Journal Articles, BioMed Central and SpringerOpen

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Browsing Journal Articles, BioMed Central and SpringerOpen by Department "Aerospace and Ocean Engineering"

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    5th International Symposium on Focused Ultrasound
    Abounader, Roger; Abraham, Christopher; Adema, Gosse; Agrawal, Punit; Airan, Raag; Aleman, Dionne; Alexander, Phillip; Alkins, Ryan; Alnazeer, Moez; Altman, Michael; Aly, Amirah; Amaral, Joao G.; Amrahli, Maral; Amraoui, Sana; Andarawewa, Kumari; Andriyakhina, Yulia; Angstadt, Mary; Ankou, Bénédicte; Arias, Ana C.; Arvanitis, Costas; Asadnia, Kiana; Aubert, Isabelle; Aubry, Jean-Francois; Aubry, Jean-Francois; Aurup, Christian; Bader, Kenneth; Badr, Lena; Baek, Hongchae; Barbato, Gaetano; Beccaria, Kevin; Bellorofonte, Carlo; Benson, Lee; Bernus, Olivier; Berriet, Rémi; Bertolina, Jim; Beskin, Viktoriya; Bessière, Francis; Bethune, Allison; Bezzi, Mario; Bond, Aaron; Bonomo, Guido; Borowsky, Alexander; Borys, Nicolas; Böttcher, Joachim; Bouley, Donna; Bour, Pierre; Bourekas, Eric; Brenin, David; Brokman, Omer; Brosh, Inbar; Buckner, Andrew; Bullock, Timothy; Cafarelli, Andrea; Cahill, Jessica; Camarena, Francisco; Camelo-Piragua, Sandra; Campbell, Benjamin; Campbell, Fiona; Cannata, Jon; Canney, Michael; Carlson, Roy; Carneiro, Antonio; Carpentier, Alexandre; Catheline, Stefan; Cavin, Ian; Cesana, Claudio; Chabok, Hamid R.; Chamanara, Marzieh; Chang, Jin H.; Chang, Won S.; Changizi, Barbara; Chapelon, Jean Y.; Chaplin, Vandiver; Chapman, Martin; Chaudhary, Neeraj; Chaussy, Christian; Chen, Cherry; Chen, Johnny; Chen, Wohsing; Chen, Xiaoming; Chevalier, Philippe; Chiou, George; Chisholm, Alexander; Christofferson, Ivy; Chung, Hyun H.; Ciuti, Gastone; Clement, Gregory; Cooper, Mark; Corea, Joseph; Corso, Cristiano; Cosman, Josh; Coughlin, Dezba; Crake, Calum; Cunitz, Bryan; Curiel, Laura; Curley, Colleen T.; Czarnota, Gregory; Dababou, Susan; Dallapiazza, Robert; de Bever, Joshua; de Jager, Bram; de Ruiter, Joost; de Senneville, Baudouin D.; Deckers, Roel; Delattre, Jean-Yves; den Brok, Martijn; Dhanaliwala, Ali; Diodato, Alessandro; Dixon, Adam; Donner, Elizabeth; Downs, Matthew; Du, Zhongmin; Dubois, Rémi; Dupre, Aurelien; Eikelenboom, Dylan; Elias, W. J.; Ellens, Nicholas; Endre, Ruby; Eran, Ayelet; Erasmus, Hans-Peter; Everstine, Ashli; Farahani, Keyvan; Farrer, Alexis; Farry, Justin; Federau, Christian; Feng, Xue; Ferrer, Cyril; Ferrera, Vincent; Fishman, Paul; Foley, Jessica; Frenkel, Victor; Fütterer, Jurgen; Gach, H. M.; Gandhi, Dheeraj; Gertner, Michael; Goldsher, Dorit; Gorgone, Alessandro; Greillier, Paul; Griesenauer, Rebekah; Grissom, William; Grondin, Julien; Guha, Chandan; Gulati, Amitabh; Gullapalli, Rao; Guo, Sijia; Gupta, Samit; Gurm, Hitinder; Gwinn, Ryder; Hadley, Rock; Haïssaguerre, Michel; Hammoud, Dima; Hananel, Arik; Hargrove, Amelia; Hatch, Robert; Haworth, Kevin; Hazan, Eilon; He, Ye; Heemels, Maurice; Heerschap, Arend; Hilas, Elaine; Hoang-Xuan, Khe; Hocini, Mélèze; Hodaie, Mojgan; Hofmann, Denis; Holland, Christy; Hoogenboom, Martijn; Hopyan, Sevan; Hossack, John; Houdouin, Alexandre; Hsu, Po-Hung; Hu, Jim; Hurwitz, Mark; Huss, Diane; Hwang, Chang-il; Hwang, Joo