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1. Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Author

Shin, Jae Y., Tajbakhsh, Nima, Hurst, R. Todd, Kendall, Christopher B., and Liang, Jianming

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations., Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 2017

Published
2017

2. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Author

Tajbakhsh, Nima, Shin, Jae Y., Gurudu, Suryakanth R., Hurst, R. Todd, Kendall, Christopher B., Gotway, Michael B., and Liang, Jianming

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: \emph{Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch?} To address this question, we considered 4 distinct medical imaging applications in 3 specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from 3 different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that (1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; (2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; (3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and (4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

Published
2017
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4. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study

Author

Abel, K, Adamali, H, Adeloye, D, Adeyemi, O, Adeyemi, F, Ahmad, S, Ahmed, R, Ainsworth, M, Al-Sheklly, B, Alamoudi, A, Aljaroof, M, Allan, L, Allen, R, Alli, A, Altmann, D, Anderson, D, Andrews, M, Angyal, A, Antoniades, C, Arbane, G, Armour, C, Armstrong, N, Armstrong, L, Arnold, H, Arnold, D, Ashworth, M, Ashworth, A, Assefa-Kebede, H, Atkin, P, Atkins, H, Atkins, A, Aul, R, Avram, C, Baggott, R, Baguley, D, Baillie, J K, Bain, S, Bakali, M, Bakau, M, Baldry, E, Baldwin, D, Ballard, C, Bambrough, J, Barker, R E, Barratt, S, Barrett, F, Basire, D, Basu, N, Batterham, R, Baxendale, H, Bayes, H, Bayley, M, Beadsworth, M, Beirne, P, Bell, R, Bell, D, Berry, C, Betts, S, Bhui, K, Bishop, L, Blaikely, J, Bloomfield, C, Bloss, A, Bolger, A, Bolton, C E, Bonnington, J, Botkai, A, Bourne, M, Bourne, C, Bradley, E, Bramham, K, Brear, L, Breen, G, Breeze, J, Briggs, A, Bright, E, Brightling, C E, Brill, S, Brindle, K, Broad, L, Broome, M, Brown, J S, Brown, M, Brown, J, Brown, R, Brown, V, Brown, A, Brugha, T, Brunskill, N, Buch, M, Bularga, A, Bullmore, E, Burn, D, Burns, G, Busby, J, Buttress, A, Byrne, S, Cairns, P, Calder, P C, Calvelo, E, Card, B, Carr, L, Carson, G, Carter, P, Cavanagh, J, Chalder, T, Chalmers, J D, Chambers, R C, Channon, K, Chapman, K, Charalambou, A, Chaudhuri, N, Checkley, A, Chen, J, Chetham, L, Chilvers, E R, Chinoy, H, Chong-James, K, Choudhury, N, Choudhury, G, Chowdhury, P, Chowienczyk, P, Christie, C, Clark, D, Clark, C, Clarke, J, Clift, P, Clohisey, S, Coburn, Z, Cole, J, Coleman, C, Connell, D, Connolly, B, Connor, L, Cook, A, Cooper, B, Coupland, C, Craig, T, Crisp, P, Cristiano, D, Crooks, M G, Cross, A, Cruz, I, Cullinan, P, Daines, L, Dalton, M, Dark, P, Dasgin, J, David, A, David, C, Davies, M, Davies, G, Davies, K, Davies, F, Davies, G A, Daynes, E, De Silva, T, De Soyza, A, Deakin, B, Deans, A, Defres, S, Dell, A, Dempsey, K, Dennis, J, Dewar, A, Dharmagunawardena, R, Diar Bakerly, N, Dipper, A, Diver, S, Diwanji, S N, Dixon, M, Djukanovic, R, Dobson, H, Dobson, C, Dobson, S L, Docherty, A B, Donaldson, A, Dong, T, Dormand, N, Dougherty, A, Dowling, R, Drain, S, Dulawan, P, Dunleavy, A, Dunn, S, Easom, N, Echevarria, C, Edwards, S, Edwardson, C, Elliott, B, Elliott, A, Ellis, Y, Elmer, A, Elneima, O, Evans, R