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1. Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions

Author

Sinha, Grant, Parmar, Krish, Azimi, Hilda, Tai, Amy, Chen, Yuhao, Wong, Alexander, and Xi, Pengcheng

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Food image segmentation is an important task that has ubiquitous applications, such as estimating the nutritional value of a plate of food. Although machine learning models have been used for segmentation in this domain, food images pose several challenges. One challenge is that food items can overlap and mix, making them difficult to distinguish. Another challenge is the degree of inter-class similarity and intra-class variability, which is caused by the varying preparation methods and dishes a food item may be served in. Additionally, class imbalance is an inevitable issue in food datasets. To address these issues, two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional Encoder representation for Image Transformers (BEiT). The models are trained and valuated using the FoodSeg103 dataset, which is identified as a robust benchmark for food image segmentation. The BEiT model outperforms the previous state-of-the-art model by achieving a mean intersection over union of 49.4 on FoodSeg103. This study provides insights into transfering knowledge using convolution and Transformer-based approaches in the food image domain.

Published
2023

5. Optimizing Storage Performance with Calibrated Interrupts.

Author

TAI, AMY, SMOLYAR, IGOR, WEI, MICHAEL, and TSAFRIR, DAN

Subjects
STORAGE, HEURISTIC, SEMANTICS, COMPUTER software
Abstract

After request completion, an I/O device must decide whether to minimize latency by immediately firing an interrupt or to optimize for throughput by delaying the interrupt, anticipating that more requests will complete soon and help amortize the interrupt cost. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Unfortunately, because devices lack the semantic information about which I/O requests are latency-sensitive, these heuristics can sometimes lead to disastrous results. Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. The device then “calibrates” its interrupts to completions of latency-sensitive requests. We focus on NVMe storage devices and show that it is natural to express these semantics in the kernel and the application and only requires a modest two-bit change to the device interface. Calibrated interrupts increase throughput by up to 35%, reduce CPU consumption by as much as 30%, and achieve up to 37% lower latency when interrupts are coalesced. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Live Recovery of Bit Corruptions in Datacenter Storage Systems

Author

Tai, Amy, Kryczka, Andrew, Kanaujia, Shobhit, Petersen, Chris, Antonov, Mikhail, Waliji, Muhammad, Jamieson, Kyle, Freedman, Michael J., and Cidon, Asaf

Subjects
FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)
Abstract

Due to its high performance and decreasing cost per bit, flash is becoming the main storage medium in datacenters for hot data. However, flash endurance is a perpetual problem, and due to technology trends, subsequent generations of flash devices exhibit progressively shorter lifetimes before they experience uncorrectable bit errors. In this paper we propose extending flash lifetime by allowing devices to expose higher bit error rates. To do so, we present DIRECT, a novel set of policies that leverages latent redundancy in distributed storage systems to recover from bit corruption errors with minimal performance and recovery overhead. In doing so, DIRECT can significantly extend the lifetime of flash devices by effectively utilizing these devices even after they begin exposing bit errors. We implemented DIRECT on two real-world storage systems: ZippyDB, a distributed key-value store backed by RocksDB, and HDFS, a distributed file system. When tested on production traces at Facebook, DIRECT reduces application-visible error rates in ZippyDB by more than 10^2 and recovery time by more than 10^4. DIRECT also allows HDFS to tolerate a 10^4--10^5 higher bit error rate without experiencing application-visible errors.

Published
2018

13. Latin American Update.

Author

O'Boyle, Marilyn, Tai, Amy, and Fraser, Caroline

Abstract

The article presents an update on Suzuki method of teaching music in Latin America. The success of Suzuki Music Schools in Colombia is reported. Highlights of the course, How to Teach Reading Music in a Natural Way, given in Cusco, Peru in September 2006 are presented. The experiences of guitarist Eric Escalaya in participating at the 4th National Suzuki Workshop in Lima, Peru are also detailed.

