58 results on '"Butler, Patrick"'
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2. Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions
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Yousuf, Raquib Bin, Biswas, Subhodip, Kaushal, Kulendra Kumar, Dunham, James, Gelles, Rebecca, Muthiah, Sathappan, Self, Nathan, Butler, Patrick, Ramakrishnan, Naren, Yousuf, Raquib Bin, Biswas, Subhodip, Kaushal, Kulendra Kumar, Dunham, James, Gelles, Rebecca, Muthiah, Sathappan, Self, Nathan, Butler, Patrick, and Ramakrishnan, Naren
- Abstract
Understanding key insights from full-text scholarly articles is essential as it enables us to determine interesting trends, give insight into the research and development, and build knowledge graphs. However, some of the interesting key insights are only available when considering full-text. Although researchers have made significant progress in information extraction from short documents, extraction of scientific entities from full-text scholarly literature remains a challenging problem. This work presents an automated End-to-end Research Entity Extractor called EneRex to extract technical facets such as dataset usage, objective task, method from full-text scholarly research articles. Additionally, we extracted three novel facets, e.g., links to source code, computing resources, programming language/libraries from full-text articles. We demonstrate how EneRex is able to extract key insights and trends from a large-scale dataset in the domain of computer science. We further test our pipeline on multiple datasets and found that the EneRex improves upon a state of the art model. We highlight how the existing datasets are limited in their capacity and how EneRex may fit into an existing knowledge graph. We also present a detailed discussion with pointers for future research. Our code and data are publicly available at https://github.com/DiscoveryAnalyticsCenter/EneRex., Comment: ACM KDD 2022 Workshop on Data-driven Science of Science
- Published
- 2022
3. Low evidence for implementation of well- documented implants regarding risk of early revision:a systematic review on total hip arthroplasty
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Butler, Patrick, Gorgis, Josef, Viberg, Bjarke, Overgaard, Søren, Butler, Patrick, Gorgis, Josef, Viberg, Bjarke, and Overgaard, Søren
- Abstract
» When introducing an implant, surgeons are subjected to steep learning curves, which may lead to a heightened revision rate. Stepwise introduction revolutionized implant introduction but lacks a last step. » No guidelines exist for the introduction of a well-documented implant not previously used in a department. This is problematic according to the European Union’s legislated tendering process, potentially leading to increased revisions. In this systematic review, the introduction of a well-documented total hip arthroplasty implant to experienced surgeons is explored amid concerns of higher revision rate. » Literature search strategies were deployed in the Embase and Medline databases, revealing a total of 14,612 articles. Using the Covidence software (Cochrane, London), two reviewers screened articles for inclusion. » No articles were found that fulfilled our eligibility criteria. A post hoc analysis retrieved two national register-based studies only missing information about the surgeon’s knowledge of the introduced implant. None of the introduced implants decreased the revision rate and around 30% of the introduced implants were associated with a higher revision rate. » The review showed that no data exist about revision rates when introducing well-documented implants. In continuation thereof, the introduction of well-documented implants might also be associated with increased revision rates, as has been shown for total knee arthroplasty. We therefore suggest that special attention should be focused on changes of implants in departments, which can be achieved by way of specific registration in national registers.
- Published
- 2021
4. Impact of COVID-19 Pandemic on Cardiovascular Testing in Asia
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Kudo, T, Lahey, R, Hirschfeld, C, Williams, M, Lu, B, Alasnag, M, Bhatia, M, Henry Bom, H, Dautov, T, Fazel, R, Karthikeyan, G, Keng, F, Rubinshtein, R, Better, N, Cerci, R, Dorbala, S, Raggi, P, Shaw, L, Villines, T, Vitola, J, Choi, A, Malkovskiy, E, Goebel, B, Cohen, Y, Randazzo, M, Pascual, T, Pynda, Y, Dondi, M, Paez, D, Einstein, A, Hinterleitner, G, Lu, Y, Morozova, O, Xu, Z, Lopez-Mattei, J, Parwani, P, Nasery, M, Goda, A, Shirka, E, Benlabgaa, R, Bouyoucef, S, Medjahedi, A, Nailli, Q, Agolti, M, Aguero, R, Alak, M, Alberguina, L, Arroñada, G, Astesiano, A, Norton, C, Benteo, P, Blanco, J, Bonelli, J, Bustos, J, Cabrejas, R, Cachero, J, Campisi, R, Canderoli, A, Carames, S, Carrascosa, P, Castro, R, Cendoya, O, Cognigni, L, Collaud, C, Cortes, C, Courtis, J, Cragnolino, D, Daicz, M, De La Vega, A, De Maria, S, Del Riego, H, Dettori, F, Deviggiano, A, Dragonetti, L, Embon, M, Enriquez, R, Ensinas, J, Faccio, F, Facello, A, Garofalo, D, Geronazzo, R, Gonza, N, Gutierrez, L, Guzzo, M, Hasbani, V, Huerin, M, Jäger, V, Lewkowicz, J, López De Munaín, M, Lotti, J, Marquez, A, Masoli, O, Mastrovito, E, Mayoraz, M, Melado, G, Mele, A, Merani, M, Meretta, A, Molteni, S, Montecinos, M, Noguera, E, Novoa, C, Sueldo, C, Ascani, S, Pollono, P, Pujol, M, Radzinschi, A, Raimondi, G, Redruello, M, Rodríguez, M, Romero, R, Acuña, A, Rovaletti, F, San Miguel, L, Solari, L, Strada, B, Traverso, S, Traverzo, S, Espeche, M, Weihmuller, J, Wolcan, J, Zeffiro, S, Sakanyan, M, Beuzeville, S, Boktor, R, Butler, P, Calcott, J, Carr, L, Chan, V, Chao, C, Chong, W, Dobson, M, Downie, D, Dwivedi, G, Elison, B, Engela, J, Francis, R, Gaikwad, A, Basavaraj, A, Goodwin, B, Greenough, R, Hamilton-Craig, C, Hsieh, V, Joshi, S, Lederer, K, Lee, K, Lee, J, Magnussen, J, Mai, N, Mander, G, Murton, F, Nandurkar, D, Neill, J, O'Rourke, E, O'Sullivan, P, Pandos, G, Pathmaraj, K, Pitman, A, Poulter, R, Premaratne, M, Prior, D, Ridley, L, Rutherford, N, Salehi, H, Saunders, C, Scarlett, L, Seneviratne, S, Shetty, D, Shrestha, G, Shulman, J, Solanki, V, Stanton, T, Stuart, M, Stubbs, M, Swainson, I, Taubman, K, Taylor, A, Thomas, P, Unger, S, Upton, A, Vamadevan, S, Van Gaal, W, Verjans, J, Voutnis, D, Wayne, V, Wilson, P, Wong, D, Wong, K, Younger, J, Feuchtner, G, Mirzaei, S, Weiss, K, Maroz-Vadalazhskaya, N, Gheysens, O, Homans, F, Moreno-Reyes, R, Pasquet, A, Roelants, V, Van De Heyning, C, Ríos, R, Soldat-Stankovic, V, Stankovic, S, Albernaz Siqueira, M, Almeida, A, Alves Togni, P, Andrade, J, Andrade, L, Anselmi, C, Araújo, R, Azevedo, G, Bezerra, S, Biancardi, R, Grossman, G, Brandão, S, Pianta, D, Carreira, L, Castro, B, Chang, T, Cunali, F, Cury, R, Dantas, R, de Amorim Fernandes, F, De Lorenzo, A, De Macedo Filho, R, Erthal, F, Fernandes, F, Fernandes, J, De Souza, T, Alves, W, Ghini, B, Goncalves, L, Gottlieb, I, Hadlich, M, Kameoka, V, Lima, R, Lima, A, Lopes, R, Machado e Silva, R, Magalhães, T, Silva, F, Mastrocola, L, Medeiros, F, Meneghetti, J, Naue, V, Naves, D, Nolasco, R, Nomura, C, Oliveira, J, Paixao, E, De Carvalho, F, Pinto, I, Possetti, P, Quinta, M, Nogueira Ramos, R, Rocha, R, Rodrigues, A, Rodrigues, C, Romantini, L, Sanches, A, Santana, S, Sara da Silva, L, Schvartzman, P, Matushita, C, Senra, T, Shiozaki, A, Menezes de Siqueira, M, Siqueira, C, Smanio, P, Soares, C, Junior, J, Bittencourt, M, Spiro, B, Mesquita, C, Torreao, J, Torres, R, Uellendahl, M, Monte, G, Veríssimo, O, Cabeda, E, Pedras, F, Waltrick, R, Zapparoli, M, Naseer, H, Garcheva-Tsacheva, M, Kostadinova, I, Theng, Y, Abikhzer, G, Barette, R, Chow, B, Dabreo, D, Friedrich, M, Garg, R, Hafez, M, Johnson, C, Kiess, M, Leipsic, J, Leung, E, Miller, R, Oikonomou, A, Probst, S, Roifman, I, Small, G, Tandon, V, Trivedi, A, White, J, Zukotynski, K, Canessa, J, Muñoz, G, Concha, C, Hidalgo, P, Lovera, C, Massardo, T, Vargas, L, Abad, P, Arturo, H, Ayala, S, Benitez, L, Cadena, A, Caicedo, C, Moncayo, A, Gomez, S, Gutierrez Villamil, C, Jaimes, C, Londoño, J, Londoño Blair, J, Pabon, L, Pineda, M, Rojas, J, Ruiz, D, Escobar, M, Vasquez, A, Vergel, D, Zuluaga, A, Gamboa, I, Castro, G, González, U, Baric, A, Batinic, T, Franceschi, M, Paar, M, Jukic, M, Medakovic, P, Persic, V, Prpic, M, Punda, A, Batista, J, Gómez Lauchy, J, Gutierrez, Y, Menéndez, R, Peix, A, Rochela, L, Panagidis, C, Petrou, I, Engelmann, V, Kaminek, M, Kincl, V, Lang, O, Simanek, M, Abdulla, J, Bøttcher, M, Christensen, M, Gormsen, L, Hasbak, P, Hess, S, Holdgaard, P, Johansen, A, Kyhl, K, Norgaard, B, Øvrehus, K, Rønnow Sand, N, Steffensen, R, Thomassen, A, Zerahn, B, Perez, A, Escorza Velez, G, Velez, M, Abdel Aziz, I, Abougabal, M, Ahmed, T, Allam, A, Asfour, A, Hassan, M, Hassan, A, Ibrahim, A, Kaffas, S, Kandeel, A, Ali, M, Mansy, A, Maurice, H, Nabil, S, Shaaban, M, Flores, A, Poksi, A, Knuuti, J, Kokkonen, V, Larikka, M, Uusitalo, V, Bailly, M, Burg, S, Deux, J, Habouzit, V, Hyafil, F, Lairez, O, Proffit, F, Regaieg, H, Sarda-Mantel, L, Tacher, V, Schneider, R, Ayetey, H, Angelidis, G, Archontaki, A, Chatziioannou, S, Datseris, I, Fragkaki, C, Georgoulias, P, Koukouraki, S, Koutelou, M, Kyrozi, E, Repasos, E, Stavrou, P, Valsamaki, P, Gonzalez, C, Gutierrez, G, Maldonado, A, Buga, K, Garai, I, Maurovich-Horvat, P, Schmidt, E, Szilveszter, B, Várady, E, Banthia, N, Bhagat, J, Bhargava, R, Bhat, V, Choudhury, P, Chowdekar, V, Irodi, A, Jain, S, Joseph, E, Kumar, S, Girijanandan Mahapatra, P, Mitra, D, Mittal, B, Ozair, A, Patel, C, Patel, T, Patel, R, Patel, S, Saxena, S, Sengupta, S, Singh, S, Singh, B, Sood, A, Verma, A, Affandi, E, Alam, P, Edison, E, Gunawan, G, Hapkido, H, Hidayat, B, Huda, A, Mukti, A, Prawiro, D, Soeriadi, E, Syawaluddin, H, Albadr, A, Assadi, M, Emami, F, Houshmand, G, Maleki, M, Rostami, M, Zakavi, S, Zaid, E, Agranovich, S, Arnson, Y, Bar-Shalom, R, Frenkel, A, Knafo, G, Lugassi, R, Maor