Search

Your search keyword '"Moreno, Pablo A."' showing total 2,319 results
Start Over Author "Moreno, Pablo A."
2,319 results on '"Moreno, Pablo A."'

1. Testing gravity with the latent heat of neutron star matter

Author

Moreno, Pablo Navarro, Wojnar, Aneta, and Llanes-Estrada, Felipe J.

Subjects
General Relativity and Quantum Cosmology, Nuclear Theory
Abstract

The Seidov limit is a bound on the maximum latent heat that a presumed first-order phase transition of neutron-star matter can have before its excess energy density, not compensated by additional pressure, results in gravitational collapse. Because latent heat forces an apparent nonanalytic behaviour in plots correlating physical quantities (kinks in two-dimensional, ridges in three-dimensional ones), it can be constrained by data. As the onset of collapse depends on the intensity of gravity, testing for sudden derivative changes and, if they are large, breaching the Seidov limit would reward with two successive discoveries: such a phase transition (which could stem from hadron matter but also from a gravitational phase transition), and a modification of General Relativity (thus breaking the matter/gravity degeneracy). We illustrate the point with $f(R)=R+\alpha R^2$ metric gravity., Comment: 27 pages, 11 figures

Published
2024
Full Text
View/download PDF

2. Formulation, Colloidal Characterization, and In Vitro Biological Effect of BMP-2 Loaded PLGA Nanoparticles for Bone Regeneration

Author

del Castillo-Santaella, Teresa, Ortega-Oller, Inmaculada, Padial-Molina, Miguel, OValle, Francisco, Galindo-Moreno, Pablo, Jodar-Reyes, Ana Belen, and Peula-Garcia, Jose Manuel

Subjects
Physics - Biological Physics
Abstract

Nanoparticles (NPs) based on the polymer poly (lactide-co-glycolide) acid (PLGA) have been widely studied in developing delivery systems for drugs and therapeutic biomolecules, due to the biocompatible and biodegradable properties of the PLGA. In this work, a synthesis method for bone morphogenetic protein (BMP-2)-loaded PLGA NPs was developed and optimized, in order to carry out and control the release of BMP-2, based on the double-emulsion (water/oil/water,W/O/W) solvent evaporation technique. The polymeric surfactant Pluronic F68 was used in the synthesis procedure, as it is known to have an effect on the reduction of the size of the NPs, the enhancement of their stability, and the protection of the encapsulated biomolecule. Spherical solid polymeric NPs were synthesized, showing a reproducible multimodal size distribution, with diameters between 100 and 500 nm. This size range appears to allow the protein to act on the cell surface and at the cytoplasm level. The effect of carrying BMP-2 co-adsorbed with bovine serum albumin on the NP surface was analyzed. The colloidal properties of these systems (morphology by SEM, hydrodynamic size, electrophoretic mobility, temporal stability, protein encapsulation, and short-term release profile) were studied. The effect of both BMP2-loaded NPs on the proliferation, migration, and osteogenic differentiation of mesenchymal stromal cells from human alveolar bone (ABSC) was also analyzed in vitro., Comment: 18 pages, 8 figures

Published
2024
Full Text
View/download PDF

5. Note on power hypergraphs with equal domination and matching numbers

Author

de Diego, María José Chávez, Moreno, Pablo Montero, and Villar-Liñán, María Trinidad

Subjects
Mathematics - Combinatorics, 05C65, 05C69, 05C70
Abstract

We present some examples that refute two recent results in the literature concerning the equality of the domination and matching numbers for power and generalized power hypergraphs. In this note we pinpoint the flaws in the proofs and suggest how they may be mended., Comment: 7 pages, 1 figure, XIII Encuentro Andaluz de Matem\'atica Discreta, (C\'adiz) Spain, july, 2023

Published
2023

6. Ridges in rotating neutron-star properties due to first order phase transitions

Author

Moreno, Pablo Navarro, Llanes-Estrada, Felipe J., and Lope-Oter, Eva

Subjects
Nuclear Theory
Abstract

We identify combinations of observables for rotating neutron stars that can one day bear on the question of whether there can be first order phase transitions in the neutron matter therein. We employ the Hartle-Thorne theory for stationary, rotating neutron stars at conventional angular velocities (in the pulsar and millisecond pulsar ranges) and extract three-dimensional sections of the ellipticity or the dynamical angular momentum as function of the star's mass and angular velocity. An eventual first order phase transition in the equation of state (EoS) leaves a clear ridge (nonanalyticity) in these observables, akin to the sudden kink in popular mass-radius diagrams for static stars. Finally, we observe that static neutron stars in General Relativity (GR) will fail to be compact enough for the light ring's position at r=3M to be outside the star, except for the most extreme equations of state. The outer light ring of a rotating star might however be formed unless the EoS softens too much, and its eventual detection can then be used to constrain the EoS (or the gravity theory)., Comment: 17 pages, 26 plots, thoroughly revised following refereeing