Published
2006

14. Effect of hydrocortisone on mortality and organ support in patients with severe COVID-19

Author

Angus, Derek, Derde, Lennie, Al-Beidh, Farah, Annane, Djillali, Arabi, Yaseen, Beane, Abigail, van Bentum-Puijk, Wilma, Berry, Lindsay, Bhimani, Zahra, Bonten, Marc, Bradbury, Charlotte, Brunkhorst, Frank, Buxton, Meredith, Buzgau, Adrian, Cheng, Allen, de Jong, Menno, Detry, Michelle, Estcourt, Lise, Fitzgerald, Mark, Goossens, Herman, Green, Cameron, Haniffa, Rashan, Higgins, Alisa, Horvat, Christopher, Hullegie, Sebastiaan, Kruger, Peter, Lamontagne, Francois, Lawler, Patrick, Linstrum, Kelsey, Litton, Edward, Lorenzi, Elizabeth, Marshall, John, Mcauley, Daniel, Mcglothin, Anna, Mcguinness, Shay, Mcverry, Bryan, Montgomery, Stephanie, Mouncey, Paul, Murthy, Srinivas, Nichol, Alistair, Parke, Rachael, Parker, Jane, Rowan, Kathryn, Sanil, Ashish, Santos, Marlene, Saunders, Christina, Seymour, Christopher, Turner, Anne, van de Veerdonk, Frank, Venkatesh, Balasubramanian, Zarychanski, Ryan, Berry, Scott, Lewis, Roger, Mcarthur, Colin, Webb, Steven, Gordon, Anthony, NIHR, Hôpital Raymond Poincaré [AP-HP], Writing Committee for the REMAP-CAP Investigators, Farah Al-Beidh, Derek Angus, Djillali Annane, Yaseen Arabi, Wilma van Bentum-Puijk, Scott Berry, Abigail Beane, Zahra Bhimani, Marc Bonten, Charlotte Bradbury, Frank Brunkhorst, Meredith Buxton, Allen Cheng, Menno De Jong, Lennie Derde, Lise Estcourt, Herman Goossens, Anthony Gordon, Cameron Green, Rashan Haniffa, Francois Lamontagne, Patrick Lawler, Edward Litton, John Marshall, Colin McArthur, Daniel McAuley, Shay McGuinness, Bryan McVerry, Stephanie Montgomery, Paul Mouncey, Srinivas Murthy, Alistair Nichol, Rachael Parke, Kathryn Rowan, Christopher Seymour, Anne Turner, Frank van de Veerdonk, Steve Webb, Ryan Zarychanski, Lewis Campbell, Andrew Forbes, David Gattas, Stephane Heritier, Lisa Higgins, Peter Kruger, Sandra Peake, Jeffrey Presneill, Ian Seppelt, Tony Trapani, Paul Young, Sean Bagshaw, Nick Daneman, Niall Ferguson, Cheryl Misak, Marlene Santos, Sebastiaan Hullegie, Mathias Pletz, Gernot Rohde, Kathy Rowan, Brian Alexander, Kim Basile, Timothy Girard, Christopher Horvat, David Huang, Kelsey Linstrum, Jennifer Vates, Richard Beasley, Robert Fowler, Steve McGloughlin, Susan Morpeth, David Paterson, Bala Venkatesh, Tim Uyeki, Kenneth Baillie, Eamon Duffy, Rob Fowler, Thomas Hills, Katrina Orr, Asad Patanwala, Steve Tong, Mihai Netea, Shilesh Bihari, Marc Carrier, Dean Fergusson, Ewan Goligher, Ghady Haidar, Beverley Hunt, Anand Kumar, Mike Laffan, Patrick Lawless, Sylvain Lother, Peter McCallum, Saskia Middeldopr, Zoe McQuilten, Matthew Neal, John Pasi, Roger Schutgens, Simon Stanworth, Alexis Turgeon, Alexandra Weissman, Neill Adhikari, Matthew Anstey, Emily Brant, Angelique de Man, Francois Lamonagne, Marie-Helene Masse, Andrew Udy, Donald Arnold, Phillipe Begin, Richard Charlewood, Michael Chasse, Mark Coyne, Jamie Cooper, James Daly, Iain Gosbell, Heli Harvala-Simmonds, Tom Hills, Sheila MacLennan, David Menon, John McDyer, Nicole Pridee, David Roberts, Manu Shankar-Hari, Helen Thomas, Alan Tinmouth, Darrell Triulzi, Tim Walsh, Erica Wood, Carolyn Calfee, Cecilia O’Kane, Murali Shyamsundar, Pratik Sinha, Taylor Thompson, Ian Young, Shailesh Bihari, Carol Hodgson, John Laffey, Danny McAuley, Neil Orford, Ary Neto, Michelle Detry, Mark Fitzgerald, Roger Lewis, Anna McGlothlin, Ashish Sanil, Christina Saunders, Lindsay Berry, Elizabeth Lorenzi, Eliza