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1,010 results on '"Brind A."'

1. Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction

Author

Zhang, Yuting, Liu, Boyang, Bunting, Karina V., Brind, David, Thorley, Alexander, Karwath, Andreas, Lu, Wenqi, Zhou, Diwei, Wang, Xiaoxia, Mobley, Alastair R., Tica, Otilia, Gkoutos, Georgios, Kotecha, Dipak, and Duan, Jinming

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). In order to quantify LVEF automatically and accurately, this paper proposes a new pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p<0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluation of cardiac systolic function., Comment: Accepted to Frontiers in Medicine

Published
2024

5. Analysis of Traffic Noise Annoyance at Intersections in Mid-Sized Indian Cities

Author

Yadav, Adarsh, Parida, Manoranjan, Choudhary, Pushpa, Kumar, Brind, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Manoj, M., editor, and Roy, Debashish, editor

Published
2024
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14. Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Author

Yuting Zhang, Boyang Liu, Karina V. Bunting, David Brind, Alexander Thorley, Andreas Karwath, Wenqi Lu, Diwei Zhou, Xiaoxia Wang, Alastair R. Mobley, Otilia Tica, Georgios V. Gkoutos, Dipak Kotecha, and Jinming Duan

Subjects
artificial intelligence, echocardiogram, ejection fraction, heart failure, atrial fibrillation, Medicine (General), R5-920
Abstract

IntroductionThe echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF).MethodsThis paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF.ResultsThis method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF

Published
2024
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16. Open Synthesis Network Research in an Undergraduate Laboratory: Development of Benzoxazole Amide Derivatives against 'Leishmania' Parasite

Author

Faville, Scott C., Harris-Hamdscomb, Kes, Harker, Owain, Mattison, Stephanie, Tamorite, Hajie, Bristowe, Joshua, Daly, Daniel, Ege, Raissa, He, Haoyuan, Jones, Julian, McCorkindale, Abby, Mei, Kerry, Monson, Ashleigh, Moree, Lana, Perkovic, Finley, Rickerby, Georgia, Robinson, Jack, Rudkin, Felix, Whibley, Luke, Worthington, Rebecca, Ennis, Courtney, de la Harpe, Sara, Brind, Thomasin, Hopkins, Andrew, Winefield, Kaleb, Hendrickx, Sarah, Caljon, Guy, Perry, Benjamin, and Vernall, Andrea J.

Abstract

An undergraduate laboratory was developed as part of the Drugs for Neglected Diseases "initiative's" Open Synthesis Network. This activity aimed to develop new compounds efficacious against visceral leishmaniasis. Students successfully synthesized, purified, and characterized ten different benzoxazole amides that were sent for biological testing against several protozoan parasites. Although all the benzoxazole amides had poor activity against "L. donovani," several (2, 4, and 9) showed moderate activity against "T. cruzi," "T. b. rhodesiense," and "T. b. brucei" paired with low cell cytotoxicity. This drug discovery laboratory activity made a measurable contribution to neglected tropical disease research and was an engaging and research-orientated experience for undergraduate students. Implementation of drug discovery laboratories across a range of student levels and backgrounds is highly achievable using existing laboratory equipment and a short investment in activity preparation and can be a sustainable course component.

Published
2022
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17. The impact of misinformation presented during jury deliberation on juror memory and decision-making

Author

Hayley J. Cullen, Natali Dilevski, Faye T. Nitschke, Gianni Ribeiro, Shobanah Brind, and Nikita Woolley

Subjects
juries, legal decision-making, memory, misinformation, jury deliberation, Psychology, BF1-990
Abstract

When deliberating, jurors may introduce misinformation that may influence other jurors’ memory and decision-making. In two studies, we explored the impact of misinformation exposure during jury deliberation. Participants in both studies read a transcript of an alleged sexual assault. In Study 1 (N = 275), participants encountered either consistent pro-prosecution misinformation, consistent pro-defense misinformation, or contradictory misinformation (pro-prosecution and pro-defense). In Study 2 (N = 339), prior to encountering either pro-prosecution or pro-defense misinformation while reading a jury deliberation transcript, participants either received or did not receive a judicial instruction about misinformation exposure during deliberation. Participants in both studies completed legal decision-making variables (e.g., defendant guilt rating) before and after deliberation, and their memory was assessed for misinformation acceptance via recall and source memory tasks. In Study 1, misinformation type did not influence legal decision-making, but pro-prosecution misinformation was more likely to be misattributed as trial evidence than pro-defense or contradictory misinformation. In Study 2, pro-defense misinformation was more likely to be misattributed to the trial than pro-prosecution misinformation, and rape myths moderated this. Furthermore, exposure to pro-defense misinformation skewed legal decision-making towards the defense’s case. However, the judicial instruction about misinformation exposure did not influence memory or decision-making. Together, these findings suggest that misinformation in jury deliberations may distort memory for trial evidence and bias decision-making, highlighting the need to develop effective safeguards for reducing the impact of misinformation in trial contexts.

Published
2024
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18. Statistical modeling of traffic noise at intersections in a mid-sized city, India

Author

Yadav Adarsh, Parida Manoranjan, and Kumar Brind

Subjects
traffic noise, influencing variables, traffic heterogeneity, intersection-specific traffic noise model, Environmental technology. Sanitary engineering, TD1-1066
Abstract

The modeling of traffic noise is more debated around intersections due to traffic flow and road geometry complexity. The available intersection-specific traffic noise models cannot be transferred to predict the traffic noise at intersections in the mid-sized Indian cities due to traffic heterogeneity, variety in driving conditions, and vehicle compositions. This article aims to develop an intersection-specific traffic noise model by collecting data at 19 intersections in Kanpur, India. The data include a wide range of traffic, road, and weather-related variables. Furthermore, significant input variables are determined and used in the statistical regression model to develop an intersection-specific traffic noise model for the mid-sized Indian cities. This study develops a separate entrance and exit arm model based on the corresponding influencing variables. The coefficient of determination (R 2) value is 0.74 and 0.69 for the developed model at the entrance and exit arms, respectively, whereas these models achieve R 2 values of 0.73 and 0.67 in the validation step. Also, the performance of developed models is evaluated on the standard and mean absolute errors as performance metrics. This study finds that traffic volume and receiver distance are relatively the most important variables in the entrance and exit arm noise models.

