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354 results on '"Núñez, Oscar"'

10. IAPT chromosome data 30

Author

Marhold, Karol, Kučera, Jaromír, de Almeida, Erton Mendonça, Alves, Lânia I.F., Araneda-Beltrán, Claudia, Baeza, Carlos M., Banaev, Evgeny V., Batista, Fabiane R.C., Cacho, N. Ivalú, Chernyagina, Olga A., Cuba-Díaz, Marely, Erst, Andrey S., Felix, Leonardo P., Martins, Eliana Regina Forni, de Andrade, Maria J. Gomes, Gudkova, Polina D., Gupta, Raghbir Chand, Kaur, Kuljit, Aulakh, Mandeep Kaur, Korobkov, Aleksandr A., Kostikova, Vera A., Kotseruba, Violetta V., Krivenko, Denis A., Kuzmin, Igor V., Li, Zhi-Min, Matos, Samara S., Neto, Enoque Medeiros, Melo, André L., Mitrenina, Elizaveta Yu., Nachychko, Viktor O., Neves, José Achilles L., Pirani, José Rubens, Probatova, Nina S., Romero-da Cruz, María V., Ruiz-Ponce, Eduardo, Saggoo, Manjit Inder Singh, Santos, Fabiano J., Santos, Géssica S., Silva, Pollyana K., Singh, Vijay, Souza, Pamela C.S.S., Stuessy, Tod, Sukhorukov, Alexander P., Sun, Hang, Sun, Wen-Guang, Tashev, Alexander N., Tomoshevich, Mariya A., Toro-Núñez, Oscar, Urtubey, Estrella, Vale, Vera L.C., Voronkova, Mariya S., Wang, Wei, Xiang, Kunli, and Zappi, Daniela C.

Published
2019

16. Empirical Second-Line Therapy in 5000 Patients of the European Registry on Helicobacter pylori Management (Hp-EuReg)

Author

Fiorinni, Giulia, Saracino, Ilaria Maria, Carrasco, Manuel Pabon, Huerga, Alma Keco, Almajano, Enrique Alfaro, Martinez Dominguez, Samuel Jesus, Galan, Horacio Alonso, Velayos, Benito, Sadornil, Carmen Dueñas, Botargues Bote, Jose Maria, Gonzalez-Cordero, Pedro Luis, Areia, Miguel, Gomez Rodriguez, Blas Jose, Pellicano, Rinaldo, Nuñez, Óscar, Franceschi, Francesco, Alekseenko, Sergey, Perona, Monica, Abdulkhakov, Rustam, Dominguez-Cajal, Manuel, Notari, Pedro Almela, Camarero, Judith Gomez, Moreno, Manuel Jimenez, Algaba, Alicia, Bermejo, Fernando, Tejada, Javier Tejedor, Susanibar, Elida Oblitas, Boltin, Doron, Georgopoulos, Sotirios, OMorain, Colm, Qasim, Asghar, Beales, Ian, Bakulina, Natalia, Fadeenko, Galina, Malfertheiner, Peter, Rosania, Rosa, Ilchishina, Tatiana, Bogomolov, Pavel, Bakulin, Igor, Zaytsev, Oleg, Gravina, Antonietta Gerarda, Romano, Marco, Di Leo, Alfredo, Losurdo, Giuseppe, Grigorieva, Ludmila, Guillena, Pedro Delgado, Marusic, Marinko, Jurcic, Dragan, Dekhnich, Natalia Nikolaevna, Iyo, Eduardo, de la Peña Negro, Luisa Carmen, Baryshnikova, Natalia, Bakanova, Natalia, Simsek, Halis, Simsek, Cem, Gridnyev, Oleksiy, Fernandez-Bermejo, Miguel, Angueira, Teresa, Ruiz-Zorrilla Lopez, Rafael, Gomez, Barbara, Kovacheva-Slavova, Mila, Lahat, Adi, Alcedo, Javier, Campillo, Ana, Belousova, Liya Nikolaevna, Villarroya, Ramon Pajares, Ljubicic, Neven, Nikolic, Marko, González-Santiago, Jesús M., Santamaría, Diego Burgos, Pakhomova, Anna, Sekulic-Spasic, Izabela, Ghisa, Matteo, Farinati, Fabio, Sagdati, Sabir Irfan, Panic, Nikola, Heluwaert, Frederic, Amorena, Edurne, Moreira, Leticia, Esparrach, Gloria Fernandez, Plotnikova, Ekaterina Yuryevna, Kukla, Michal, Kamburov, Victor, Lamuela Calvo, Luis Javier, Rankovic, Ivan, Lavín, Antonio Cuadrado, Lazaro, Yolanda Arguedas, Carrera Agnieszka Dobrowolska, Victor Gonzalez, Eder, Piotr, Kononova, Alla, Nyssen, Olga P., Vaira, Dino, Pérez Aísa, Ángeles, Rodrigo, Luis, Castro-Fernandez, Manuel, Jonaitis, Laimas, Tepes, Bojan, Vologzhanina, Liudmila, Caldas, María, Lanas, Angel, Lucendo, Alfredo J., Bujanda, Luis, Ortuño, Juan, Barrio, Jesús, Huguet, Jose M., Voynovan, Irina, Lasala, Jorge Perez, Sarsenbaeva, Aiman Silkanovna, Fernandez-Salazar, Luis, Molina-Infante, Javier, Jurecic, Natasa Brglez, Gasbarrini, Antonio, Kupčinskas, Juozas, Bordin, Dmitry, Marcos-Pinto, Ricardo, Lerang, Frode, Leja, Marcis, Buzas, Gyorgy M., Niv, Yaron, Rokkas, Theodore, Phull, Perminder, Smith, Sinead, Shvets, Oleg, Venerito, Marino, Milivojevic, Vladimir, Simsek, Ilkay, Lamy, Vincent, Bytzer, Peter, Boyanova, Lyudmila, Kunovský, Lumír, Beglinger, Christoph, Doulberis, Michael, Marlicz, Wojciech, Goldis, Adrian, Tonkić, Ante, Capelle, Lisette, Puig, Ignasi, Megraud, Francis, Morain, Colm O’, and Gisbert, Javier P.