Moalem, I, Mor, M, Muskal, N, Ranser, S, Shalev, A, Albano, D, Alongi, P, Arnone, G, Bagatin, E, Baldari, S, Bauckneht, M, Bertelli, P, Bianco, F, Bonfiglioli, R, Boni, R, Bruno, A, Bruno, I, Busnardo, E, Califaretti, E, Camoni, L, Carnevale, A, Casoni, R, Cavallo, A, Cavenaghi, G, Chierichetti, F, Chiocchi, M, Cittanti, C, Colletta, M, Conti, U, Cossu, A, Cuocolo, A, Cuzzocrea, M, De Rimini, M, De Vincentis, G, Del Giudice, E, Del Torto, A, Della Tommasina, V, Durmo, R, Erba, P, Evangelista, L, Faletti, R, Faragasso, E, Farsad, M, Ferro, P, Florimonte, L, Frantellizzi, V, Fringuelli, F, Gatti, M, Gaudiano, A, Gimelli, A, Giubbini, R, Giuffrida, F, Ialuna, S, Laudicella, R, Leccisotti, L, Leva, L, Liga, R, Liguori, C, Longo, G, Maffione, M, Mancini, M, Marcassa, C, Milan, E, Nardi, B, Pacella, S, Pepe, G, Pontone, G, Pulizzi, S, Quartuccio, N, Rampin, L, Ricci, F, Rossini, P, Rubini, G, Russo, V, Sacchetti, G, Sambuceti, G, Scarano, M, Sciagrà, R, Sperandio, M, Stefanelli, A, Ventroni, G, Zoboli, S, Baugh, D, Chambers, D, Madu, E, Nunura, F, Asano, H, Chimura, C, Fujimoto, S, Fujisue, K, Fukunaga, T, Fukushima, Y, Fukuyama, K, Hashimoto, J, Ichikawa, Y, Iguchi, N, Imai, M, Inaki, A, Ishimura, H, Isobe, S, Kadokami, T, Kato, T, Kumita, S, Maruno, H, Mataki, H, Miyagawa, M, Morimoto, R, Moroi, M, Nagamachi, S, Nakajima, K, Nakata, T, Nakazato, R, Nanasato, M, Naya, M, Norikane, T, Ohta, Y, Okayama, S, Okizaki, A, Otomi, Y, Otsuka, H, Saito, M, Sakata, S, Sarai, M, Sato, D, Shiraishi, S, Suwa, Y, Takanami, K, Takehana, K, Taki, J, Tamaki, N, Taniguchi, Y, Teragawa, H, Tomizawa, N, Tsujita, K, Umeji, K, Wakabayashi, Y, Yamada, S, Yamazaki, S, Yoneyama, T, Rawashdeh, M, Batyrkhanov, D, Makhdomi, K, Ombati, K, Alkandari, F, Garashi, M, Coie, T, Rajvong, S, Kalinin, A, Kalnina, M, Haidar, M, Komiagiene, R, Kviecinskiene, G, Mataciunas, M, Vajauskas, D, Picard, C, Karim, N, Reichmuth, L, Samuel, A, Allarakha, M, Naojee, A, Alexanderson-Rosas, E, Barragan, E, González-Montecinos, A, Cabada, M, Rodriguez, D, Carvajal-Juarez, I, Cortés, V, Cortés, F, De La Peña, E, Gama-Moreno, M, González, L, Ramírez, N, Jiménez-Santos, M, Matos, L, Monroy, E, Morelos, M, Ornelas, M, Ortga Ramirez, J, Preciado-Anaya, A, Preciado-Gutiérrez, Ó, Barragan, A, Rosales Uvera, S, Sandoval, S, Tomas, M, Sierra-Galan, L, Siu, S, Vallejo, E, Valles, M, Faraggi, M, Sereegotov, E, Ilic, S, Ben-Rais, N, Alaoui, N, Taleb, S, Pa Myo, K, Thu, P, Ghimire, R, Rajbanshi, B, Barneveld, P, Glaudemans, A, Habets, J, Koopmans, K, Manders, J, Pool, S, Scholte, A, Scholtens, A, Slart, R, Thimister, P, Van Asperen, E, Veltman, N, Verschure, D, Wagenaar, N, Edmond, J, Ellis, C, Johnson, K, Keenan, R, Kueh, S, Occleshaw, C, Sasse, A, To, A, Van Pelt, N, Young, C, Cuadra, T, Roque Vanegas, H, Soli, I, Issoufou, D, Ayodele, T, Madu, C, Onimode, Y, Efros-Monsen, E, Forsdahl, S, Hildre Dimmen, J, Jørgensen, A, Krohn, I, Løvhaugen, P, Bråten, A, Al Dhuhli, H, Al Kindi, F, Al-Bulushi, N, Jawa, Z, Tag, N, Afzal, M, Fatima, S, Younis, M, Riaz, M, Saadullah, M, Herrera, Y, Lenturut-Katal, D, Vázquez, M, Ortellado, J, Akhter, A, Cao, D, Cheung, S, Dai, X, Gong, L, Han, D, Hou, Y, Li, C, Li, T, Li, D, Li, S, Liu, J, Liu, H, Ng, M, Sun, K, Tang, G, Wang, J, Wang, X, Wang, Z, Wang, Y, Wu, J, Wu, Z, Xia, L, Xiao, J, Xu, L, Yang, Y, Yin, W, Yu, J, Yuan, L, Zhang, T, Zhang, L, Zhang, Y, Zhang, X, Zhu, L, Alfaro, A, Abrihan, P, Barroso, A, Cruz, E, Gomez, M, Magboo, V, Medina, J, Obaldo, J, Pastrana, D, Pawhay, C, Quinon, A, Tang, J, Tecson, B, Uson, K, Uy, M, Kostkiewicz, M, Kunikowska, J, Bettencourt, N, Cantinho, G, Ferreira, A, Syed, G, Arnous, S, Atyani, S, Byrne, A, Gleeson, T, Kerins, D, Meehan, C, Murphy, D, Murphy, M, Murray, J, O'Brien, J, Bang, J, Bom, H, Cho, S, Hong, C, Jang, S, Jeong, Y, Kang, W, Kim, J, Namgung, C, So, Y, Won, K, Majstorov, V, Vavlukis, M, Salobir, B, Štalc, M, Benedek, T, Benedek, I, Mititelu, R, Stan, C, Ansheles, A, Dariy, O, Drozdova, O, Gagarina, N, Gulyaev, V, Itskovich, I, Karalkin, A, Kokov, A, Migunova, E, Pospelov, V, Ryzhkova, D, Saifullina, G, Sazonova, S, Sergienko, V, Shurupova, I, Trifonova, T, Ussov, W, Vakhromeeva, M, Valiullina, N, Zavadovsky, K, Zhuravlev, K, Okarvi, S, Saranovic, D, Jason See, J, Sekar, R, Yew, M, Vondrak, A, Bejai, S, Bennie, G, Bester, R, Engelbrecht, G, Evbuomwan, O, Gongxeka, H, Vuuren, M, Kaplan, M, Khushica, P, Lakhi, H, Louw, L, Malan, N, Milos, K, Modiselle, M, More, S, Naidoo, M, Scholtz, L, Vangu, M, Aguadé-Bruix, S, Blanco, I, Cabrera, A, Camarero, A, Casáns-Tormo, I, Cuellar-Calabria, H, Flotats, A, Fuentes Cañamero, M, García, M, Jimenez-Heffernan, A, Leta, R, Diaz, J, Lumbreras, L, Marquez-Cabeza, J, Martin, F, Martinez de Alegria, A, Medina, F, Canal, M, Peiro, V, Pubul-Nuñez, V, Rayo Madrid, J, Rey, C, Perez, R, Ruiz, J, Hernández, G, Sevilla, A, Zeidán, N, Nanayakkara, D, Udugama, C, Simonsson, M, Alkadhi, H, Buechel, R, Burger, P, Ceriani, L, De Boeck, B, Gräni, C, Juillet de Saint Lager Lucas, A, Kamani, C, Kawel-Boehm, N, Manka, R, Prior, J, Rominger, A, Vallée, J, Khiewvan, B, Premprabha, T, Thientunyakit, T, Sellem, A, Kir, K, Sayman, H, Sebikali, M, Muyinda, Z, Kmetyuk, Y, Korol, P, Mykhalchenko, O, Pliatsek, V, Satyr, M, Albalooshi, B, Ahmed Hassan, M, Anderson, J, Bedi, P, Biggans, T, Bularga, A, Bull, R, Burgul, R, Carpenter, J, Coles, D, Cusack, D, Deshpande, A, Dougan, J, Fairbairn, T, Farrugia, A, Gopalan, D, Gummow, A, Ramkumar, P, Hamilton, M, Harbinson, M, Hartley, T, Hudson, B, Joshi, N, Kay, M, Kelion, A, Khokhar, A, Kitt, J, Low, C, Mak, S, Marousa, N, Martin, J, Mcalindon, E, Menezes, L, Morgan-Hughes, G, Moss, A, Murray, A, Nicol, E, Patel, D, Peebles, C, Pugliese, F, Luis Rodrigues, J, Rofe, C, Sabharwal, N, Schofield, R, Semple, T, Sharma, N, Strouhal, P, Subedi, D, Topping, W, Tweed, K, Weir-Mccall, J, Abbara, S, Abbasi, T, Abbott, B, Abohashem, S, Abramson, S, Al-Abboud, T, Al-Mallah, M, Almousalli, O, Ananthasubramaniam, K, Kumar, M, Askew, J, Attanasio, L, Balmer-Swain, M, Bayer, R, Bernheim, A, Bhatti, S, Bieging, E, Blankstein, R, Bloom, S, Blue, S, Bluemke, D, Borges, A, Branch, K, Bravo, P, Brothers, J, Budoff, M, Bullock-Palmer, R, Burandt, A, Burke, F, Bush, K, Candela, C, Capasso, E, Cavalcante, J, Chang, D, Chatterjee, S, Chatzizisis, Y, Cheezum, M, Chen, T, Chen, J, Chen, M, Clarcq, J, Cordero, A, Crim, M, Danciu, S, Decter, B, Dhruva, N, Doherty, N, Doukky, R, Dunbar, A, Duvall, W, Edwards, R, Esquitin, K, Farah, H, Fentanes, E, Ferencik, M, Fisher, D, Fitzpatrick, D, Foster, C, Fuisz, T, Gannon, M, Gastner, L, Gerson, M, Ghoshhajra, B, Goldberg, A, Goldner, B, Gonzalez, J, Gore, R, Gracia-López, S, Hage, F, Haider, A, Haider, S, Hamirani, Y, Hassen, K, Hatfield, M, Hawkins, C, Hawthorne, K, Heath, N, Hendel, R, Hernandez, P, Hill, G, Horgan, S, Huffman, J, Hurwitz, L, Iskandrian, A, Janardhanan, R, Jellis, C, Jerome, S, Kalra, D, Kaviratne, S, Kay, F, Kelly, F, Khalique, O, Kinkhabwala, M, Iii, G, Kircher, J, Kirkbride, R, Kontos, M, Kottam, A, Krepp, J, Layer, J, Lee, S, Leppo, J, Lesser, J, Leung, S, Lewin, H, Litmanovich, D, Liu, Y, Magurany, K, Markowitz, J, Marn, A, Matis, S, Mckenna, M, Mcrae, T, Mendoza, F, Merhige, M, Min, D, Moffitt, C, Moncher, K, Moore, W, Morayati, S, Morris, M, Mossa-Basha, M, Mrsic, Z, Murthy, V, Nagpal, P, Napier, K, Nelson, K, Nijjar, P, Osman, M, Passen, E, Patel, A, Patil, P, Paul, R, Phillips, L, Polsani, V, Poludasu, R, Pomerantz, B, Porter, T, Prentice, R, Pursnani, A, Rabbat, M, Ramamurti, S, Rich, F, Luna, H, Robinson, A, Robles, K, Rodríguez, C, Rorie, M, Rumberger, J, Russell, R, Sabra, P, Sadler, D, Schemmer, M, Schoepf, U, Shah, S, Shah, N, Shanbhag, S, Sharma, G, Shayani, S, Shirani, J, Shivaram, P, Sigman, S, Simon, M, Slim, A, Smith, D, Smith, A, Soman, P, Srichai-Parsia, M, Streeter, J, T, A, Tawakol, A, Thomas, D, Thompson, R, Torbet, T, Trinidad, D, Ullery, S, Unzek, S, Uretsky, S, Vallurupalli, S, Verma, V, Waller, A, Wang, E, Ward, P, Weissman, G, Wesbey, G, White, K, Winchester, D, Wolinsky, D, Yost, S, Zgaljardic, M, Alonso, O, Beretta, M, Ferrando, R, Kapitan, M, Mut, F, Djuraev, O, Rozikhodjaeva, G, Le Ngoc, H, Mai, S, Nguyen, X, Kudo, Takashi, Lahey, Ryan, Hirschfeld, Cole B., Williams, Michelle C., Lu, Bin, Alasnag, Mirvat, Bhatia, Mona, Henry Bom, Hee-Seung, Dautov, Tairkhan, Fazel, Reza, Karthikeyan, Ganesan, Keng, Felix Y. J., Rubinshtein, Ronen, Better, Nathan, Cerci, Rodrigo Julio, Dorbala, Sharmila, Raggi, Paolo, Shaw, Leslee J., Villines, Todd C., Vitola, João V., Choi, Andrew D., Malkovskiy, Eli, Goebel, Benjamin, Cohen, Yosef A., Randazzo, Michael, Pascual, Thomas N. B., Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew J., Cerci, Rodrigo, Vitola, Joao V., Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Cohen, Yosef, Choi, Andrew, Lopez-Mattei, Juan, Parwani, Purvi, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qais, Agolti, Mariela, Aguero, Roberto Nicolas, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Bas, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlos, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Lucas, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, López De Munaín, Maria Nieves A., Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matias, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marcos, Noguera, Eduardo, Novoa, Carlos, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matías, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Lucas, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charles, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetrius, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroos, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnès, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlos, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Vinícius, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martins, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlos, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soares, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthias, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chris, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Luis, Cadena, Alberto, Caicedo, Carlos, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Luis, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlos, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andres, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulises, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marcos, Menéndez, Rayner, Peix, Amalia, Rochela, Luis, Panagidis, Christoforos, Petrou, Ioannis, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Anders, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-François, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioannis, Fragkaki, Christina, Georgoulias, Panagiotis, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelos, Stavrou, Petros, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Maurovich-Horvat, Pál, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam, Padma