Published
2023
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19. The effect of blade row interaction on rotor film cooling

Author

Brind, James and Pullan, Graham

Subjects
629.132, Turbomachinery, Aerodynamics, Film cooling, Heat transfer, Blade row interaction, Turbine
Abstract

In gas turbines, film cooling is required to protect metal parts from hot combustion gases. Reduction in coolant mass flow increases cycle efficiency, and hence reduces greenhouse gas emissions. However, the lifespan of a cooled component is sensitive to the metal temperature within the part. A designer requires predictions of cooling performance to make this compromise, yet present design methods are subject to uncertainty and are not viable without empirical input. Flow through a turbine is inherently unsteady due to relative motion of stators and rotors, termed blade row interaction. Blade row interaction is not captured in flat-plate and cascade testing, or present design methods, contributing to uncertainty in predicted cooling performance. The aims of this thesis are to establish the mechanisms by which blade row interaction affects rotor film cooling, and quantify their influence on cooling performance in a representative case. A new experimental rig is developed to facilitate aerodynamic and heat transfer measurements of cooling holes subject to unsteady main-stream boundary conditions. The effect of unsteadiness is set by non-linearity in the hole response. Unsteadiness reduces film effectiveness by up to 31% with cylindrical holes at a low momentum flux ratio, because the response to perturbations is non-linear. Cylindrical holes at a high momentum flux ratio, and fan-shaped holes, are robust to unsteadiness because they respond linearly. Non-film-resolved computations are used to identify the blade row interaction mechanisms generating unsteady main-stream boundary conditions in a turbine rotor. A quasi-steady model is used to predict instantaneous excursions in cooling hole momentum flux ratio. Fluctuations of at least ±30% are present for all hole locations, due to both upstream vane wake and potential field interaction. A hybrid URANS-LES computational approach is implemented, validated against experimental data, and applied to a turbine stage cascade model. Compared to steady conditions, blade row interaction reduces rotor film effectiveness: by up to 18% on the pressure side, due to migration of vane coolant across the passage; and by up to 30% on the suction side, due to wake interactions increasing the film mixing rate.