Miller, Vanessa Singh, Claire Zammit, Wilma van Bentum Puijk, Wietske Bouwman, Yara Mangindaan, Lorraine Parker, Svenja Peters, Ilse Rietveld, Kik Raymakers, Radhika Ganpat, Nicole Brillinger, Rene Markgraf, Kate Ainscough, Kathy Brickell, Aisha Anjum, Janis-Best Lane, Alvin Richards-Belle, Michelle Saull, Daisy Wiley, Julian Bion, Jason Connor, Simon Gates, Victoria Manax, Tom van der Poll, John Reynolds, Marloes van Beurden, Evelien Effelaar, Joost Schotsman, Craig Boyd, Cain Harland, Audrey Shearer, Jess Wren, Giles Clermont, William Garrard, Kyle Kalchthaler, Andrew King, Daniel Ricketts, Salim Malakoutis, Oscar Marroquin, Edvin Music, Kevin Quinn, Heidi Cate, Karen Pearson, Joanne Collins, Jane Hanson, Penny Williams, Shane Jackson, Adeeba Asghar, Sarah Dyas, Mihaela Sutu, Sheenagh Murphy, Dawn Williamson, Nhlanhla Mguni, Alison Potter, David Porter, Jayne Goodwin, Clare Rook, Susie Harrison, Hannah Williams, Hilary Campbell, Kaatje Lomme, James Williamson, Jonathan Sheffield, Willian van’t Hoff, Phobe McCracken, Meredith Young, Jasmin Board, Emma Mart, Cameron Knott, Julie Smith, Catherine Boschert, Julia Affleck, Mahesh Ramanan, Ramsy D’Souza, Kelsey Pateman, Arif Shakih, Winston Cheung, Mark Kol, Helen Wong, Asim Shah, Atul Wagh, Joanne Simpson, Graeme Duke, Peter Chan, Brittney Cartner, Stephanie Hunter, Russell Laver, Tapaswi Shrestha, Adrian Regli, Annamaria Pellicano, James McCullough, Mandy Tallott, Nikhil Kumar, Rakshit Panwar, Gail Brinkerhoff, Cassandra Koppen, Federica Cazzola, Matthew Brain, Sarah Mineall, Roy Fischer, Vishwanath Biradar, Natalie Soar, Hayden White, Kristen Estensen, Lynette Morrison, Joanne Smith, Melanie Cooper, Monash Health, Yahya Shehabi, Wisam Al-Bassam, Amanda Hulley, Christina Whitehead, Julie Lowrey, Rebecca Gresha, James Walsham, Jason Meyer, Meg Harward, Ellen Venz, Patricia Williams, Catherine Kurenda, Kirsy Smith, Margaret Smith, Rebecca Garcia, Deborah Barge, Deborah Byrne, Kathleen Byrne, Alana Driscoll, Louise Fortune, Pierre Janin, Elizabeth Yarad, Naomi Hammond, Frances Bass, Angela Ashelford, Sharon Waterson, Steve Wedd, Robert McNamara, Heidi Buhr, Jennifer Coles, Sacha Schweikert, Bradley Wibrow, Rashmi Rauniyar, Erina Myers, Ed Fysh, Ashlish Dawda, Bhaumik Mevavala, Ed Litton, Janet Ferrier, Priya Nair, Hergen Buscher, Claire Reynolds, John Santamaria, Leanne Barbazza, Jennifer Homes, Roger Smith, Lauren Murray, Jane Brailsford, Loretta Forbes, Teena Maguire, Vasanth Mariappa, Judith Smith, Scott Simpson, Matthew Maiden, Allsion Bone, Michelle Horton, Tania Salerno, Martin Sterba, Wenli Geng, Pieter Depuydt, Jan De Waele, Liesbet De Bus, Jan Fierens, Stephanie Bracke, Brenda Reeve, William Dechert, Michaël Chassé, François Martin Carrier, Dounia Boumahni, Fatna Benettaib, Ali Ghamraoui, David Bellemare, Ève Cloutier, Charles Francoeur, François Lamontagne, Frédérick D’Aragon, Elaine Carbonneau, Julie Leblond, Gloria Vazquez-Grande, Nicole Marten, Maggie Wilson, Martin Albert, Karim Serri, Alexandros Cavayas, Mathilde Duplaix, Virginie Williams, Bram Rochwerg, Tim Karachi, Simon Oczkowski, John Centofanti, Tina Millen, Erick Duan, Jennifer Tsang, Lisa Patterson, Shane English, Irene Watpool, Rebecca Porteous, Sydney Miezitis, Lauralyn McIntyre, Laurent Brochard, Karen Burns, Gyan Sandhu, Imrana Khalid, Alexandra Binnie, Elizabeth Powell, Alexandra McMillan, Tracy Luk, Noah Aref, Zdravko Andric, Sabina Cviljevic, Renata Đimoti, Marija Zapalac, Gordan Mirković, Bruno