Recent advances in heart sound analysis

"This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/aa7ec8".[EN] Objective: Auscultation of heart sound recordings or the phonocardiogram (PCG) has been shown to be valuable for the detection of disease and pathologies (Leatham 1975, Raghu et al 2015). The automated classification of pathology in heart sounds has been studied for over 50 years. Typical methods can be grouped into: artificial neural network-based approaches (Uguz 2012), support vector machines (Ari et al 2010), hidden Markov model-based approaches (Saracoglu 2012) and clustering-based approaches (Quiceno-Manrique et al 2010). However, accurate automated classification still remains a significant challenge due to the lack of highquality, rigorously validated, and standardized open databases of heart sound recordings. Approach: The 2016 PhysioNet/Computing in Cardiology (CinC) Challenge sought to create a large database to facilitate this, by assembling recordings from multiple research groups across the world, acquired in different real-world clinical and nonclinical environments (such as in-home visits), to encourage the development of algorithms to accurately identify, from a single short recording (10-60s), as normal, abnormal or poor signal quality, and thus to further identify whether the subject of the recording should be referred on for an expert diagnosis (Liu et al 2016). Until this Challenge, no significant open-access heart sound database was available for researchers to train and evaluate the automated diagnostics algorithms upon (Clifford et al 2016). Moreover, no open source heart sound segmentation and classification algorithms were available. The Challenge changed this situation significantly. Main results and Significance: This editorial reviews the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for promising research avenues in the field of heart sound signal processing and classification as a result of the Challenge.This work was funded in part by the National Institutes of Health, grant R01-GM104987, the International Postdoctoral Exchange Programme of the National Postdoctoral Management Committee of China and Emory University. We are also grateful to Mathworks for providing free software licenses and sponsoring the Challenge prize money, and Computing in Cardiology for sponsoring the Challenge prize money and providing a forum to present the Challenge results. We would also like to thank the database contributors, and data annotators for their invaluable assistance. Finally, we would like to thank all the competitors and researchers themselves, without whom there would be no Challenge or special issue.Clifford, GD.; Liu, C.; Moody, B.; Millet Roig, J.; Schmidt, S.; Li, Q.; Silva, I.... (2017). Recent advances in heart sound analysis. Physiological Measurement. 38(8):10-25. https://doi.org/10.1088/1361-6579/aa7ec8S1025388Abdollahpur, M., Ghaffari, A., Ghiasi, S., & Mollakazemi, M. J. (2017). Detection of pathological heart sounds. Physiological Measurement, 38(8), 1616-1630. doi:10.1088/1361-6579/aa7840Ari, S., Hembram, K., & Saha, G. (2010). Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Systems with Applications, 37(12), 8019-8026. doi:10.1016/j.eswa.2010.05.088Chauhan, S., Wang, P., Sing Lim, C., & Anantharaman, V. (2008). A computer-aided MFCC-based HMM system for automatic auscultation. Computers in Biology and Medicine, 38(2), 221-233. doi:10.1016/j.compbiomed.2007.10.006Nabhan Homsi, M., & Warrick, P. (2017). Ensemble methods with outliers for phonocardiogram classification. Physiological Measurement, 38(8), 1631-1644. doi:10.1088/1361-6579/aa7982Kay, E., & Agarwal, A. (2017). DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. Physiological Measurement, 38(8), 1645-1657. doi:10.1088/1361-6579/aa6a3dLangley, P., & Murray, A. (2017). Heart sound classification from unsegmented phonocardiograms. Physiological Measurement, 38(8), 1658-1670. doi:10.1088/1361-6579/aa724cLiu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., … Clifford, G. D. (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement, 37(12), 2181-2213. doi:10.1088/0967-3334/37/12/2181Maknickas, V., & Maknickas, A. (2017). Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiological Measurement, 38(8), 1671-1684. doi:10.1088/1361-6579/aa7841Plesinger, F., Viscor, I., Halamek, J., Jurco, J., & Jurak, P. (2017). Heart sounds analysis using probability assessment. Physiological Measurement, 38(8), 1685-1700. doi:10.1088/1361-6579/aa7620Da Poian, G., Liu, C., Bernardini, R., Rinaldo, R., & Clifford, G. D. (2017). Atrial fibrillation detection on compressed sensed ECG. Physiological Measurement, 38(7), 1405-1425. doi:10.1088/1361-6579/aa7652Quiceno-Manrique, A. F., Godino-Llorente, J. I., Blanco-Velasco, M., & Castellanos-Dominguez, G. (2009). Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals. Annals of Biomedical Engineering, 38(1), 118-137. doi:10.1007/s10439-009-9838-3Jull, J., Giles, A., Boyer, Y., & Stacey, D. (2015). 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A switchable pH-differential unitized regenerative fuel cell with high performance

Regenerative fuel cells are a potential candidate for future energy storage, but their applications are limited by the high cost and poor round-trip efficiency. Here we present a switchable pH-differential unitized regenerative fuel cell capable of addressing both the obstacles. Relying on a membraneless laminar flow-based design, pH environments in the cell are optimized independently for different electrode reactions and are switchable together with the cell process to ensure always favorable thermodynamics for each electrode reaction. Benefiting from the thermodynamic advantages of the switchable pH-differential arrangement, the cell allows water electrolysis at a voltage of 0.57 V, and a fuel cell open circuit voltage of 1.89 V, rendering round-trip efficiencies up to 74%. Under room conditions, operating the cell in fuel cell mode yields a power density of 1.3 W cm¯², which is the highest performance to date for laminar flow-based cells and is comparable to state-of-the-art polymer electrolyte membrane fuel cells

