Czech Technical University in Prague Technická 4 166 07 Praha 6 Czech Republic M. Hromčík is with Deptartment of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Technická 4 166 07 Praha 6 Czech Republic

We employ recent control design algorithms for delay systems to address the mixed sensitivity design of a dynamic controller for multiple degree of freedom systems interconnected with an inverse signal shaper. A delay based inverse shaper is included within a feedback loop in order to eliminate oscillatory modes due to an attached flexible substructure, which has been shown to be advantageous to inducing directly the filtering property by an appropriate controller design. Due to the presence of the inverse shaper, the closed loop dynamics become infinite dimensional. Furthermore we design fixed-order controllers, aiming at an easy implementation. As a consequence of these two features standard techniques from H-infinity control can no longer be used. We present a two-stage method, which firstly makes the closed-loop system asymptotically stable and, secondly, computes a low-order controller that minimizes the H-infinity norm of the weighted closed-loop transfer functions. The approach is grounded in recent methods for fixed-order H-infinity control of delay systems, and the delay-differential algebraic equations (DDAE) modelling and control framework. © 2017

关键词： H-infinity control inverse shapers spectral optimization time-delay

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Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology

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Biological psychiatry. Cognitive neuroscience and neuroimaging 2025年

作者： Ana I Silva Ida E Sønderby George Kirov Abdel Abdellaoui Ingrid Agartz David Ames Nicola J Armstrong Eric Artiges Tobias Banaschewski Anne S Bassett Carrie E Bearden John Blangero Rune Boen Dorret I Boomsma Robin Bülow Nancy J Butcher Vince Calhoun Linda E Campbell Eva W C Chow Simone Ciufolini Michael C Craig Benedicto Crespo-Farroco Adam C Cunningham Shareefa Dalvie Eileen Daly Paola Dazzan Eco J C de Geus Greig I de Zubicaray Joanne L Doherty Gary Donohoe Mark Drakesmith Thomas Espeseth Vincent Frouin Hugh Garavan David C Glahn Naomi J Goodrich-Hunsaker Penny A Gowland Hans J Grabe Antoine Grigis Maria Gudbrandsen Boris A Gutman Jan Haavik Asta K Håberg Jeremy Hall Andreas Heinz Sarah Hohmann Jouke-Jan Hottenga Sébastien Jacquemont Neda Jahanshad Rachel K Jonas Derek K Jones Erik G Jönsson Sanne Koops Kuldeep Kumar Stephanie Le Hellard Herve Lemaitre Jingyu Liu Astri J Lundervold Jean-Luc Martinot Karen A Mather Donna M McDonald-McGinn Katie L McMahon Allan F McRae Sarah E Medland Clara A Moreau Kieran C Murphy Declan Murphy Robin M Murray Frauke Nees Michael J Owen Marie-Laure Paillère Martinot Diimitri Papadopoulos Orfanos Tomas Paus Luise Poustka Tiago Reis Marques David R Roalf Perminder S Sachdev Freda Scheffler J Eric Schmitt Gunter Schumann Vidar M Steen Dan J Stein Lachlan T Strike Alexander Teumer Anbupalam Thalamuthu Sophia I Thomopoulos Diana Tordesillas-Gutiérrez Julian N Trollor Anne Uhlmann Ariana Vajdi Dennis van 't Ent Therese van Amelsvoort Marianne B M van den Bree Dennis van der Meer Javier Vázquez-Bourgon Julio E Villalón-Reina Uwe Völker Henry Völzke Jacob A S Vorstman Lars T Westlye Nigel Williams Katharina Wittfeld Margaret J Wright Paul M Thompson Ole A Andreassen David E J Linden School for Mental Health and Neuroscience Department of Psychiatry and Neuropsychology Faculty of Health Medicine and Life Sciences Maastricht University Maastricht The Netherlands Neuroscience and Mental Health Innovation Institute Cardiff University Cardiff United Kingdom Center for Magnetic Resonance Research Department of Radiology University of Minnesota Minneapolis MN USA. Electronic address: silvaai@um.edu. Centre for Precision Psychiatry Institute of Clinical Medicine University of Oslo Oslo Norway Department of Medical Genetics Oslo University Hospital Oslo Norway KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo Oslo Norway. Division of Psychological Medicine and Clinical Neurosciences Cardiff University Cardiff United Kingdom Centre for Neuropsychiatric Genetics and Genomics School of Medicine Cardiff University Cardiff United Kingdom. Department of Psychiatry Amsterdam UMC University of Amsterdam Amsterdam the Netherlands. Norwegian Centre for Mental Disorders Research (NORMENT) Institute of Clinical Medicine University of Oslo Oslo Norway Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet & Stockholm Health Care Services Stockholm Region Stockholm Sweden Department of Psychiatric Research Diakonhjemmet Hospital Oslo Norway. University of Melbourne Academic Unit for Psychiatry of Old Age Kew Victoria Australia. Mathematics and Statistics Curtin University Perth WA Australia. Institut National de la Santé et de la Recherche Médicale INSERM U1299 "Trajectoires développementales et psychiatrie" Université Paris-Saclay Ecole Normale supérieure Paris-Saclay CNRS Centre Borelli Gif-sur-Yvette France Psychiatry Department EPS Barthélémy Durand Etampes Île-de-France France. Department of Child and Adolescent Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim University of Heidelberg Mannheim Germany. Clinical Genetics Research Program Centre for

