84 research outputs found

    Stellar Metallicity Gradients in SDSS galaxies

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    We infer stellar metallicity and abundance ratio gradients for a sample of red galaxies in the Sloan Digital Sky Survey (SDSS) Main galaxy sample. Because this sample does not have multiple spectra at various radii in a single galaxy, we measure these gradients statistically. We separate galaxies into stellar mass bins, stack their spectra in redshift bins, and calculate the measured absorption line indices in projected annuli by differencing spectra in neighboring redshift bins. After determining the line indices, we use stellar population modeling from the EZ\_Ages software to calculate ages, metallicities, and abundance ratios within each annulus. Our data covers the central regions of these galaxies, out to slightly higher than 1Re1 R_{e}. We find detectable gradients in metallicity and relatively shallow gradients in abundance ratios, similar to results found for direct measurements of individual galaxies. The gradients are only weakly dependent on stellar mass, and this dependence is well-correlated with the change of ReR_e with mass. Based on this data, we report mean equivalent widths, metallicities, and abundance ratios as a function of mass and velocity dispersion for SDSS early-type galaxies, for fixed apertures of 2.5 kpc and of 0.5 ReR_e.Comment: 19 pages; 8 tables, 12 figures. Submitted to ApJ for publicatio

    Recent advances in heart sound analysis

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    "This is an author-created, un-copyedited versĂ­on of an article published in Physiological Measurement. IOP Publishing Ltd is not responsĂ­ble for any errors or omissĂ­ons in this versĂ­on of the manuscript or any versĂ­on derived from it. The VersĂ­on 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). Cultural adaptation of a shared decision making tool with Aboriginal women: a qualitative study. BMC Medical Informatics and Decision Making, 15(1). doi:10.1186/s12911-015-0129-7Saraçoğlu, R. (2012). Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction. Engineering Applications of Artificial Intelligence, 25(7), 1523-1528. doi:10.1016/j.engappai.2012.07.005Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J. (2010). Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological Measurement, 31(4), 513-529. doi:10.1088/0967-3334/31/4/004Springer, D. B., Brennan, T., Ntusi, N., Abdelrahman, H. Y., ZĂŒhlke, L. J., Mayosi, B. M., 
 Clifford, G. D. (2016). Automated signal quality assessment of mobile phone-recorded heart sound signals. Journal of Medical Engineering & Technology, 40(7-8), 342-355. doi:10.1080/03091902.2016.1213902Springer, D., Tarassenko, L., & Clifford, G. (2015). Logistic Regression-HSMM-based Heart Sound Segmentation. IEEE Transactions on Biomedical Engineering, 1-1. doi:10.1109/tbme.2015.2475278Uğuz, H. (2010). A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases. Journal of Medical Systems, 36(1), 61-72. doi:10.1007/s10916-010-9446-7Whitaker, B. M., Suresha, P. B., Liu, C., Clifford, G. D., & Anderson, D. V. (2017). Combining sparse coding and time-domain features for heart sound classification. Physiological Measurement, 38(8), 1701-1713. doi:10.1088/1361-6579/aa7623Zhu, T., Dunkley, N., Behar, J., Clifton, D. A., & Clifford, G. D. (2015). Fusing Continuous-Valued Medical Labels Using a Bayesian Model. Annals of Biomedical Engineering, 43(12), 2892-2902. doi:10.1007/s10439-015-1344-1Zhu, T., Johnson, A. E. W., Behar, J., & Clifford, G. D. (2013). Crowd-Sourced Annotation of ECG Signals Using Contextual Information. Annals of Biomedical Engineering, 42(4), 871-884. doi:10.1007/s10439-013-0964-

    An open access database for the evaluation of heart sound algorithms

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    This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

    Releasing activity disengages Cohesin’s Smc3/Scc1 interface in a process blocked by Acetylation

