5 results on '"Reyes-Munoz, P"'
Search Results
2. Tenecteplase versus standard of care for minor ischaemic stroke with proven occlusion (TEMPO-2): a randomised, open label, phase 3 superiority trial
- Author
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Coutts, Shelagh B, Ankolekar, Sandeep, Appireddy, Ramana, Arenillas, Juan F, Assis, Zarina, Bailey, Peter, Barber, Philip A, Bazan, Rodrigo, Buck, Brian H, Butcher, Ken S, Camden, Marie-Christine, Campbell, Bruce C V, Casaubon, Leanne K, Catanese, Luciana, Chatterjee, Kausik, Choi, Philip M C, Clarke, Brian, Dowlatshahi, Dar, Ferrari, Julia, Field, Thalia S, Ganesh, Aravind, Ghia, Darshan, Goyal, Mayank, Greisenegger, Stefan, Halse, Omid, Horn, Mackenzie, Hunter, Gary, Imoukhuede, Oje, Kelly, Peter J, Kennedy, James, Kenney, Carol, Kleinig, Timothy J, Krishnan, Kailash, Lima, Fabricio, Mandzia, Jennifer L, Marko, Martha, Martins, Sheila O, Medvedev, George, Menon, Bijoy K, Mishra, Sachin M, Molina, Carlos, Moussaddy, Aimen, Muir, Keith W, Parsons, Mark W, Penn, Andrew M W, Pille, Arthur, Pontes-Neto, Octávio M, Roffe, Christine, Serena, Joaquin, Simister, Robert, Singh, Nishita, Spratt, Neil, Strbian, Daniel, Tham, Carol H, Wiggam, M Ivan, Williams, David J, Willmot, Mark R, Wu, Teddy, Yu, Amy Y X, Zachariah, George, Zafar, Atif, Zerna, Charlotte, Hill, Michael D, Salluzzi, Marina, Blenkin, Nicole, Dueck, Ashley, Doram, Craig, Zhang, Qiao, Kenney, Carol, Ryckborst, Karla, Bohn, Shelly, Collier, Quentin, Taylor, Frances, Lethebe, B. Cord, Jambula, Anitha, Sage, Kayla, Toussaint, Lana, Save, Supryia, Lee, Jaclyn, Laham, N, Sultan, A.A., Deepak, A., Sitaram, A., Demchuk, Andrew M., Lockey, A., Micielli, A., Wadhwa, A., Arabambi, B., Graham, B., Bogiatzi, Chrysi, Doshi, Darshan, Chakraborty, D., Kim, Diana, Vasquez, D, Singh, D, Tse, Dominic, Harrison, E., Smith, E.E., Teleg, E., Klourfeld, E., Klein, G., Sebastian, I.A., Evans, J, Hegedus, J, Kromm, J, Lin, K, Ignacio, K, Ghavami, Kimia, Ismail, M., Moores, M., Panzini, M.A., Boyko, M., Almekhlafi, M.A., Newcommon, Nancy, Maraj, N., Imoukhuede, O., Volny, O., Stys, Peter, Couillard, Phillipe, Ojha, P., Eswaradass, P., Joundi, Raed, Singh, R., Asuncion, R.M., Muir, R.T., Dey, S., Mansoor, S., Wasyliw, S., Nagendra, S., Hu, Sherry, Althubait, S., Chen, S., Bal, S., Van Gaal, Stephen, Peters, Steven, Ray, Sucharita, Chaturvedi, S., Subramaniam, Suresh, Fu, Vivian, Villaluna, K., Maclean, G., King-Azote, P., Ma, C., Plecash, A., Murphy, C., Gorman, J., Wilson, L., Zhou, L., Benevente, O., Teal, P., Yip, S., Mann, S., Dewar, B., Demetroff, M., Shamloul, R., Beardshaw, R., Roberts, S., Blaquiere, D., Stotts, G., Shamy, M., Bereznyakova, O., Fahed, R., Alesefir, W., Lavoie, Suzy, Hache, A., Collard, K, Mackey, A., Gosselin-Lefebvre, S., Verreault, S., Beauchamp, B., Lambourn, L., Khaw, A., Mai, L., Sposato, L., Bres Bullrich, M., Azarpazhooh, R., Fridman, S., Kapoor, A., Southwell, A., Bardi, E., Fatakdawala, I., Kamra, M, Lopes, K., Popel, N., Norouzi, V., Liu, A., Liddy, A.M., Ghoari, B., Hawkes, C., Enriquez, C.A., Gladstone, D.J., Manosalva Alzate, H.A., Khosravani, H., Hopyan, J.J., Sivakumar, K., Son, M., Boulos, M.I., Hamind, M.A., Swartz, R.H., Murphy, R., Reiter, S., Fitzpatrick, T., Bhandari, V., Good, J., Penn, M., Naylor, M., Frost, S., Cayley, A., Akthar, F., Williams, J., Kalman, L., Crellin, L., Wiegner, R., Singh, R.S., Stewart, T., To, W., Singh, S., Pikula, A., Jaigobin, C., Carpani, F., Silver, F., Janssen, H., Schaafsma, J., del