A, Evans, J, Evans, H, Evans, D, Evans, R I, Evans, R, Evans, T, Fabbri, L, Fairbairn, S, Fairman, A, Fallon, K, Faluyi, D, Favager, C, Felton, T, Finch, J, Finney, S, Fisher, H, Fletcher, S, Flockton, R, Foote, D, Ford, A, Forton, D, Francis, R, Francis, S, Francis, C, Frankel, A, Fraser, E, Free, R, French, N, Fuld, J, Furniss, J, Garner, L, Gautam, N, Geddes, J R, George, P M, George, J, Gibbons, M, Gilmour, L, Gleeson, F, Glossop, J, Glover, S, Goodman, N, Gooptu, B, Gorsuch, T, Gourlay, E, Greenhaff, P, Greenhalf, W, Greenhalgh, A, Greening, N J, Greenwood, J, Greenwood, S, Gregory, R, Grieve, D, Gummadi, M, Gupta, A, Gurram, S, Guthrie, E, Hadley, K, Haggar, A, Hainey, K, Haldar, P, Hall, I, Hall, L, Halling-Brown, M, Hamil, R, Hanley, N A, Hardwick, H E, Hardy, E, Hargadon, B, Harrington, K, Harris, V, Harrison, E M, Harrison, P, Hart, N, Harvey, A, Harvey, M, Harvie, M, Havinden-Williams, M, Hawkes, J, Hawkings, N, Haworth, J, Hayday, A, Heaney, L G, Heeney, J L, Heightman, M, Heller, S, Henderson, M, Hesselden, L, Hillman, T, Hingorani, A, Hiwot, T, Ho, L P, Hoare, A, Hoare, M, Hogarth, P, Holbourn, A, Holdsworth, L, Holgate, D, Holmes, K, Holroyd-Hind, B, Horsley, A, Hosseini, A, Hotopf, M, Houchen, L, Howard, L, Howell, A, Hufton, E, Hughes, A, Hughes, J, Hughes, R, Humphries, A, Huneke, N, Hurst, J R, Hurst, R, Husain, M, Hussell, T, Ibrahim, W, Ient, A, Ingram, L, Ismail, K, Jackson, T, Jacob, J, James, W Y, Janes, S, Jarvis, H, Jayaraman, B, Jenkins, R G, Jezzard, P, Jiwa, K, Johnson, S, Johnson, C, Johnston, D, Jolley, C, Jolley, C J, Jones, I, Jones, S, Jones, D, Jones, H, Jones, G, Jones, M, Jose, S, Kabir, T, Kaltsakas, G, Kamwa, V, Kar, P, Kausar, Z, Kelly, S, Kerr, S, Key, A L, Khan, F, Khunti, K, King, C, King, B, Kitterick, P, Klenerman, P, Knibbs, L, Knight, S, Knighton, A, Kon, O M, Kon, S, Kon, S S, Korszun, A, Kotanidis, C, Koychev, I, Kurupati, P, Kwan, J, Laing, C, Lamlum, H, Landers, G, Langenberg, C, Lasserson, D, Lawrie, A, Lea, A, Leavy, O C, Lee, D, Lee, E, Leitch, K, Lenagh, R, Lewis, K, Lewis, V, Lewis, K E, Lewis, J, Lewis-Burke, N, Light, T, Lightstone, L, Lim, L, Linford, S, Lingford-Hughes, A, Lipman, M, Liyanage, K, Lloyd, A, Logan, S, Lomas, D, Lone, N I, Loosley, R, Lord, J M, Lota, H, Lucey, A, MacGowan, G, Macharia, I, Mackay, C, Macliver, L, Madathil, S, Madzamba, G, Magee, N, Mairs, N, Majeed, N, Major, E, Malim, M, Mallison, G, Man, W, Mandal, S, Mangion, K, Mansoori, P, Marciniak, S, Mariveles, M, Marks, M, Marshall, B, Martineau, A, Maskell, N, Matila, D, Matthews, L, Mayet, J, McAdoo, S, McAllister-Williams, H, McArdle, P, McArdle, A, McAulay, D, McAuley, H J C, McAuley, D F, McCafferty, K, McCann, G P, McCauley, H, McCourt, P, Mcgarvey, L, McGinness, J, McGovern, A, McGuinness, H, McInnes, I B, McIvor, K, McIvor, E, McMahon, A, McMahon, M J, McMorrow, L, Mcnally, T, McNarry, M, McQueen, A, McShane, H, Megson, S, Meiring, J, Menzies, D, Michael, A, Michael, B D, Milligan, L, Mills, N, Mitchell, J, Mohamed, A, Molyneaux, P L, Monteiro, W, Morley, A, Morrison, L, Morriss, R, Morrow, A, Moss, A, Moss, A J, Moss, P, Mukaetova-Ladinska, E, Munawar, U, Murali, E, Murira, J, Nassa, H, Neill, P, Neubauer, S, Newby, D, Newell, H, Newton Cox, A, Nicholson, T, Nicoll, D, Nolan, C M, Noonan, M J, Novotny, P, Nunag, J, Nyaboko, J, O'Brien, L, Odell, N, Ogg, G, Olaosebikan, O, Oliver, C, Omar, Z, Openshaw, P J