Published
2023
Full Text
View/download PDF

7. A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data

Author

Shi, Yue, Han, Liangxiu, González-Moreno, Pablo, Dancey, Darren, Huang, Wenjiang, Zhang, Zhiqiang, Liu, Yuanyuan, Huan, Mengning, Miao, Hong, and Dai, Min

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series., Comment: 24 pages

Published
2023

9. Exploring the Potential of Pumice Stone as Coarse Aggregate: An Experimental Approach to Reduce Concrete Self-Weight

Author

Arunachalam, Krishna Prakash, Avudaiappan, Siva, Senepa, Maria Vanessa Gutierrez, Cardenas, Cristian Alexis Canales, Moreno, Pablo Ignacio Cuello, Marzialetti, Teresita, Ahmed, Saddam M., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Saavedra Flores, Erick I., editor, Astroza, Rodrigo, editor, and Das, Raj, editor

Published
2024
Full Text
View/download PDF

11. Perceptions of the impact of individual allergic rhinitis symptoms: A survey of ARIA clinical experts

Author

Asllani, Julijana, Douagui, Habib, Asayag, Estrella, Elizabeth, Castro Maria, Crisci, Carlos D., Gomez, René Maximiliano, Ivancevich, Juan Carlos, Jares, Edgardo, Máspero, Jorge Fernando, Moreno, Pablo, Neffen, Hugo Eduardo, Zernotti, Mario Emilio, Bosnic-Anticevich, Sinthia, Chahhoud Carson, Kristin, Cvetkovski, Biljana, Davies, Janet M., Kritikos, Vicky, O'Hehir, Robyn, Oliver, Brian, Tan, House Rachel, Tattersall, Jessica, Zubrinich, Celia, Aberer, Werner, Niederberger-Leppin, Verena, Pali-Schöll, Isabella, Doyen, Virginie, Ebo, Didier, Louis, Renaud, Rombaux, Philippe, Scheire, Sophie, Bennoor, Kazi, Barreto, Bruno A., Camargos, Paulo, Chong-Neto, Herberto Jose, Cruz, Alvaro A., da Silva, Jane, Marta, Guidacci, Rizzo, José Angelo, Rosario Filho, Nelson, Faradiba, Sarquis Serpa, Solé, Dirceu, Urrutia-Pereira, Marilyn, Christoff, George, Mariana, Mandazhieva-Pepelanova, Cvetanka, Odjakova, Popov, Todor A., Vicheva, Dilyana, Teixeira Maria, do Ceu, Bouchard, Jacques, Brozek, Jan, Chu, Derek K., Denburg, Judah, Eiwegger, Thomas, Keith, Paul K., To, Teresa, Waserman, Susan, Abusada, Nancy, Alvarez Cuesta, Emilio, Calvo-Gil, Mario A., Guzmán, Maria Antonieta, Pérez Gomez, Tamara, Wong Gary, Wing Kin, Zhang, Luo, Caraballo, Luis, Cepeda Sarabia, Alfonso, Nyembue, Dieudonné, Soto-Martinez, Manuel, Miculinic, Neven, Plavec, Davor, Pitsios, Constantinos, Yiallouros, Panayiotis, Panzner, Petr, Sova, Milan, Vachova, Martina, Bindslev-Jensen, Carsten, Dahl, Ronald, Malling, Hans-Jorgen, Münter, Lars, Poulsen, Lars K., Charlotte, Suppli Ulrik, Tannert, Line Kring, Mahboub, Bassam, Salameh, Laila, Cherrez-Ojeda, Ivan, Robles, de Guevara Karla, El-Owaidy, Rasha Hassan, El-Sayed, Zeinab A., El-Sayed, Shereen Saad, Hossny, Elham, Mostafa, Badr Eldin, Julge, Kaja, Billo, Nils Eric, Eklund, Patrik, Erhola, Marina, Haahtela, Tari, Karjalainen, Jussi, Makela, Mika, Toppila-Salmi, Sanna, Valovirta, Erkka, Vasankari, Tuula, Annesi-Maesano, Isabella, Bossé, Isabelle, Bourgoin-Heck, Melisande, Charpin, Denis, Coste, André, de Blay, Frédéric, Devillier, Philippe, Didier, Alain, Dinh-Xuan, Anh Tuan, Fontaine, Jean-François, Just, Jocelyne, Nadif, Rachel, Pham-Thi, Nhân, Pigearias, Bernard, Roche, Nicolas, Ganse Eric, Van, Chkhartishvili, Ekaterine, Gamkrelidze, Amiran, Gotua, Maia, Becker, Sven, Bergmann, Karl-Christian, Bieber, Thomas, Brehler, Randolf, Buhl, Roland, Chaker, Adam M., Darsow, Ulf, Hamelmann, Eckard, Heinrich, Joachim, Keil, Thomas, Klimek, Ludger, Kolkhir, Pavel, Lau, Susanne, Maurer, Marcus, Mösges, Ralph, Neou, Angelos, Pfaar, Oliver, Ring, Johannes, Wagenmann, Martin, Zuberbier, Torsten, Ramanauskaite, Aiste, Lingnau, Anja, Christou, Demetrios, Neisinger, Sophia, Aggelidis, Xenophon, Constantinidis, Jannis, Dimou, Maria, Grigoreas, Christos, Kapsali, Trisevgeni, Katotomichelakis, Michael, Loukides, Stelios, Makris, Michael, Manousakis, Emmanouil, Mikos, Nikolaos, Pagkalos, Aris, Papadopoulos, Nikolaos G., Papanikolaou, Vasileios, Paraskevopoulos, Giannis, Piskou, Konstantina, Prokopakis, Emmanuel, Psarros, Fotios, Samitas, Konstantinos, Stamataki, Sofia, Stefanaki Lina, Evangelia, Syrigou, Ekaterini, Tsiligianni, Ioanna, Vallianatou, Mina, Vourdas, Dimitrios, Xepapadaki, Paraskevi, Karavelia, Aspasia, Wai San Ko, Fanny, Wong, Gary, Hirschberg, Andor, Kraxner, Helga, Nekam, Kristof, Lane, Stephen, Rottem, Menachem, Artesani Maria, Cristina, Barbaglia, Simona, Beghé, Bianca, Bilo