Published
2020
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23. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Abbasizanjani, Hoda, Ahmed, Nida, Ahmed, Badar, Akbari, Ashley, Akinoso-Imran, Abdul Qadr, Allara, Elias, Allery, Freya, Angelantonio, Emanuele Di, Ashworth, Mark, Ayyar-Gupta, Vandana, Babu-Narayan, Sonya, Bacon, Seb, Ball, Steve, Banerjee, Ami, Barber, Mark, Barrett, Jessica, Bennie, Marion, Berry, Colin, Beveridge, Jennifer, Birney, Ewan, Bojanić, Lana, Bolton, Thomas, Bone, Anna, Boyle, Jon, Braithwaite, Tasanee, Bray, Ben, Briffa, Norman, Brind, David, Brown, Katherine, Buch, Maya, Canoy, Dexter, Caputo, Massimo, Carragher, Raymond, Carson, Alan, Cezard, Genevieve, Chang, Jen-Yu Amy, Cheema, Kate, Chin, Richard, Chudasama, Yogini, Cooper, Jennifer, Copland, Emma, Crallan, Rebecca, Cripps, Rachel, Cromwell, David, Curcin, Vasa, Curry, Gwenetta, Dale, Caroline, Danesh, John, Das-Munshi, Jayati, Dashtban, Ashkan, Davies, Alun, Davies, Joanna, Davies, Gareth, Davies, Neil, Day, Joshua, Delmestri, Antonella, Denaxas, Spiros, Denholm, Rachel, Dennis, John, Denniston, Alastair, Deo, Salil, Dhillon, Baljean, Docherty, Annemarie, Dong, Tim, Douiri, Abdel, Downs, Johnny, Dregan, Alexandru, Ellins, Elizabeth A, Elwenspoek, Martha, Falck, Fabian, Falter, Florian, Fan, Yat Yi, Firth, Joseph, Fraser, Lorna, Friebel, Rocco, Gavrieli, Amir, Gerstung, Moritz, Gilbert, Ruth, Gillies, Clare, Glickman, Myer, Goldacre, Ben, Goldacre, Raph, Greaves, Felix, Green, Mark, Grieco, Luca, Griffiths, Rowena, Gurdasani, Deepti, Halcox, Julian, Hall, Nick, Hama, Tuankasfee, Handy, Alex, Hansell, Anna, Hardelid, Pia, Hardy, Flavien, Harris, Daniel, Harrison, Camille, Harron, Katie, Hassaine, Abdelaali, Hassan, Lamiece, Healey, Russell, Hemingway, Harry, Henderson, Angela, Herz, Naomi, Heyl, Johannes, Hidajat, Mira, Higginson, Irene, Hinchliffe, Rosie, Hippisley-Cox, Julia, Ho, Frederick, Hocaoglu, Mevhibe, Hollings, Sam, Horne, Elsie, Hughes, David, Humberstone, Ben, Inouye, Mike, Ip, Samantha, Islam, Nazrul, Jackson, Caroline, Jenkins, David, Jiang, Xiyun, Johnson, Shane, Kadam, Umesh, Kallis, Costas, Karim, Zainab, Kasan, Jake, Katsoulis, Michalis, Kavanagh, Kim, Kee, Frank, Keene, Spencer, Kent, Seamus, Khalid, Sara, Khawaja, Anthony, Khunti, Kamlesh, Killick, Richard, Kinnear, Deborah, Knight, Rochelle, Kolamunnage-Dona, Ruwanthi, Kontopantelis, Evan, Kurdi, Amanj, Lacey, Ben, Lai, Alvina, Lambarth, Andrew, Larzjan, Milad Nazarzadeh, Lawler, Deborah, Lawrence, Thomas, Lawson, Claire, Li, Qiuju, Li, Ken, Llinares, Miguel Bernabeu, Lorgelly, Paula, Lowe, Deborah, Lyons, Jane, Lyons, Ronan, Machado, Pedro, Macleod, Mary Joan, Macleod, John, Malgapo, Evaleen, Mamas, Mamas, Mamouei, Mohammad, Manohar, Sinduja, Mapeta, Rutendo, Martelli, Javiera Leniz, Martos, David Moreno, Mateen, Bilal, McCarthy, Aoife, Melville, Craig, Milton, Rebecca, Mizani, Mehrdad, Moncusi, Marta Pineda, Morales, Daniel, Mordi, Ify, Morrice, Lynn, Morris, Carole, Morris, Eva, Mu, Yi, Mueller, Tanja, Murdock, Lars, Nafilyan, Vahé, Nicholson, George, Nikiphorou, Elena, Nolan, John, Norris, Tom, Norris, Ruth, North, Laura, North, Teri-Louise, O'Connell, Dan, Oliver, Dominic, Oluyase, Adejoke, Olvera-Barrios, Abraham, Omigie, Efosa, Onida, Sarah, Padmanabhan, Sandosh, Palmer, Tom, Pasea, Laura, Patel, Riyaz, Payne, Rupert, Pell, Jill, Petitjean, Carmen, Pherwani, Arun, Pickrell, Owen, Pierotti, Livia, Pirmohamed, Munir, Priedon, Rouven, Prieto-Alhambra, Dani, Proudfoot, Alastair, Quinn, Terry, Quint, Jennifer, Raffetti, Elena, Rahimi, Kazem, Rao, Shishir, Razieh, Cameron, Roberts, Brian, Rogers, Caroline, Rossdale, Jennifer, Salim, Safa, Samani, Nilesh, Sattar, Naveed, Schnier, Christian, Schwartz, Roy, Selby, David, Seminog, Olena, Shabnam, Sharmin, Shah, Ajay, Shelton, Jon, Sheppard, James, Sinha, Shubhra, Skrypak, Mirek, Slapkova, Martina, Sleeman, Katherine, Smith, Craig, Sofat, Reecha, Sosenko, Filip, Sperrin, Matthew, Steeg, Sarah, Sterne, Jonathan, Stoica, Serban, Sudell, Maria, Sudlow, Cathie, Sun, Luanluan, Suseeladevi, Arun Karthikeyan, Sweeting, Michael, Sydes, Matt, Takhar, Rohan, Tang, Howard, Thygesen, Johan, Tilston, George, Tochel, Claire, Toit, Clea du, Tomlinson, Christopher, Toms, Renin, Torabi, Fatemeh, Torralbo, Ana, Townson, Julia, Tufail, Adnan, Tungamirai, Tapiwa, Varma, Susheel, Vollmer, Sebastian, Walker, Venexia, Wang, Tianxiao, Wang, Huan, Warwick, Alasdair, Watkinson, Ruth, Watson, Harry, Whiteley, William, Whittaker, Hannah, Wilde, Harry, Wilkinson, Tim, Williams, Gareth, Williams, Michelle, Williams, Richard, Withnell, Eloise, Wolfe, Charles, Wood, Angela, Wright, Lucy, Wu, Honghan, Wu, Jinge, Wu, Jianhua, Yates, Tom, Zaccardi, Francesco, Zhang, Haoting, Zhang, Huayu, Zuccolo, Luisa, Thygesen, Johan H, Mizani, Mehrdad A, Banerjee, Amitava, Lai, Alvina G, Li, Kezhi, Mateen, Bilal A, Sterne, Jonathan A C, Pagel, Christina, and Whiteley, William N

Published
2022
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25. Statistical Analysis of Factors Influencing the Liquid and Plastic Limits of Fine-Grained Soil Mixtures

Author

Verma, Gaurav, Kumar, Brind, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Biswas, Sabyasachi, editor, Metya, Subhadeep, editor, Kumar, Sanjay, editor, and Samui, Pijush, editor

Published
2021
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30. An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs

Author

Siminovitch, Katherine A., Hirschfield, Gideon M., Mason, Andrew, Vincent, Catherine, Xie, Gang, Zhang, Jinyi, Tang, Ruqi, Ma, Xiong, Li, Zhiqiang, Shi, Yongyong, Affronti, Andrea, Almasio, Piero L., Alvaro, Domenico, Andreone, Pietro, Andriulli, Angelo, Azzaroli, Francesco, Battezzati, Pier Maria, Benedetti, Antonio, Bragazzi, MariaConsiglia, Brunetto, Maurizia, Bruno, Savino, Calvaruso, Vincenza, Cardinale, Vincenzo, Casella, Giovanni, Cazzagon, Nora, Ciaccio, Antonio, Coco, Barbara, Colli, Agostino, Colloredo, Guido, Colombo, Massimo, Colombo, Silvia, Cristoferi, Laura, Cursaro, Carmela, Crocè, Lory Saveria, Crosignani, Andrea, D’Amato, Daphne, Donato, Francesca, Elia, Gianfranco, Fabris, Luca, Fagiuoli, Stefano, Ferrari, Carlo, Floreani, Annarosa, Galli, Andrea, Giannini, Edoardo, Grattagliano, Ignazio, Lampertico, Pietro, Lleo, Ana, Malinverno, Federica, Mancuso, Clara, Marra, Fabio, Marzioni, Marco, Massironi, Sara, Mattalia, Alberto, Miele, Luca, Milani, Chiara, Morini, Lorenzo, Morisco, Filomena, Muratori, Luigi, Muratori, Paolo, Niro, Grazia A., O’Donnell, Sarah, Picciotto, Antonio, Portincasa, Piero, Rigamonti, Cristina, Ronca, Vincenzo, Rosina, Floriano, Spinzi, Giancarlo, Strazzabosco, Mario, Tarocchi, Mirko, Tiribelli, Claudio, Toniutto, Pierluigi, Valenti, Luca, Vinci, Maria, Zuin, Massimo, Nakamura, Hitomi, Abiru, Seigo, Nagaoka, Shinya, Komori, Atsumasa, Yatsuhashi, Hiroshi, Ishibashi, Hiromi, Ito, Masahiro, Migita, Kiyoshi, Ohira, Hiromasa, Katsushima, Shinji, Naganuma, Atsushi, Sugi, Kazuhiro, Komatsu, Tatsuji, Mannami, Tomohiko, Matsushita, Kouki, Yoshizawa, Kaname, Makita, Fujio, Nikami, Toshiki, Nishimura, Hideo, Kouno, Hiroshi, Kouno, Hirotaka, Ota, Hajime, Komura, Takuya, Nakamura, Yoko, Shimada, Masaaki, Hirashima, Noboru, Komeda, Toshiki, Ario, Keisuke, Nakamuta, Makoto, Yamashita, Tsutomu, Furuta, Kiyoshi, Kikuchi, Masahiro, Naeshiro, Noriaki, Takahashi, Hironao, Mano, Yutaka, Tsunematsu, Seiji, Yabuuchi, Iwao, Shimada, Yusuke, Yamauchi, Kazuhiko, Sugimoto, Rie, Sakai, Hironori, Mita, Eiji, Koda, Masaharu, Tsuruta, Satoru, Kamitsukasa, Hiroshi, Sato, Takeaki, Masaki, Naohiko, Kobata, Tatsuro, Fukushima, Nobuyoshi, Ohara, Yukio, Muro, Toyokichi, Takesaki, Eiichi, Takaki, Hitoshi, Yamamoto, Tetsuo, Kato, Michio, Nagaoki, Yuko, Hayashi, Shigeki, Ishida, Jinya, Watanabe, Yukio, Kobayashi, Masakazu, Koga, Michiaki, Saoshiro, Takeo, Yagura, Michiyasu, Hirata, Keisuke, Tanaka, Atsushu, Takikawa, Hajime, Zeniya, Mikio, Abe, Masanori, Onji, Morikazu, Kaneko, Shuichi, Honda, Masao, Arai, Kuniaki, Arinaga-Hino, Teruko, Hashimoto, Etsuko, Taniai, Makiko, Umemura, Takeji, Joshita, Satoru, Nakao, Kazuhiko, Ichikawa, Tatsuki, Shibata, Hidetaka, Yamagiwa, Satoshi, Seike, Masataka, Honda, Koichi, Sakisaka, Shotaro, Takeyama, Yasuaki, Harada, Masaru, Senju, Michio, Yokosuka, Osamu, Kanda, Tatsuo, Ueno, Yoshiyuki, Kikuchi, Kentaro, Ebinuma, Hirotoshi, Himoto, Takashi, Yasunami, Michio, Murata, Kazumoto, Mizokami, Masashi, Kawata, Kazuhito, Shimoda, Shinji, Miyake, Yasuhiro, Takaki, Akinobu, Yamamoto, Kazuhide, Hirano, Katsuji, Ichida, Takafumi, Ido, Akio, Tsubouchi, Hirohito, Chayama, Kazuaki, Harada, Kenichi, Nakanuma, Yasuni, Maehara, Yoshihiko, Taketomi, Akinobu, Shirabe, Ken, Soejima, Yuji, Mori, Akira, Yagi, Shintaro, Uemoto, Shinji, H, Egawa, Tanaka, Tomohiro, Yamashiki, Noriyo, Tamura, Sumito, Sugawara, Yasuhiro, Kokudo, Norihiro, Juran, Brian D., Atkinson, Elizabeth J., Cheung, Angela, de Andrade, Mariza, Lazaridis, Konstantinos N., Chalasani, Naga, Luketic, Vel, Odin, Joseph, Chopra, Kapil, Baras, Aris, Horowitz, Julie, Abecasis, Goncalo, Cantor, Michael, Coppola, Giovanni, Economides, Aris, Lotta, Luca A., Overton, John D., Reid, Jeffrey G., Shuldiner, Alan, Beechert, Christina, Forsythe, Caitlin, Fuller, Erin D., Gu, Zhenhua, Lattari, Michael, Lopez, Alexander, Schleicher, Thomas D., Padilla, Maria