M, Osbourne, R, Ostermann, M, Overton, C, Oxton, J, Pacpaco, E, Paddick, S, Papineni, P, Paradowski, K, Pareek, M, Parekh, D, Parfrey, H, Pariante, C, Parker, S, Parkes, M, Parmar, J, Parvin, R, Patale, S, Patel, B, Patel, S, Patel, M, Pathmanathan, B, Pavlides, M, Pearl, J E, Peckham, D, Pendlebury, J, Peng, Y, Pennington, C, Peralta, I, Perkins, E, Peto, T, Petousi, N, Petrie, J, Pfeffer, P, Phipps, J, Pimm, J, Piper Hanley, K, Pius, R, Plein, S, Plekhanova, T, Poinasamy, K, Polgar, O, Poll, L, Porter, J C, Portukhay, S, Powell, N, Price, L, Price, D, Price, A, Price, C, Prickett, A, Quaid, S, Quigley, J, Quint, J, Qureshi, H, Rahman, N, Rahman, M, Ralser, M, Raman, B, Ramos, A, Rangeley, J, Rees, T, Regan, K, Richards, A, Richardson, M, Rivera-Ortega, P, Robertson, E, Rodgers, J, Ross, G, Rossdale, J, Rostron, A, Routen, A, Rowland, A, Rowland, M J, Rowland, J, Rowland-Jones, S L, Roy, K, Rudan, I, Russell, R, Russell, E, Sabit, R, Sage, E K, Samani, N, Samuel, R, Sapey, E, Saralaya, D, Saratzis, A, Sargeant, J, Sass, T, Sattar, N, Saunders, K, Saunders, R, Saxon, W, Sayer, A, Schwaeble, W, Scott, J, Scott, K, Selby, N, Semple, M G, Sereno, M, Shah, K, Shah, A, Shah, P, Sharma, M, Sharpe, M, Sharpe, C, Shaw, V, Sheikh, A, Shevket, K, Shikotra, A, Short, J, Siddiqui, S, Sigfrid, L, Simons, G, Simpson, J, Singapuri, A, Singh, S J, Singh, C, Singh, S, Skeemer, J, Smith, I, Smith, J, Smith, L, Smith, A, Soares, M, Southern, D, Spears, M, Spencer, L G, Speranza, F, Stadon, L, Stanel, S, Steiner, M, Stensel, D, Stern, M, Stewart, I, Stockley, J, Stone, R, Storrie, A, Storton, K, Stringer, E, Subbe, C, Sudlow, C, Suleiman, Z, Summers, C, Summersgill, C, Sutherland, D, Sykes, D L, Sykes, R, Talbot, N, Tan, A L, Taylor, C, Taylor, A, Te, A, Tedd, H, Tee, C J, Tench, H, Terry, S, Thackray-Nocera, S, Thaivalappil, F, Thickett, D, Thomas, D, Thomas, D C, Thomas, A K, Thompson, A A R, Thompson, T, Thornton, T, Thwaites, R S, Tobin, M, Toingson, G F, Tong, C, Toshner, M, Touyz, R, Tripp, K A, Tunnicliffe, E, Turner, E, Turtle, L, Turton, H, Ugwuoke, R, Upthegrove, R, Valabhji, J, Vellore, K, Wade, E, Wain, L V, Wajero, L O, Walder, S, Walker, S, Wall, E, Wallis, T, Walmsley, S, Walsh, S, Walsh, J A, Watson, L, Watson, J, Watson, E, Welch, C, Welch, H, Welsh, B, Wessely, S, West, S, Wheeler, H, Whitehead, V, Whitney, J, Whittaker, S, Whittam, B, Wild, J, Wilkins, M, Wilkinson, D, Williams, N, Williams, B, Williams, J, Williams-Howard, S A, Willicombe, M, Willis, G, Wilson, D, Wilson, I, Window, N, Witham, M, Wolf-Roberts, R, Woodhead, F, Woods, J, Wootton, D, Worsley, J, Wraith, D, Wright, L, Wright, C, Wright, S, Xie, C, Yasmin, S, Yates, T, Yip, K P, Young, B, Young, S, Young, A, Yousuf, A J, Yousuf, A, Zawia, A, Zhao, B, Zongo, O, Evans, Rachael A, McAuley, Hamish J C, Harrison, Ewen M, Shikotra, Aarti, Singapuri, Amisha, Sereno, Marco, Elneima, Omer, Docherty, Annemarie B, Lone, Nazir I, Leavy, Olivia C, Daines, Luke, Baillie, J Kenneth, Brown, Jeremy S, Chalder, Trudie, De Soyza, Anthony, Diar Bakerly, Nawar, Easom, Nicholas, Geddes, John R, Greening, Neil J, Hart, Nick, Heaney, Liam G, Heller, Simon, Howard, Luke, Hurst, John R, Jacob, Joseph, Jenkins, R Gisli, Jolley, Caroline, Kerr, Steven, Kon, Onn M, Lewis, Keir, Lord, Janet M, McCann, Gerry P, Neubauer, Stefan, Openshaw, Peter J M, Parekh, Dhruv, Pfeffer, Paul, Rahman, Najib M, Raman, Betty, Richardson, Matthew, Rowland, Matthew, Semple, Malcolm G, Shah, Ajay M, Singh, Sally J, Sheikh, Aziz, Thomas, David, Toshner, Mark, Chalmers, James D, Ho, Ling-Pei, Horsley, Alex, Marks, Michael, Poinasamy, Krisnah, Wain, Louise V, and Brightling, Christopher E