Savenadia, Edison, Edison, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindaugas, Vajauskas, Donatas, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Luis, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisés, Matos, Luis, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, Andrés, Preciado-Gutiérrez, Óscar Ulises, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Pa Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niels, Verschure, Derk, Wagenaar, Nils, Edmond, John, Ellis, Chris, Johnson, Kerryanne, Keenan, Ross, Kueh, Shaw Hua (Anthony), Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niels, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Sergienko, Vladimir, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Luis, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Julius, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thomas, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thomas, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charles, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thomas, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Blankstein, Ron, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, Cavalcante, Joao, Chang, Donald, Chatterjee, Saurav, Chatzizisis, Yiannis, Cheezum, Michael, Chen, Tiffany, Chen, Jennifer, Chen, Marcus, Clarcq, James, Cordero, Ayreen, Crim, Matthew, Danciu, Sorin, Decter, Bruce, Dhruva, Nimish, Doherty, Neil, Doukky, Rami, Dunbar, Anjori, Duvall, William, Edwards, Rachael, Esquitin, Kerry, Farah, Husam, Fentanes, Emilio, Ferencik, Maros, Fisher, Daniel, Fitzpatrick, Daniel, Foster, Cameron, Fuisz, Tony, Gannon, Michael, Gastner, Lori, Gerson, Myron, Ghoshhajra, Brian, Goldberg, Alan, Goldner, Brian, Gonzalez, Jorge, Gore, Rosco, Gracia-López, Sandra, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hatfield, Mallory, Hawkins, Carolyn, Hawthorne, Katie, Heath, Nicholas, Hendel, Robert, Hernandez, Phillip, Hill, Gregory, Horgan, Stephen, Huffman, Jeff, Hurwitz, Lynne, Iskandrian, Ami, Janardhanan, Rajesh, Jellis, Christine, Jerome, Scott, Kalra, Dinesh, Kaviratne, Summanther, Kay, Fernando, Kelly, Faith, Khalique, Omar, Kinkhabwala, Mona, Iii, George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thomas, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, 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Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, James, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vikas, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Winchester, David, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Kudo, T, Lahey, R, Hirschfeld, C, Williams, M, Lu, B, Alasnag, M, Bhatia, M, Henry Bom, H, Dautov, T, Fazel, R, Karthikeyan, G, Keng, F, Rubinshtein, R, Better, N, Cerci, R, Dorbala, S, Raggi, P, Shaw, L, Villines, T, 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B., Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew J., Cerci, Rodrigo, Vitola, Joao V., Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Cohen, Yosef, Choi, Andrew, Lopez-Mattei, Juan, Parwani, Purvi, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qais, Agolti, Mariela, Aguero, Roberto Nicolas, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Bas, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlos, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Lucas, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, López De Munaín, Maria Nieves A., Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matias, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marcos, Noguera, Eduardo, Novoa, Carlos, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matías, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Lucas, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charles, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetrius, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroos, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnès, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlos, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Vinícius, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martins, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlos, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soares, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthias, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chris, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Luis, Cadena, Alberto, Caicedo, Carlos, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Luis, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlos, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andres, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulises, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marcos, Menéndez, Rayner, Peix, Amalia, Rochela, Luis, Panagidis, Christoforos, Petrou, Ioannis, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Anders, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-François, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioannis, Fragkaki, Christina, Georgoulias, Panagiotis, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelos, Stavrou, Petros, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Maurovich-Horvat, Pál, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam, Padma Savenadia, Edison, Edison, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindaugas, Vajauskas, Donatas, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Luis, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisés, Matos, Luis, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, Andrés, Preciado-Gutiérrez, Óscar Ulises, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Pa Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niels, Verschure, Derk, Wagenaar, Nils, Edmond, John, Ellis, Chris, Johnson, Kerryanne, Keenan, Ross, Kueh, Shaw Hua (Anthony), Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niels, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Sergienko, Vladimir, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Luis, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Julius, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thomas, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thomas, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charles, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thomas, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Blankstein, Ron, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, Cavalcante, Joao, Chang, Donald, Chatterjee, Saurav, Chatzizisis, Yiannis, Cheezum, Michael, Chen, Tiffany, Chen, Jennifer, Chen, Marcus, Clarcq, James, Cordero, Ayreen, Crim, Matthew, Danciu, Sorin, Decter, Bruce, Dhruva, Nimish, Doherty, Neil, Doukky, Rami, Dunbar, Anjori, Duvall, William, Edwards, Rachael, Esquitin, Kerry, Farah, Husam, Fentanes, Emilio, Ferencik, Maros, Fisher, Daniel, Fitzpatrick, Daniel, Foster, Cameron, Fuisz, Tony, Gannon, Michael, Gastner, Lori, Gerson, Myron, Ghoshhajra, Brian, Goldberg, Alan, Goldner, Brian, Gonzalez, Jorge, Gore, Rosco, Gracia-López, Sandra, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hatfield, Mallory, Hawkins, Carolyn, Hawthorne, Katie, Heath, Nicholas, Hendel, Robert, Hernandez, Phillip, Hill, Gregory, Horgan, Stephen, Huffman, Jeff, Hurwitz, Lynne, Iskandrian, Ami, Janardhanan, Rajesh, Jellis, Christine, Jerome, Scott, Kalra, Dinesh, Kaviratne, Summanther, Kay, Fernando, Kelly, Faith, Khalique, Omar, Kinkhabwala, Mona, Iii, George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thomas, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, James, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vikas, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Winchester, David, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, and Nguyen, Xuan Canh
- Abstract
Background: The coronavirus disease-2019 (COVID-19) pandemic significantly affected management of cardiovascular disease around the world. The effect of the pandemic on volume of cardiovascular diagnostic procedures is not known. Objectives: This study sought to evaluate the effects of the early phase of the COVID-19 pandemic on cardiovascular diagnostic procedures and safety practices in Asia. Methods: The International Atomic Energy Agency conducted a worldwide survey to assess changes in cardiovascular procedure volume and safety practices caused by COVID-19. Testing volumes were reported for March 2020 and April 2020 and were compared to those from March 2019. Data from 180 centers across 33 Asian countries were grouped into 4 subregions for comparison. Results: Procedure volumes decreased by 47% from March 2019 to March 2020, showing recovery from March 2020 to April 2020 in Eastern Asia, particularly in China. The majority of centers cancelled outpatient activities and increased time per study. Practice changes included implementing physical distancing and restricting visitors. Although COVID testing was not commonly performed, it was conducted in one-third of facilities in Eastern Asia. The most severe reductions in procedure volumes were observed in lower-income countries, where volumes decreased 81% from March 2019 to April 2020. Conclusions: The COVID-19 pandemic in Asia caused significant reductions in cardiovascular diagnostic procedures, particularly in low-income countries. Further studies on effects of COVID-19 on cardiovascular outcomes and changes in care delivery are warranted.