Genetically engineered probiotic E. coli Nissle to consume amino acids associated with orphan metabolic diseases

Orphan metabolic diseases are rare genetic defects that interfere with metabolism due to ineffective or missing enzymes. Two of them, Phenylketonuria (PKU) and Maple Syrup Urine Disease (MSUD) are defined by accumulation of amino acids to toxic levels due to defective metabolism of protein break down products. PKU is caused by a defect in the gene encoding phenylalanine hydroxylase (PAH). MSUD is caused by a defect in a multi-enzyme complex found in mitochondria called branched chain ɑ-ketoacid dehydrogenase “BCKDH”. Without the activity of these enzymes, the amino acid phenylalanine (Phe) in the case of PKU or the branched-chain amino acids leucine (Leu), isoleucine and valine for MSUD build up to neurotoxic levels in the blood and brain, leading to neurological deficits. Current treatment options focus on dietary protein restriction, are insufficient and, unfortunately, can lead to a failure to thrive. Lifelong compliance with a prescription diet is also a concern. We have genetically engineered Nissle, a probiotic strain of E. coli, to reduce serum phenylalanine and leucine levels in patients with PKU or MSUD; preclinical data supporting the activity of these strains are described. Please click Additional Files below to see the full abstract

A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2

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Postural stability during standing balance and sit-to-stand in master athlete runners compared with nonathletic old and young adults

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Coastal New England pilot study to determine fossil and biogenic formaldehyde source contributions using radiocarbon

Author Posting. © American Geophysical Union, 2010. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 115 (2010): D10301, doi:10.1029/2009JD012810.Compound specific radiocarbon analyses of atmospheric formaldehyde are reported as fraction modern (Fm) for a limited number of winter and summer air samples collected in coastal southern New England in 2007. The 11 of 13 samples with Fm 0.2 (max ∼ 0.35) were collected on days with strong northwesterly flow and the least urban impact. The Fm data were combined with VOC observations from the Rhode Island Department of Environmental Management, estimates of oxygenated VOC (OVOC), and back trajectories to interpret the relative contributions of biogenic and fossil carbon sources. It is argued that CH2O sources were dominated by pollutant VOCs and OVOCs from upwind coastal cities as opposed to more local biogenic VOCs at the times of sample collection.This research was supported by a graduate student internship program at WHOI National Ocean Sciences Accelerator Mass Spectrometry Facility (NSF OCE‐9807266) and by NASA project NNG04GB38G

Canine respiratory coronavirus employs caveolin-1-mediated pathway for internalization to HRT-18G cells

Canine respiratory coronavirus (CRCoV), identified in 2003, is a member of the Coronaviridae family. The virus is a betacoronavirus and a close relative of human coronavirus OC43 and bovine coronavirus. Here, we examined entry of CRCoV into human rectal tumor cells (HRT-18G cell line) by analyzing co-localization of single virus particles with cellular markers in the presence or absence of chemical inhibitors of pathways potentially involved in virus entry. We also targeted these pathways using siRNA. The results show that the virus hijacks caveolin-dependent endocytosis to enter cells via endocytic internalization

Synthetic glycans control gut microbiome structure and mitigate colitis in mice

Relative abundances of bacterial species in the gut microbiome have been linked to many diseases. Species of gut bacteria are ecologically differentiated by their abilities to metabolize different glycans, making glycan delivery a powerful way to alter the microbiome to promote health. Here, we study the properties and therapeutic potential of chemically diverse synthetic glycans (SGs). Fermentation of SGs by gut microbiome cultures results in compound-specific shifts in taxonomic and metabolite profiles not observed with reference glycans, including prebiotics. Model enteric pathogens grow poorly on most SGs, potentially increasing their safety for at-risk populations. SGs increase survival, reduce weight loss, and improve clinical scores in mouse models of colitis. Synthetic glycans are thus a promising modality to improve health through selective changes to the gut microbiome

Ezrin interacts with the SARS coronavirus spike protein and restrains infection at the entry stage

© 2012 Millet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Entry of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and its envelope fusion with host cell membrane are controlled by a series of complex molecular mechanisms, largely dependent on the viral envelope glycoprotein Spike (S). There are still many unknowns on the implication of cellular factors that regulate the entry process. Methodology/Principal Findings: We performed a yeast two-hybrid screen using as bait the carboxy-terminal endodomain of S, which faces the cytosol during and after opening of the fusion pore at early stages of the virus life cycle. Here we show that the ezrin membrane-actin linker interacts with S endodomain through the F1 lobe of its FERM domain and that both the eight carboxy-terminal amino-acids and a membrane-proximal cysteine cluster of S endodomain are important for this interaction in vitro. Interestingly, we found that ezrin is present at the site of entry of S-pseudotyped lentiviral particles in Vero E6 cells. Targeting ezrin function by small interfering RNA increased S-mediated entry of pseudotyped particles in epithelial cells. Furthermore, deletion of the eight carboxy-terminal amino acids of S enhanced S-pseudotyped particles infection. Expression of the ezrin dominant negative FERM domain enhanced cell susceptibility to infection by SARS-CoV and S pseudotyped particles and potentiated S-dependent membrane fusion. Conclusions/Significance: Ezrin interacts with SARS-CoV S endodomain and limits virus entry and fusion. Our data present a novel mechanism involving a cellular factor in the regulation of S-dependent early events of infection.This work was supported by the Research Grant Council of Hong Kong (RGC#760208)and the RESPARI project of the International Network of Pasteur Institutes