BACKGROUND:Copy number variants (CNVs) increase risk for neurodevelopmental conditions. The neurobiological mechanisms linking these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals carrying CNVs are associated with their degree of penetrance. METHODS:We investigated if increased CNV-penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA-CNV consortium. In the main analyses, we included 9,268 individuals (aged 7 to 90 years, 54% females), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 non-carriers. CNV-penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk to develop either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder and congenital malformations). RESULTS:For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. CONCLUSIONS:Our findings link global and regional cortical morphometric features with CNV-penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

关键词： autism neuroimaging psychiatric disorders schizophrenia structural imaging

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Deep cross-Modal correlation learning for audio and lyrics in music retrieval

arXiv

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arXiv 2017年

作者： Yu, Yi Tang, Suhua Raposo, Francisco Chen, Lei Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Dept. of Communication Engineering and Informatics University of Electro-Communications Tokyo Japan Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Computer Science and Engineering Hong Kong University of Science and Technology

Deep cross-modal learning has successfully demonstrated excellent performances in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics are taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Different modality data are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study on understanding the correlation between language and music audio through deep architectures for learning the paired temporal correlation of audio and lyrics. Pre-trained Doc2vec model followed by fully-connected layers (fully-connected deep neural network) is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) pretrained CNN followed by fully-connected layers is investigated for representing music audio. ii) We further suggest an end-toend architecture that simultaneously trains convolutional layers and fully-connected layers to better learn temporal structures of music audio. Particularly, our end-to-end deep architecture contains two properties: simultaneously implementing feature learning and cross-modal correlation learning, and learning joint representation by considering temporal structures. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval. Copyrigh

关键词： Music

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Inductive identification of functional status information and establishing a gold standard corpus: A case study on the Mobility domain

Inductive identification of functional status information an...

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2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

作者： Thieu, Thanh Camacho, Jonathan Ho, Pei-Shu Porcino, Julia Ding, Min Nelson, Lisa Rasch, Elizabeth Zhou, Chunxiao Chan, Leighton Brandt, Diane Newman-Griffis, Denis Yuan, Ao Lai, Albert M. Rehabilitation Medicine Department National Institutes of Health Clinical Center BethesdaMD United States Social Security Advisory Board WashingtonDC United States Computer Science and Engineering Department Ohio State University ColumbusOH United States Biostatistics Bioinformatics and Biomathematics Dept. Georgetown University WashingtonDC United States Institute for Informatics Washington University St. LouisMO United States