- Published
- 2021
5. Tradespace Exploration of the Next Generation Communication Satellites
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Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, Aguilar, Alexa, Butler, Patrick, Collins, Jennifer, Guerster, Markus, Kristinsson, Bjarni, McKeen, Patrick, Cahoy, Kerri, Crawley, Edward F, Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, Aguilar, Alexa, Butler, Patrick, Collins, Jennifer, Guerster, Markus, Kristinsson, Bjarni, McKeen, Patrick, Cahoy, Kerri, and Crawley, Edward F
- Abstract
© 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. With this paper, we describe a tradespace exploration analysis for the next generation constellation of communication satellites resulting in a recommendation for a future system. In particular, we compare our proposal with ViaSat-3 and SpaceX’s Starlink constellation. In order to arrive at a recommendation for an optimal constellation design, we first identify the design space by creating a morphological matrix and applying necessary constraints (see Table 1 for the architectural decision). The morphological matrix decisions are selected based on variability in heritage versus state-of-the-art designs, and include options with different Technology Readiness Level (TRL). The resulting 3,120 feasible architectures are evaluated using both cost and performance estimates. Costs are determined from component costs, TRL, and heritage. Performance scoring is based on a modified Signal-to-Noise Ratio (SNR) calculation, which includes technical factors such as the downlink budget and latency, as well as system factors such as crosslinks, architectures, and coverage. The final design recommendation is a Radio Frequency (RF) crosslink, bent pipe, Ka/Ku-band satellite with an electronically steered antenna and projected mass of 125 kg. The system is a constellation of 312 satellites, spread across 6 orbital planes at 444 km of altitude with global coverage and an estimated system capacity of about 2 Tbps. Estimates place the cost at $8.9 billion with a NPV of $1.4 billion over a total lifetime of ten years. Latency is expected to be around 25 ms. As with many space systems, our proposed design comes with a number of risks. Outside of typical regulatory, technological, and programmatic risks, providing satellite communications, particularly data services, comes with a unique risk: the price of user terminals. In order to provide public consumer broadband, in addition to other attractive m
- Published
- 2021
6. Low evidence for implementation of well- documented implants regarding risk of early revision:a systematic review on total hip arthroplasty
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Butler, Patrick, Gorgis, Josef, Viberg, Bjarke, Overgaard, Søren, Butler, Patrick, Gorgis, Josef, Viberg, Bjarke, and Overgaard, Søren
- Abstract
» When introducing an implant, surgeons are subjected to steep learning curves, which may lead to a heightened revision rate. Stepwise introduction revolutionized implant introduction but lacks a last step. » No guidelines exist for the introduction of a well-documented implant not previously used in a department. This is problematic according to the European Union’s legislated tendering process, potentially leading to increased revisions. In this systematic review, the introduction of a well-documented total hip arthroplasty implant to experienced surgeons is explored amid concerns of higher revision rate. » Literature search strategies were deployed in the Embase and Medline databases, revealing a total of 14,612 articles. Using the Covidence software (Cochrane, London), two reviewers screened articles for inclusion. » No articles were found that fulfilled our eligibility criteria. A post hoc analysis retrieved two national register-based studies only missing information about the surgeon’s knowledge of the introduced implant. None of the introduced implants decreased the revision rate and around 30% of the introduced implants were associated with a higher revision rate. » The review showed that no data exist about revision rates when introducing well-documented implants. In continuation thereof, the introduction of well-documented implants might also be associated with increased revision rates, as has been shown for total knee arthroplasty. We therefore suggest that special attention should be focused on changes of implants in departments, which can be achieved by way of specific registration in national registers.
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- 2021
7. Using AntiPatterns to avoid MLOps Mistakes
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Muralidhar, Nikhil, Muthiah, Sathappah, Butler, Patrick, Jain, Manish, Yu, Yu, Burne, Katy, Li, Weipeng, Jones, David, Arunachalam, Prakash, McCormick, Hays 'Skip', Ramakrishnan, Naren, Muralidhar, Nikhil, Muthiah, Sathappah, Butler, Patrick, Jain, Manish, Yu, Yu, Burne, Katy, Li, Weipeng, Jones, David, Arunachalam, Prakash, McCormick, Hays 'Skip', and Ramakrishnan, Naren
- Abstract
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and methodologies. Here we catalog and document numerous antipatterns in financial ML operations (MLOps). Some antipatterns are due to technical errors, while others are due to not having sufficient knowledge of the surrounding context in which ML results are used. By providing a common vocabulary to discuss these situations, our intent is that antipatterns will support better documentation of issues, rapid communication between stakeholders, and faster resolution of problems. In addition to cataloging antipatterns, we describe solutions, best practices, and future directions toward MLOps maturity.
- Published
- 2021
8. Flexibility, variability and constraint in energy management patterns across vertebrate taxa revealed by long‐term heart rate measurements
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Halsey, Lewis G., Green, Jonathan A., Twiss, Sean D., Arnold, Walter, Burthe, Sarah J., Butler, Patrick J., Cooke, Steven J., Grémillet, David, Ruf, Thomas, Hicks, Olivia, Minta, Katarzyna J., Prystay, Tanya S., Wascher, Claudia A.F., Careau, Vincent, Halsey, Lewis G., Green, Jonathan A., Twiss, Sean D., Arnold, Walter, Burthe, Sarah J., Butler, Patrick J., Cooke, Steven J., Grémillet, David, Ruf, Thomas, Hicks, Olivia, Minta, Katarzyna J., Prystay, Tanya S., Wascher, Claudia A.F., and Careau, Vincent
- Abstract
1. Animals are expected to be judicious in the use of the energy they gain due to the costs and limits associated with its intake. The management of energy expenditure (EE) exhibited by animals has previously been considered in terms of three patterns: the constrained, independent and performance patterns of energy management. These patterns can be interpreted by regressing daily EE against maintenance EE measured over extended periods. From the multiple studies on this topic, there is equivocal evidence about the existence of universal patterns in certain aspects of energy management. 2. The implicit assumption that animals exhibit specifically one of three discrete energy management patterns, and without variation, seems simplistic. We suggest that animals can exhibit gradations of different energy management patterns and that the exact pattern will fluctuate as their environmental context changes. 3. To investigate these ideas, and for possible large‐scale patterns in energy management, we analysed long‐term heart rate data—a strong proxy for EE—across and within individuals in 16 species of birds, mammals and fish. 4. Our analyses of 292 individuals representing 46,539 observation‐days suggest that vertebrates typically exhibit predominantly the independent or performance energy patterns at the across‐individual level, and that the pattern does not associate with taxonomic group. Within individuals, however, animals generally exhibit some degree of energy constraint. Together, these findings indicate that across diverse species, some individuals supply more energy to all aspects of their life than do others, however all individuals must trade‐off deployment of their available energy between competing functions. This demonstrates that within‐individual analyses are essential for the interpretation of energy management patterns. 5. We also found that species do not necessarily exhibit a fixed energy management pattern but rather temporal variation in their energy
- Published
- 2019
9. Flexibility, variability and constraint in energy management patterns across vertebrate taxa revealed by long‐term heart rate measurements
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Halsey, Lewis G., Green, Jonathan A., Twiss, Sean D., Arnold, Walter, Burthe, Sarah J., Butler, Patrick J., Cooke, Steven J., Grémillet, David, Ruf, Thomas, Hicks, Olivia, Minta, Katarzyna J., Prystay, Tanya S., Wascher, Claudia A.F., Careau, Vincent, Halsey, Lewis G., Green, Jonathan A., Twiss, Sean D., Arnold, Walter, Burthe, Sarah J., Butler, Patrick J., Cooke, Steven J., Grémillet, David, Ruf, Thomas, Hicks, Olivia, Minta, Katarzyna J., Prystay, Tanya S., Wascher, Claudia A.F., and Careau, Vincent
- Abstract
1. Animals are expected to be judicious in the use of the energy they gain due to the costs and limits associated with its intake. The management of energy expenditure (EE) exhibited by animals has previously been considered in terms of three patterns: the constrained, independent and performance patterns of energy management. These patterns can be interpreted by regressing daily EE against maintenance EE measured over extended periods. From the multiple studies on this topic, there is equivocal evidence about the existence of universal patterns in certain aspects of energy management. 2. The implicit assumption that animals exhibit specifically one of three discrete energy management patterns, and without variation, seems simplistic. We suggest that animals can exhibit gradations of different energy management patterns and that the exact pattern will fluctuate as their environmental context changes. 3. To investigate these ideas, and for possible large‐scale patterns in energy management, we analysed long‐term heart rate data—a strong proxy for EE—across and within individuals in 16 species of birds, mammals and fish. 4. Our analyses of 292 individuals representing 46,539 observation‐days suggest that vertebrates typically exhibit predominantly the independent or performance energy patterns at the across‐individual level, and that the pattern does not associate with taxonomic group. Within individuals, however, animals generally exhibit some degree of energy constraint. Together, these findings indicate that across diverse species, some individuals supply more energy to all aspects of their life than do others, however all individuals must trade‐off deployment of their available energy between competing functions. This demonstrates that within‐individual analyses are essential for the interpretation of energy management patterns. 5. We also found that species do not necessarily exhibit a fixed energy management pattern but rather temporal variation in their energy
- Published
- 2019
10. Feasibility Study of Short Takeoff and Landing Urban Air Mobility Vehicles Using Geometric Programming
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Courtin, Christopher, Burton, Michael, Butler, Patrick, Yu, Alison, Vascik, Parker D., Hansman, R. John, Courtin, Christopher, Burton, Michael, Butler, Patrick, Yu, Alison, Vascik, Parker D., and Hansman, R. John
- Abstract
Electric Short Takeoff and Landing (eSTOL) vehicles are proposed as a path towards implementing an Urban Air Mobility (UAM) network that reduces critical vehicle certification risks and offers advantages in vehicle performance compared to the widely proposed Electric Vertical Takeoff and Landing (eVTOL) aircraft. An overview is given of the system constraints and key enabling technologies that must be incorporated into the design of the vehicle. The tradeoffs between vehicle performance and runway length are investigated using geometric programming, a robust optimization framework. Runway lengths as short as 100-300 ft are shown to be feasible, depending on the level of technology and the desired cruise speed. The tradeoffs between runway length and the potential to build new infrastructure in urban centers are investigated using Boston as a representative case study. The placement of some runways up to 600ft is shown to be possible in the urban center, with a significant increase in the number of potential locations for runways shorter than 300ft. Key challenges and risks to implementation are discussed.
