Izal-Azcárate, I. (Íñigo)

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    Isolation, culture and characterization of adult carotid body-derived cells
    (Elsevier, 2009) San-Sebastian, W. (Waldy); Gutierrez-Perez, M. (María); Izal-Azcárate, I. (Íñigo); Marcilla, I. (Irene); Belzunegui, S. (S.); Izal-Azcarate, A. (A.); Luquin, M.R. (María Rosario); Prosper-Cardoso, F. (Felipe); Lopez, B. (Berta)
    Recent studies indicate that carotid body (CB) could be a suitable cell source for cell therapy in Parkinson’s disease.We have isolated and successfully expanded in culture as monolayer adult CB-derived cells using a modification of the culture medium employed for bone marrow multipotent adult progenitor cells (MAPCs). These cells express variable amounts of tyrosine hydroxylase (TH), -III tubulin and Sox2. In addition, CB-derived cells showed high expression of Sox2 related to a high rate of proliferation and consistent with an undifferentiated state. Under culture conditions that reduced cell proliferation, Sox2 expression decreased while TH and -III tubulin expression was increased. This could indicate that the differentiation of some cells occurs in the culture, thus accounting for a certain neural differentiation potential of CB-derived cells.
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    Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks
    (Elsevier, 2022) Vadillo, J. (Javier); Izal-Azcárate, I. (Íñigo); Galarza-Rodríguez, A. (Ainhoa); Rodríguez-Lalanne, F. (Fermín)
    Due to the expected lack of fossil fuels in near future as well as climate change produced by greenhouse effect as consequence of environmental emissions, renewable energy generation, and specifically solar photovoltaic generation, has become relevant in present energy generation challenge. Photovoltaic generators have strong relationship with solar irradiation and outdoor temperature in energy generation process. These meteorological parameters are volatile and uncertain in nature so, unexpected changes on these parameters produce variations on solar photovoltaic generators’ output power. While many researchers have been focused in recent years on the development of novel models for forecasting involved meteorological parameters in photovoltaic generation, they commonly do not consider an analysis step of the data before using it in the developed models. Hence, the aim of this study consists in assembling a wavelet based time-frequency analysis of the used data with deep learning neural networks to forecast solar irradiation, in next 10 min, to compute solar photovoltaic generation. Results of the validation step showed that the deviation of the proposed forecaster was lower than 4% in 90.60% of studied sample days. Final forecaster’s root mean square error was 35.77 W/m2, which was an accuracy improvement of 37.52% compared against persistence benchmark model
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    Enseñando Biología III
    (Servicio de Publicaciones de la Universidad de Navarra, 2018) Ibañez-Gaston, R. (Ricardo); González-Fernández, D. (David); Galicia-Paredes, D. (David); Fernández-Rodríguez, J. (Juana); Izal-Azcárate, I. (Íñigo); Martín-Rodríguez, M. (Marina); Bodegas-Frías, E. (Elena)