ISBN: (纸本)9781509030491

The importance of functional status information (FSI) has become increasingly evident in recent years [1, 2]. However, implementation, application, and normalization of FSI in health care and Electronic Health Records (EHRs) have been largely underexplored. The World Health Organization's International Classification of Functioning, Disability and Health (ICF) [3] is considered to be the international standard for describing and coding function and health states. Nevertheless, the ICF provides only a limited vocabulary for recognizing FSI descriptions, since its purpose is to organize concepts related to functioning rather than to provide a comprehensive terminology or a complete set of relations between concepts. While the free text portion of EHRs might provide a more complete picture of health status, treatment, and progress, current Natural Language Processing (NLP) methods largely focus on extracting medical conditions (e.g. diagnoses and symptoms, etc.). The absence of a standardized functional terminology and incompleteness of the ICF as a vocabulary source makes it challenging to build a NLP system to extract FSI from EHR free text. Our work takes the first step towards extraction of FSI from free text by systematically identifying the structure of FSI related to Mobility, a key domain of the ICF and an important domain in the determination of work disability. Our interdisciplinary research group inductively evaluated examples extracted from over 1,200 Physical Therapy (PT) notes from the Clinical Center of the National Institutes of Health (NIH). This extensive work resulted in a nested entity structure comprised of 2 entities, 3 sub-entities, 8 attributes, and 21 attribute values. Furthermore, we have manually curated the first gold standard corpus of 200 double-annotated and 50 triple-annotated PT notes. Our inter-annotator agreement (IAA) averages 97% F1-score on partial textual span matching and from 0.4 to 0.9 Siegel & Castellan's kappa on attribute va

关键词： Terminology

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Gamma/hadron discrimination using a small-WCD with four PMTs 37

Gamma/hadron discrimination using a small-WCD with four PMTs

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37th International Cosmic Ray Conference, ICRC 2021

作者： Conceição, R. Assis, P. Assunção, F. Bakalová, A. Barres de Almeida, U. Bom, C.R. Correia, J. De Angelis, A. Dias, L. González, B.S. Guillén, A. La Mura, G. Lourenço, N. Machado, P. Marques, S. Mendes, L. Pimenta, M. Shellard, R. Tomé, B. Vicha, J. Abreu, P. Albert, A. Angüner, E.O. Arcaro, C. Arnaldi, L.H. Arteaga-Velázquez, J.C. Batković, I. Bellido, J. Belmont-Moreno, E. Bisconti, F. Blanco, A. Bohacova, M. Bottacini, E. Bretz, T. Brisbois, C. Brogueira, P. Brown, A.M. Bulik, T. Caballero Mora, K.S. Campos, S.M. Chiavassa, A. Chytka, L. Consolati, G. Cotzomi Paleta, J. Dasso, S. De Bom, C.R. de la Fuente, E. de Souza, V. Depaoli, D. Di Sciascio, G. Dib, C.O. Dorner, D. Doro, M. Du Vernois, M. Ergin, T. Fan, K.L. Fraija, N. Funk, S. García, J.I. García-González, J.A. García Roca, S.T. Giacinti, G. Goksu, H. Guarino, F. Haist, F. Hansen, P.M. Harding, J.P. Hinton, J. Hofmann, W. Hona, B. Hoyos, D. Huentemeyer, P. Hueyotl-Zahuantitla, F. Insolia, A. Janecek, P. Joshi, V. Khelifi, B. Kunwar, S. Lapington, J. Laspiur, M.R. Leitl, F. Longo, F. Lopes, L. Lopez-Coto, R. Mandat, D. Mariazzi, A.G. Mariotti, M. Marques Moraes, A. Martínez-Castro, J. Martínez-Huerta, H. May, S. Melo, D.G. Mendes, L.F. Mendes, L.M. Mineeva, T. Mitchell, A. Mohan, S. Morales Olivares, O.G. Moreno-Barbosa, E. Nellen, L. Novotny, V. Olivera-Nieto, L. Orlando, E. Pech, M. Pichel, A. Portes de Albuquerque, M. Prandini, E. Rado Cuchills, M.S. Reisenegger, A. Reville, B. Rho, C.D. Rovero, A.C. Ruiz-Velasco, E. Salazar, G.A. Sandoval, A. Santander, M. Schoorlemmer, H. Schüssler, F. Serrano, V.H. Shellard, R.C. Sinha, A. Smith, A.J. Surajbali, P. Torres Aguilar, I. van Eldik, C. Vergara-Quispe, I.D. Viana, A. Vigorito, C.F. Wang, X. Werner, F. White, R. Zamalloa Jara, M.A. LIP - Laboratório de Instrumentação e Física Experimental de Partículas Lisbon Portugal IST - Instituto Superior Técnico Lisboa Portugal Department of Informatics Engineering CISUC University of Coimbra Coimbra Portugal Institute of Physics of the Czech Academy of Sciences Prague Czech Republic CBPF - Centro Brasileiro de Pesquisas Físicas Rio de Janeiro Brazil INFN Università di Padova Italy Computer Architecture and Technology Department University of Granada Granada Spain Av. Prof. Gama Pinto 2 Lisboa1649-003 Portugal Physics Division Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States Aix Marseille Univ CNRS IN2P3 CPPM 163 avenue de Luminy - Case 902 Marseillecedex 09 13288 France University of Padova Department of Physics and Astronomy INFN Padova Via Marzolo 8 Padova35131 Italy Centro Atómico Bariloche Comisión Nacional de Energía Atómica RN S. C. de Bariloche8400 Argentina Universidad Michoacana de San Nicolás de Hidalgo Calle de Santiago Tapia 403 Centro Mich. Morelia58000 Mexico FZU Institute of Physics of the Czech Academy of Sciences Na Slovance 1999/2 Praha 8182 00 Czech Republic Centro Brasileiro de Pesquisas Físicas R. Dr. Xavier Sigaud 150 RJ Rio de Janeiro22290-180 Brazil Av. de la Cultura 733 Pabellón CuscoC-358 Peru Instituto de Física Universidad Nacional Autónoma de México Sendero Bicipuma C.U. Coyoacán CDMX Ciudad de México04510 Mexico Dipartimento di Fisica Università degli Studi di Torino Via Pietro Giuria 1 Torino10125 Italy RWTH Aachen University Physics Institute 3 Otto-Blumenthal-Straße Aachen52074 Germany University of Maryland College ParkMD20742 United States Durham University Stockton Road DurhamDH1 3LE United Kingdom Astronomical Observatory University of Warsaw Aleje Ujazdowskie 4 Warsaw00478 Poland Facultad de Ciencias en Física y Matemáticas UNACH Boulevard Belisario Domínguez Km. 1081 Sin Número Terán Chiapas Tuxtla Gutiérrez Mexico Facultad de