- Published
- 2018
11. Feasibility Study of Short Takeoff and Landing Urban Air Mobility Vehicles Using Geometric Programming
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Courtin, Christopher, Burton, Michael, Butler, Patrick, Yu, Alison, Vascik, Parker D., Hansman, R. John, Courtin, Christopher, Burton, Michael, Butler, Patrick, Yu, Alison, Vascik, Parker D., and Hansman, R. John
- Abstract
Electric Short Takeoff and Landing (eSTOL) vehicles are proposed as a path towards implementing an Urban Air Mobility (UAM) network that reduces critical vehicle certification risks and offers advantages in vehicle performance compared to the widely proposed Electric Vertical Takeoff and Landing (eVTOL) aircraft. An overview is given of the system constraints and key enabling technologies that must be incorporated into the design of the vehicle. The tradeoffs between vehicle performance and runway length are investigated using geometric programming, a robust optimization framework. Runway lengths as short as 100-300 ft are shown to be feasible, depending on the level of technology and the desired cruise speed. The tradeoffs between runway length and the potential to build new infrastructure in urban centers are investigated using Boston as a representative case study. The placement of some runways up to 600ft is shown to be possible in the urban center, with a significant increase in the number of potential locations for runways shorter than 300ft. Key challenges and risks to implementation are discussed.
- Published
- 2018
12. Key Case Law Rules for Government Contract Formation Ed. 1
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Butler, Patrick, Butler, Patrick, Butler, Patrick, and Butler, Patrick
- Abstract
Go Beyond the FAR! The guidance contained in the almost 2000 pages of the Federal Acquisition Regulation and the various agency supplements are just a part of the resources government acquisition professionals need to do their jobs effectively. Accessing and understanding case law is equally important to a thorough understanding of government contracting. Legal decisions explain the Government Accountability Office's and the courts' views on how procurement statutes and regulations apply in a wide range of situations. Case law also gives potential bid protesters and agencies a way to gauge the likely outcome of a protest. Until now, it has been difficult to find and understand the legal decisions that could be relevant to a particular situation. Key Case Law Rules for Government Contract Formation changes that by organizing and explaining the most important protest grounds in a readily accessible and comprehensible way. With an emphasis on more recent cases, the book is organized around the key protest grounds, such as pricing issues, allegations that the government wrongfully prevented competition, or improper sealed-bidding procedures. Bridging the gap of understanding between the legal and the contracting communities, this book is a much-needed addition to the essential resources for acquisition professionals.
- Published
- 2014
13. Can Self-Censorship in News Media be Detected Algorithmically? A Case Study in Latin America
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Tao, Rongrong, Zhou, Baojian, Chen, Feng, Liu, Naifeng, Mares, David, Butler, Patrick, Ramakrishnan, Naren, Tao, Rongrong, Zhou, Baojian, Chen, Feng, Liu, Naifeng, Mares, David, Butler, Patrick, and Ramakrishnan, Naren
- Abstract
Censorship in social media has been well studied and provides insight into how governments stifle freedom of expression online. Comparatively less (or no) attention has been paid to detecting (self) censorship in traditional media (e.g., news) using social media as a bellweather. We present a novel unsupervised approach that views social media as a sensor to detect censorship in news media wherein statistically significant differences between information published in the news media and the correlated information published in social media are automatically identified as candidate censored events. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords, and a new near-linear-time algorithm (called GraphDPD) to identify the highest scoring clusters as indicators of censorship. We outline extensive experiments on semi-synthetic data as well as real datasets (with Twitter and local news media) from Mexico and Venezuela, highlighting the capability to accurately detect real-world self censorship events.
- Published
- 2016
14. EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System
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Muthiah, Sathappan, Butler, Patrick, Khandpur, Rupinder Paul, Saraf, Parang, Self, Nathan, Rozovskaya, Alla, Zhao, Liang, Cadena, Jose, Lu, Chang-Tien, Vullikanti, Anil, Marathe, Achla, Summers, Kristen, Katz, Graham, Doyle, Andy, Arredondo, Jaime, Gupta, Dipak K., Mares, David, Ramakrishnan, Naren, Muthiah, Sathappan, Butler, Patrick, Khandpur, Rupinder Paul, Saraf, Parang, Self, Nathan, Rozovskaya, Alla, Zhao, Liang, Cadena, Jose, Lu, Chang-Tien, Vullikanti, Anil, Marathe, Achla, Summers, Kristen, Katz, Graham, Doyle, Andy, Arredondo, Jaime, Gupta, Dipak K., Mares, David, and Ramakrishnan, Naren
- Abstract
EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events, currency rates, and food prices. In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with specific attention to the discoveries it has enabled, correct as well as missed forecasts, and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations., Comment: Submitted to a conference
- Published
- 2016
15. Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study
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Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., Brownstein, John S., Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., and Brownstein, John S.
- Abstract
Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.
- Published
- 2015
- Full Text
- View/download PDF
16. Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study
- Author
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Computer Science, Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., Brownstein, John S., Computer Science, Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., and Brownstein, John S.
- Abstract
Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.
- Published
- 2015
17. Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study
- Author
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Computer Science, Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., Brownstein, John S., Computer Science, Nsoesie, Elaine O., Butler, Patrick, Ramakrishnan, Naren, Mekaru, Sumiko R., and Brownstein, John S.
- Abstract
Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.
- Published
- 2015
18. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
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Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, Lu, Chang-Tien, Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, and Lu, Chang-Tien
- Abstract
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
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- 2014
19. Knowledge Discovery in Intelligence Analysis
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Butler, Patrick Julian Carey and Butler, Patrick Julian Carey
- Abstract
Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data, as well as rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. These problems are further exacerbated by the sheer volume of data that is available to intelligence analysts. Machine learning methods enable the automated transduction of such large datasets from raw feeds to actionable knowledge but successful use of such methods require integrated frameworks for contextualizing them within the work processes of the analyst. Intelligence analysts typically distinguish between three classes of problems: collections, analysis, and operations. This dissertation specifically focuses on two problems in analysis: i) the reconstruction of shredded documents using a visual analytic framework combining computer vision techniques and user input, and ii) the design and implementation of a system for event forecasting which allows an analyst to not just consume forecasts of significant societal events but also understand the rationale behind these alerts and the use of data ablation techniques to determine the strength of conclusions. This work does not attempt to replace the role of the analyst with machine learning but instead outlines several methods to augment the analyst with machine learning. In doing so this dissertation also explores the responsibilities of an analyst in evaluating complex models and decisions made by these models. Finally, this dissertation defines a list of responsibilities for models designed to aid the analyst's work in evaluating and verifying the models.
- Published
- 2014
20. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
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Computer Science, Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, Lu, Chang-Tien, Computer Science, Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, and Lu, Chang-Tien
- Abstract
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
- Published
- 2014
21. ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
- Author
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Computer Science, Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, Lu, Chang-Tien, Computer Science, Ramakrishnan, Naren, Butler, Patrick, Self, Nathan, Khandpur, Rupinder P., Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil Kumar S., Korkmaz, Gizem, Kuhlman, Christopher J., Marathe, Achla, Zhao, Liang, Ting, Hua, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Muthia, Sathappan, Chen, Feng, and Lu, Chang-Tien
- Abstract
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
- Published
- 2014
22. 'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators
- Author
-
Ramakrishnan, Naren, Butler, Patrick, Muthiah, Sathappan, Self, Nathan, Khandpur, Rupinder, Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil, Korkmaz, Gizem, Kuhlman, Chris, Marathe, Achla, Zhao, Liang, Hua, Ting, Chen, Feng, Lu, Chang-Tien, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, Mares, David, Ramakrishnan, Naren, Butler, Patrick, Muthiah, Sathappan, Self, Nathan, Khandpur, Rupinder, Saraf, Parang, Wang, Wei, Cadena, Jose, Vullikanti, Anil, Korkmaz, Gizem, Kuhlman, Chris, Marathe, Achla, Zhao, Liang, Hua, Ting, Chen, Feng, Lu, Chang-Tien, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, Getoor, Lise, Katz, Graham, Doyle, Andy, Ackermann, Chris, Zavorin, Ilya, Ford, Jim, Summers, Kristen, Fayed, Youssef, Arredondo, Jaime, Gupta, Dipak, and Mares, David
- Abstract
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
- Published
- 2014
23. Spatio-Temporal Storytelling on Twitter
- Author
-
Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, Ramakrishnan, Naren, Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, and Ramakrishnan, Naren
- Abstract
Social media, e.g.,Twitter, have provided us an unprecedented opportunity to observe events un-folding in real-time. The rapid pace at which situations play out on social media necessitates new tools for capturing and summarizing the spatio-temporal progression of events. This technical report describes methods for generating dynamic real-world storylines from Twitter Sources and shares the results of related experiments.