Maria, Beatrice, Boner, Attilio, Bonini, Matteo, Bonini, Sergio, Braido, Fulvio, Brussino, Luisa, Canonica, G Walter, Cecchi, Lorenzo, Ciprandi, Giorgio, Compalati, Enrico, D'Amato, Gennaro, De Feo, Giulia, Del Giacco, Stefano, Fiocchi, Alessandro, Heffler, Enrico, Lombardi, Carlo, Monti, Riccardo, Muraro, Antonella, Nettis, Eustachio, Nicola, Stefania, Passalacqua, Giovanni, Patella, Vincenzo, Puggioni, Francesca, Rolla, Giovanni, Romano, Antonino, Scichilone, Nicola, Triggiani, Massimo, Ventura Maria, Teresa, Viegi, Giovanni, Boccabella, Cristina, Giovannini, Mattia, Mahesh Padukudru, Anand, Iinuma, Tomohisa, Ohta, Ken, Okamoto, Yoshitaka, Okubo, Kimihiro, Pawankar, Ruby, Sakurai, Daiju, Suzukawa, Maho, Yamaguchi, Masao, Ispayeva, Zhanat, Chang, Yoon-Seok, Park, Hae-Sim, Al-Ahmad, Mona, Cirule, Ieva, Grisle, Ineta, Irani, Carla, Rouadi, Philip, Zaitoun, Fares, Dubakiene, Ruta, Emuzyte, Regina, Kvedariene, Violeta, Gradauskiene, Brigita, Valiulis, Arunas, Ollert, Markus, Hannachi, Farah, Dokic, Dejan, Abdul Latiff, Amir Hamzah, Abdullah, Baharudin, Palaniappan, S.P., Woo, Kent, Montefort, Stephen, Bedolla Barajas, Martín, Costa Domínguez, María del Carmen, Espinoza Contreras, Jesús Guillermo, Fuentes Pèrez, Jose Miguel, Gálvez Romero, José Luis, García Cruz, María de la Luz Hortensia, González Diaz, Sandra, Huerta Villalobos, Yunuen Rocío, Larenas-Linnemann, Désiree E., Luna-Pech, Jorge Agustin, Matta Campos, Juan José, Rivero Yeverino, Daniela, Rodríguez González, Mónica, Rodríguez Zagal, Eréndira, Lkhagvaa, Battur, Mavale-Manuel, Sandra, Nunes Elizabete, Abrantes, Chavannes, Niels, Fokkens, Wytske J., Koppelman, Gerard, Maitland-van der Zee, Anke-Hilse, Reitsma, Sietze, Chandrasekharan, Ramanathan, Yusuf, Osman, Repka-Ramirez, Maria Susana, Sisul, Juan Carlos, Gereda, José E., Recto, Marysia T., Bialek, Slawomir, Bialoszewski, Artur, Chelminska, Marta, Gawlik, Radoslaw, Jassem, Ewa, Jutel, Marek, Kuna, Piotr, Kupczyk, Maciej, Moniuszko, Marcin, Niedoszytko, Marek, Raciborski, Filip, Romantowski, Jan, Samolinski, Boleslaw, Specjalski, Krzysztof, Carreiro Martins, Pedro, Correia de Sousa, Jaime, Costa, Elisio, Fonseca, Joao A., Lourenço, Olga, Morais-Almeida, Mario, Pereira Ana, Margarida, Regateiro, Frederico, Robalo Cordeiro, Carlos, Rosado Pinto, Jose, Sousa-Pinto, Bernardo, Taborda Barata, Luis, Todo-Bom, Ana Maria, Vieira Rafael, José, Carvalho, Daniela, Al-Nesf, Maryam Ali, Agache, Ioana, Berghea Elena, Camelia, Bumbacea, Roxana, Deleanu, Diana, Mihaltan, Florin, Panaitescu Bunu, Carmen, Khaitov, Musa, Namazova-Baranova, Leyla, Vishneva, Elena, Alshaikh Nada, Ali, Dimic-Janjic, Sanja, Milenkovic, Branislava, Wang, De Yun, Hrubiško, Martin, Edelbaher, Natalija, Jošt, Maja, Kopac, Peter, Košnik, Mitja, Lalek, Nika, Poplas Susic, Antonija, Rozman Sinur, Irma, Soklic Košak, Tanja, Triller, Nadja, Triller, Katja, Urbancic, Jure, Zidarn, Mihaela, Levin, Michael, Potter, Paul, Zar, Heather, Alobid, Isam, Anto, Aram, Bartra, Joan, Bobolea, Irina, Campo, Paloma, Cardona, Vicky, Carriazo Ana, Maria, Castillo Vizuete, José Antonio, Chivato, Tomas, Davila Gonzalez, Ignacio Jesus, Mullol, Joaquim, Nieto Garcia, Antonio, Picado, César, Quirce, Santiago, Sastre, Joaquin, de las Vecillas, Leticia, Benson, Mikael, Kull, Inger, van Hage, Marianne, Wickman, Magnus, Khaltaev, Nikolai, Mohammad, Yousser, Tantilipikorn, Pongsakorn, Vichyanond, Pakit, Cingi, Cemal, Gemicioglu, Bilun, Goksel, Ozlem, Kalayci, Omer, Kalyoncu, Ali Fuat, Meço, Cem, Vardaloglu, Koyuncu Ilgim, Yorgancioglu, Arzu, Karabacak, Deniz Eyice, Kirenga, Bruce, Adcock, Ian Michael, Arshad, Hasan S., Bewick, Mike, Bond, Christine, Brightling, Christopher, Bush, Andrew, Calderon, Moïses, Chung, Kian Fan, Custovic, Adnan, Djukanovic, Ratko, Durham, Stephen, Farrell, John, Halpin, David, Hyland, Michael, Johnston, Sebastian, Lipworth, Brian, Nakonechna, Alla, Price, David, Roberts, Graham, Ryan, Dermot, Schwarze, Jürgen, Sheikh, Aziz, Shields, Mike, Walker, Samantha, Williams, Sian, Kaidashev, Igor, Kurchenko, Andrii Igorevich, Tsaryk, Vladyslav, Bernstein, David, Bernstein, Jonathan A., Casale, Thomas B., Chinthrajah, Sharon, Dykewicz, Mark, Marshall, Gailen D., Meltzer, Eli O., Naclerio, Robert M., Togias, Alkis, Toskala, Elina, Wallace, Dana, Williams, Dennis M., Yawn, Barbara, Caballero-Fonseca, Fernan, Le Thi Tuyet, Lan, Vu, Tran Thien Quan, Gil-Mata, Sara, Teixeira, Tatiana, Bedbrook, Anna, Bousquet, Jean, and Vieira, Rafael José