Published
2021
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8. On the Potential of Large Ring Lasers

Author

Stedman, G. E., Hurst, R. B., and Schreiber, K. U.

Subjects
Physics - Optics, Physics - General Physics
Abstract

We describe a new ring laser with area A = 833 m^2 and update performance statistics for several such machines. Anandan & Chaio 1982 judged ring lasers inferior to matter interferometers as possible detectors of gravitational waves. However, we note that geophysically interesting results have been obtained from large ring lasers and that there is still a lot of room for improvements., Comment: accepted optics communications

Published
2007
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9. Observation of a resonance in the K$_s$p decay channel at a mass of 1765 MeV/c$^2$

Author

WA89 Collaboration, Adamovich, M. I., Alexandrov, Yu. A., Barberis, D., Beck, M., Bérat, C., Beusch, W., Boss, M., Brons, S., Brückner, W., Buénerd, M., Busch, C., Büscher, C., Charignon, F., Chauvin, J., Chudakov, E. A., Dersch, U., Dropmann, F., Engelfried, J., Faller, F., Fournier, A., Gerassimov, S. G., Godbersen, M., Grafström, P., Haller, Th., Heidrich, M., Hubbard, E., Hurst, R. B., Königsmann, K., Konorov, I., Keller, N., Martens, K., Martin, Ph., Masciocchi, S., Michaels, R., Müller, U., Neeb, H., Newbold, D., Newsom, C., Paul, S., Pochodzalla, J., Potashnikova, I., Povh, B., Ren, Z., Rey-Campagnolle, M., Rosner, G., Rossi, L., Rudolph, H., Scheel, C., Schmitt, L., Siebert, H. -W., Simon, A., Smith, V., Thilmann, O., Trombini, A., Vesin, E., Volkemer, B., Vorwalter, K., Walcher, Th., Wälder, G., Werding, R., Wittmann, E., and Zavertyaev, M. V.

