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2,733 results on '"Stevens G"'

1. Evaluation of pulsed electromagnetic field therapy for the treatment of chronic postoperative pain following lumbar surgery: a pilot, double-blind, randomized, sham-controlled clinical trial

Author

Sorrell RG, Muhlenfeld J, Moffett J, Stevens G, and Kesten S

Subjects
failed back surgery syndrome, lumbar surgery, neuropathic pain, pulsed electromagnetic field therapy, neuromodulation, chronic pain, nociceptive pathways, Medicine (General), R5-920
Abstract

Robert Gordon Sorrell,1 Jamie Muhlenfeld,2 John Moffett,2 Gary Stevens,3 Steven Kesten4 1Alabama Orthopedic Center, Birmingham, AL, USA; 2Regenesis Biomedical Inc., Scottsdale, AZ, USA; 3Dynastat Consulting Inc., Bastrop, TX, USA; 4SKC Life Sciences, Carlsbad, CA, USA Background: The incidence of chronic postoperative pain following lumbar spinal surgery has increased with the overall increase in the prevalence of lumbar surgery. This study was conducted to evaluate the analgesic effectiveness of pulsed electromagnetic field (PEMF) therapy in subjects with persistent pain following lumbar surgery. Patients and methods: A randomized, double-blind, sham-controlled, multicenter study in 36 subjects with persistent low-back and/or radiating leg pain after lumbar surgery was conducted. Eligible subjects were randomized (1:1:1) to receive one of two doses of therapy (42-μs or 38-μs pulse width) or treatment with a sham device. Subjects self-treated twice daily for 60 days. The primary end point was change in pain intensity (∆PI) using the Numerical Pain Rating Scale between average baseline (Days −5 to −1) and end of treatment (Days 56–60) for lumbar and radiating leg pain. Secondary outcome measures included the Oswestry Disability Index, Beck Depression Inventory-II, Patient Global Impression of Change, and consumption of analgesics. Results: Low-back pain scores for the 42-µs group decreased by 40.2% (p = 0.028), compared to 18.6% for the 38-µs pulse width group (p = 0.037) and 25.6% for the sham group (p = 0.013 per protocol population). Average leg pain scores decreased by 45.0% (42 μs, p = 0.009), 17.0% (38 μs, p = 0.293), and 24.5% (sham, p = 0.065). The proportion of subjects responding to therapy, time to 30% reduction in pain scores, and Patient Global Impression of Change were improved with the PEMF 42-μs device over the sham control, although results were associated with p-values >0.05. Conclusion: PEMF therapy (42-μs pulse width) was associated with trends for a reduction in pain, compared to sham treatment. Secondary endpoints were consistent with an overall beneficial effect of the PEMF 42-μs pulse width device. Keywords: failed back surgery syndrome, lumbar surgery, neuropathic pain, pulsed electromagnetic field therapy, neuromodulation, chronic pain, nociceptive pathways

Published
2018

2. Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Author

Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, published in A&A

Published
2024
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10. Empagliflozin in the treatment of heart failure with reduced ejection fraction in addition to background therapies and therapeutic combinations (EMPEROR-Reduced): a post-hoc analysis of a randomised, double-blind trial