H.; Idbaih, Ahmed; Ikeuchi, Masahiko; Ingham, Elizabeth; Ives, Kimberly; Izumi, Masashi; Jackson-Lewis, Vernice; Janát-Amsbury, Margit; Jang, Kee W.; Jedruszczuk, Kathleen; Jiménez-Gambín, Sergio; Jiménez, Noé; Johnson, Sara; Jonathan, Sumeeth; Joy, Joyce; Jung, Hyun H.; Jung, Na Y.; Kahn, Itamar; Kamimura, Hermes; Kamrava, Seyed K.; Kang, Jeeun; Kang, Kook J.; Kang, Soo Y.; Kao, Yi-tzu; Katti, Prateek; Kawasaki, Motohiro; Kaye, Elena; Keupp, Jochen; Kim, AeRang; Kim, Harry; Kim, Hyun-Chul; Kim, Hyuncheol; Kim, Hyungmin; Kim, Min S.; Kim, Namho; Kiyasu, Katsuhito; Kneepkens, Esther; Knopp, Michael; Kobus, Thiele; Koral, Korgun; Kreider, Wayne; Krishna, Vibhor; Krug, Roland; Krupa, Steve; Kuo, Chia-Chun; Kwiecinski, Wojciech; Lacoste, Romain; Lam, Heather; Lamberti-Pasculli, Maria; Lang, Brian; Larner, James; Larrabee, Zachary; Leach, J. K.; LeBlang, Suzanne; Leclercq, Delphine; Lee, Hak J.; Lee, Jong-Hwan; Lehericy, Stéphane; Leighton, Wan; Leung, Steven; Lewis, Bobbi; Lewis, Matthew; Li, Dawei; Linn, Sabine; Lipsman, Nir; Liu, Hao-Li; Liu, Jingfei; Lopes, M. B.; Lotz, Jeff; Lu, Xin; Lundt, Jonathan; Luo, Xi; Lustgarten, Lior; Lustig, Micheal; Macoskey, Jonathan; Madore, Bruno; Maev, Roman; Magat, Julie; Maimbourg, Guillaume; Maimon, Noam; Mainprize, Todd; Malayer, Jerry; Maples, Danny; Marquet, Fabrice; Marrocchio, Cristina; Marx, Mike; Mastorakos, Panagiotis; Mauri, Giovanni; McLean, Hailey; McMichael, John; Mead, Brian P.; Melodelima, David; Melot-Dusseau, Sandrine; Menciassi, Arianna; Merrill, Robb; Meyer, Joshua; Midiri, Massimo; Miga, Michael; Migliore, Ilaria G.; Miller, Eric; Minalga, Emilee; Moon, Hyungwon; Moore, David; Mourad, Pierre; Mouratidis, Petros; Mueller, Michael; Mugler, John; Muller, Sébastien; Namba, Hirofumi; Naor, Omer; Nassar, Maria; Nazai, Navid; Negron, Karina; Negussie, Ayele; Nguyen, Thai-Son; Nicolay, Klaas; Nikolaeva, Anastasia V.; Oetgen, Matthew; Olive, Kenneth; Olumolade, Oluyemi; Orsi, Franco; Owens, Gabe; Ozilgen, Arda; Padegimas, Linas; Palermo, Carmine; Pan, Chia-Hsin; Pandey, Aditya; Papadakis, Georgios; Park, Chang K.; Park, Sang M.; Parker, Jonathon; Parvizi, Mohammad H.; Pascal-Tenorio, Aurea; Patel, Janish; Patz, Sam; Payen, Thomas; Perich, Eloi; Pernot, Mathieu; Perol, David; Perry, James; Pillarisetty, Venu; Pioche, Mathieu; Pizzuto, Matthew; Plaksin, Michael; Plata, Juan; Price, Karl; Prince, Jessica; Przedborski, Serge; Quinones-Hinojosa, Alfredo; Ramachandran, Akhilesh; Ranjan, Ashish; Ravikumar, Vinod; Reichenbach, Juergen; Repasky, Elizabeth; Rezai, Ali; Ritter, Philippe; Rivoire, Michel; Rochman, Carrie; Rosenberg, Jarrett; Rosnitskiy, Pavel B.; Ruiz, Antonio; Sahgal, Arjun; Samiotaki, Gesthimani; Sanghvi, Narendra; Santin, Mathieu D.; Santos, Domiciano; Sasaki, Noboru; Sastra, Steve; Schade, George; Schall, Jeffrey; Schlesinger, Ilana; Schmitt, Paul; Schwaab, Julia; Scionti, Stephen; Scipione, Roberto; Scoarughi, Gian