Subjects
High Energy Physics - Experiment
Abstract

We report on the observation of a K$_s$p resonance signal at a mass of 1765$\pm$5 MeV/c$^2$, with intrinsic width $\Gamma = 108\pm 22$ MeV/c$^2$, produced inclusively in $\Sigma^-$-nucleus interactions at 340 GeV/c in the hyperon beam experiment WA89 at CERN. The signal was observed in the kinematic region $x_F>0.7$, in this region its production cross section rises approximately linearly with $(1-x_F)$, reaching $BR(X\to K_S p)\cdot d\sigma /dx_F = (5.2\pm 2.3) \mu b $ per nucleon at $x_F=0.8$. The hard \xf spectrum suggests the presence of a strong leading particle effect in the production and hence the identification as a $\Sigma^{*+}$ state. No corresponding peaks were observed in the $K^- p$ and $\Lambda \pi^{\pm}$ mass spectra.

Published
2007
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21. Contributors

Author

Babu, R. Venkatesh, primary, Bar, Yaniv, additional, Blair, Joseph, additional, Bradley, Andrew P., additional, Carneiro, Gustavo, additional, Chen, Hao, additional, Dam, Erik B., additional, Diamant, Idit, additional, Dou, Qi, additional, Feldman, Michael D., additional, Frank, Renee, additional, Gao, Yaozong, additional, Georgescu, Bogdan, additional, Geva, Ofer, additional, Ghesu, Florin C., additional, Greenspan, Hayit, additional, Guo, Yanrong, additional, Heng, Pheng-Ann, additional, Hornegger, Joachim, additional, Hurst, R. Todd, additional, Igel, Christian, additional, Ithapu, Vamsi K., additional, Janowczyk, Andrew, additional, Johnson, Sterling C., additional, Kallenberg, Michiel, additional, Kendall, Christopher B., additional, Kim, Minjeong, additional, Konen, Eli, additional, Kruthiventi, Srinivas S.S., additional, Liang, Jianming, additional, Liao, Rui, additional, Lieberman, Sivan, additional, Lu, Le, additional, Madabhushi, Anant, additional, Margulies, Kenneth B., additional, Metaxas, Dimitris, additional, Miao, Shun, additional, Mok, Vincent C.T., additional, Mopuri, Konda R., additional, Nascimento, Jacinto, additional, Nguyen, Hien Van, additional, Nielsen, Mads, additional, Nirschl, Jeffrey J., additional, Pai, Akshay, additional, Peyster, Eliot G., additional, Prabhu, Nikita, additional, Qin, Jing, additional, Sarvadevabhatla, Ravi K., additional, Shen, Dinggang, additional, Shi, Lin, additional, Shin, Hoo-Chang, additional, Shin, Jae Y., additional, Singh, Vikas, additional, Sommer, Stefan, additional, Srinivas, Suraj, additional, Suk, Heung-Il, additional, Summers, Ronald M., additional, Tajbakhsh, Nima, additional, Teng, Yuan-Ching, additional, Vemulapalli, Raviteja, additional, Wang, Defeng, additional, Wang, Jane Z., additional, Wang, Shaoyu, additional, Wolf, Lior, additional, Wu, Guorong, additional, Xie, Yuanpu, additional, Xing, Fuyong, additional, Yan, Zhennan, additional, Yang, Lin, additional, Yu, Lequan, additional, Zhan, Yiqiang, additional, Zhang, Shaoting, additional, Zhao, Lei, additional, Zhou, S. Kevin, additional, Zhou, Xiang Sean, additional, and Zimmerman, Gali, additional

Published
2017
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28. A regulatory shellacking: BOEM finalizes rules for Arctic OCS operators

Author

Hurst, R. Isaak

Subjects
United States. Department of the Interior, United States. Coast Guard, Shell PLC, Petroleum industry, Business, Business, regional
Abstract

The definition of shellacking is 'to beat someone up repeatedly.' With that idea in mind, it is no exaggeration to say that Royal Dutch Shell took a regulatory and legal [...]