Author

Packer, M, Anker, S, Butler, J, Filippatos, G, Pocock, S, Zannad, F, Ferreira, JP, Brueckmann, M, George, J, Jamal, W, Welty, FK, Palmer, M, Clayton, T, Parhofer, KG, Pedersen, TR, Greenberg, B, Konstam, MA, Lees, KR, Carson, P, Doehner, W, Miller, A, Haas, M, Pehrson, S, Komajda, M, Anand, I, Teerlink, J, Rabinstein, A, Steiner, T, Kamel, H, Tsivgoulis, G, Lewis, J, Freston, J, Kaplowitz, N, Mann, J, Petrie, J, Perrone, S, Nicholls, S, Janssens, S, Bocchi, E, Giannetti, N, Verma, S, Zhang, J, Spinar, J, Seronde, M-F, Boehm, M, Merkely, B, Chopra, V, Senni, M, Taddi, S, Tsutsui, H, Choi, D-J, Chuquiure, E, La Rocca, HPB, Ponikowski, P, Juanatey, JRG, Squire, I, Januzzi, J, Pina, I, Bernstein, R, Cheung, A, Green, J, Kaul, S, Lam, C, Lip, G, Marx, N, McCullough, P, Mehta, C, Rosenstock, J, Sattar, N, Scirica, B, Shah, S, Wanner, C, Aizenberg, D, Cartasegna, L, Colombo Berra, F, Colombo, H, Fernandez Moutin, M, Glenny, J, Alvarez Lorio, C, Anauch, D, Campos, R, Facta, A, Fernandez, A, Ahuad Guerrero, R, Lobo Márquez, L, Leon de la Fuente, RA, Mansilla, M, Hominal, M, Hasbani, E, Najenson, M, Moises Azize, G, Luquez, H, Guzman, L, Sessa, H, Amuchástegui, M, Salomone, O, Perna, E, Piskorz, D, Sicer, M, Perez de Arenaza, D, Zaidman, C, Nani, S, Poy, C, Resk, J, Villarreal, R, Majul, C, Smith Casabella, T, Sassone, S, Liberman, A, Carnero, G, Caccavo, A, Berli, M, Budassi, N, Bono, J, Alvarisqueta, A, Amerena, J, Kostner, K, Hamilton, A, Begg, A, Beltrame, J, Colquhoun, D, Gordon, G, Sverdlov, A, Vaddadi, G, Wong, J, Coller, J, Prior, D, Friart, A, Leone, A, Vervoort, G, Timmermans, P, Troisfontaines, P, Franssen, C, Sarens, T, Vandekerckhove, H, Van De Borne, P, Chenot, F, De Sutter, J, De Vuyst, E, Debonnaire, P, Dupont, M, Pereira Dutra, O, Canani, LH, Vieira Moreira, MdC, de Souza, W, Backes, LM, Maia, L, De Souza Paolino, B, Manenti, ER, Saporito, W, Villaça Guimarães Filho, F, Franco Hirakawa, T, Saliba, LA, Neuenschwander, FC, de Freitas Zerbini, CA, Gonçalves, G, Gonçalves Mello, Y, Ascenção de Souza, J, Beck da Silva Neto, L, Bocchi, EA, Da Silveira, J, de Moura Xavier Moraes Junior, JB, de Souza Neto, JD, Hernandes, M, Finimundi, HC, Sampaio, CR, Vasconcellos, E, Neves Mancuso, FJ, Noya Rabelo, MM, Rodrigues Bacci, M, Santos, F, Vidotti, M, Simões, MV, Gomes, FL, Vieira Nascimento, C, Precoma, D, Helfenstein Fonseca, FA, Ribas Fortes, JA, Leães, PE, Campos de Albuquerque, D, Kerr Saraiva, JF, Rassi, S, Alves da Costa, FA, Reis, G, Zieroth, S, Dion, D, Savard, D, Bourgeois, R, Constance, C, Anderson, K, Leblanc, M-H, Yung, D, Swiggum, E, Pliamm, L, Pesant, Y, Tyrrell, B, Huynh, T, Spiegelman, J, Lavoie, J-P, Hartleib, M, Bhargava, R, Straatman, L, Virani, S, Costa-Vitali, A, Hill, L, Heffernan, M, Khaykin, Y, Ricci, J, Senaratne, M, Zhai, A, Lubelsky, B, Toma, M, Yao, L, McKelvie, R, Noronha, L, Babapulle, M, Pandey, A, Curnew, G, Lavoie, A, Berlingieri, J, Kouz, S, Lonn, E, Chehayeb, R, Zheng, Y, Sun, Y, Cui, H, Fan, Z, Han, X, Jiang, X, Tang, Q, Zhou, J, Zheng, Z, Zhang, X, Zhang, N, Zhang, Y, Shen, A, Yu, J, Ye, J, Yao, Y, Yan, J