- Published
- 2013
24. Spatio-Temporal Storytelling on Twitter
- Author
-
Computer Science, Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, Ramakrishnan, Naren, Computer Science, Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, and Ramakrishnan, Naren
- Abstract
Social media, e.g.,Twitter, have provided us an unprecedented opportunity to observe events un-folding in real-time. The rapid pace at which situations play out on social media necessitates new tools for capturing and summarizing the spatio-temporal progression of events. This technical report describes methods for generating dynamic real-world storylines from Twitter Sources and shares the results of related experiments.
- Published
- 2013
25. Spatio-Temporal Storytelling on Twitter
- Author
-
Computer Science, Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, Ramakrishnan, Naren, Computer Science, Dos Santos Jr, Raimundo F., Shah, Sumit, Chen, Feng, Boedihardjo, Arnold P., Butler, Patrick, Lu, Chang-Tien, and Ramakrishnan, Naren
- Abstract
Social media, e.g.,Twitter, have provided us an unprecedented opportunity to observe events un-folding in real-time. The rapid pace at which situations play out on social media necessitates new tools for capturing and summarizing the spatio-temporal progression of events. This technical report describes methods for generating dynamic real-world storylines from Twitter Sources and shares the results of related experiments.
- Published
- 2013
26. FEMA Urban Search and Rescue Teams : Considering an Improved Strategy for an Evolving Homeland Security Enterprise
- Author
-
Morag, Nadav, Butler, Patrick, Security Studies, Poirier, Alfred, Morag, Nadav, Butler, Patrick, Security Studies, and Poirier, Alfred
- Abstract
The United States governments role in preparing for, preventing, responding to, and recovering from all domestic disasters is coordinated by the Federal Emergency Management Agency (FEMA). Further, FEMA is designated as the primary agency responsible for coordinating Structural Collapse (Urban) Search and Rescue (US and R) situations in the National Response Framework. Since the inception of FEMA resources intended for response to US and R missions, the national search and rescue system has evolved, along with the numbers and types of other resources available to assist in US and R missions. Nonetheless, a disconnect remains, with no common national US and R strategy that effectively brings together available federal resources from FEMA, the Department of Defense, and other partner agencies. FEMA states that urban search and rescue is considered a multi-hazard discipline, as the teams can hypothetically be utilized for response to a wide variety of natural and man-made emergencies or disasters. Although the present FEMA US and R task force model has worked well for certain types of disasters, this thesis explores responses to past events and considers a new strategy that could allow the US and R teams to be used more effectively and efficiently in an evolving Homeland Security enterprise.
- Published
- 2012
27. Succession planning in homeland security - how can we ensure the effective transfer of knowledge to a new generation of employees?
- Author
-
Josefek, Robert, Bergin, Richard, Naval Postgraduate School (U.S.), Security Studies, Butler, Patrick I., Josefek, Robert, Bergin, Richard, Naval Postgraduate School (U.S.), Security Studies, and Butler, Patrick I.
- Abstract
CHDS State/Local, In the past five years, the Los Angeles Fire Department (LAFD) has seen the mass retirements of tenured, experienced personnel and the hiring of new generations. Because a large percentage of the department is currently eligible for retirement, this trend will continue over the next five to seven years. The drain of experience and knowledge will directly affect the operational capabilities of the department, as well as our nation's homeland security. Through the use of case studies, the research will examine how the LAFD can maximize institutional memory, and transfer this knowledge to a new generation of employees. The practical significance of this project is to 1) identify the challenges of current succession planning of the LAFD; 2) identify solutions to these challenges through evaluating precedent cases; and 3) develop a conceptual and tailored succession planning guide based on identified solutions. In today's world, the workforce is an organization's most important asset, often differentiating highly successful agencies from those that struggle. By developing a succession planning guide that focuses on assessment, development, identification and selection, organizations can align its goals with its human capital needs and ensure it can keep pace with the complexities in homeland security., http://archive.org/details/successionplanni109455384, Assistance Fire Chief, Los Angeles Fire Department Special Operations Division author (civilian), Approved for public release; distribution is unlimited.
- Published
- 2012
28. Succession Planning in Homeland Security - How Can We Ensure the Effective Transfer of Knowledge to a New Generation of Employees
- Author
-
NAVAL POSTGRADUATE SCHOOL MONTEREY CA, Butler, Patrick I., NAVAL POSTGRADUATE SCHOOL MONTEREY CA, and Butler, Patrick I.
- Abstract
In the past five years, the Los Angeles Fire Department (LAFD) has seen the mass retirements of tenured, experienced personnel and the hiring of new generations. Because a large percentage of the department is currently eligible for retirement, this trend will continue over the next five to seven years. The drain of experience and knowledge will directly affect the operational capabilities of the department, as well as our nation's homeland security. Through the use of case studies, the research will examine how the LAFD can maximize institutional memory, and transfer this knowledge to a new generation of employees. The practical significance of this project is to: (1) identify the challenges of current succession planning of the LAFD; (2) identify solutions to these challenges through evaluating precedent cases; and (3) develop a conceptual and tailored succession planning guide based on identified solutions. In today's world, the workforce is an organization's most important asset, often differentiating highly successful agencies from that struggle. By developing a succession planning guide that focuses on assessment, development, identification and selection, organizations can align its goals with its human capital needs and ensure it can keep pace with the complexities in homeland security., The original document contains color images.
- Published
- 2010
29. Succession planning in homeland security - how can we ensure the effective transfer of knowledge to a new generation of employees?
- Author
-
Josefek, Robert, Bergin, Richard, Naval Postgraduate School (U.S.), Security Studies, Butler, Patrick I., Josefek, Robert, Bergin, Richard, Naval Postgraduate School (U.S.), Security Studies, and Butler, Patrick I.
- Abstract
In the past five years, the Los Angeles Fire Department (LAFD) has seen the mass retirements of tenured, experienced personnel and the hiring of new generations. Because a large percentage of the department is currently eligible for retirement, this trend will continue over the next five to seven years. The drain of experience and knowledge will directly affect the operational capabilities of the department, as well as our nation's homeland security. Through the use of case studies, the research will examine how the LAFD can maximize institutional memory, and transfer this knowledge to a new generation of employees. The practical significance of this project is to 1) identify the challenges of current succession planning of the LAFD; 2) identify solutions to these challenges through evaluating precedent cases; and 3) develop a conceptual and tailored succession planning guide based on identified solutions. In today's world, the workforce is an organization's most important asset, often differentiating highly successful agencies from those that struggle. By developing a succession planning guide that focuses on assessment, development, identification and selection, organizations can align its goals with its human capital needs and ensure it can keep pace with the complexities in homeland security.
- Published
- 2010
30. Tracking macaroni penguins during long foraging trips using 'behavioural geolocation'
- Author
-
Green, Jonathan A., Wilson, Rory P., Boyd, Ian L., Woakes, Anthony J., Green, Chris J., Butler, Patrick J., Green, Jonathan A., Wilson, Rory P., Boyd, Ian L., Woakes, Anthony J., Green, Chris J., and Butler, Patrick J.
- Abstract
The movement of marine vertebrates has been tracked using a variety of techniques, all of which depend on the external attachment of a transmitting or recording device. However, these devices can have negative effects on the subject animals, limiting both the quantity and quality of data collected. We present a new method for monitoring large-scale movement of marine vertebrates that uses behavioural data stored on a surgically implanted data logger. The technique ('behavioural geolocation') relies on the principles of light-based geolocation but rather than measuring ambient light levels, changes in diving behaviour associated with sunrise and sunset are used to infer daylength and time of local sunrise, and hence location. We present data from a trial, post-hoc, analysis of diving data collected from macaroni penguins Eudyptes chrysolophus during long foraging trips associated with incubation and preparation for moult. Our results showed that the penguins usually travelled to the polar frontal zone to the north of their breeding colony at South Georgia, an area broadly consistent with previously measured behaviour and the availability of preferred prey at this period of the annual cycle.
- Published
- 2009
31. Evaluating the prudence of parents: daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Boyd, Ian L., Woakes, Anthony J., Warren, Nicholas L., Butler, Patrick J., Green, Jonathan A., Boyd, Ian L., Woakes, Anthony J., Warren, Nicholas L., and Butler, Patrick J.
- Abstract
We measured daily energy expenditure (DEE) continuously for a whole year in a free ranging bird, the macaroni penguin Eudyptes chrysolophus. We combined these measurements with concurrently recorded foraging behaviour, and literature information on body mass and dietary factors to estimate prey consumption rates and foraging success. DEE was at a maximum during late chick-rearing but was equally high during all other active phases of the breeding season. DEE was approximately 4xresting metabolic rate, which accords with established theory and suggests a common 'energetic ceiling' throughout the summer period. However, whether this represents a maximum in physiological capacity, or a rate which optimises fitness is still unclear. Rates of prey consumption and foraging success followed different patterns from daily energy expenditure. Daily prey consumption was high as the penguins prepared for long fasts associated with moulting and incubation but relatively low during chick-rearing, when foraging areas were restricted and foraging success lower. It appears that the energy intake of macaroni penguins is subject to extrinisic or environmental constraints rather than to intrinsic physiological limits.
- Published
- 2009
32. Tracking macaroni penguins during long foraging trips using 'behavioural geolocation'
- Author
-
Green, Jonathan A., Wilson, Rory P., Boyd, Ian L., Woakes, Anthony J., Green, Chris J., Butler, Patrick J., Green, Jonathan A., Wilson, Rory P., Boyd, Ian L., Woakes, Anthony J., Green, Chris J., and Butler, Patrick J.