Sotiropoulos, Toledo, Karina, Widom, Louis, Wolf, Sarah E., Pradhan, Manasi, Manoochehri, Kia, Ulloa, Ricardo H., Bai, Xiaodong, Balasubramanian, Suganthi, Barnard, Leland, Blumenfeld, Andrew, Eom, Gisu, Habegger, Lukas, Hawes, Alicia, Khalid, Shareef, Maxwell, Evan K., Salerno, William, Staples, Jeffrey C., Jones, Marcus B., Mitnaul, Lyndon J., Sturgess, Richard, Healey, Christopher, Yeoman, Andrew, Gunasekera, Anton VJ., Kooner, Paul, Kapur, Kapil, Sathyanarayana, V., Kallis, Yiannis, Subhani, Javaid, Harvey, Rory, McCorry, Roger, Rooney, Paul, Ramanaden, David, Evans, Richard, Mathialahan, Thiriloganathan, Gasem, Jaber, Shorrock, Christopher, Bhalme, Mahesh, Southern, Paul, Tibble, Jeremy A., Gorard, David A., Jones, Susan, Mells, George, Mulcahy, Victoria, Srivastava, Brijesh, Foxton, Matthew R., Collins, Carole E., Elphick, David, Karmo, Mazn, Porras-Perez, Francisco, Mendall, Michael, Yapp, Tom, Patel, Minesh, Ede, Roland, Sayer, Joanne, Jupp, James, Fisher, Neil, Carter, Martyn J., Koss, Konrad, Shah, Jayshri, Piotrowicz, Andrzej, Scott, Glyn, Grimley, Charles, Gooding, Ian R., Williams, Simon, Tidbury, Judith, Lim, Guan, Cheent, Kuldeep, Levi, Sass, Mansour, Dina, Beckley, Matilda, Hollywood, Coral, Wong, Terry, Marley, Richard, Ramage, John, Gordon, Harriet M., Ridpath, Jo, Ngatchu, Theodore, Bob Grover, Vijay Paul, Shidrawi, Ray G., Abouda, George, Corless, L., Narain, Mark, Rees, Ian, Brown, Ashley, Taylor-Robinson, Simon, Wilkins, Joy, Grellier, Leonie, Banim, Paul, Das, Debasish, Heneghan, Michael A., Curtis, Howard, Matthews, Helen C., Mohammed, Faiyaz, Aldersley, Mark, Srirajaskanthan, Raj, Walker, Giles, McNair, Alistair, Sharif, Amar, Sen, Sambit, Bird, George, Prince, Martin I., Prasad, Geeta, Kitchen, Paul, Barnardo, Adrian, Oza, Chirag, Sivaramakrishnan, Nurani N., Gupta, Prakash, Shah, Amir, Evans, Chris DJ., Saha, Subrata, Pollock, Katharine, Bramley, Peter, Mukhopadhya, Ashis, Barclay, Stephen T., McDonald, Natasha, Bathgate, Andrew J., Palmer, Kelvin, Dillon, John F., Rushbrook, Simon M., Przemioslo, Robert, McDonald, Chris, Millar, Andrew, Tai, Cheh, Mitchell, Stephen, Metcalf, Jane, Shaukat, Syed, Ninkovic, Mary, Shmueli, Udi, Davis, Andrew, Naqvi, Asifabbas, Lee, Tom JW., Ryder, Stephen, Collier, Jane, Klass, Howard, Cramp, Matthew E., Sharer, Nichols, Aspinall, Richard, Ghosh, Deb, Douds, Andrew C., Booth, Jonathan, Williams, Earl, Hussaini, Hyder, Christie, John, Mann, Steven, Thorburn, Douglas, Marshall, Aileen, Patanwala, Imran, Ala, Aftab, Maltby, Julia, Matthew, Ray, Corbett, Chris, Vyas, Sam, Singhal, Saket, Gleeson, Dermot, Misra, Sharat, Butterworth, Jeff, George, Keith, Harding, Tim, Douglass, Andrew, Mitchison, Harriet, Panter, Simon, Shearman, Jeremy, Bray, Gary, Roberts, Michael, Butcher, Graham, Forton, Daniel, Mahmood, Zahid, Cowan, Matthew, Das, Debashis, Ch’ng, Chin Lye, Rahman, Mesbah, Whatley, Gregory C.A., Wesley, Emma, Mandal, Aditya, Jain, Sanjiv, Pereira, Stephen P., Wright, Mark, Trivedi, Palak, Gordon, Fiona H., Unitt, Esther, Palejwala, Altaf, Austin, Andrew, Vemala, Vishwaraj, Grant, Allister, Higham, Andrew D., Brind, Alison, Mathew, Ray, Cox, Mark, Ramakrishnan, Subramaniam, King, Alistair, Whalley, Simon, Fraser, Jocelyn, Thomson, S.J., Bell, Andrew, Wong, Voi Shim, Kia, Richard, Gee, Ian, Keld, Richard, Ransford, Rupert, Gotto, James, Millson, Charles, Cordell, Heather J., Fryett, James J., Ueno, Kazuko, Darlay, Rebecca, Aiba, Yoshihiro, Hitomi, Yuki, Kawashima, Minae, Nishida, Nao, Khor, Seik-Soon, Gervais, Olivier, Kawai, Yosuke, Nagasaki, Masao, Tokunaga, Katsushi, Gerussi, Alessio, Carbone, Marco, Asselta, Rosanna, Ferreira, Manuel A.R., Sun, Dylan, Jones, David E., Flack, Steven, Spicer, Ann, Mulcahy, Victoria L., Byan, Jinyoung, Han, Younghun, Sandford, Richard N., Amos, Christopher I., Seldin, Michael F., Invernizzi, Pietro, Nakamura, Minoru, and Mells, George F.