Campo, M., Alskaini, M., Rajendram, P., Fairall, P., Granfield, B., Crawford, D., Jabs, J., White, L., Sivakumar, L., Piquette, L., Nguyen, T., Nomani, A., Wagner, A., Alrohimi, A., Butt, A., D'Souza, A., Gajurel, B., Vekhande, C., Kamble, H., Kalashyan, H., Lloret, M., Benguzzi, M., Arsalan, N., Ishaque, N., Ashayeriahmadabad, R., Samiento, R., Hosseini, S., Kazi, S., Das, S., Sugumar, T., Selchen, D., Kostyrko, P., Muccilli, A., Saposnik, A.G., Vandervelde, C., Ratnayake, K., McMillan, S., Katsanos, A., Shoamanesh, A., Sahlas, D.J., Naidoo, V., Todorov, V., Toma, H., Brar, J., Lee, J., Horton, M., Chen, S., Shand, E., Weatherby, S., Jin, A., Durafourt, B., Jalini, S., Gardner, A., Tyson, C., Junk, E., Foster, K., Bolt, K., Sylvain, N., Maley, S., Urroz, L., Peeling, L., Kelly, M., Whelan, R., Cooley, R., Teitelbaum, J., Boutayeb, A., Moore, A., Cole, E., Waxman, L., Ben-Amor, N., Sanchez, R., Khalil, S., Nehme, A., Legault, C., Tampieri, D., Ehrensperger, E., Vieira, L., Cortes, M., Angle, M., Hannouche, M., Badawy, M., Werner, K., Wieszmuellner, S., Langer, A., Gisold, A., Zach, H., Rommer, P., Macher, S., Blechinger, S., Marik, W., Series, W., Baumgartinger, M., Krebs, S., Koski, J., Eirola, S., Ivanoff, T., Erakanto, A., Kupari, L., Sibolt, G., Panula, J., Tomppo, L., Tiainen, M., Ahlstrom, M., Martinez Majander, N., Suomalainen, O., Raty, S., Levi, C., Kerr, E., Allen, J., Kaauwai, L.P., Belevski, L., Russell, M., Ormond, S., Chew, A., Loiselle, A., Royan, A., Hughes, B., Garcia Esperon, C., Pepper, E., Miteff, F., He, J., Lycett, M., Min, M., Murray, N., Pavey, N., Starling de Barros, R., Gangadharan, S., Dunkerton, S., Waller, S., Canento Sanchez, T., Wellings, T., Edmonds, G., Whittaker, K.A., Ewing, M., Lee, P., Singkang, R., McDonald, A., Dos Santos, A., Shin, C., Jackson, D., Tsoleridis, J., Fisicchia, L., Parsons, N., Shenoy, N., Smith, S., Sharobeam, A., Balabanski, A., Park, A., Williams, C., Pavlin-Premri, D., Rodrigues, E., Alemseged, F., Ng, F., Zhao, H., Beharry, J., Ng, J.L., Williamson, J., Wong, J.Z.W., Li, K., Kwan, M.K., Valente, M., Yassi, N., Cooley, R., Yogendrakumar, V., McNamara, B., Buchanan, C., McCarthy, C., Thomas, G., Stephens, K., Chung, M., Chung, M.F., Tang, M., Busch, T., Frost, T., Lee, R., Stuart, N., Pachani, N., Menon, A., Borojevic, B., Linton, C.M., Garcia, G., Callaly, E.P., Dewey, H., Liu, J., Chen, J., Wong, J., Nowak, K., To, K., Lizak, N.S., Bhalala, O., Park, P., Tan, P., Martins, R., Cody, R., Forbes, R., Chen, S.K., Ooi, S., Tu, S., Dang, Y.L., Ling, Z., Cranefield, J., Drew, R., Tan, A., Kurunawai, C., Harvey, J., Mahadevan, J.J., Cagi, L., Palanikumar, L., Chia, L.N., Goh, R., El-Masri, S., Urbi, B., Rapier, C., Berrill, H., McEvoy, H., Dunning, R., Kuriakose, S., Chad, T., Sapaen, V., Sabet, A., Shah, D., Yeow, D., Lilley, K., Ward, K., Mozhy Mahizhnan, M., Tan, M., Lynch, C., Coveney, S., Tobin, K., McCabe, J., Marnane, M., Murphy, S., Large, M., Moynihan, B., Boyle, K., Sanjuan, E., Sanchis, M., Boned, S., Pancorbo, O., Sala, V., Garcia, L., Garcia-Tornel, A., Juega, J., Pagola, J., Santana, K., Requena, M., Muchada, M., Olive, M., Lozano, P.J., Rubiera, M., Deck, M., Rodriguez, N., Gomez, B., Reyes