Published
2024
Full Text
View/download PDF

17. Current Emerging Concepts on the Pathogenesis of Peri-implant Diseases: A Narrative Review.

Author

Guarnieri, Renzo, Testarelli, Luca, Galindo-Moreno, Pablo, Del Fabbro, Massimo, and Testori, Tiziano

Subjects
BACTERIAL disease complications, STOMATITIS, PERI-implantitis, FOREIGN bodies, HUMAN microbiota
Abstract

Purpose: To describe the emerging evidence concerning etiologic factors and pathophysiologic mechanisms involved in peri-implant inflammatory diseases. Materials and Methods: An electronic search for articles published until November 2022 was conducted in MEDLINE by three independent reviewers to identify the manuscripts' reporting data on etiologic factors and pathophysiologic mechanisms associated with peri-implant diseases. Results: Current evidence suggests that peri-implant mucositis and peri-implantitis are inflammatory conditions linked to a microbial challenge. However, in recent years there has been increasing evidence indicating that certain peri-implant inflammatory conditions may not be primarily related to biofilm-mediated infectious processes but rather to other biologic mechanisms, such as a foreign body response. Conclusions: Current evidence in the dental and medical literature opens new avenues for a more complex interpretation of the etiopathogenetic factors involved in peri-implant diseases. A better understanding of various factors related to the host response, including dysbiosis mechanisms associated with changes in microbiota composition, is necessary for a more precise physiopathologic characterization of these diseases. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

19. Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence–Assisted ARIA Care Pathways (ARIA 2024)

Author

Bousquet, Jean, Schünemann, Holger J., Sousa-Pinto, Bernardo, Zuberbier, Torsten, Togias, Alkis, Samolinski, Boleslaw, Bedbrook, Anna, Czarlewski, Wienczyslawa, Hofmann-Apitius, Martin, Litynska, Justyna, Vieira, Rafael José, Anto, Josep M., Fonseca, Joao A., Brozek, Jan, Bognanni, Antonio, Brussino, Luisa, Canonica, G. Walter, Cherrez-Ojeda, Ivan, Cruz, Alvaro A., Vecillas, Leticia de las, Dykewicz, Mark, Gemicioglu, Bilun, Giovannini, Mattia, Haahtela, Tari, Jacobs, Marc, Jacomelli, Cristina, Klimek, Ludger, Kvedariene, Violeta, Larenas-Linnemann, Desiree E., Louis, Gilles, Lourenço, Olga, Leemann, Lucas, Morais-Almeida, Mario, Neves, Ana Luisa, Nadeau, Kari C., Nowak, Artur, Palamarchuk, Yuliia, Palkonen, Susanna, Papadopoulos, Nikolaos G., Parmelli, Elena, Pereira, Ana Margarida, Pfaar, Oliver, Regateiro, Frederico S., Savouré, Marine, Taborda-Barata, Luis, Toppila-Salmi, Sanna K., Torres, Maria J., Valiulis, Arunas, Ventura, Maria Teresa, Williams, Sian, Yepes-Nuñez, Juan J., Yorgancioglu, Arzu, Zhang, Luo, Zuberbier, Jaron, Abdul Latiff, Amir Hamzah, Abdullah, Baharudin, Agache, Ioana, Al-Ahmad, Mona, Al-Nesf, Maryam Ali, Al Shaikh, Nada A., Amaral, Rita, Ansotegui, Ignacio J., Asllani, Julijana, Balotro-Torres, Maria Cristina, Bergmann, Karl-Christian, Bernstein, Jonathan A., Bindslev-Jensen, Carsten, Blaiss, Michael S., Bonaglia, Cristina, Bonini, Matteo, Bossé, Isabelle, Braido, Fulvio, Caballero-Fonseca, Fernan, Camargos, Paulo, Carreiro-Martins, Pedro, Casale, Thomas, Castillo-Vizuete, José-Antonio, Cecchi, Lorenzo, Teixeira, Maria do Ceu, Chang, Yoon-Seok, Loureiro, Claudia Chaves, Christoff, George, Ciprandi, Giorgio, Cirule, Ieva, Correia-de-Sousa, Jaime, Costa, Elisio M., Cvetkovski, Biljana, de Vries, Govert, Del Giacco, Stefano, Devillier, Philippe, Dokic, Dejan, Douagui, Habib, Durham, Stephen R., Enecilla, Maria Lourdes, Fiocchi, Alessandro, Fokkens, Wytske J., Fontaine, Jean-François, Gawlik, Radoslaw, Gereda, Jose E., Gil-Mata, Sara, Giuliano, Antonio F.M., Gotua, Maia, Gradauskiene, Brigita, Guzman, Maria Antonieta, Hossny, Elham, Hrubiško, Martin, Iinuma, Tomohisa, Irani, Carla, Ispayeva, Zhanat, Ivancevich, Juan Carlos, Jartti, Tuomas, Jeseňák, Miloš, Julge, Kaja, Jutel, Marek, Kaidashev, Igor, Bennoor, Kazi S., Khaltaev, Nicolai, Kirenga, Bruce, Kraxner, Helga, Kull, Inger, Kulus, Marek, Kuna, Piotr, Kupczyk, Maciej, Kurchenko, Andriy, La Grutta, Stefania, Lane, Stephen, Miculinic, Neven, Lee, Sang Min, Le Thi Tuyet, Lan, Lkhagvaa, Battur, Louis, Renaud, Mahboub, Bassam, Makela, Mika, Makris, Michael, Maurer, Marcus, Melén, Eric, Milenkovic, Branislava, Mohammad, Yousser, Moniuszko, Marcin, Montefort, Stephen, Moreira, Andre, Moreno, Pablo, Mullol, Joaquim, Nadif, Rachel, Nakonechna, Alla, Navarro-Locsin, Cecilia Gretchen, Neffen, Hugo E., Nekam, Kristof, Niedoszytko, Marek, Nunes, Elizabete, Nyembue, Dieudonné, O’Hehir, Robyn, Ollert, Markus, Ohta, Ken, Okamoto, Yoshitaka, Okubo, Kimihiro, Olze, Heidi, Padukudru, Mahesh Anand, Palomares, Oscar, Pali-Schöll, Isabella, Panzner, Petr, Palosuo, Kati, Park, Hae S., Passalacqua, Giovanni, Patella, Vincenzo, Pawankar, Ruby, Pétré, Benoît, Pitsios, Constantinos, Plavec, Davor, Popov, Todor A., Puggioni, Francesca, Quirce, Santiago, Raciborski, Filip, Ramonaité, Agné, Recto, Marysia, Repka-Ramirez, Susana, Roberts, Graham, Robles-Velasco, Karla, Roche, Nicolas, Rodriguez-Gonzalez, Monica, Romualdez, Joel A., Rottem, Menachem, Rouadi, Philip W., Salapatas, Marianella, Sastre, Joaquin, Serpa, Faradiba S., Sayah, Zineb, Scichilone, Nicola, Senna, Gianenrico, Sisul, Juan Carlos, Solé, Dirceu, Soto-Martinez, Manuel E., Sova, Milan, Sozinova, Olga, Stevanovic, Katarina, Ulrik, Charlotte Suppli, Szylling, Anna, Tan, Frances M., Tantilipikorn, Pongsakorn, Todo-Bom, Ana, Tomic-Spiric, Vesna, Tsaryk, Vladyslav, Tsiligianni, Ioanna, Urrutia-Pereira, Marilyn, Rostan, Marylin Valentin, Sofiev, Mikhail, Valovirta, Erkka, Van Eerd, Michiel, Van Ganse, Eric, Vasankari, Tuula, Vichyanond, Pakit, Viegi, Giovanni, Wallace, Dana, Wang, De Yun, Waserman, Susan, Wong, Gary, Worm, Margitta, Yusuf, Osman M., Zaitoun, Fares, and Zidarn, Mihaela