L.; Scott, Serena; Sebeke, Lukas; Seifabadi, Reza; Seo, Jai; Sesenoglu-Laird, Ozge; Shah, Binit; Shahriari, Kian; Shaikh, Sumbul; Shea, Jill; Shi, Jiaqi; Shim, Jenny; Shinkov, Alexander; Shuman, Jillian; Silvestrini, Matthew; Sim, Changbeom; Sin, Vivian; Sinai, Alon; Singh, Manoj; Sinilshchikov, Ilya; Skalina, Karin; Slingluff, Craig; So, Po-Wah; Solomon, Stephen; Son, Keon H.; Sperling, Scott; Stein, Ruben; Stein, Sherman; Stevens, Aaron; Stimec, Jennifer; Storm, Gert; Straube, William; Suelmann, Britt; Sutton, Jonathan; Svedin, Bryant; Takemasa, Ryuichi; Takiguchi, Mitsuyoshi; Tam, Emily; Tan, Jeremy; Tang, Xinyan; Tanter, Mickael; Tebebi, Pamela; Tehrani, Seruz; Temple, Michael; Teofilovic, Dejan; ter Haar, Gail; Terzi, Marina E.; Thueroff, Stefan; Timbie, Kelsie; Tognarelli, Selene; Tretbar, Steffen; Trudeau, Maureen; Tsai, Yi-Chieh; Tsysar, Sergey A.; Tucci, Samantha; Tuveson, David; Ushida, Takahiro; Vaessen, Paul; Vaillant, Fanny; Van Arsdell, Glen; van Breugel, Johanna; Van der Jeugd, Anneke; Van der Jeugd, Anneke; Van der Wall, Elsken; van Diest, Paul; van Stralen, Marijn; Varano, Gianluca; Velat, Manuela; Vidal-Jove, Joan; Vigna, Paolo D.; Vignot, Alexandre; Vincenot, Jeremy; Vykhodtseva, Natalia; Wang, Bin; Wang, Han; Wang, Kevin; Wang, Qi; Wang, Qingguo; Wang, Shengping; Wang, Yak-Nam; Wang, Zhaorui; Wardlow, Rachel; Warren, Amy; Waszczak, Barbara; Watson, Katherine; Webb, Taylor; Wei-Bin, Shen; Wei, Kuo-Chen; Weiss, Steffen; Weissler, Yoni; Werner, Beat; Wesseling, Pieter; Williams, Noelle; Wilson, Emmanuel; Wintermark, Max; Witkamp, Arjen; Wong, Carlos; Wu, Jing-Fu; Wydra, Adrian; Xu, Alexis; Xu, Doudou; Xu, Su; Yang, Georgiana; Yang, Nai-Yi; Yao, Chen; Yarowsky, Paul; Ye, Patrick P.; Yuldashev, Petr; Zaaroor, Menashe; Zachiu, Cornel; Zahos, Peter; Zangos, Stephan; Zhang, Dandan; Zhang, Hua; Zhang, Jimin; Zhang, Junhai; Zhang, Xi; Zhao, Li; Zhong, Pei; Zhuo, Jiachen; Zidowitz, Stephan; Zinke, Wolf; Zorgani, Ali (2016-11-21)
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    Predicting network modules of cell cycle regulators using relative protein abundance statistics
    Oguz, Cihan; Watson, Layne T.; Baumann, William T.; Tyson, John J. (2017-02-28)
    Background Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of “feasible” parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. Results Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in “regulation of cell size” and “regulation of G1/S transition” contribute most to predictive variability, whereas proteins involved in “positive regulation of transcription involved in exit from mitosis,” “mitotic spindle assembly checkpoint” and “negative regulation of cyclin-dependent protein kinase by cyclin degradation” contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50. Conclusions By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.
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    Predicting the combined effect of multiple genetic variants
    Liu, Mingming; Watson, Layne T.; Zhang, Liqing (2015-07-30)