Munoz, F.J., Gomez, A.S., Sanz, A.C., Garcia, E.C., Penacoba, G., Ramos, M.E., de Lera Alfonso, M., Feliu, A, Pardo, L., Ramirez, P., Murillo, A., Lopez Dominguez, D., Rodriguez, J., Terceno Izaga, M., Reina, M., Viturro, S.B., Bojaryn, U., Vera Monge, V.A., Silva Blas, Y., R Siew, R., Agustin, S J, Seet, C., Tianming, T., d'Emden, A., Murray, A., Welch, A., Hatherley, K., Day, N., Smith, W., MacRae, E., Mitchell, E.S., Mahmood, A., Elliot, J., Neilson, S., Biswas, V., Brown, C., Lewis, A., Ashton, A., Werring, D., Perry, R., Muhammad, R., Lee, Y.C., Black, A., Robinson, A., Williams, A., Banaras, A., Cahoy, C., Raingold, G., Marinescu, M., Atang, N., Bason, N., Francia, N., Obarey, S., Feerick, S., Joseph, J., Schulz, U., Irons, R., Benjamin, J., Quinn, L., Jhoots, M., Teal, R., Ford, G., Harston, G., Bains, H., Gbinigie, I., Mathieson, P., Irons, R., Sim, C.H., Hayter, E., Kennedy, K., Binnie, L., Priestley, N., Williams, R., Ghatala, R., Stratton, S., Blight, A., Zhang, L., Davies, A., Duffy, H., Roberts, J., Homer, J., Roberts, K., Dodd, K., Cawley, K., Martin, M., Leason, S., Cotgreave, S., Taylor, T., Nallasivan, A., Haider, S., Chakraborty, T., Webster, T., Gil, A., Martin, B., Joseph, B., Cabrera, C., Jose, D., Man, J., Aquino, J., Sebastian, S., Osterdahl, M., Kwan, M., Matthew, M., Ike, N., Bello, P., Wilding, P., Fuentes, R., Shah, R., Mashate, S., Patel, T., Nwanguma, U., Dave, V., Haber, A., Lee, A., O'Sullivan, A., Drumm, B., Dawson, A.C., Matar, T., Biswas, V., Roberts, D., Taylor, E., Rounis, E., El-Masry, A., O'Hare, C., Kalladka, D., Jamil, S., Auger, S., Raha, O., Evans, M., Vonberg, F., Kalam, S., Ali Sheikh, A., Jenkins, I.H., George, J., Kwan, J., Blagojevic, J., Saeed, M., Haji-Coll, M., Tsuda, M., Sayed, M., Winterkron, N., Thanbirajah, N., Vittay, O., Karim, R., Smail, R.C., Gauhar, S., Elmamoun, S., Malani, S., Pralhad Kelavkar, S., Hiden, J., Ferdinand, P., Sanyal, R., Varquez, R., Smith, B., Okechukwu, C., Fox, E., Collins, E., Courtney, K., Tauro, S., Patterson, C., McShane, D., Kerr, E., Roberts, G., McIImoyle, J., McGuire, K., Fearon, P., Gordon, P., Isaacs, K., Lucas, K., Smith, L., Dews, L., Bates, M., Lawrence, S., Heeley, S., Patel, V., Chin, Y.M., Sims, D., Littleton, E., Khaira, J., Nadar, K., Kieliszkowska, A., Sari, B., Domingos Belo, C., Smith, E., Manolo, E.Y., Aeron-Thomas, J., Doheny, M., Garcia Pardo, M., Recaman, M., Tibajia, M.C., Aissa, M., Mah, Y., Yu, T., Patel, V., Meenakshisundaram, S., Heller, S., Alsukhni, R., Williams, O., Farag, M., Benger, M., Engineer, A., Aissa, M., Bayhonan, S., Conway, S., Bhalla, A., Nouvakis, D., Theochari, E., Boyle, F., Teo, J., King-Robson, J., Law, K.Y., Sztriha, L., Ismail, M., McGovern, A., Day, D., Mitchell-Douglas, J., Francis, J., Iqbal, A., Punjabivaryani, P., Anonuevo Reyes, J., Anonuevo Reyes, M., Pauls, M., Buch, A., Hedstrom, A., Hutchinson, C., Kirkland, C., Newham, J., Wilkes, G., Fleming, L., Fleck, N., Franca, A., Chwal, B., Oldoni, C., Mantovani, G., Noll, G., Zanella, L., Soma, M., Secchi, T., Borelli, W., Rimoli, B.P., da Cunha Silva, G.H., Machado Galvao Mondin, L.A., Barbosa Cerantola, R., Imthon, A.K., Esaki, A.S., Camilo, M., Vincenzi, O.C., ds Cruz, R.R., Morillos, M.B., Riccioppa Rodrigues, G.G., Santos Ferreira, K., Pazini, A.M., Pena Pereira, M.A., de Albuquerque, A.L.A., Massote Fontanini, C.E., Matinez Rubio, C.F., dos Santos, D.T., Dias, F.A., Alves, F.F.A., Milani, C., Pegorer Santos, B., Winckler, F., De Souza, J.T., Bonome, L.A.M., Cury Silva, V.A., Teodoro, R.S., Modolo, G.P., Ferreira, N.C., Barbosa dos Santos, D.F., dos Santos Moreira, J.C., Cruz Guedes de Morais, A.B., Vieira, J., Mendes, G., and de Queiroz, J.P.