Published
2024
Full Text
View/download PDF

20. The Global Distribution of Acacia

Author

Botella, Christophe, primary, Marchante, Hélia, additional, Celesti-Grapow, Laura, additional, Brundu, Giuseppe, additional, Geerts, Sjirk, additional, Ramirez-Albores, Jorge E., additional, González-Moreno, Pablo, additional, Ritter, Matt, additional, and Richardson, David M., additional

Published
2023
Full Text
View/download PDF

25. Fast Adaptation with Linearized Neural Networks

Author

Maddox, Wesley J., Tang, Shuai, Moreno, Pablo Garcia, Wilson, Andrew Gordon, and Damianou, Andreas

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuning neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning. Code is available at https://github.com/amzn/xfer/tree/master/finite_ntk., Comment: AISTATS 2021

Published
2021

26. UCSC Cell Browser: visualize your single-cell data

Author

Speir, Matthew L, Bhaduri, Aparna, Markov, Nikolay S, Moreno, Pablo, Nowakowski, Tomasz J, Papatheodorou, Irene, Pollen, Alex A, Raney, Brian J, Seninge, Lucas, Kent, W James, and Haeussler, Maximilian

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Bioengineering, Networking and Information Technology R&D (NITRD), 1.5 Resources and infrastructure (underpinning), Genomics, Software, Databases, Genetic, Metadata, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

SummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets.Availability and implementationWe provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Published
2021

29. Transcriptomic profiles and diagnostic biomarkers in the Mediterranean seagrasses Posidonia oceanica and Cymodocea nodosa reveal mechanistic insights of adaptative strategies upon desalination brine stress

Author

Rodríguez-Rojas, Fernanda, Navarrete, Camilo, Rámila, Consuelo, Tapia-Reyes, Patricio, Celis-Plá, Paula S.M., González, Christian, Pereira-Rojas, Jeniffer, Blanco-Murillo, Fabio, Moreno, Pablo, Gutiérrez-Campos, Catalina, Sánchez-Lizaso, José Luis, and Sáez, Claudio A.

Published
2024
Full Text
View/download PDF

31. Interpretable Anomaly Detection with Mondrian P{\'o}lya Forests on Data Streams

Author

Dickens, Charlie, Meissner, Eric, Moreno, Pablo G., and Diethe, Tom

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do not fit the expected behaviour. Recent work has coalesced on variations of (random) $k$\emph{d-trees} to summarise data for anomaly detection. However, these methods rely on ad-hoc score functions that are not easy to interpret, making it difficult to asses the severity of the detected anomalies or select a reasonable threshold in the absence of labelled anomalies. To solve these issues, we contextualise these methods in a probabilistic framework which we call the Mondrian \Polya{} Forest for estimating the underlying probability density function generating the data and enabling greater interpretability than prior work. In addition, we develop a memory efficient variant able to operate in the modern streaming environments. Our experiments show that these methods achieves state-of-the-art performance while providing statistically interpretable anomaly scores.

