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1. Presence of Recombinant Bat Coronavirus GCCDC1 in Cambodian Bats

Author

Feng Zhu, Veasna Duong, Xiao Fang Lim, Vibol Hul, Tanu Chawla, Lucy Keatts, Tracey Goldstein, Alexandre Hassanin, Vuong Tan Tu, Philippe Buchy, October M. Sessions, Lin-Fa Wang, Philippe Dussart, Danielle E. Anderson, Duke-NUS Medical School [Singapore], Institut Pasteur du Cambodge, Réseau International des Instituts Pasteur (RIIP), Unité des Virus Emergents (UVE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Wildlife Conservation Society (WCS), University of California [Davis] (UC Davis), University of California (UC), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Vietnam Academy of Science and Technology (VAST), University of Melbourne, and DEA and L-FW were supported by Grants NRF2012NRFCRP001-056 and NRF2016NRF-NSFC002-013 from the Singapore National Research Foundation. This study was made possible by the generous support of the American people through the United States Agency for International Development (USAID) Emerging Pandemic Threats PREDICT project (cooperative agreement number GHN-A-OO-09-00010-00 and AID-OAA-A-14-00102). The contents of this paper are the responsibility of the authors and do not necessarily reflect the views of the US Agency for International Development or the US Government. The fieldwork was supported in part by the National Authority for Preah Vihear, UNESCO, 'Société des amis du Muséum et du Jardin des Plantes', and the Muséum national d’Histoire naturelle.

Subjects
Recombination, Genetic, China, Coronaviridae, Coronaviridae Infections, [SDV]Life Sciences [q-bio], viruses, bats, coronavirus, virus diseases, cross-species transmission, Genome, Viral, zoonosis, recombination, respiratory tract diseases, Evolution, Molecular, Phylogeography, co-infection, Infectious Diseases, Chiroptera, Virology, Animals, GCCDC1, Cambodia, Phylogeny, Disease Reservoirs
Abstract

International audience; Bats have been recognized as an exceptional viral reservoir, especially for coronaviruses. At least three bat zoonotic coronaviruses (SARS-CoV, MERS-CoV and SARS-CoV-2) have been shown to cause severe diseases in humans and it is expected more will emerge. One of the major features of CoVs is that they are all highly prone to recombination. An extreme example is the insertion of the P10 gene from reoviruses in the bat CoV GCCDC1, first discovered in Rousettus leschenaultii bats in China. Here, we report the detection of GCCDC1 in four different bat species (Eonycteris spelaea, Cynopterus sphinx, Rhinolophus shameli and Rousettus sp.) in Cambodia. This finding demonstrates a much broader geographic and bat species range for this virus and indicates common cross-species transmission. Interestingly, one of the bat samples showed a co-infection with an Alpha CoV most closely related to RsYN14, a virus recently discovered in the same genus (Rhinolophus) of bat in Yunnan, China, 2020. Taken together, our latest findings highlight the need to conduct active surveillance in bats to assess the risk of emerging CoVs, especially in Southeast Asia.

Published
2022
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2. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

Author

Luis A. Hernández Gómez, José Luis Blanco Murillo, José Alcázar Ramírez, Eduardo López Gonzalo, Rubén Fernández Pozo, Doroteo Torre Toledano, [Fernández Pozo,R, Blanco Murillo,JL, Hernández Gómez,L, López Gonzalo,E] Signal, Systems and Radiocommunications Departament, Universidad Politécnica de Madrid, Madrid, Spain. [Alcázar Ramírez,J] Respiratory Departament, Hospital Torrecárdenas, Almería, Spain. [Toledano,DT] ATVS Biometric Recognition group, Universidad Autónoma de Madrid, Madrid, Spain., The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project., UAM. Departamento de Ingeniería Informática, and Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002)

Subjects
Sustained speech, Artificial intelligence, Information Science::Information Science::Computing Methodologies::Software::Speech Recognition Software [Medical Subject Headings], Computer science, Speech recognition, 0206 medical engineering, lcsh:TK7800-8360, 02 engineering and technology, computer.software_genre, Voice analysis, Nasalization, lcsh:Telecommunication, Vowel, lcsh:TK5101-6720, 0202 electrical engineering, electronic engineering, information engineering, Distribución normal, Diseases::Respiratory Tract Diseases::Respiration Disorders::Apnea::Sleep Apnea Syndromes::Sleep Apnea, Obstructive [Medical Subject Headings], Audio signal processing, Telecomunicaciones, Phenomena and Processes::Mathematical Concepts::Statistical Distributions::Normal Distribution [Medical Subject Headings], lcsh:Electronics, 020206 networking & telecommunications, Phonetics, Speaker recognition, Speech processing, Diseases::Otorhinolaryngologic Diseases::Laryngeal Diseases::Voice Disorders [Medical Subject Headings], 020601 biomedical engineering, Continuous speech, Obstructive sleep apnea, respiratory tract diseases, Apnea del sueño obstructiva, Pattern recognition (psychology), Speech dynamics, Gaussian mixture models, computer, Programa informático para el reconocimiento del lenguaje hablado, Trastornos de la voz, Classification and regression tree (CART)
Abstract

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531, This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry., The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project.

Published
2009