The Southern Wide-field Gamma-ray Observatory (SWGO) is the next-generation gamma-ray observatory, currently in an R&D phase. The experiment is expected to have a large array of water Cherenkov detectors (WCD) placed at a high elevation (> 4.4 km a.s.l.) in South America. Here we present a WCD concept with reduced surface area and height of stations comprising four PMTs at the bottom. We show that it is possible to reach an excellent gamma/hadron discrimination by analysing the data gathered by this station with machine learning techniques. Such performance can be achieved by analysing the shower patterns at the ground or through the PMTs signal time structure to tag muons. Moreover, it is shown that the station's performance does not depend on the array configuration (dense or sparse) nor on the shower inclination (θ◦). Such a concept reduces the cost associated with the transport of massive amounts of water to high elevation sites while keeping a high physics performance. Therefore, it could be a good candidate station for SWGO, enabling to reach good sensitivities from low energies (∼ 100 GeV) up to the PeV region, covering large ground surface areas (few square km). © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Observatories

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Revisiting graph construction for fast image segmentation

arXiv

引用

arXiv 2017年

作者： Zhang, Zizhao Xing, Fuyong Wang, Hanzi Yan, Yan Huang, Ying Shi, Xiaoshuang Yang, Lin Dept. of Computer and Information Science and Engineering University of Florida FL32611 United States Dept. of Biostatistics and Informatics Colorado School of Public Health University of Colorado Denver DenverCO80045 United States J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida FL32611 United States Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Fujian361005 China

In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph over image regions with both global and local connections. Our novel approach to build graph connections relies on two key observations: 1) local region pairs that co-occur frequently will have a high probability to reside on a common object;2) spatially distant regions in a common object often exhibit similar visual saliency, which implies their neighborship in a manifold. We present a novel energy function to efficiently conduct graph partitioning. Based on multiple high quality partitions, we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi-class segmentation. Sufficient experiments, on the BSDS500 benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts. Copyright © 2017, The Authors. All rights reserved.

关键词： Image segmentation

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