    Background Many genetic variants have been identified in the human genome. The functional effects of a single variant have been intensively studied. However, the joint effects of multiple variants in the same genes have been largely ignored due to their complexity or lack of data. This paper uses HMMvar, a hidden Markov model based approach, to investigate the combined effect of multiple variants from the 1000 Genomes Project. Two tumor suppressor genes, TP53 and phosphatase and tensin homolog (PTEN), are also studied for the joint effect of compensatory indel variants. Results Results show that there are cases where the joint effect of having multiple variants in the same genes is significantly different from that of a single variant. The deleterious effect of a single indel variant can be alleviated by their compensatory indels in TP53 and PTEN. Compound mutations in two genes, β-MHC and MyBP-C, leading to severer cardiovascular disease compared to single mutations, are also validated. Conclusions This paper extends the functionality of HMMvar, a tool for assigning a quantitative score to a variant, to measure not only the deleterious effect of a single variant but also the joint effect of multiple variants. HMMvar is the first tool that can predict the functional effects of both single and general multiple variations on proteins. The precomputed scores for multiple variants from the 1000 Genomes Project and the HMMvar package are available at https://bioinformatics.cs.vt.edu/zhanglab/HMMvar/
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    Sensitivity study for ICON tidal analysis
    Cullens, Chihoko Y.; Immel, Thomas J.; Triplett, Colin C.; Wu, Yen-Jung; England, Scott L.; Forbes, Jeffrey M.; Liu, Guiping (2020-05-22)
    Retrieval of the properties of the middle and upper atmosphere can be performed using several different interferometric and photometric methods. The emission-shape and Doppler shift of both atomic and molecular emissions can be observed from the ground and space to provide temperature and bulk velocity. These instantaneous measurements can be combined over successive times/locations along an orbit track, or successive universal/local times from a ground station to quantify the motion and temperature of the atmosphere needed to identify atmospheric tides. In this report, we explore how different combinations of space-based wind and temperature measurements affect the retrieval of atmospheric tides, a ubiquitous property of planetary atmospheres. We explore several scenarios informed by the use of a tidally forced atmospheric circulation model, an empirically based emissions reference, and a low-earth orbit satellite observation geometry based on the ICON mission design. This capability provides a necessary tool for design of an optimal mission concept for retrieval of atmospheric tides from ICON remote-sensing observations. Here it is used to investigate scenarios of limited data availability and the effects of rapid changes in the total wave spectrum on the retrieval of the correct tidal spectrum. An approach such as that described here could be used in the design of future missions, such as the NASA DYNAMIC mission (National Research Council, Solar and space physics: a science for a technological society, 2013).