Published
2022
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23. Exploring the Functionality of the Krüppel-like Factors in Kidney Development, Metabolism, and Diseases.

Author

Salmon-Cabrales, Itzel S., de la Garza-Kalife, David A., García-González, Gabriel, Estrada-Rodríguez, Ana E., Jiménez-Gutiérrez, Marco Antonio, Santoyo-Suárez, Michelle G., Rodríguez-Núñez, Oscar, Garza-Treviño, Elsa N., Benítez-Chao, Diego F., Padilla-Rivas, Gerardo R., and Islas, Jose Francisco

Subjects
KRUPPEL-like factors, KIDNEY development, RENAL fibrosis, METABOLIC regulation, TRANSCRIPTION factors
Abstract

The kidneys contribute to the overall health of an organism by maintaining systemic homeostasis. This process involves various biological mechanisms, in which the Krüppel-like factors (KLFs), a family of transcription factors, are essential for regulating development, differentiation, proliferation, and cellular apoptosis. They also play a role in the metabolic regulation of essential nutrients, such as glucose and lipids. The dysregulation of these transcription factors is associated with the development of various pathologies, which can ultimately lead to renal fibrosis, severely compromising kidney function. In this context, the present article provides a comprehensive review of the existing literature, offering an enriching analysis of the findings related to the role of KLFs in nephrology, while also highlighting their potential therapeutic role in the treatment of renal diseases. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Solving Viscous Burgers' Equation: Hybrid Approach Combining Boundary Layer Theory and Physics-Informed Neural Networks.

Author

Ortiz Ortiz, Rubén Darío, Martínez Núñez, Oscar, and Marín Ramírez, Ana Magnolia

Subjects
*BURGERS' equation, *NONLINEAR differential equations, *PARTIAL differential equations, *BOUNDARY layer (Aerodynamics), *SHOCK waves, *HYBRID systems
Abstract

In this paper, we develop a hybrid approach to solve the viscous Burgers' equation by combining classical boundary layer theory with modern Physics-Informed Neural Networks (PINNs). The boundary layer theory provides an approximate analytical solution to the equation, particularly in regimes where viscosity dominates. PINNs, on the other hand, offer a data-driven framework that can address complex boundary and initial conditions more flexibly. We demonstrate that PINNs capture the key dynamics of the Burgers' equation, such as shock wave formation and the smoothing effects of viscosity, and show how the combination of these methods provides a powerful tool for solving nonlinear partial differential equations. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration.

