341 research outputs found

    CoRoT 102918586: a Gamma Dor pulsator in a short period eccentric eclipsing binary

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    Pulsating stars in eclipsing binary systems are powerful tools to test stellar models. Binarity enables to constrain the pulsating component physical parameters, whose knowledge drastically improves the input physics for asteroseismic studies. The study of stellar oscillations allows us, in its turn, to improve our understanding of stellar interiors and evolution. The space mission CoRoT discovered several promising objects suitable for these studies, which have been photometrically observed with unprecedented accuracy, but needed spectroscopic follow-up. A promising target was the relatively bright eclipsing system CoRoT 102918586, which turned out to be a double-lined spectroscopic binary and showed, as well, clear evidence of Gamma Dor type pulsations. We obtained phase resolved high-resolution spectroscopy with the Sandiford spectrograph at the McDonald 2.1m telescope and the FEROS spectrograph at the ESO 2.2m telescope. Spectroscopy yielded both the radial velocity curves and, after spectra disentangling, the component effective temperatures, metallicity and line-of-sight projected rotational velocities. The CoRoT light curve was analyzed with an iterative procedure, devised to disentangle eclipses from pulsations. We obtained an accurate determination of the system parameters, and by comparison with evolutionary models strict constraints on the system age. Finally, the residuals obtained after subtraction of the best fitting eclipsing binary model were analyzed to determine the pulsator properties. We achieved a quite complete and consistent description of the system. The primary star pulsates with typical {\gamma} Dor frequencies and shows a splitting in period which is consistent with high order g-mode pulsations in a star of the corresponding physical parameters. The value of the splitting, in particular, is consistent with pulsations in l = 1 modes.Comment: 12 pages, 10 figures. Accepted for publication in Astronomy and Astrophysic

    Modeling wave propagation through an analytical surface model

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    Nowadays there is a certain development in the use of railway, especially in the form of trams and underground lines in urban areas. Despite its many advantages, this kind of transport is a significant source of vibrations, which may affect residents and buildings near to the track. Wave transmission through the ground is therefore a phenomenon of particular interest. The object of this article is to formulate and test an analytical model of vibration propagation through the terrain surface. The model is based on the wave equation and takes into account wave scattering and reflection in the interfaces between different soil layers. A sensitivity analysis of its main parameters is carried out, and then a comprehensive set of simulations is made to test model performance and analyze factors such as load magnitude and velocity or soil configuration. The model has proved to be an interesting instrument to study the vibration phenomenon from a theoretical point of view and some improvements are proposed to turn it into a tool for engineers and designers

    Kepler-91b: a planet at the end of its life. Planet and giant host star properties via light-curve variations

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    The evolution of planetary systems is intimately linked to the evolution of their host star. Our understanding of the whole planetary evolution process is based on the large planet diversity observed so far. To date, only few tens of planets have been discovered orbiting stars ascending the Red Giant Branch. Although several theories have been proposed, the question of how planets die remains open due to the small number statistics. In this work we study the giant star Kepler-91 (KOI-2133) in order to determine the nature of a transiting companion. This system was detected by the Kepler Space Telescope. However, its planetary confirmation is needed. We confirm the planetary nature of the object transiting the star Kepler-91 by deriving a mass of Mp=0.880.33+0.17 MJup M_p=0.88^{+0.17}_{-0.33} ~M_{\rm Jup} and a planetary radius of Rp=1.3840.054+0.011 RJupR_p=1.384^{+0.011}_{-0.054} ~R_{\rm Jup}. Asteroseismic analysis produces a stellar radius of R=6.30±0.16 RR_{\star}=6.30\pm 0.16 ~R_{\odot} and a mass of M=1.31±0.10 MM_{\star}=1.31\pm 0.10 ~ M_{\odot} . We find that its eccentric orbit (e=0.0660.017+0.013e=0.066^{+0.013}_{-0.017}) is just 1.320.22+0.07 R1.32^{+0.07}_{-0.22} ~ R_{\star} away from the stellar atmosphere at the pericenter. Kepler-91b could be the previous stage of the planet engulfment, recently detected for BD+48 740. Our estimations show that Kepler-91b will be swallowed by its host star in less than 55 Myr. Among the confirmed planets around giant stars, this is the planetary-mass body closest to its host star. At pericenter passage, the star subtends an angle of 4848^{\circ}, covering around 10% of the sky as seen from the planet. The planetary atmosphere seems to be inflated probably due to the high stellar irradiation.Comment: 21 pages, 8 tables and 11 figure

    Light elements in stars with exoplanets

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    It is well known that stars orbited by giant planets have higher abundances of heavy elements when compared with average field dwarfs. A number of studies have also addressed the possibility that light element abundances are different in these stars. In this paper we will review the present status of these studies. The most significant trends will be discussed.Comment: 10 pages, 6 figures. Submitted to the proceedings of IAU symposium 268: Light elements in the universe

    Galactic Archaeology with CoRoT and APOGEE: Creating mock observations from a chemodynamical model