Published
2021
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31. Acoustic Boundary Conditions for Can-Annular Combustors

Author

James Brind

Subjects
can-annular, combustor, turbine, acoustic impedance, aeroacoustics, Mechanical engineering and machinery, TJ1-1570
Abstract

This paper derives and validates an analytical model for acoustic boundary conditions on a can-annular gas turbine combustion system composed of discrete cans connected to an open annulus upstream of a turbine. The analytical model takes one empirical parameter: a connection impedance between adjacent cans. This impedance is extracted from time-marching computations of two-can sectors of representative combustors. The computations show that reactance follows the Rayleigh conductivity, while resistance takes a value of order 0.1 as a weak function of geometry. With a calibrated value of acoustic resistance, the analytical model reproduces can-to-can transfer functions predicted by full-annulus computations to within 0.03 magnitude at compact frequencies. Varying the combustor–turbine gap length, both model and computations exhibit a minimum in reflected energy, which drops by 63% compared to the datum gap. A parametric study yields a design guideline for gap length at the minimum reflected energy, allowing the designer to maximise transmission from the combustion system and reduce damping requirements.

Published
2023
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33. X Chromosome Contribution to the Genetic Architecture of Primary Biliary Cholangitis

Author

Walker, Erin, Xie, Gang, Mason, Andy, Myers, Robert, Peltekian, Kevork, Ghent, Cameron, Atkinson, Elizabeth, Juran, Bruce, Lazaridis, Kostas, Lu, Yue, Gu, Xiangjun, Jing, Kaiyan, Amos, Chris, Affronti, Andrea, Brunetto, Maurizia, Coco, Barbara, Spinzi, Giancarlo, Elia, Gianfranco, Ferrari, Carlo, Lleo, Ana, Muratori, Luigi, Muratori, Paolo, Portincasa, Piero, Colli, Agostino, Bruno, Savino, Colloredo, Guido, Azzaroli, Francesco, Andreone, Pietro, Bragazzi, MariaConsiglia, Alvaro, Domenico, Cardinale, Vincenzo, Cazzagon, Nora, Rigamonti, Cristina, Floreani, Annarosa, Rosina, Floriano, Ciaccio, Antonio, Cristoferi, Laura, D’Amato, Daphne, Malinverno, Federica, Mancuso, Clara, Massironi, Sara, Milani, Chiara, O’Donnell, Sarah E., Ronca, Vincenzo, Barisani, Donatella, Lampertico, Pietro, Donato, Francesca, Fagiuoli, Stefano, Almasio, Piero L., Giannini, Edoardo, Cursaro, Carmela, Colombo, Massimo, Valenti, Luca, Miele, Luca, Andriulli, Angelo, Niro, Grazia A., Grattagliano, Ignazio, Morini, Lorenzo, Casella, Giovanni, Vinci, Maria, Battezzati, Pier Maria, Crosignani, Andrea, Zuin, Massimo, Mattalia, Alberto, Calvaruso, Vincenza, Colombo, Silvia, Benedetti, Antonio, Marzioni, Marco, Galli, Andrea, Marra, Fabio, Tarocchi, Mirko, Picciotto, Antonio, Morisco, Filomena, Fabris, Luca, Crocè, Lory Saveria, Tiribelli, Claudio, Toniutto, Pierluigi, Strazzabosco, Mario, Ch’ng, Chin Lye, Rahman, Mesbah, Yapp, Tom, Sturgess, Richard, Healey, Christopher, Czajkowski, Marek, Gunasekera, Anton, Gyawali, Pranab, Premchand, Purushothaman, Kapur, Kapil, Marley, Richard, Foster, Graham, Watson, Alan, Dias, Aruna, Subhani, Javaid, Harvey, Rory, McCorry, Roger, Ramanaden, David, Gasem, Jaber, Evans, Richard, Mathialahan, Thiriloganathan, Shorrock, Christopher, Lipscomb, George, Southern, Paul, Tibble, Jeremy, Gorard, David, Palegwala, Altaf, Jones, Susan, Dawwas, Mohamed, Alexander, Graeme, Dolwani, Sunil, Prince, Martin, Foxton, Matthew, Elphick, David, Mitchison, Harriet, Gooding, Ian, Karmo, Mazn, Saksena, Sushma, Mendall, Mike, Patel, Minesh, Ede, Roland, Austin, Andrew, Sayer, Joanna, Hankey, Lorraine, Hovell, Christopher, Fisher, Neil, Carter, Martyn, Koss, Konrad, Piotrowicz, Andrzej, Grimley, Charles, Neal, David, Lim, Guan, Levi, Sass, Ala, Aftab, Broad, Andrea, Saeed, Athar, Wood, Gordon, Brown, Jonathan, Wilkinson, Mark, Gordon, Harriet, Ramage, John, Ridpath, Jo, Ngatchu, Theodore, Grover, Bob, Shaukat, Syed, Shidrawi, Ray, Abouda, George, Ali, Faiz, Rees, Ian, Salam, Imroz, Narain, Mark, Brown, Ashley, Taylor-Robinson, Simon, Williams, Simon, Grellier, Leonie, Banim, Paul, Das