, Xu, X, Wang, Z, Ma, J, Li, Y, Li, S, Lu, S, Kong, X, Song, Y, Yang, G, Yao, Z, Pan, Y, Guo, X, Sun, Z, Dong, Y, Zhu, J, Peng, D, Yuan, Z, Lin, J, Yin, Y, Jerabek, O, Burianova, H, Fiala, T, Hubac, J, Ludka, O, Monhart, Z, Vodnansky, P, Zeman, K, Foldyna, D, Krupicka, J, Podpera, I, Busak, L, Radvan, M, Vomacka, Z, Prosecky, R, Cifkova, R, Durdil, V, Vesely, J, Vaclavik, J, Cervinka, P, Linhart, A, Brabec, T, Miklik, R, Bourhaial, H, Olbrich, H-G, Genth-Zotz, S, Kemala, E, Lemke, B, Böhm, M, Schellong, S, Rieker, W, Heitzer, T, Ince, H, Faghih, M, Birkenfeld, A, Begemann, A, Ghanem, A, Ujeyl, A, von Haehling, S, Dorsel, T, Bauersachs, J, Prull, M, Weidemann, F, Darius, H, Nickenig, G, Wilke, A, Sauter, J, Rauch-Kroehnert, U, Frey, N, Schulze, CP, König, W, Maier, L, Menzel, F, Proskynitopoulos, N, Ebert, H-H, Sarnighausen, H-E, Düngen, H-D, Licka, M, Stellbrink, C, Winkelmann, B, Menck, N, López-Sendón, JL, de la Fuente Galán, L, Delgado Jiménez, JF, Manito Lorite, N, Pérez de Juan Romero, M, Galve Basilio, E, Cereto Castro, F, González Juanatey, JR, Gómez, JJ, Sanmartín Fernández, M, Garcia-Moll Marimon, X, Pascual Figal, D, Bover Freire, R, Bonnefoy Cudraz, E, Jobbe Duval, A, Tomasevic, D, Habib, G, Isnard, R, Picard, F, Khanoyan, P, Dubois-Rande, J-L, Galinier, M, Roubille, F, Alexandre, J, Babuty, D, Delarche, N, Berneau, J-B, Girerd, N, Saxena, M, Rosano, G, Yousef, Z, Clifford, C, Arden, C, Bakhai, A, Boos, C, Jenkins, G, Travill, C, Price, D, Koenyves, L, Lakatos, F, Matoltsy, A, Noori, E, Zilahi, Z, Andrassy, P, Kancz, S, Simon, G, Sydo, T, Vorobcsuk, A, Kiss, RG, Toth, K, Szakal, I, Nagy, L, Barany, T, Nagy, A, Szolnoki, E, Chopra, VK, Mandal, S, Rastogi, V, Shah, B, Mullasari, A, Shankar, J, Mehta, V, Oomman, A, Kaul, U, Komarlu, S, Kahali, D, Bhagwat, A, Vijan, V, Ghaisas, NK, Mehta, A, Kashyap, J, Kothari, Y, TaddeI, S, Scherillo, M, Zacà, V, Genovese, S, Salvioni, A, Fucili, A, Fedele, F, Cosmi, F, Volpe, M, Mazzone, C, Esposito, G, Doi, M, Yamamoto, H, Sakagami, S, Oishi, S, Yasaka, Y, Tsuboi, H, Fujino, Y, Matsuoka, S, Watanabe, Y, Himi, T, Ide, T, Ichikawa, M, Kijima, Y, Koga, T, Yuda, S, Fukui, K, Kubota, T, Manita, M, Fujinaga, H, Matsumura, T, Fukumoto, Y, Kato, R, Kawai, Y, Hiasa, G, Kazatani, Y, Mori, M, Ogimoto, A, Inoko, M, Oguri, M, Kinoshita, M, Okuhara, K, Watanabe, N, Ono, Y, Otomo, K, Sato, Y, Matsunaga, T, Takaishi, A, Miyagi, N, Uehara, H, Takaishi, H, Urata, H, Kataoka, T, Matsubara, H, Matsumoto, T, Suzuki, T, Takahashi, N, Imamaki, M, Yoshitama, T, Saito, T, Sekino, H, Furutani, Y, Koda, M, Shinozaki, T, Hirabayashi, K, Tsunoda, R, Yonezawa, K, Hori, H, Yagi, M, Arikawa, M, Hashizume, T, Ishiki, R, Koizumi, T, Nakayama, K, Taguchi, S, Nanasato, M, Yoshida, Y, Tsujiyama, S, Nakamura, T, Oku, K, Shimizu, M, Suwa, M, Momiyama, Y, Sugiyama, H, Kobayashi, K, Inoue, S, Kadokami, T, Maeno, K, Kawamitsu, K, Maruyama, Y, Nakata, A, Shibata, T, Wada, A, Cho, H-J, Na, JO, Yoo, B-S, Choi, J-O, Hong, SK, Shin, J-H, Cho, M-C, Han, SH, Jeong, J-O, Kim, J-J, Kang, SM, Kim, D-S, Kim, MH, Llamas Esperon, G, Illescas Díaz, J, Fajardo