Published
2020

32. Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data

Author

Tonolini, Francesco, Moreno, Pablo G., Damianou, Andreas, and Murray-Smith, Roderick

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this setting, direct application of classical variational methods often gives rise to collapsed densities that do not adequately explore the solution space. Instead, we derive our novel reduced entropy condition approximate inference method that results in rich posteriors. We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model., Comment: 8+12 pages

Published
2020

33. Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

Author

Hu, Shell Xu, Moreno, Pablo G., Xiao, Yang, Shen, Xi, Obozinski, Guillaume, Lawrence, Neil D., and Damianou, Andreas

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a more powerful model for each task. To develop our framework, we revisit the empirical Bayes formulation for multi-task learning. The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task. We derive a novel amortized variational inference that couples all the variational posteriors via a meta-model, which consists of a synthetic gradient network and an initialization network. Each variational posterior is derived from synthetic gradient descent to approximate the true posterior on the query set, although where we do not have access to the true gradient. Our results on the Mini-ImageNet and CIFAR-FS benchmarks for episodic few-shot classification outperform previous state-of-the-art methods. Besides, we conduct two zero-shot learning experiments to further explore the potential of the synthetic gradient., Comment: ICLR 2020

Published
2020

34. Putative cell type discovery from single-cell gene expression data

Author

Miao, Zhichao, Moreno, Pablo, Huang, Ni, Papatheodorou, Irene, Brazma, Alvis, and Teichmann, Sarah A

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Genomics
Abstract

We present a novel method for automated identification of putative cell types from single-cell RNA-seq (scRNA-seq) data. By iteratively applying a machine learning approach to an initial clustering of gene expression profiles of a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The feature genes, which are differentially expressed in the particular cell group, jointly discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterised by the feature genes as markers. To benchmark this approach, we use expert-annotated scRNA-seq datasets from a range of experiments, as well as comparing to existing cell annotation methods, which are all based on a pre-existing reference. We show that our method automatically identifies the 'ground truth' cell assignments with high accuracy. Moreover, our method, Single Cell Clustering Assessment Framework (SCCAF) predicts new putative biologically meaningful cell-states in published data on haematopoiesis and the human cortex. SCCAF is available as an open-source software package on GitHub (https://github.com/SCCAF/sccaf) and as a Python package index and has also been implemented as a Galaxy tool in the Human Cell Atlas.

Published
2020

35. Preferential Batch Bayesian Optimization

Author

Siivola, Eero, Dhaka, Akash Kumar, Andersen, Michael Riis, Gonzalez, Javier, Moreno, Pablo Garcia, and Vehtari, Aki

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests, or recommender systems, there is a need for methods that can replace direct feedback with \emph{preferential feedback}, obtained via rankings or pairwise comparisons. In this work, we present preferential batch Bayesian optimization (PBBO), a new framework that allows finding the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets., Comment: 6 pages + 7 pages in supplementary material

Published
2020

39. Hackathon in Teaching: Applying Machine Learning to Life Sciences Tasks

Author

Guijo-Rubio, David, Vargas, Víctor M., Barbero-Gómez, Javier, Die, Jose V., González-Moreno, Pablo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
Full Text
View/download PDF

41. Identification of potential invasive alien species in Spain through horizon scanning

Author

Cano-Barbacil, Carlos, Carrete, Martina, Castro-Díez, Pilar, Delibes-Mateos, Miguel, Jaques, Josep A., López-Darias, Marta, Nogales, Manuel, Pino, Joan, Ros, Macarena, Traveset, Anna, Turon, Xavier, Vilà, Montserrat, Altamirano, María, Álvarez, Inés, Arias, Andrés, Boix, Dani, Cabido, Carlos, Cacabelos, Eva, Cobo, Fernando, Cruz, Joaquín, Cuesta, José A., Dáder, Beatriz, del Estal, Pedro, Gallardo, Belinda, Gómez Laporta, Miguel, González-Moreno, Pablo, Hernández, José Carlos, Jiménez-Alfaro, Borja, Lázaro Lobo, Adrián, Leza, Mar, Montserrat, Marta, Oliva-Paterna, Francisco J., Piñeiro, Laura, Ponce, Carlos, Pons, Pere, Rotchés-Ribalta, Roser, Roura-Pascual, Núria, Sánchez, Marta, Trillo, Alejandro, Viñuela, Elisa, and García-Berthou, Emili

Published
2023
Full Text
View/download PDF

43. Single cell transcriptomic analysis of Graves’ disease thyroid glands reveals the broad immunoregulatory potential of thyroid follicular and stromal cells and implies a major re-interpretation of the role of aberrant HLA class II expression in autoimmunity