- Abstract
The movement of marine vertebrates has been tracked using a variety of techniques, all of which depend on the external attachment of a transmitting or recording device. However, these devices can have negative effects on the subject animals, limiting both the quantity and quality of data collected. We present a new method for monitoring large-scale movement of marine vertebrates that uses behavioural data stored on a surgically implanted data logger. The technique ('behavioural geolocation') relies on the principles of light-based geolocation but rather than measuring ambient light levels, changes in diving behaviour associated with sunrise and sunset are used to infer daylength and time of local sunrise, and hence location. We present data from a trial, post-hoc, analysis of diving data collected from macaroni penguins Eudyptes chrysolophus during long foraging trips associated with incubation and preparation for moult. Our results showed that the penguins usually travelled to the polar frontal zone to the north of their breeding colony at South Georgia, an area broadly consistent with previously measured behaviour and the availability of preferred prey at this period of the annual cycle.
- Published
- 2009
33. Evaluating the prudence of parents: daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Boyd, Ian L., Woakes, Anthony J., Warren, Nicholas L., Butler, Patrick J., Green, Jonathan A., Boyd, Ian L., Woakes, Anthony J., Warren, Nicholas L., and Butler, Patrick J.
- Abstract
We measured daily energy expenditure (DEE) continuously for a whole year in a free ranging bird, the macaroni penguin Eudyptes chrysolophus. We combined these measurements with concurrently recorded foraging behaviour, and literature information on body mass and dietary factors to estimate prey consumption rates and foraging success. DEE was at a maximum during late chick-rearing but was equally high during all other active phases of the breeding season. DEE was approximately 4xresting metabolic rate, which accords with established theory and suggests a common 'energetic ceiling' throughout the summer period. However, whether this represents a maximum in physiological capacity, or a rate which optimises fitness is still unclear. Rates of prey consumption and foraging success followed different patterns from daily energy expenditure. Daily prey consumption was high as the penguins prepared for long fasts associated with moulting and incubation but relatively low during chick-rearing, when foraging areas were restricted and foraging success lower. It appears that the energy intake of macaroni penguins is subject to extrinisic or environmental constraints rather than to intrinsic physiological limits.
- Published
- 2009
34. Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors
- Author
-
Cao, Yong, Patnaik, Debprakash, Ponce, Sean, Archuleta, Jeremy, Butler, Patrick, Feng, Wu-chun, Ramakrishnan, Naren, Cao, Yong, Patnaik, Debprakash, Ponce, Sean, Archuleta, Jeremy, Butler, Patrick, Feng, Wu-chun, and Ramakrishnan, Naren
- Abstract
Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams from these chips is critical to understanding the firing patterns of neurons and to gaining insight into the underlying cellular activity. We present a GPGPU solution to mining spike trains. We focus on mining frequent episodes which captures coordinated events across time even in the presence of intervening background/"junk" events. Our algorithmic contributions are two-fold: MapConcatenate, a new computation-to-core mapping scheme, and a two-pass elimination approach to quickly find supported episodes from a large number of candidates. Together, they help realize a real-time "chip-on-chip" solution to neuroscience data mining, where one chip (the multi-electrode array) supplies the spike train data and another (the GPGPU) mines it at a scale unachievable previously. Evaluation on both synthetic and real datasets demonstrate the potential of our approach.
- Published
- 2009
35. On Utilization of Contributory Storage in Desktop Grids
- Author
-
Computer Science, University Libraries, Miller, Chreston, Butler, Patrick, Shah, Ankur, Butt, Ali R., Computer Science, University Libraries, Miller, Chreston, Butler, Patrick, Shah, Ankur, and Butt, Ali R.
- Abstract
The availability of desktop grids and shared computing platforms has popularized the use of contributory resources, such as desktops, as computing substrates for a variety of applications. However, addressing the exponentially growing storage demands of applications, especially in a contributory environment, remains a challenging research problem. In this report, we propose a transparent distributed storage system that harnesses the storage contributed by grid participants arranged in a peer-to-peer network to yield a scalable, robust, and self-organizing system. The novelty of our work lies in (i) design simplicity to facilitate actual use; (ii) support for easy integration with grid platforms; (iii) ingenious use of striping and error coding techniques to support very large data files; and (iv) the use of multicast techniques for data replication. Experimental results through simulations and an actual implementation show that our system can provide reliable and efficient storage with large file support for desktop grid applications.
- Published
- 2007
36. On Utilization of Contributory Storage in Desktop Grids
- Author
-
Computer Science, University Libraries, Miller, Chreston, Butler, Patrick, Shah, Ankur, Butt, Ali R., Computer Science, University Libraries, Miller, Chreston, Butler, Patrick, Shah, Ankur, and Butt, Ali R.
- Abstract
The availability of desktop grids and shared computing platforms has popularized the use of contributory resources, such as desktops, as computing substrates for a variety of applications. However, addressing the exponentially growing storage demands of applications, especially in a contributory environment, remains a challenging research problem. In this report, we propose a transparent distributed storage system that harnesses the storage contributed by grid participants arranged in a peer-to-peer network to yield a scalable, robust, and self-organizing system. The novelty of our work lies in (i) design simplicity to facilitate actual use; (ii) support for easy integration with grid platforms; (iii) ingenious use of striping and error coding techniques to support very large data files; and (iv) the use of multicast techniques for data replication. Experimental results through simulations and an actual implementation show that our system can provide reliable and efficient storage with large file support for desktop grid applications.
- Published
- 2007
37. The Transportation of Convicts from County Tipperary to Australia 1836-1853
- Author
-
Butler, Patrick and Butler, Patrick
- Abstract
The aims and objectives of this work are to consider the socio-economic conditions prevailing in county Tipperary during the period in question. The affects on those residing within the county, both rural and urban, and to try and understand how socioeconomic conditions had such an influence in the perpetration of crime and the resulting cases of transportation. The investigation of why, when and how convicts were transported to Australia from the late eighteenth up to the middle of the nineteenth century has been looked at in depth by both historians and those hoping, to trace their convict ancestral roots. Some have studied conditions on the ships, some have studied the colonies and others such as A.G.L Shaw1 have studied the whole transportation era itself. However for the purposes of this thesis it was decided to focus on one particular era 1836-1853 and on one county, namely Tipperary. This period was selected because not only was Tipperary badly affected by agrarian agitation and faction fighting, more so than any other, but also by the Famine a combination of which led to high incidents of transportation. This thesis provides an analysis of some of those transported, looks at their backgrounds and establishes the link with the socio-economic conditions, which in so many ways shaped the lives and futures of those affected by these conditions. The colonies will also be looked at, in light of what awaited those who were transported along with a brief glimpse of how they fared.
- Published
- 2005
38. The Transportation of Convicts from County Tipperary to Australia 1836-1853
- Author
-
Butler, Patrick and Butler, Patrick
- Abstract
The aims and objectives of this work are to consider the socio-economic conditions prevailing in county Tipperary during the period in question. The affects on those residing within the county, both rural and urban, and to try and understand how socioeconomic conditions had such an influence in the perpetration of crime and the resulting cases of transportation. The investigation of why, when and how convicts were transported to Australia from the late eighteenth up to the middle of the nineteenth century has been looked at in depth by both historians and those hoping, to trace their convict ancestral roots. Some have studied conditions on the ships, some have studied the colonies and others such as A.G.L Shaw1 have studied the whole transportation era itself. However for the purposes of this thesis it was decided to focus on one particular era 1836-1853 and on one county, namely Tipperary. This period was selected because not only was Tipperary badly affected by agrarian agitation and faction fighting, more so than any other, but also by the Famine a combination of which led to high incidents of transportation. This thesis provides an analysis of some of those transported, looks at their backgrounds and establishes the link with the socio-economic conditions, which in so many ways shaped the lives and futures of those affected by these conditions. The colonies will also be looked at, in light of what awaited those who were transported along with a brief glimpse of how they fared.
- Published
- 2005
39. Effects of long-term implanted data loggers on macaroni penguins Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Tanton, Jane Lynda, Woakes, Anthony J., Boyd, Ian L., Butler, Patrick J., Green, Jonathan A., Tanton, Jane Lynda, Woakes, Anthony J., Boyd, Ian L., and Butler, Patrick J.
- Abstract
We tested the hypothesis that implanted data loggers have no effect on the survival, breeding success and behaviour of macaroni penguins Eudyptes chrysolophus . Seventy penguins were implanted with heart rate data loggers (DLs) for periods of up to 15 months. When compared to control groups, implanted penguins showed no significant difference in over-wintering survival rates, arrival date and mass at the beginning of the breeding season. Later in the breeding season, implanted penguins showed no significant difference in the duration of their incubation foraging trip, breeding success, fledging mass of their chicks, date of arrival to moult and mass at the beginning of the moult fast. We conclude that implanted devices had no effects on the behaviour, breeding success and survival of this species. We contrast these results to those from studies using externally attached devices, which commonly affect the behaviour of penguins. We suggest that implanted devices should be considered as an alternative to externally attached devices in order to obtain the most accurate representation of the freeranging behaviour, ecology and physiology of penguins.
- Published
- 2004
40. Energetics of the moult fast in female macaroni penguins Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Butler, Patrick J., Woakes, Anthony J., Boyd, Ian L., Green, Jonathan A., Butler, Patrick J., Woakes, Anthony J., and Boyd, Ian L.
- Abstract
The metabolic rate of moulting macaroni penguins Eudyptes chrysolophus was investigated using three techniques in order to test two hypotheses concerning the energetic cost of the moult fast in penguins. First, that energy expenditure during the moult is greater than that while penguins are onshore during the breeding season. Second, techniques that do not measure energy expenditure throughout the moult fast do not accurately determine the true cost of the moult. Mass loss calculations, measurement of the rate of oxygen consumption and estimation of the rate of oxygen consumption from heart rate in the field were used with captive and free-ranging penguins. Comparison of the results from these techniques suggest that metabolic rate is higher in the field than in a respirometer due to an increase in thermoregulatory costs. Furthermore the average metabolic rate of female penguins during the moult at 5.04±0.85 W kg−1 was not significantly different from that of female penguins on-shore during the breeding season at 6.27±0.38 W kg−1. Metabolic rate in the field changed during the moult fast as feather loss and increased vascularisation of the skin caused increased heat production, illustrating the importance of determining energy expenditure from animals in the field throughout the whole of the moult fast.
- Published
- 2004
41. Effects of long-term implanted data loggers on macaroni penguins Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Tanton, Jane Lynda, Woakes, Anthony J., Boyd, Ian L., Butler, Patrick J., Green, Jonathan A., Tanton, Jane Lynda, Woakes, Anthony J., Boyd, Ian L., and Butler, Patrick J.