Campos, P, Almeida Alvarado, J, Bazzoni Ruiz, A, Echeverri Rico, J, Lopez Alcocer, I, Valle Molina, L, Hernandez Herrera, C, Calvo Vargas, C, Padilla Padilla, FG, Rodriguez Briones, I, Chuquiure Valenzuela, EJJR, Aguilera Real, ME, Carrillo Calvillo, J, Alpizar Salazar, M, Cervantes Escárcega, JL, Velasco Sanchez, R, Al - Windy, N, van Heerebeek, L, Bellersen, L, Brunner-La Rocca, H-P, Post, J, Linssen, GCM, van de Wetering, M, Peters, R, van Stralen, R, Groutars, R, Smits, P, Yilmaz, A, Kok, WEM, Van der Meer, P, Dijkmans, P, Troquay, R, van Alem, AP, Van de Wal, R, Handoko, L, Westendorp, ICD, van Bergen, PFMM, Rensing, BJWM, Hoogslag, P, Kietselaer, B, Kragten, JA, den Hartog, FR, Alings, A, Danilowicz-Szymanowicz, L, Raczak, G, Piesiewicz, W, Zmuda, W, Kus, W, Podolec, P, Musial, W, Drelich, G, Kania, G, Miekus, P, Mazur, S, Janik, A, Spyra, J, Peruga, J, Balsam, P, Krakowiak, B, Szachniewicz, J, Ginel, M, Grzybowski, J, Chrustowski, W, Wojewoda, P, Kalinka, A, Zurakowski, A, Koc, R, Debinski, M, Fil, W, Kujawiak, M, Forys, J, Kasprzak, M, Krol, M, Michalski, P, Mirek-Bryniarska, E, Radwan, K, Skonieczny, G, Stania, K, Skoczylas, G, Madej, A, Jurowiecki, J, Firek, B, Wozakowska-Kaplon, B, Cymerman, K, Neutel, J, Adams, K, Balfour, P, Deswal, A, Djamson, A, Duncan, P, Hong, M, Murray, C, Rinde-Hoffman, D, Woodhouse, S, MacNevin, R, Rama, B, Broome-Webster, C, Kindsvater, S, Abramov, D, Barettella, M, Pinney, S, Herre, J, Cohen, A, Vora, K, Challappa, K, West, S, Baum, S, Cox, J, Jani, S, Karim, A, Akhtar, A, Quintana, O, Paukman, L, Goldberg, R, Bhatti, Z, Budoff, M, Bush, E, Potler, A, Delgado, R, Ellis, B, Dy, J, Fialkow, J, Sangrigoli, R, Ferdinand, K, East, C, Falkowski, S, Donahoe, S, Ebrahimi, R, Kline, G, Harris, B, Khouzam, R, Jaffrani, N, Jarmukli, N, Kazemi, N, Koren, M, Friedman, K, Herzog, W, Silva Enciso, J, Cheung, D, Grover-McKay, M, Hauptman, P, Mikhalkova, D, Hegde, V, Hodsden, J, Khouri, S, McGrew, F, Littlefield, R, Bradley, P, McLaurin, B, Lupovitch, S, Labin, I, Rao, V, Leithe, M, Lesko, M, Lewis, N, Lombardo, D, Mahal, S, Malhotra, V, Dauber, I, Banerjee, A, Needell, J, Miller, G, Paladino, L, Munuswamy, K, Nanna, M, McMillan, E, Mumma, M, Napoli, M, Nelson, W, O'Brien, T, Adlakha, A, Onwuanyi, A, Serota, H, Schmedtje, J, Paraschos, A, Potu, R, Sai-Sudhakar, C, Saltzberg, M, Sauer, A, Shah, P, Skopicki, H, Bui, H, Carr, K, Stevens, G, Tahirkheli, N, Tallaj, J, Yousuf, K, Trichon, B, Welker, J, Tolerico, P, Vest, A, Vivo, R, Wang, X, Abadier, R, Dunlap, S, Weintraub, N, Malik, A, Kotha, P, Zaha, V, Kim, G, Uriel, N, Greene, T, Salacata, A, Arora, R, Gazmuri, R, Kobayashi, J, Iteld, B, Vijayakrishnan, R, Dab, R, Mirza, Z, Marques, V, Nallasivan, M, Bensimhon, D, Peart, B, Saint-Jacques, H, Barringhaus, K, Contreras, J, Gupta, A, Koneru, S, Nguyen, V, Verma, Subodh, Dhingra, Nitish K, Butler, Javed, Anker, Stefan D, Ferreira, Joao Pedro, Filippatos, Gerasimos, Januzzi, James L, Lam, Carolyn S P, Sattar, Naveed, Peil, Barbara, Nordaby, Matias, Brueckmann, Martina, Pocock, Stuart J, Zannad, Faiez, and Packer, Milton