Author

Caredda, Marco, Ciulu, Marco, Tilocca, Francesca, Langasco, Ilaria, Núñez, Oscar, Sentellas, Sònia, Saurina, Javier, Pilo, Maria Itria, Spano, Nadia, Sanna, Gavino, and Mara, Andrea

Subjects
FISHER discriminant analysis, NEAR infrared spectroscopy, ADULTERATIONS, HONEY, FRUCTOSE, CHEMOMETRICS
Abstract

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Combination of Bismuth and Standard Triple Therapy Eradicates Helicobacter pylori Infection in More than 90% of Patients

Author

McNicholl, Adrian G., Bordin, Dmitry S., Lucendo, Alfredo, Fadeenko, Galina, Fernandez, Manuel Castro, Voynovan, Irina, Zakharova, Natalia Valerievna, Sarsenbaeva, Aiman Silkanovna, Bujanda, Luis, Perez-Aisa, Ángeles, Vologzhanina, Liudmila, Zaytsev, Oleg, Ilchishina, Tatiana, Coba, Cristobal de la, Lasala, Jorge Perez, Alekseenko, Sergey, Modolell, Ines, Molina-Infante, Javier, Ruiz-Zorrilla Lopez, Rafael, Alonso-Galan, Horacio, Moreno, Nuria Fernandez, Hinojosa, Jen, Santaella, Inmaculada, Varela, Pilar, Gonzalez-Cordero, Pedro Luis, Barrio, Jesus, Dominguez-Jimenez, Jose Luis, Nuñez, Oscar, Alcedo, Javier, Nyssen, Olga P., Caldas, Maria, Donday, Maria G., Shvetz, Oleg, Megraud, Francis, O'Morain, Colm, and Gisbert, Javier P.

Published
2020
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35. Methodology for Monitoring Sustainable Development of Isolated Microgrids in Rural Communities

Author

Rahmann, Claudia, Núñez, Oscar, Valencia, Felipe, Arrechea, Susana, Sager, Jalel, and Kammen, Daniel

Subjects
Engineering, Electrical Engineering, Rural Health, Behavioral and Social Science, sustainable development, isolated microgrids, composite index, policy makers, Built Environment and Design
Abstract

Microgrids are a rapidly evolving and increasingly common form of local power generation used to serve the needs of both rural and urban communities. In this paper, we present a methodology to evaluate the evolution of the sustainability of stand-alone microgrids projects. The proposed methodology considers a composite sustainability index (CSI) that includes both positive and negative impacts of the operation of the microgrid in a given community. The CSI is constructed along environmental, social, economic and technical dimensions of the microgrid. The sub-indexes of each dimension are aggregated into the CSI via a set of adaptive weighting factors, which indicate the relative importance of the corresponding dimension in the sustainability goals. The proposed methodology aims to be a support instrument for policy makers especially when defining sound corrective measures to guarantee the sustainability of small, isolated microgrid projects. To validate the performance of the proposed methodology, a microgrid installed in the northern part of Chile (Huatacondo) has been used as a benchmarking project..

Published
2016

44. Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions.

Author

Mara, Andrea, Migliorini, Matteo, Ciulu, Marco, Chignola, Roberto, Egido, Carla, Núñez, Oscar, Sentellas, Sònia, Saurina, Javier, Caredda, Marco, Deroma, Mario A., Deidda, Sara, Langasco, Ilaria, Pilo, Maria I., Spano, Nadia, and Sanna, Gavino

Subjects
INDUCTIVELY coupled plasma mass spectrometry, FISHER discriminant analysis, MACHINE learning, HONEY, RARE earth metals, HONEY composition
Abstract

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.

Author

Núñez, Nerea, Saurina, Javier, and Núñez, Oscar

Subjects
MASS spectrometry, COFFEE beans, COFFEE, CHEMOMETRICS, COFFEE brewing, PRINCIPAL components analysis, HOT water
Abstract

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica—Vietnam Robusta, Vietnam Arabica—Cambodia and Vietnam Robusta—Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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