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    Sister chromatid cohesion conferred by entrapment of sister DNAs within a tripartite ring formed between cohesin’s Scc1, Smc1, and Smc3 subunits is created during S and destroyed at anaphase through Scc1 cleavage by separase. Cohesin’s association with chromosomes is controlled by opposing activities: loading by Scc2/4 complex and release by a separase- independent releasing activity as well as by cleavage. Coentrapment of sister DNAs at replication is accompanied by acetylation of Smc3 by Eco1, which blocks releasing activity and ensures that sisters remain connected. Because fusion of Smc3 to Scc1 prevents release and bypasses the requirement for Eco1, we suggested that release is mediated by disengagement of the Smc3/Scc1 interface. We show that mutations capable of bypassing Eco1 in Smc1, Smc3, Scc1, Wapl, Pds5, and Scc3 subunits reduce dissociation of N-terminal cleavage fragments of Scc1 (NScc1) from Smc3. This process involves interaction between Smc ATPase heads and is inhibited by Smc3 acetylation

    The AIM2 inflammasome exacerbates atherosclerosis in clonal haematopoiesis

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    Clonal haematopoiesis, which is highly prevalent in older individuals, arises from somatic mutations that endow a proliferative advantage to haematopoietic cells. Clonal haematopoiesis increases the risk of myocardial infarction and stroke independently of traditional risk factors(1). Among the common genetic variants that give rise to clonal haematopoiesis, the JAK2(V617F) (JAK2(VF)) mutation, which increases JAK-STAT signalling, occurs at a younger age and imparts the strongest risk of premature coronary heart disease(1,2). Here we show increased proliferation of macrophages and prominent formation of necrotic cores in atherosclerotic lesions in mice that express Jak2(VF) selectively in macrophages, and in chimeric mice that model clonal haematopoiesis. Deletion of the essential inflammasome components caspase 1 and 11, or of the pyroptosis executioner gasdermin D, reversed these adverse changes. Jak2(VF) lesions showed increased expression of AIM2, oxidative DNA damage and DNA replication stress, and Aim2 deficiency reduced atherosclerosis. Single-cell RNA sequencing analysis of Jak2(VF) lesions revealed a landscape that was enriched for inflammatory myeloid cells, which were suppressed by deletion of Gsdmd. Inhibition of the inflammasome product interleukin-1 beta reduced macrophage proliferation and necrotic formation while increasing the thickness of fibrous caps, indicating that it stabilized plaques. Our findings suggest that increased proliferation and glycolytic metabolism in Jak2(VF) macrophages lead to DNA replication stress and activation of the AIM2 inflammasome, thereby aggravating atherosclerosis. Precise application of therapies that target interleukin-1 beta or specific inflammasomes according to clonal haematopoiesis status could substantially reduce cardiovascular risk

    Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

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    The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000ĝ€-mmĝ€-yrĝ'1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000ĝ€-mmĝ€-yrĝ'1. Author(s) 2016.Fil: Wagner, Fabien H.. Instituto Nacional de Pesquisas Espaciais; BrasilFil: HĂ©rault, Bruno. Ecologie Des Forets de Guyane; BrasilFil: Bonal, Damien. Institut National de la Recherche Agronomique; FranciaFil: Stahl, Clment. Universiteit Antwerp; BĂ©lgicaFil: Anderson, Liana O.. National Center For Monitoring And Early Warning Of Natural Disasters; BrasilFil: Baker, Timothy R.. University Of Leeds; Reino UnidoFil: Sebastian Becker, Gabriel. Universidad de Hohenheim; AlemaniaFil: Beeckman, Hans. Royal Museum For Central Africa; BĂ©lgicaFil: Boanerges Souza, Danilo. MinistĂ©rio da CiĂȘncia, Tecnologia, InovaçÔes. Instituto Nacional de Pesquisas da AmazĂŽnia; BrasilFil: Cesar Botosso, Paulo. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Bowman, David M. J. S.. University of Tasmania; AustraliaFil: BrĂ€uning, Achim. Universitat Erlangen-Nuremberg; AlemaniaFil: Brede, Benjamin. Wageningen University And Research Centre; PaĂ­ses BajosFil: Irving Brown, Foster. Universidade Federal Do Acre; BrasilFil: Julio Camarero, Jesus. Instituto Boliviano de Investigacion Forestal Bolivia; BoliviaFil: Camargo, Plnio Barbosa. Universidade de Sao Paulo; BrasilFil: Cardoso, Fernanda C.G.. Universidade Federal do ParanĂĄ; BrasilFil: Carvalho, Fabrcio Alvim. Universidade Federal de Juiz de Fora; BrasilFil: Castro, Wendeson. Universidade Federal Do Acre; BrasilFil: Koloski Chagas, Rubens. Universidade de Sao Paulo; BrasilFil: Chave, Jrome. Centre National de la Recherche Scientifique; FranciaFil: Chidumayo, Emmanuel N.. University Of Zambia; ZambiaFil: Clark, Deborah A.. University Of Missouri-st. Louis; Estados UnidosFil: Regina Capellotto Costa, Flavia. MinistĂ©rio da CiĂȘncia, Tecnologia, InovaçÔes. Instituto Nacional de Pesquisas da AmazĂŽnia; BrasilFil: Couralet, Camille. Royal Museum For Central Africa; BĂ©lgicaFil: Henrique Da Silva Mauricio, Paulo. Universidade Federal Do Acre; BrasilFil: Dalitz, Helmut. Universidad de Hohenheim; AlemaniaFil: Resende De Castro, Vinicius. Universidade Federal de Vicosa; BrasilFil: Milani, Jaanan Eloisa De Freitas. Universidade Federal do ParanĂĄ; BrasilFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - Mendoza. Instituto Argentino de NivologĂ­a, GlaciologĂ­a y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de NivologĂ­a, GlaciologĂ­a y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de NivologĂ­a, GlaciologĂ­a y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; Argentin

    Optimization of lipase production by solid-state fermentation of olive pomace: from flask to laboratory-scale packed-bed bioreactor

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    Lipases are versatile catalysts with many applications and can be produced by solid-state fermentation (SSF) using agro-industrial wastes. The aim of this work was to maximize the production of Aspergillus ibericus lipase under SSF of olive pomace (OP) and wheat bran (WB), evaluating the effect on lipase production of C/N ratio, lipids, phenols, content of sugars of substrates and nitrogen source addition. Moreover, the implementation of the SSF process in a packed-bed bioreactor and the improvement of lipase extraction conditions were assessed. Low C/N ratios and high content of lipids led to maximum lipase production. Optimum SSF conditions were achieved with a C/N mass ratio of 25.2 and 10.2% (w/w) lipids in substrate, by the mixture of OP:WB (1:1) and supplemented with 1.33% (w/w) (NH4)2SO4. Studies in a packed-bed bioreactor showed that the lower aeration rates tested prevented substrate dehydration, improving lipase production. In this work, the important role of Triton X-100 on lipase extraction from the fermented solid substrate has been shown. A final lipase activity of 223 ± 5 U g1 (dry basis) was obtained after 7 days of fermentation.Felisbela Oliveira acknowledges the ïŹnancial support from Fundação para a CiĂȘncia e Tecnologia (FCT) of Portugal through grant SFRH/BD/87953/2012. JosĂ© Manuel Salgado was supported by grant CEB/N2020–INV/01/2016 from Project ‘‘BIOTECNORTE-Underpinning Biotechnology to foster the north of Portugal bioeconomy’’ (NORTE-01-0145-FEDER-000004). Luı ÂŽs Abrunhosa was supported by grant UMINHO/BPD/51/2015 from project UID/BIO/04469/2013 ïŹnanced by FCT/MEC (OE). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684) and BioTecNorte operation (NORTE-01-0145-FEDER000004) funded by the European Regional Development Fund under the scope of Norte2020–Programa Operacional Regional do Norte. Noelia PĂ©rez-RodrĂ­guez acknowledges the ïŹnancial support of FPU fellowship from the Spanish Ministry of Education, Culture and Sports. The authors thank the Spanish Ministry of Economy and Competitiveness for the ïŹnancial support of this work (Project CTQ2015-71436-C2-1-R), which has partial ïŹnancial support from the FEDER funds of the European Union.info:eu-repo/semantics/publishedVersio