Published
2022
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12. Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning

Author

Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Zinchenko, I. A., Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Zinchenko, I. A.

Abstract

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, submitted to A&A

Published
2024

17. Non-Richtmyer–Meshkov instability ejecta production based on shallow bubble collapse.

Author

Maskaly, G. R., Stevens, G. D., La Lone, B. M., Turley, W. D., Staska, M. D., Najjar, F. M., and Hartsfield, T. M.

Subjects
*RICHTMYER-Meshkov instability, *SURFACES (Technology), *SURFACE finishing, *FREE material, *FREE surfaces, *BUBBLES
Abstract

The study of shock-driven ejecta production has focused on Richtmyer–Meshkov instability (RMI) growth from geometric features of the material surface. Extensive study of this mechanism under both single- and multiple-shock conditions has found that the ejected mass tends to be closely associated with the shocked surface phase, and its temperature is not dramatically greater than the hydrodynamic shock temperature of the bulk. In this work, we propose and demonstrate a new ejecta production mechanism that can occur under multiple-shock conditions based on the collapse of bubbles near the free surface of the material. This mechanism produces ejected mass that is much greater in quantity than observed in the RMI case. The particles are much hotter than predicted by the shock Hugoniot state, and the ejected mass does not appear to be strongly dependent upon initial surface finish. The ejecta source extends into the material with no clear remaining free surface. We name this mechanism Shallow Bubble Collapse (SBC) and discuss the conditions under which it activates. We demonstrate resolved modeling methods that enable the calculation, design, and study of SBC as a mechanism and perform a series of experiments to compare with the models. Under some multiple-shock conditions, SBC ejection produces ten times more ejected mass than RMI growth. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Clinical Outcomes for Patients with Brain Metastases from Upper Gastrointestinal Cancer Treated with Stereotactic Radiosurgery

Author

Bommireddy, A., primary, Billena, C., additional, Mayo, Z.S., additional, Koro, S., additional, Davis, R.W., additional, Chao, S.T., additional, Murphy, E.S., additional, Suh, J.H., additional, Chan, T.A., additional, Yu, J.S., additional, Barnett, G.H., additional, Mohammadi, A.M., additional, Angelov, L., additional, Stevens, G., additional, Estfan, B., additional, Kamath, S., additional, Khorana, A., additional, and Balagamwala, E.H., additional

Published
2023
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23. Development of an RPA for Prediction of Radiation Necrosis Following Single Fraction Gamma Knife Radiosurgery for Brain Metastases