- Abstract
Individuals with minor ischaemic stroke and intracranial occlusion are at increased risk of poor outcomes. Intravenous thrombolysis with tenecteplase might improve outcomes in this population. We aimed to test the superiority of intravenous tenecteplase over non-thrombolytic standard of care in patients with minor ischaemic stroke and intracranial occlusion or focal perfusion abnormality.
- Published
- 2024
- Full Text
- View/download PDF
3. Mapping landscape canopy nitrogen content from space using PRISMA data
- Author
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Verrelst, J, Rivera-Caicedo, J, Reyes-Munoz, P, Morata, M, Amin, E, Tagliabue, G, Panigada, C, Hank, T, Berger, K, Verrelst J., Rivera-Caicedo J. P., Reyes-Munoz P., Morata M., Amin E., Tagliabue G., Panigada C., Hank T., Berger K., Verrelst, J, Rivera-Caicedo, J, Reyes-Munoz, P, Morata, M, Amin, E, Tagliabue, G, Panigada, C, Hank, T, Berger, K, Verrelst J., Rivera-Caicedo J. P., Reyes-Munoz P., Morata M., Amin E., Tagliabue G., Panigada C., Hank T., and Berger K.
- Abstract
Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g/m2 and coefficient of determination (R2) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates ove
- Published
- 2021
4. Mapping Essential Vegetation Variables Over Europe Using Gaussian Process Regression and Sentinel-3 Data in Google Earth Engine
- Author
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Reyes-Munoz, P., primary, Pipia, L., additional, Salinero-Delgado, M., additional, de Grave, C., additional, Estevez, J., additional, Belda, S., additional, and Verrelst, J., additional
- Published
- 2021
- Full Text
- View/download PDF
5. Mapping landscape canopy nitrogen content from space using PRISMA data
- Author
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G Tagliabue, Eatidal Amin, Juan Pablo Rivera-Caicedo, Pablo Reyes-Muñoz, Cinzia Panigada, Miguel Morata, Tobias Hank, Jochem Verrelst, Katja Berger, Verrelst, J, Rivera-Caicedo, J, Reyes-Munoz, P, Morata, M, Amin, E, Tagliabue, G, Panigada, C, Hank, T, and Berger, K
- Subjects
Active learning ,Active learning (machine learning) ,Computer science ,Dimensionality reduction ,Hyperspectral imaging ,PRISMA ,Context (language use) ,Collinearity ,Hybrid retrieval ,Imaging spectroscopy ,Atomic and Molecular Physics, and Optics ,Computer Science Applications ,CHIME ,Kriging ,EnMAP ,Canopy nitrogen content ,Computers in Earth Sciences ,Engineering (miscellaneous) ,Gaussian process regression ,Remote sensing - Abstract
Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g / m 2 and coefficient of determination ( R 2 ) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates over the whole landscape and meaningful associated uncertainties. These promising results demonstrate the feasibility of routinely quantifying CNC from space, such as in an operational context as part of the future CHIME mission.
- Published
- 2021
- Full Text
- View/download PDF
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