, Debasish, Chilton, Andrew, Heneghan, Michael, Curtis, Howard, Gess, Markus, Drake, Ian, Aldersley, Mark, Davies, Mervyn, Jones, Rebecca, McNair, Alastair, Srirajaskanthan, Raj, Pitcher, Maxton, Sen, Sambit, Bird, George, Barnardo, Adrian, Kitchen, Paul, Yoong, Kevin, Chirag, Oza, Sivaramakrishnan, Nurani, MacFaul, George, Jones, David, Shah, Amir, Evans, Chris, Saha, Subrata, Pollock, Katharine, Bramley, Peter, Mukhopadhya, Ashis, Fraser, Andrew, Mills, Peter, Shallcross, Christopher, Campbell, Stewart, Bathgate, Andrew, Shepherd, Alan, Dillon, John, Rushbrook, Simon, Przemioslo, Robert, Macdonald, Christopher, Metcalf, Jane, Shmueli, Udi, Davis, Andrew, Naqvi, Asifabbas, Lee, Tom, Ryder, Stephen D., Collier, Jane, Klass, Howard, Ninkovic, Mary, Cramp, Matthew, Sharer, Nicholas, Aspinall, Richard, Goggin, Patrick, Ghosh, Deb, Douds, Andrew, Hoeroldt, Barbara, Booth, Jonathan, Williams, Earl, Hussaini, Hyder, Stableforth, William, Ayres, Reuben, Thorburn, Douglas, Marshall, Eileen, Burroughs, Andrew, Mann, Steven, Lombard, Martin, Richardson, Paul, Patanwala, Imran, Maltby, Julia, Brookes, Matthew, Mathew, Ray, Vyas, Samir, Singhal, Saket, Gleeson, Dermot, Misra, Sharat, Butterworth, Jeff, George, Keith, Harding, Tim, Douglass, Andrew, Panter, Simon, Shearman, Jeremy, Bray, Gary, Butcher, Graham, Forton, Daniel, Mclindon, John, Cowan, Matthew, Whatley, Gregory, Mandal, Aditya, Gupta, Hemant, Sanghi, Pradeep, Jain, Sanjiv, Pereira, Steve, Prasad, Geeta, Watts, Gill, Wright, Mark, Neuberger, James, Gordon, Fiona, Unitt, Esther, Grant, Allister, Delahooke, Toby, Higham, Andrew, Brind, Alison, Cox, Mark, Ramakrishnan, Subramaniam, King, Alistair, Collins, Carole, Whalley, Simon, Li, Andy, Fraser, Jocelyn, Bell, Andrew, Wong, Voi Shim, Singhal, Amit, Gee, Ian, Ang, Yeng, Ransford, Rupert, Gotto, James, Millson, Charles, Bowles, Jane, Thomas, Caradog, Harrison, Melanie, Galaska, Roman, Kendall, Jennie, Whiteman, Jessica, Lawlor, Caroline, Gray, Catherine, Elliott, Keith, Mulvaney-Jones, Caroline, Hobson, Lucie, Van Duyvenvoorde, Greta, Loftus, Alison, Seward, Katie, Penn, Ruth, Maiden, Jane, Damant, Rose, Hails, Janeane, Cloudsdale, Rebecca, Silvestre, Valeria, Glenn, Sue, Dungca, Eleanor, Wheatley, Natalie, Doyle, Helen, Kent, Melanie, Hamilton, Caroline, Braim, Delyth, Wooldridge, Helen, Abrahams, Rachel, Paton, Alison, Lancaster, Nicola, Gibbins, Andrew, Hogben, Karen, Desousa, Phillipa, Muscariu, Florin, Musselwhite, Janine, McKay, Alexandra, Tan, LaiTing, Foale, Carole, Brighton, Jacqueline, Flahive, Kerry, Nambela, Estelle, Townshend, Paula, Ford, Chris, Holder, Sophie, Palmer, Caroline, Featherstone, James, Nasseri, Mariam, Sadeghian, Joy, Williams, Bronwen, Thomas, Carol, Rolls, Sally-Ann, Hynes, Abigail, Duggan, Claire, Jones, Sarah, Crossey, Mary, Stansfield, Glynis, MacNicol, Carolyn, Wilkins, Joy, Wilhelmsen, Elva, Raymode, Parizade, Lee, Hye-Jeong, Durant, Emma, Bishop, Rebecca, Ncube, Noma, Tripoli, Sherill, Casey, Rebecca, Cowley, Caroline, Miller, Richard, Houghton, Kathryn, Ducker, Samantha, Wright, Fiona, Bird, Bridget, Baxter, Gwen, Keggans, Janie, Hughes, Maggie, Grieve, Emma, Young, Karin, Williams, D., Ocker, Kate, Hines, Frances, Martin, Kirsty, Innes, Caron, Valliani, Talal, Fairlamb, Helen, Thornthwaite, Sarah, Eastick, Anne, Tanqueray, Elizabeth, Morrison, Jennifer, Holbrook, Becky, Browning, Julie, Walker, Kirsten, Congreave, Susan, Verheyden, Juliette, Slininger, Susan, Stafford, Lizzie, O’Donnell, Denise, Ainsworth, Mark, Lord, Susan, Kent, Linda, March, Linda, Dickson, Christine, Simpson, Diane, Longhurst, Beverley, Hayes, Maria, Shpuza, Ervin, White, Nikki, Besley, Sarah, Pearson, Sallyanne, Wright, Alice, Jones, Linda, Gunter, Emma, Dewhurst, Hannah, Fouracres, Anna, Farrington, Liz, Graves, Lyn, Marriott, Suzie, Leoni, Marina, Tyrer, David, Martin, Kate, Dali-kemmery, Lola, Lambourne, Victoria, Green, Marie, Sirdefield, Dawn, Amor, Kelly, Colley, Julie, Shinder, Bal, Jones, Jayne, Mills, Marisa, Carnahan, Mandy, Taylor, Natalie, Boulton, Kerenza, Tregonning, Julie, Brown, Carly, Clifford, Gayle, Archer, Emily, Hamilton, Maria, Curtis, Janette, Shewan, Tracey, Walsh, Sue, Warner