Author

Mayo, Z.S., primary, Reddy, C.A., additional, Billena, C., additional, Davies, E.M., additional, Bommireddy, A., additional, Davis, R.W., additional, Murphy, E.S., additional, Suh, J.H., additional, Balagamwala, E.H., additional, Chan, T.A., additional, Yu, J.S., additional, Barnett, G.H., additional, Mohammadi, A.M., additional, Angelov, L., additional, Stevens, G., additional, and Chao, S.T., additional

Published
2023
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24. Clinical Outcomes of Patients with Brain Metastases from Colorectal Cancer Treated with Stereotactic Radiosurgery

Author

Bommireddy, A., primary, Billena, C., additional, Mayo, Z.S., additional, Koro, S., additional, Chao, S.T., additional, Murphy, E.S., additional, Suh, J.H., additional, Chan, T.A., additional, Yu, J.S., additional, Barnett, G.H., additional, Mohammadi, A.M., additional, Angelov, L., additional, Stevens, G., additional, Valente, M., additional, Steele, S.R., additional, Gorgun, E., additional, Liska, D., additional, Khorana, A., additional, Krishnamurthi, S., additional, and Balagamwala, E.H., additional

Published
2023
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26. Frameless Fractionated Stereotactic Radiosurgery for Brain Metastases: An Institutional Series of 145 Cases

Author

Kilic, S.S., primary, Halima, A., additional, Neyman, G., additional, Guo, B., additional, Magnelli, A., additional, Kolar, M.D., additional, Cho, Y.B., additional, Qi, P., additional, Stevens, G., additional, Barnett, G.H., additional, Angelov, L., additional, Mohammadi, A.M., additional, Woody, N.M., additional, Chan, T.A., additional, Yu, J.S., additional, Murphy, E.S., additional, Suh, J.H., additional, and Chao, S.T., additional

Published
2023
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30. Controlling the coexistence of structural phases and the optical properties of gallium nanoparticles with optical excitation

Author

MacDonald, K. F., Fedotov, V. A., Pochon, S., Stevens, G., Kusmartsev, F. V., and Zheludev, N. I.

Subjects
Condensed Matter
Abstract

We have observed reversible structural transformations, induced by optical excitation at 1.55 micrometer, between the beta, gamma and liquid phases of gallium in self-assembled gallium nanoparticles, with a narrow size distribution around 50 nm, on the tip of an optical fiber. Only a few tens of nanowatts of optical excitation per particle are required to control the transformations, which take the form of a dynamic phase coexistence and are accompanied by substantial changes in the optical properties of the nanoparticle film. The time needed to achieve phase equilibrium is in the microsecond range, and increases critically at the transition temperature., Comment: 4 pages, 3 figures

Published
2003
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32. Gallium nanoparticles grow where light is

Author

MacDonald, K. F., Brocklesby, W. S., Emelyanov, V. I., Fedotov, V. A., Pochon, S., Ross, K. J., Stevens, G., and Zheludev, N. I.

Subjects
Physics - Optics, Physics - General Physics
Abstract

The study of metallic nanoparticles has a long tradition in linear and nonlinear optics [1], with current emphasis on the ultrafast dynamics, size, shape and collective effects in their optical response [2-6]. Nanoparticles also represent the ultimate confined geometry:high surface-to-volume ratios lead to local field enhancements and a range of dramatic modifications of the material's properties and phase diagram [7-9]. Confined gallium has become a subject of special interest as the light-induced structural phase transition recently observed in gallium films [10, 11] has allowed for the demonstration of all-optical switching devices that operate at low laser power [12]. Spontaneous self-assembly has been the main approach to the preparation of nanoparticles (for a review see 13). Here we report that light can dramatically influence the nanoparticle self-assembly process: illumination of a substrate exposed to a beam of gallium atoms results in the formation of nanoparticles with a relatively narrow size distribution. Very low light intensities, below the threshold for thermally-induced evaporation, exert considerable control over nanoparticle formation through non-thermal atomic desorption induced by electronic excitation., Comment: 11 pages, 4 figures

Published
2001
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33. The temperatures of ejecta transporting in vacuum and gases.