Published
2016

32. A-T and the Minicourse Concept.

Author

Purdue Univ., Lafayette, IN. Dept. of Biological Sciences. and Hurst, R. N.

Abstract

Two introductory biology courses which are given as service courses for agriculture students have been broken down into small conceptual units (mini-courses). Students cannot fail the course and are awarded a C grade when they have reached a predetermined level of mastery in each level for any unit, they may revise the unit and retake the test. If students do not complete the requirements, they are not given a grade or recorded as a fail. To obtain grades of A or B students elect to do extra reading and take a test measuring higher cognitive levels than knowledge. The mini-courses are taught using an audio-tutorial technique. (AL)

Published
1970

36. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study

Author

Evans, Rachael A, primary, McAuley, Hamish, additional, Harrison, Ewen M, additional, Shikotra, Aarti, additional, Singapuri, Amisha, additional, Sereno, Marco, additional, Elneima, Omer, additional, Docherty, Annemarie B, additional, Lone, Nazir I, additional, Leavy, Olivia C, additional, Daines, Luke, additional, Baillie, J Kenneth, additional, Brown, Jeremy S, additional, Chalder, Trudie, additional, De Soyza, Anthony, additional, Diar Bakerly, Nawar, additional, Easom, Nicholas, additional, Geddes, John R, additional, Greening, Neil J, additional, Hart, Nick, additional, Heaney, Liam G, additional, Heller, Simon, additional, Howard, Luke, additional, Hurst, John R, additional, Jacob, Joseph, additional, Jenkins, R Gisli, additional, Jolley, Caroline, additional, Kerr, Steven, additional, Kon, Onn M, additional, Lewis, Keir, additional, Lord, Janet M, additional, McCann, Gerry P, additional, Neubauer, Stefan, additional, Openshaw, Peter J M, additional, Parekh, Dhruv, additional, Pfeffer, Paul, additional, Rahman, Najib M, additional, Raman, Betty, additional, Richardson, Matthew, additional, Rowland, Matthew, additional, Semple, Malcolm G, additional, Shah, Ajay M, additional, Singh, Sally J, additional, Sheikh, Aziz, additional, Thomas, David, additional, Toshner, Mark, additional, Chalmers, James D, additional, Ho, Ling-Pei, additional, Horsley, Alex, additional, Marks, Michael, additional, Poinasamy, Krisnah, additional, Wain, Louise V, additional, Brightling, Christopher E, additional, Abel, K, additional, Adamali, H, additional, Adeloye, D, additional, Adeyemi, O, additional, Adeyemi, F, additional, Ahmad, S, additional, Ahmed, R, additional, Ainsworth, M, additional, Alamoudi, A, additional, Aljaroof, M, additional, Allan, L, additional, Allen, R, additional, Alli, A, additional, Al-Sheklly, B, additional, Altmann, D, additional, Anderson, D, additional, Andrews, M, additional, Angyal, A, additional, Antoniades, C, additional, Arbane, G, additional, Armour, C, additional, Armstrong, N, additional, Armstrong, L, additional, Arnold, H, additional, Arnold, D, additional, Ashworth, M, additional, Ashworth, A, additional, Assefa-Kebede, H, additional, Atkin, P, additional, Atkins, H, additional, Atkins, A, additional, Aul, R, additional, Avram, C, additional, Baggott, R, additional, Baguley, D, additional, Baillie, J K, additional, Bain, S, additional, Bakali, M, additional, Bakau, M, additional, Baldry, E, additional, Baldwin, D, additional, Ballard, C, additional, Bambrough, J, additional, Barker, R E, additional, Barratt, S, additional, Barrett, F, additional, Basu, N, additional, Batterham, R, additional, Baxendale, H, additional, Bayes, H, additional, Bayley, M, additional, Beadsworth, M, additional, Beirne, P, additional, Bell, R, additional, Bell, D, additional, Berry, C, additional, Betts, S, additional, Bhui, K, additional, Bishop, L, additional, Blaikely, J, additional, Bloomfield, C, additional, Bloss, A, additional, Bolger, A, additional, Bolton, C E, additional, Bonnington, J, additional, Botkai, A, additional, Bourne, M, additional, Bourne, C, additional, Bradley, E, additional, Bramham, K, additional, Brear, L, additional, Breen, G, additional, Breeze, J, additional, Briggs, A, additional, Bright, E, additional, Brightling, C E, additional, Brill, S, additional, Brindle, K, additional, Broad, L, additional, Broome, M, additional, Brown, J S, additional, Brown, M, additional, Brown, J, additional, Brown, R, additional, Brown, V, additional, Brown, A, additional, Brugha, T, additional, Brunskill, N, additional, Buch, M, additional, Bularga, A, additional, Bullmore, E, additional, Burn, D, additional, Burns, G, additional, Busby, J, additional, Buttress, A, additional, Byrne, S, additional, Cairns, P, additional, Calder, P C, additional, Calvelo, E, additional, Card, B, additional, Carr, L, additional, Carson, G, additional, Carter, P, additional, Cavanagh, J, additional, Chalder, T, additional, Chalmers, 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Published
2021
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43. Automatic Detection of Larval Zebrafish ECG: Computational Tool for High-throughput Cardiac Activity Analysis