Author

Álvarez-Sierra, Daniel, Rodríguez-Grande, Jorge, Gómez-Brey, Aroa, Bello, Irene, Caubet, Enric, González, Óscar, Zafón, Carles, Iglesias, Carmela, Moreno, Pablo, Ruiz, Núria, Marín-Sánchez, Ana, Colobran, Roger, and Pujol-Borrell, Ricardo

Published
2023
Full Text
View/download PDF

44. Implications of Quantum Computing for Artificial Intelligence alignment research

Author

Sevilla, Jaime and Moreno, Pablo

Subjects
Computer Science - Emerging Technologies, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

We explain some key features of quantum computing via three heuristics and apply them to argue that a deep understanding of quantum computing is unlikely to be helpful to address current bottlenecks in Artificial Intelligence Alignment. Our argument relies on the claims that Quantum Computing leads to compute overhang instead of algorithmic overhang, and that the difficulties associated with the measurement of quantum states do not invalidate any major assumptions of current Artificial Intelligence Alignment research agendas. We also discuss tripwiring, adversarial blinding, informed oversight and side effects as possible exceptions., Comment: 10 pages

Published
2019

45. Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction

Author

Salman, Shaeke, Payrovnaziri, Seyedeh Neelufar, Liu, Xiuwen, Rengifo-Moreno, Pablo, and He, Zhe

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

Deep neural networks have achieved remarkable success in various challenging tasks. However, the black-box nature of such networks is not acceptable to critical applications, such as healthcare. In particular, the existence of adversarial examples and their overgeneralization to irrelevant, out-of-distribution inputs with high confidence makes it difficult, if not impossible, to explain decisions by such networks. In this paper, we analyze the underlying mechanism of generalization of deep neural networks and propose an ($n$, $k$) consensus algorithm which is insensitive to adversarial examples and can reliably reject out-of-distribution samples. Furthermore, the consensus algorithm is able to improve classification accuracy by using multiple trained deep neural networks. To handle the complexity of deep neural networks, we cluster linear approximations of individual models and identify highly correlated clusters among different models to capture feature importance robustly, resulting in improved interpretability. Motivated by the importance of building accurate and interpretable prediction models for healthcare, our experimental results on an ICU dataset show the effectiveness of our algorithm in enhancing both the prediction accuracy and the interpretability of deep neural network models on one-year patient mortality prediction. In particular, while the proposed method maintains similar interpretability as conventional shallow models such as logistic regression, it improves the prediction accuracy significantly., Comment: 8 pages, 6 figures

Published
2019

46. Metabolomics in the Cloud: Scaling Computational Tools to Big Data

Author

Gao, Jianliang, Sadawi, Noureddin, Karaman, Ibrahim, Pearce, Jake T M, Moreno, Pablo, Larsson, Anders, Capuccini, Marco, Elliott, Paul, Nicholson, Jeremy K, Ebbels, Timothy M D, and Glen, Robert

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Background: Metabolomics datasets are becoming increasingly large and complex, with multiple types of algorithms and workflows needed to process and analyse the data. A cloud infrastructure with portable software tools can provide much needed resources enabling faster processing of much larger datasets than would be possible at any individual lab. The PhenoMeNal project has developed such an infrastructure, allowing users to run analyses on local or commercial cloud platforms. We have examined the computational scaling behaviour of the PhenoMeNal platform using four different implementations across 1-1000 virtual CPUs using two common metabolomics tools. Results: Our results show that data which takes up to 4 days to process on a standard desktop computer can be processed in just 10 min on the largest cluster. Improved runtimes come at the cost of decreased efficiency, with all platforms falling below 80% efficiency above approximately 1/3 of the maximum number of vCPUs. An economic analysis revealed that running on large scale cloud platforms is cost effective compared to traditional desktop systems. Conclusions: Overall, cloud implementations of PhenoMeNal show excellent scalability for standard metabolomics computing tasks on a range of platforms, making them a compelling choice for research computing in metabolomics., Comment: 25 pages, 5 figures

Published
2019

49. Thyroid cells from normal and autoimmune thyroid glands suppress T lymphocytes proliferation upon contact revealing a new regulatory inhibitory type of interaction independent of PD1/PDL1

Author

Álvarez-Sierra, Daniel, Sánchez-Gaona, Nerea, Cruz Cobo, María, Escriche, Alba, Abad, María, Gómez-Brey, Aroa, Bello, Irene, Caubet, Enric, González, Óscar, Zafón, Carles, Iglesias, Carmela, Moreno, Pablo, Petit, Anna, Fernández-Sanmartín, Marco Antonio, Martínez-Gallo, Mónica, and Pujol-Borrell, Ricardo

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results