Author

Hartsfield, T. M., Schulze, R. K., La Lone, B. M., Charonko, J. J., Hammerberg, J. E., Regele, J. D., Schauer, M. M., Schwarzkopf, J. D., Sheppard, D. G., Stevens, G. D., Cooley, J. C., Donovan, P. M., Manzanares, R., Martinez, J. I., Schmidt, D. W., Staska, M. D., Valencia, R. J., Turley, W. D., and Buttler, W. T.

Subjects
GASES, LIQUID metals, TEMPERATURE, RADIANCE, THERMAL analysis
Abstract

In this work, we measure continuous thermal radiance from evolving clouds of liquid metal fragments ejected into vacuum, nonreactive, and reactive gas. We implement a model for the thermalization of the ejecta and gas and use this to constrain the absolute temperature of the ejecta cloud. This model enables further analyses of ejecta thermal behavior under a variety of conditions. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Time trends in associations between neighbourhood and school characteristics and mental health problems among Dutch adolescents

Author

Brons, H, Helbich, M, Bolt, G, Visser, K, Stevens, G, Brons, H, Helbich, M, Bolt, G, Visser, K, and Stevens, G

Abstract

We investigated trends in associations between physical and social neighbourhood and school characteristics and adolescent mental health problems between 2005 and 2017. Nationally representative, repeated cross-sectional data collected in the Netherlands among primary (N = 5,871) and secondary school students (N = 20,778) were analysed through cross-classified multilevel models. Hardly any evidence was found for over-time changes in associations between neighbourhood and school characteristics and adolescent mental health problems. Findings showed that for both groups of students, only social characteristics within the two contexts were associated with adolescent mental health problems. For secondary school students, school effects were larger than neighbourhood effects, while the opposite was true for primary school students. Specifically, primary school students residing in more socially fragmented or lower socioeconomic status (SES) neighbourhoods, and lower SES schools, reported more conduct problems and peer relationship problems. For secondary school students, only the SES of the neighbourhood and the school was associated with all four aspects of mental health problems. Remarkably, the direction of the associations between neighbourhood/ school SES and adolescent mental health problems varied across the different mental health outcomes. More research is warranted to replicate our findings.

Published
2023

42. National-Level Schoolwork Pressure, Family Structure, Internet Use, and Obesity as Drivers of Time Trends in Adolescent Psychological Complaints Between 2002 and 2018

Author

Youth in Changing Cultural Contexts, Leerstoel Finkenauer, Leerstoel Stevens, Boer, M., Cosma, A., Twenge, J. M., Inchley, J., Jeriček Klanšček, H., Stevens, G. W.J.M., Youth in Changing Cultural Contexts, Leerstoel Finkenauer, Leerstoel Stevens, Boer, M., Cosma, A., Twenge, J. M., Inchley, J., Jeriček Klanšček, H., and Stevens, G. W.J.M.

Published
2023

43. Time trends in associations between neighbourhood and school characteristics and mental health problems among Dutch adolescents

Author

Youth in Changing Cultural Contexts, Leerstoel Finkenauer, Urban Accessibility and Social Inclusion, Urban Living and Social Networks, Leerstoel Stevens, Brons, H, Helbich, M, Bolt, G, Visser, K, Stevens, G, Youth in Changing Cultural Contexts, Leerstoel Finkenauer, Urban Accessibility and Social Inclusion, Urban Living and Social Networks, Leerstoel Stevens, Brons, H, Helbich, M, Bolt, G, Visser, K, and Stevens, G

Published
2023

49. E-book: Calciumhuishouding – Uitgave 2022

Author

Verbeke, L., primary, Kamoen, K., additional, Aeyels, L., additional, Veramme, J., additional, De Cono, N., additional, Carpentier, I., additional, Peeters, E., additional, Vanzieleghem, B., additional, Strobbe, A., additional, Slock, D., additional, Maeyaert, B., additional, Raaijmakers, A., additional, Van Winckel, M., additional, Plaete, J., additional, Bovijn, L., additional, Van Overmeire, B., additional, Vandenplas, Y., additional, Arts, W., additional, De Greef, E., additional, Declercq, D., additional, Veereman, G., additional, Hauser, B., additional, Hebbelynck, J., additional, Huysentruyt, K., additional, Vyncke, K., additional, Neven, L., additional, and Stevens, G., additional

Published
2023
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