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    In a companion paper, we have presented the combined asteroseismic-spectroscopic dataset obtained from CoRoT lightcurves and APOGEE infra-red spectra for 678 solar-like oscillating red giants in two fields of the Galactic disc (CoRoGEE). We have measured chemical abundance patterns, distances, and ages of these field stars which are spread over a large radial range of the Milky Way's disc. Here we show how to simulate this dataset using a chemodynamical Galaxy model. We also demonstrate how the observation procedure influences the accuracy of our estimated ages.Comment: 5 pages, 6 figures. To appear in Astronomische Nachrichten, special issue "Reconstruction the Milky Way's History: Spectroscopic surveys, Asteroseismology and Chemo-dynamical models", Guest Editors C. Chiappini, J. Montalb\'an, and M. Steffe

    Design and validation of a railway inspection system to detect lateral track geometry defects based on axle-box accelerations registered from in-service trains

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    Metropolitan railway transport has become an efficient solution to the mobility necessities in urban areas. Railway track maintenance tasks have to be improved and adjusted to metropolitan requirements, in particular the few hours available to operate due to the high frequency service offered. This paper describes and proposes an inertial monitoring system to detect and estimate track irregularities by using in-service vehicles. A new maintenance strategy is established, based on the railway track conditions and continuous monitoring is provided to do so. The system proposed consists of at least two accelerometers mounted on the bogie axle-box and a GPS (Global Positioning System). Lateral accelerations have been analyzed to study gauge and lateral alignment deviations. Accelerations have been treated and processed by high-pass filtering and validation has been carried out by comparison with measurements provided by a track monitoring trolley. Measurements were made on Line 1 of the Alicante metropolitan and tram network (Spain)

    Railway traffic induced vibrations: comparison of analytical and finite element models

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    The recent increase in the use of the railway and the establishment of more restrictive policies of harmful environmental effects of railway transport highlights the need to investigate ground vibrations related to trains. Therefore models to evaluate how this phenomenon affects have been performed. This article aims to expose both analytical and 3D-FE models and to compare theoretical formulation and results. Models have been calibrated and validated with real data. Furthermore, a simulation of the acceleration level of different railway infrastructure elements has been achieved

    Grids of Stellar Models and Frequencies with CLES + LOSC

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    We present a grid of stellar models, obtained with the CLES evolution code, following the specification of ESTA-Task1, and the corresponfing seismic properties, computed with the LOSC code. We provide a complete description of the corresponding files that will be available on the ESTA web-pages.Comment: 8 pages, accepted for publication in Astrophys. Space Sci. (CoRoT/ESTA Volume