Author

Ferenc Müller, E.J. Tarte, Barrett R, Attila Sik, and Hurst R

Subjects
Larva, Heartbeat, biology, Computer science, business.industry, fungi, Cardiac activity, Pattern recognition, biology.organism_classification, Zebrafish larvae, cardiovascular diseases, Artificial intelligence, business, Throughput (business), Zebrafish
Abstract

Automatic analysis of larval zebrafish electrocardiographs (ECG) is essential for high-throughput measurements in environmental toxicity assays, cardiotoxicity measurements and drug screening. We have developed a MATLAB based software is built on methods that have previously been used to analyze human ECG, such as the Pan-Tompkins algorithm and Wavelet. For the first time these sophisticated tools have been applied to larval zebrafish ECG to automatically characterize the heart-beat waveforms. The ability of the automated algorithm to detect the QT interval for both normal and pharmacologically altered larval ECG is found and compared to previously used software that is built into LabChart® (AD Instruments). Using wavelet transforms it is shown that the typical larval ECG features are within the frequency range of 1 to 31 Hz. It is also shown that the automated software is capable of detecting QTc (heartrate corrected heartbeat interval) changes within pharmacologically altered zebrafish larval ECG. The automated process is a significant improvement on the approaches that were previously applied to the zebrafish ECG. The automated process described here that is based on established techniques of analyzing ECG can sensitively measure pharmacologically induced changes in the ECG. The novel, automated software is faster, more sensitive at detecting ECG changes and less dependent on user involvement, thus minimizing user error and human bias. The automated process can also be applied to human ECG.

Published
2021

47. Carotid intima-media thickness and coronary artery calcium score as indications of subclinical atherosclerosis

Author

Lester, Steven J., Eleid, Mackram F., Khandheria, Buoy K., and Hurst, R. Todd

Subjects
Atherosclerosis -- Risk factors, Atherosclerosis -- Diagnosis, Atherosclerosis -- Prevention, Calcium, Dietary -- Health aspects, Calcium, Dietary -- Measurement, Carotid artery -- Physiological aspects, Carotid artery -- Medical examination, Health risk assessment -- Methods
Abstract

OBJECTIVE: To determine the ability of carotid intima-media thickness (CIMT) and coronary artery calcium score (CACS) to detect subclinical atherosclerosis in a young to middle-aged, low-risk, primary-prevention population. PATIENTS AND [...]

Published
2009

48. DOP29 The Normalization and Transition of J-Pouch Phenotype in the Chicago Classification of Pouchitis

Author

Akiyama, S, primary, Cohen, N, additional, Traboulsi, C, additional, Rai, V, additional, Glick, L, additional, Yi, Y, additional, Runde, J, additional, Cohen, R, additional, Skowron, K, additional, Hurst, R, additional, Umanskiy, K, additional, Shogan, B, additional, Hyman, N, additional, Rubin, M, additional, Dalal, S, additional, Sakuraba, A, additional, Pekow, J, additional, Chang, E, additional, Ollech, J, additional, and Rubin, D, additional

Published
2021
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