- Abstract
We tested the hypothesis that implanted data loggers have no effect on the survival, breeding success and behaviour of macaroni penguins Eudyptes chrysolophus . Seventy penguins were implanted with heart rate data loggers (DLs) for periods of up to 15 months. When compared to control groups, implanted penguins showed no significant difference in over-wintering survival rates, arrival date and mass at the beginning of the breeding season. Later in the breeding season, implanted penguins showed no significant difference in the duration of their incubation foraging trip, breeding success, fledging mass of their chicks, date of arrival to moult and mass at the beginning of the moult fast. We conclude that implanted devices had no effects on the behaviour, breeding success and survival of this species. We contrast these results to those from studies using externally attached devices, which commonly affect the behaviour of penguins. We suggest that implanted devices should be considered as an alternative to externally attached devices in order to obtain the most accurate representation of the freeranging behaviour, ecology and physiology of penguins.
- Published
- 2004
42. Energetics of the moult fast in female macaroni penguins Eudyptes chrysolophus
- Author
-
Green, Jonathan A., Butler, Patrick J., Woakes, Anthony J., Boyd, Ian L., Green, Jonathan A., Butler, Patrick J., Woakes, Anthony J., and Boyd, Ian L.
- Abstract
The metabolic rate of moulting macaroni penguins Eudyptes chrysolophus was investigated using three techniques in order to test two hypotheses concerning the energetic cost of the moult fast in penguins. First, that energy expenditure during the moult is greater than that while penguins are onshore during the breeding season. Second, techniques that do not measure energy expenditure throughout the moult fast do not accurately determine the true cost of the moult. Mass loss calculations, measurement of the rate of oxygen consumption and estimation of the rate of oxygen consumption from heart rate in the field were used with captive and free-ranging penguins. Comparison of the results from these techniques suggest that metabolic rate is higher in the field than in a respirometer due to an increase in thermoregulatory costs. Furthermore the average metabolic rate of female penguins during the moult at 5.04±0.85 W kg−1 was not significantly different from that of female penguins on-shore during the breeding season at 6.27±0.38 W kg−1. Metabolic rate in the field changed during the moult fast as feather loss and increased vascularisation of the skin caused increased heat production, illustrating the importance of determining energy expenditure from animals in the field throughout the whole of the moult fast.
- Published
- 2004
43. Fatty acid signature analysis from the milk of Antarctic fur seals and Southern elephant seals from South Georgia:implications for diet determination
- Author
-
Brown, David J., Boyd, Ian L., Cripps, Geoff C., Butler, Patrick J., Brown, David J., Boyd, Ian L., Cripps, Geoff C., and Butler, Patrick J.
- Abstract
Fatty acid signature analysis (FASA) makes use of specific fatty acids, as well as entire profiles, to study dietary relationships at different trophic levels. Previously, FASA has been used in marine ecosystems in which diet determination by more direct methods is difficult and sometimes misleading. This study examined fatty acid profiles in milk from 2 species of pinniped from the Southern Ocean that were expected to have highly contrasting diets. Milk samples were collected from Antarctic fur seals Arctocephalus gazella in 3 consecutive years, from 1991 to 1993 (n = 72), and from Southern elephant seals Mirounga leonina in 1988 (n = 53) at South Georgia. Lipids were extracted and fatty acid profiles determined by temperature-programmed gas chromatography. Possible prey species collected from waters around South Georgia were also analysed. Cluster analysis as well as classification and regression trees (CART) indicated that profiles from fur seals and elephant seals were significantly different. Southern elephant seal data could be distinguished from Antarctic fur seals by lower levels of the fatty acids 16:4 n1, 18:2 n6, 18:4 n3, 18:4 n1 and 20:5 n3 and by higher levels of 18:0, 18:1 n9/ n11 (i.e. 18:1 n9 co-eluting with 18:1 n11) and 20:1 n9. Fatty acid signatures from the milk of Antarctic fur seals were closest to krill and fish species that were also known to feed on krill. Southern elephant seal fatty acid profiles were closest to species that are not known as krill predators such as larger notothenids and myctophids. The fatty acid profiles of Antarctic fur seals showed considerable inter- and intra-annual variability, which was congruent with diet variability detected using scat analyses. Southern elephant seals showed little variation in profile through lactation. In contrast to previous diet analyses based on examination of stomach contents, the results from FASA were consistent with a fish-based diet for Southern elephant seals.
- Published
- 1999
44. Fatty acid signature analysis from the milk of Antarctic fur seals and Southern elephant seals from South Georgia:implications for diet determination
- Author
-
Brown, David J., Boyd, Ian L., Cripps, Geoff C., Butler, Patrick J., Brown, David J., Boyd, Ian L., Cripps, Geoff C., and Butler, Patrick J.
- Abstract
Fatty acid signature analysis (FASA) makes use of specific fatty acids, as well as entire profiles, to study dietary relationships at different trophic levels. Previously, FASA has been used in marine ecosystems in which diet determination by more direct methods is difficult and sometimes misleading. This study examined fatty acid profiles in milk from 2 species of pinniped from the Southern Ocean that were expected to have highly contrasting diets. Milk samples were collected from Antarctic fur seals Arctocephalus gazella in 3 consecutive years, from 1991 to 1993 (n = 72), and from Southern elephant seals Mirounga leonina in 1988 (n = 53) at South Georgia. Lipids were extracted and fatty acid profiles determined by temperature-programmed gas chromatography. Possible prey species collected from waters around South Georgia were also analysed. Cluster analysis as well as classification and regression trees (CART) indicated that profiles from fur seals and elephant seals were significantly different. Southern elephant seal data could be distinguished from Antarctic fur seals by lower levels of the fatty acids 16:4 n1, 18:2 n6, 18:4 n3, 18:4 n1 and 20:5 n3 and by higher levels of 18:0, 18:1 n9/ n11 (i.e. 18:1 n9 co-eluting with 18:1 n11) and 20:1 n9. Fatty acid signatures from the milk of Antarctic fur seals were closest to krill and fish species that were also known to feed on krill. Southern elephant seal fatty acid profiles were closest to species that are not known as krill predators such as larger notothenids and myctophids. The fatty acid profiles of Antarctic fur seals showed considerable inter- and intra-annual variability, which was congruent with diet variability detected using scat analyses. Southern elephant seals showed little variation in profile through lactation. In contrast to previous diet analyses based on examination of stomach contents, the results from FASA were consistent with a fish-based diet for Southern elephant seals.
- Published
- 1999
45. Interview with Patrick Butler
- Author
-
Butler, Patrick, Butler, Patrick, Butler, Patrick, and Butler, Patrick
- Abstract
Brief outline of childhood in Rothley, Leicestershire. Describes becoming interested in boxing at competition at Glenfield Working Mens' Club, training with Sammy Lester at Bull and Mouth, Mountsorrel, then with Len Alderwick at Fish and Quart, Churchgate, Leicester. Describes training regime, first fights, signing with promoter George Biddles. Talks about championship fight in 1934, memorable characters such as Larry Gaines, Reggie Mean. Mentions boxing booths, reflects on changes in boxers and boxing. Mention of money earned.
46. Interview with Patrick Butler
- Author
-
Butler, Patrick and Butler, Patrick
- Abstract
Brief outline of childhood in Rothley, Leicestershire. Describes becoming interested in boxing at competition at Glenfield Working Mens' Club, training with Sammy Lester at Bull and Mouth, Mountsorrel, then with Len Alderwick at Fish and Quart, Churchgate, Leicester. Describes training regime, first fights, signing with promoter George Biddles. Talks about championship fight in 1934, memorable characters such as Larry Gaines, Reggie Mean. Mentions boxing booths, reflects on changes in boxers and boxing. Mention of money earned.
- Published
- 1987
47. Interview with Patrick Butler
- Author
-
Butler, Patrick and Butler, Patrick
- Abstract
Brief outline of childhood in Rothley, Leicestershire. Describes becoming interested in boxing at competition at Glenfield Working Mens' Club, training with Sammy Lester at Bull and Mouth, Mountsorrel, then with Len Alderwick at Fish and Quart, Churchgate, Leicester. Describes training regime, first fights, signing with promoter George Biddles. Talks about championship fight in 1934, memorable characters such as Larry Gaines, Reggie Mean. Mentions boxing booths, reflects on changes in boxers and boxing. Mention of money earned.
- Published
- 1987
48. AN ANALYSIS OF SKILL REQUIREMENTS FOR OPERATORS OF AMPHIBIOUS AIR CUSHION VEHICLES (ACVs)
- Author
-
HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, McKnight, A. James, Butler, Patrick J., Behringer, Richard D., HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, McKnight, A. James, Butler, Patrick J., and Behringer, Richard D.
- Abstract
The report describes the skills required in the operation of an amphibious Air Cushion Vehicle (ACV) in Army tactical and logistic missions. The research involved an analysis of the ACV characteristics, operating requirements, and environment, and results of a simulation experiment. The analysis indicates that ACV operation is complicated by (a) an inherently slow vehicle response in certain control dimensions, (b) a need for complex control coordinations in performing certain necessary maneuvers, and (c) the ACV's sensitivity to various aspects of the natural and man-made environment. The ACV also poses unique requirements for navigation, maintenance, and collision avoidance. The simulator study showed that ACVs vary considerably in operability as a function of their control configuration and pointed to the need for further attention to the control problem in developing ACV use overland. A training program of from one to three months' duration appears necessary to qualify an operator fully.
- Published
- 1969
49. Requirements for Personnel and Training Information.
- Author
-
HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, McKnight,A James, Butler,Patrick J, HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, McKnight,A James, and Butler,Patrick J
- Abstract
This document contains two-papers--one of which proposes requirements relating to the nature of personnel and training information to be provided concurrently with material development, and the other which describes the bases upon which the proposed requirements were established. (Author)
- Published
- 1963
50. WOrk Unit JACK.
- Author
-
HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, Trexler,Robert C, Butler,Patrick J, HUMAN RESOURCES RESEARCH ORGANIZATION ALEXANDRIA VA, Trexler,Robert C, and Butler,Patrick J
- Abstract
HumRRO Division 1 research personnel were asked to supply technical advisory service to the Southeastern Signal School in preparing and administering a performance test of graduates of a revised course of instruction in switchboard operation. The revised course was designed to provide training for classes containing a broad mix of personnel by AFQT level. Portions of the HumRRO developed test were also administered to greauates of a standard course to compare the courses. (Author)
- Published
- 1968
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