    MultiBaC: A strategy to remove batch effects between different omic data types

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    [EN] Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is part of a research project that is totally funded by Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana) through PROMETEO grants program for excellence research groups.Ugidos, M.; Tarazona Campos, S.; Prats-Montalbán, JM.; Ferrer, A.; Conesa, A. (2020). MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research. 29(10):2851-2864. https://doi.org/10.1177/0962280220907365S285128642910Kupfer, P., Guthke, R., Pohlers, D., Huber, R., Koczan, D., & Kinne, R. W. (2012). Batch correction of microarray data substantially improves the identification of genes differentially expressed in Rheumatoid Arthritis and Osteoarthritis. BMC Medical Genomics, 5(1). doi:10.1186/1755-8794-5-23Gregori, J., Villarreal, L., Méndez, O., Sánchez, A., Baselga, J., & Villanueva, J. (2012). Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics. Journal of Proteomics, 75(13), 3938-3951. doi:10.1016/j.jprot.2012.05.005Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47-e47. doi:10.1093/nar/gkv007Gagnon-Bartsch, J. A., & Speed, T. P. (2011). Using control genes to correct for unwanted variation in microarray data. Biostatistics, 13(3), 539-552. doi:10.1093/biostatistics/kxr034Nueda, M. j., Ferrer, A., & Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics, 13(3), 553-566. doi:10.1093/biostatistics/kxr042Jansen, J. J., Hoefsloot, H. C. J., van der Greef, J., Timmerman, M. E., Westerhuis, J. A., & Smilde, A. K. (2005). ASCA: analysis of multivariate data obtained from an experimental design. Journal of Chemometrics, 19(9), 469-481. doi:10.1002/cem.952Nueda, M. J., Conesa, A., Westerhuis, J. A., Hoefsloot, H. C. J., Smilde, A. K., Talón, M., & Ferrer, A. (2007). Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA. Bioinformatics, 23(14), 1792-1800. doi:10.1093/bioinformatics/btm251Giordan, M. (2013). A Two-Stage Procedure for the Removal of Batch Effects in Microarray Studies. Statistics in Biosciences, 6(1), 73-84. doi:10.1007/s12561-013-9081-1Nyamundanda, G., Poudel, P., Patil, Y., & Sadanandam, A. (2017). A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies. Scientific Reports, 7(1). doi:10.1038/s41598-017-11110-6Reese, S. E., Archer, K. J., Therneau, T. M., Atkinson, E. J., Vachon, C. M., de Andrade, M., … Eckel-Passow, J. E. (2013). A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics, 29(22), 2877-2883. doi:10.1093/bioinformatics/btt480Papiez, A., Marczyk, M., Polanska, J., & Polanski, A. (2018). BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm. Bioinformatics, 35(11), 1885-1892. doi:10.1093/bioinformatics/bty900Keel, B. N., Zarek, C. M., Keele, J. W., Kuehn, L. A., Snelling, W. M., Oliver, W. T., … Lindholm-Perry, A. K. (2018). RNA-Seq Meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers. BMC Genomics, 19(1). doi:10.1186/s12864-018-4769-8Li, M. D., Burns, T. C., Morgan, A. A., & Khatri, P. (2014). Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases. Acta Neuropathologica Communications, 2(1). doi:10.1186/s40478-014-0093-yAndres-Terre, M., McGuire, H. M., Pouliot, Y., Bongen, E., Sweeney, T. E., Tato, C. M., & Khatri, P. (2015). Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses. Immunity, 43(6), 1199-1211. doi:10.1016/j.immuni.2015.11.003Sandhu, V., Labori, K. J., Borgida, A., Lungu, I., Bartlett, J., Hafezi-Bakhtiari, S., … Haibe-Kains, B. (2019). Meta-Analysis of 1,200 Transcriptomic Profiles Identifies a Prognostic Model for Pancreatic Ductal Adenocarcinoma. JCO Clinical Cancer Informatics, (3), 1-16. doi:10.1200/cci.18.00102Huang, H., Liu, C.-C., & Zhou, X. J. (2010). Bayesian approach to transforming public gene expression repositories into disease diagnosis databases. Proceedings of the National Academy of Sciences, 107(15), 6823-6828. doi:10.1073/pnas.0912043107Pelechano, V., & Pérez-Ortín, J. E. (2010). There is a steady-state transcriptome in exponentially growing yeast cells. Yeast, 27(7), 413-422. doi:10.1002/yea.1768Garcı́a-Martı́nez, J., Aranda, A., & Pérez-Ortı́n, J. E. (2004). Genomic Run-On Evaluates Transcription Rates for All Yeast Genes and Identifies Gene Regulatory Mechanisms. Molecular Cell, 15(2), 303-313. doi:10.1016/j.molcel.2004.06.004Pelechano, V., Chávez, S., & Pérez-Ortín, J. E. (2010). A Complete Set of Nascent Transcription Rates for Yeast Genes. PLoS ONE, 5(11), e15442. doi:10.1371/journal.pone.0015442Zid, B. M., & O’Shea, E. K. (2014). Promoter sequences direct cytoplasmic localization and translation of mRNAs during starvation in yeast. Nature, 514(7520), 117-121. doi:10.1038/nature13578Freeberg, M. A., Han, T., Moresco, J. J., Kong, A., Yang, Y.-C., Lu, Z., … Kim, J. K. (2013). Pervasive and dynamic protein binding sites of the mRNA transcriptome in Saccharomyces cerevisiae. Genome Biology, 14(2), R13. doi:10.1186/gb-2013-14-2-r13McKinlay, A., Araya, C. L., & Fields, S. (2011). Genome-Wide Analysis of Nascent Transcription in Saccharomyces cerevisiae. G3 Genes|Genomes|Genetics, 1(7), 549-558. doi:10.1534/g3.111.000810Castells-Roca, L., García-Martínez, J., Moreno, J., Herrero, E., Bellí, G., & Pérez-Ortín, J. E. (2011). Heat Shock Response in Yeast Involves Changes in Both Transcription Rates and mRNA Stabilities. PLoS ONE, 6(2), e17272. doi:10.1371/journal.pone.0017272Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130. doi:10.1016/s0169-7439(01)00155-1Folch-Fortuny, A., Vitale, R., de Noord, O. E., & Ferrer, A. (2017). Calibration transfer between NIR spectrometers: New proposals and a comparative study. Journal of Chemometrics, 31(3), e2874. doi:10.1002/cem.2874García Muñoz, S., MacGregor, J. F., & Kourti, T. (2005). Product transfer between sites using Joint-Y PLS. Chemometrics and Intelligent Laboratory Systems, 79(1-2), 101-114. doi:10.1016/j.chemolab.2005.04.009Andrade, J. M., Gómez-Carracedo, M. P., Krzanowski, W., & Kubista, M. (2004). Procrustes rotation in analytical chemistry, a tutorial. Chemometrics and Intelligent Laboratory Systems, 72(2), 123-132. doi:10.1016/j.chemolab.2004.01.007Hurley, J. R., & Cattell, R. B. (2007). The procrustes program: Producing direct rotation to test a hypothesized factor structure. Behavioral Science, 7(2), 258-262. doi:10.1002/bs.3830070216Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Applied Statistics, 28(1), 100. doi:10.2307/234683
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