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Overview Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 30,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. Private Set and Testing Protocol Researchers interested in testing their models on the private test dataset should contact us at coughvid@epfl.ch, briefly explaining the type of validation they wish to make, and their obtained results obtained through cross-validation with the public data. Then, access to the unlabeled recordings will be provided, and the researchers should send the predictions of their models on these recordings. Finally, the performance metrics of the predictions will be sent to the researchers. The private testing data is not included in any file within our Zenodo record, and it can only be accessed by contacting the COUGHVID team at the aforementioned e-mail address. New Semi-Supervised Labeling The third version of the COUGHVID dataset contains thousands of additional recordings obtained through October 2021. Additionally, the recordings containing coughs were re-labeled according to a semi-supervised learning algorithm that combined the user labels with those of the expert physicians, which were modeled using ML and expanded on the previously unlabeled data. These labels can be found in the "status_SSL" column of the "metadata_compiled.csv" file. For more information about the data collection, pre-processing, validation, and data structure, please refer to the following publication: https://www.nature.com/articles/s41597-021-00937-4 The cough pre-processing and feature extraction code is available from the following c4science repository: https://c4science.ch/diffusion/10770/

Macroautophagy/autophagy is a conserved transport pathway where targeted structures are sequestered by phagophores, which mature into autophagosomes, and then delivered into lysosomes for degradation. Autophagy is involved in the pathophysiology of numerous diseases and its modulation is beneficial for the outcome of numerous specific diseases. Several lysosomal inhibitors such as bafilomycin A1 (BafA1), protease inhibitors and chloroquine (CQ), have been used interchangeably to block autophagy in in vitro experiments assuming that they all primarily block lysosomal degradation. Among them, only CQ and its derivate hydroxychloroquine (HCQ) are FDA-approved drugs and are thus currently the principal compounds used in clinical trials aimed to treat tumors through autophagy inhibition. However, the precise mechanism of how CQ blocks autophagy remains to be firmly demonstrated. In this study, we focus on how CQ inhibits autophagy and directly compare its effects to those of BafA1. We show that CQ mainly inhibits autophagy by impairing autophagosome fusion with lysosomes rather than by affecting the acidity and/or degradative activity of this organelle. Furthermore, CQ induces an autophagy-independent severe disorganization of the Golgi and endo-lysosomal systems, which might contribute to the fusion impairment. Strikingly, HCQ-treated mice also show a Golgi disorganization in kidney and intestinal tissues. Altogether, our data reveal that CQ and HCQ are not bona fide surrogates for other types of late stage lysosomal inhibitors for in vivo experiments. Moreover, the multiple cellular alterations caused by CQ and HCQ call for caution when interpreting results obtained by blocking autophagy with this drug.

Autophagy is a conserved intracellular catabolic pathway that allows cells to maintain homeostasis through the degradation of deleterious components via specialized double-membrane vesicles called autophagosomes. During the past decades, it has been revealed that numerous pathogens, including viruses, usurp autophagy in order to promote their propagation. Nidovirales are an order of enveloped viruses with large single-stranded positive RNA genomes. Four virus families (Arterividae, Coronaviridae, Mesoniviridae, and Roniviridae) are part of this order, which comprises several human and animal pathogens of medical and veterinary importance. In host cells, Nidovirales induce membrane rearrangements including autophagosome formation. The relevance and putative mechanism of autophagy usurpation, however, remain largely elusive. Here, we review the current knowledge about the possible interplay between Nidovirales and autophagy.