Výsledky vyhledávání

Akademický článek

Confinement of unliganded EGFR by tetraspanin nanodomains gates EGFR ligand binding and signaling

Autor: Michael G. Sugiyama, Aidan I. Brown, Jesus Vega-Lugo, Jazlyn P. Borges, Andrew M. Scott, Khuloud Jaqaman, Gregory D. Fairn, Costin N. Antonescu

Publikováno v: Nature Communications, Vol 14, Iss 1, Pp 1-17 (2023)

Abstract The epidermal growth factor receptor (EGFR) is a central regulator of cell physiology. EGFR is activated by ligand binding, triggering receptor dimerization, activation of kinase activity, and intracellular signaling. EGFR is transiently con

Externí odkaz: https://doaj.org/article/a76bca4ec1bb40f1bbe42379e8c8888d

Zobrazit plný text záznamu

Akademický článek

Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic

Autor: Ricardo Correa, Gonzalo Farias, Ernesto Fabregas, Sebastián Dormido-Canto, Ignacio Pastor, Jesus Vega

Publikováno v: Sensors, Vol 24, Iss 9, p 2764 (2024)

Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very inter

Externí odkaz: https://doaj.org/article/fdf026bd2eaf44e8af9985cebf2092ea

Zobrazit plný text záznamu

Plný text ve formátu HTML

Report

Simulation-based inference of the 2D ex-situ stellar mass fraction distribution of galaxies using variational autoencoders

Autor: Angeloudi, Eirini, Huertas-Company, Marc, Falcón-Barroso, Jesús, Sarmiento, Regina, Walo-Martín, Daniel, Pillepich, Annalisa, Ferrero, Jesús Vega

Galaxies grow through star formation (in-situ) and accretion (ex-situ) of other galaxies. Reconstructing the relative contribution of these two growth channels is crucial for constraining the processes of galaxy formation in a cosmological context. I

Externí odkaz: http://arxiv.org/abs/2410.24069

Zobrazit plný text záznamu

Akademický článek

Considerations on Stellarator’s Optimization from the Perspective of the Energy Confinement Time Scaling Laws

Autor: Andrea Murari, Emmanuele Peluso, Luca Spolladore, Jesus Vega, Michela Gelfusa

Publikováno v: Applied Sciences, Vol 12, Iss 6, p 2862 (2022)

The Stellarator is a magnetic configuration considered a realistic candidate for a future thermonuclear fusion commercial reactor. The most widely accepted scaling law of the energy confinement time for the Stellarator is the ISS04, which employs a r

Externí odkaz: https://doaj.org/article/ab4afb94798f4564819ad680e3e8ac23

Zobrazit plný text záznamu

Akademický článek

Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices

Autor: Gonzalo Farias, Ernesto Fabregas, Sebastian Dormido-Canto, Jesus Vega, Sebastian Vergara, Sebastian Dormido Bencomo, Ignacio Pastor, Alvaro Olmedo

Publikováno v: IEEE Access, Vol 6, Pp 72345-72356 (2018)

Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine

Externí odkaz: https://doaj.org/article/9f5802444ce54d75989d71d35a924152

Zobrazit plný text záznamu

Akademický článek

Zymomonas mobilis immobilized on loofa sponge: levan and ethanol production in semi-continuous fermentation

Autor: Vidiany Aparecida Queiroz Santos, Jesus Vega-Estrada, Maria Del Carmen Montes-Orcasitas, Crispin Humberto Garcia-Cruz

Publikováno v: Acta Scientiarum: Technology, Vol 39, Iss 2, Pp 135-141 (2017)

Zymomonas mobilis is a promising microorganism in the biotechnological production of ethanol and levan due to its low biomass and high tolerance to ethanol concentrations. Ethanol and levan production by Z. mobilis CDBB-603 immobilized on loofa spong

Externí odkaz: https://doaj.org/article/bafc4329e90d43b1a6cb13999b75c125

Zobrazit plný text záznamu

Report

A Deep Learning Approach for Characterizing Major Galaxy Mergers

Autor: Koppula, Skanda, Bapst, Victor, Huertas-Company, Marc, Blackwell, Sam, Grabska-Barwinska, Agnieszka, Dieleman, Sander, Huber, Andrea, Antropova, Natasha, Binkowski, Mikolaj, Openshaw, Hannah, Recasens, Adria, Caro, Fernando, Deke, Avishai, Dubois, Yohan, Ferrero, Jesus Vega, Koo, David C., Primack, Joel R., Back, Trevor

Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end, we demonstrate a CNN-based regression model that is able to predict, f

Externí odkaz: http://arxiv.org/abs/2102.05182

Zobrazit plný text záznamu