, Karen, Netherton, Kimberley, Mupudzi, Mcdonald, Gunson, Bridget, Gitahi, Jane, Gocher, Denise, Batham, Sally, Pateman, Hilary, Desmennu, Senayon, Conder, Jill, Clement, Darren, Gallagher, Susan, Orpe, Jacky, Chan, PuiChing, Currie, Lynn, O’Donohoe, Lynn, Oblak, Metod, Morgan, Lisa, Quinn, Marie, Amey, Isobel, Baird, Yolanda, Cotterill, Donna, Cumlat, Lourdes, Winter, Louise, Greer, Sandra, Spurdle, Katie, Allison, Joanna, Dyer, Simon, Sweeting, Helen, Kordula, Jean, Aiba, Yoshihiro, Nakamura, Hitomi, Abiru, Seigo, Nagaoka, Shinya, Komori, Atsumasa, Yatsuhashi, Hiroshi, Ishibashi, Hiromi, Ito, Masahiro, Kawai, Yosuke, Kohn, Seik-Soon, Gervais, Olivier, Migita, Kiyoshi, Katsushima, Shinji, Naganuma, Atsushi, Sugi, Kazuhiro, Komatsu, Tatsuji, Mannami, Tomohiko, Matsushita, Kouki, Yoshizawa, Kaname, Makita, Fujio, Nikami, Toshiki, Nishimura, Hideo, Kouno, Hiroshi, Kouno, Hirotaka, Ota, Hajime, Komura, Takuya, Nakamura, Yoko, Shimada, Masaaki, Hirashima, Noboru, Komeda, Toshiki, Ario, Keisuke, Nakamuta, Makoto, Yamashita, Tsutomu, Furuta, Kiyoshi, Kikuchi, Masahiro, Naeshiro, Noriaki, Takahashi, Hironao, Mano, Yutaka, Tsunematsu, Seiji, Yabuuchi, Iwao, Shimada, Yusuke, Yamauchi, Kazuhiko, Sugimoto, Rie, Sakai, Hironori, Mita, Eiji, Koda, Masaharu, Tsuruta, Satoru, Kamitsukasa, Hiroshi, Sato, Takeaki, Masaki, Naohiko, Kobata, Tatsuro, Fukushima, Nobuyoshi, Higuchi, Nobito, Ohara, Yukio, Muro, Toyokichi, Takesaki, Eiichi, Takaki, Hitoshi, Yamamoto, Tetsuo, Kato, Michio, Nagaoki, Yuko, Hayashi, Shigeki, Ishida, Jinya, Watanabe, Yukio, Kobayashi, Masakazu, Koga, Michiaki, Saoshiro, Takeo, Yagura, Michiyasu, Hirata, Keisuke, Takikawa, Hajime, Ohira, Hiromasa, Zeniya, Mikio, Abe, Masanori, Onji, Morikazu, Kaneko, Shuichi, Honda, Masao, Arai, Kuniaki, Arinaga-Hino, Teruko, Hashimoto, Etsuko, Taniai, Makiko, Umemura, Takeji, Joshita, Satoru, Nakao, Kazuhiko, Ichikawa, Tatsuki, Shibata, Hidetaka, Yamagiwa, Satoshi, Seike, Masataka, Honda, Koichi, Sakisaka, Shotaro, Takeyama, Yasuaki, Harada, Masaru, Senju, Michio, Yokosuka, Osamu, Kanda, Tatsuo, Ueno, Yoshiyuki, Kikuchi, Kentaro, Ebinuma, Hirotoshi, Himoto, Takashi, Yasunami, Michio, Murata, Kazumoto, Mizokami, Masashi, Shimoda, Shinji, Miyake, Yasuhiro, Takaki, Akinobu, Yamamoto, Kazuhide, Hirano, Katsuji, Ichida, Takafumi, Ido, Akio, Tsubouchi, Hirohito, Chayama, Kazuaki, Harada, Kenichi, Nakanuma, Yasuni, Maehara, Yoshihiko, Taketomi, Akinobu, Shirabe, Ken, Soejima, Yuji, Mori, Akira, Yagi, Shintaro, Uemoto, Shinji, Tanaka, Tomohiro, Yamashiki, Noriyo, Tamura, Sumito, Sugawara, Yasuhiro, Kokudo, Norihiro, Asselta, Rosanna, Paraboschi, Elvezia M., Gerussi, Alessio, Cordell, Heather J., Mells, George F., Sandford, Richard N., Jones, David E., Nakamura, Minoru, Ueno, Kazuko, Hitomi, Yuki, Kawashima, Minae, Nishida, Nao, Tokunaga, Katsushi, Nagasaki, Masao, Tanaka, Atsushi, Tang, Ruqi, Li, Zhiqiang, Shi, Yongyong, Liu, Xiangdong, Xiong, Ma, Hirschfield, Gideon, Siminovitch, Katherine A., Carbone, Marco, Cardamone, Giulia, Duga, Stefano, Gershwin, M. Eric, Seldin, Michael F., and Invernizzi, Pietro

Published
2021
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43. Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Author

Zhang, Yuting, primary, Liu, Boyang, additional, Bunting, Karina V., additional, Brind, David, additional, Thorley, Alexander, additional, Karwath, Andreas, additional, Lu, Wenqi, additional, Zhou, Diwei, additional, Wang, Xiaoxia, additional, Mobley, Alastair R., additional, Tica, Otilia, additional, Gkoutos, Georgios V., additional, Kotecha, Dipak, additional, and Duan, Jinming, additional

Published
2024
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47. Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Author

Zhang, Y, Liu, B, Bunting, KV, Brind, D, Thorley, A, Karwath, A, Lu, W, Zhou, D, Wang, X, Mobley, AR, Tica, O, Gkoutos, GV, Kotecha, D, Duan, J, Zhang, Y, Liu, B, Bunting, KV, Brind, D, Thorley, A, Karwath, A, Lu, W, Zhou, D, Wang, X, Mobley, AR, Tica, O, Gkoutos, GV, Kotecha, D, and Duan, J

Abstract

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.

Published
2024

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