Baršić, Marko Kutleša, Viktor Kotarski, Ana Vujaklija Brajković, Jakša Babel, Helena Sever, Lidija Dragija, Ira Kušan, Suvi Vaara, Leena Pettilä, Jonna Heinonen, Anne Kuitunen, Sari Karlsson, Annukka Vahtera, Heikki Kiiski, Sanna Ristimäki, Amine Azaiz, Cyril Charron, Mathieu Godement, Guillaume Geri, Antoine Vieillard-Baron, Franck Pourcine, Mehran Monchi, David Luis, Romain Mercier, Anne Sagnier, Nathalie Verrier, Cecile Caplin, Shidasp Siami, Christelle Aparicio, Sarah Vautier, Asma Jeblaoui, Muriel Fartoukh, Laura Courtin, Vincent Labbe, Cécile Leparco, Grégoire Muller, Mai-Anh Nay, Toufik Kamel, Dalila Benzekri, Sophie Jacquier, Emmanuelle Mercier, Delphine Chartier, Charlotte Salmon, PierreFrançois Dequin, Francis Schneider, Guillaume Morel, Sylvie L’Hotellier, Julio Badie, Fernando Daniel Berdaguer, Sylvain Malfroy, Chaouki Mezher, Charlotte Bourgoin, Bruno Megarbane, Sebastian Voicu, Nicolas Deye, Isabelle Malissin, Laetitia Sutterlin, Christophe Guitton, Cédric Darreau, Mickaël Landais, Nicolas Chudeau, Alain Robert, Pierre Moine, Nicholas Heming, Virginie Maxime, Isabelle Bossard, Tiphaine Barbarin Nicholier, Gwenhael Colin, Vanessa Zinzoni, Natacham Maquigneau, André Finn, Gabriele Kreß, Uwe Hoff, Carl Friedrich Hinrichs, Jens Nee, Mathias Pletz, Stefan Hagel, Juliane Ankert, Steffi Kolanos, Frank Bloos, Sirak Petros, Bastian Pasieka, Kevin Kunz, Peter Appelt, Bianka Schütze, Stefan Kluge, Axel Nierhaus, Dominik Jarczak, Kevin Roedl, Dirk Weismann, Anna Frey, Vivantes Klinikum Neukölln, Lorenz Reill, Michael Distler, Astrid Maselli, János Bélteczki, István Magyar, Ágnes Fazekas, Sándor Kovács, Viktória Szőke, Gábor Szigligeti, János Leszkoven, Daniel Collins, Patrick Breen, Stephen Frohlich, Ruth Whelan, Bairbre McNicholas, Michael Scully, Siobhan Casey, Maeve Kernan, Peter Doran, Michael O’Dywer, Michelle Smyth, Leanne Hayes, Oscar Hoiting, Marco Peters, Els Rengers, Mirjam Evers, Anton Prinssen, Jeroen Bosch Ziekenhuis, Koen Simons, Wim Rozendaal, F Polderman, P de Jager, M Moviat, A Paling, A Salet, Emma Rademaker, Anna Linda Peters, E de Jonge, J Wigbers, E Guilder, M Butler, Keri-Anne Cowdrey, Lynette Newby, Yan Chen, Catherine Simmonds, Rachael McConnochie, Jay Ritzema Carter, Seton Henderson, Kym Van Der Heyden, Jan Mehrtens, Tony Williams, Alex Kazemi, Rima Song, Vivian Lai, Dinu Girijadevi, Robert Everitt, Robert Russell, Danielle Hacking, Ulrike Buehner, Erin Williams, Troy Browne, Kate Grimwade, Jennifer Goodson, Owen Keet, Owen Callender, Robert Martynoga, Kara Trask, Amelia Butler, Livia Schischka, Chelsea Young, Eden Lesona, Shaanti Olatunji, Yvonne Robertson, Nuno José, Teodoro Amaro dos Santos Catorze, Tiago Nuno Alfaro de Lima Pereira, Lucilia Maria Neves Pessoa, Ricardo Manuel Castro Ferreira, Joana Margarida Pereira Sousa Bastos, Simin Aysel Florescu, Delia Stanciu, Miahela Florentina Zaharia, Alma Gabriela Kosa, Daniel Codreanu, Yaseen Marabi, Eman Al Qasim, Mohamned Moneer Hagazy, Lolowa Al Swaidan, Hatim Arishi, Rosana Muñoz-Bermúdez, Judith Marin-Corral, Anna Salazar Degracia, Francisco Parrilla Gómez, Maria Isabel Mateo López, Jorge Rodriguez Fernandez, Sheila Cárcel Fernández, Rosario Carmona Flores, Rafael León López, Carmen de la Fuente Martos, Angela Allan, Petra Polgarova, Neda Farahi, Stephen McWilliam, Daniel Hawcutt, Laura Rad, Laura O’Malley, Jennifer Whitbread, Olivia Kelsall, Laura Wild, Jessica Thrush, Hannah Wood, Karen Austin, Adrian Donnelly, Martin Kelly, Sinéad O’Kane, Declan McClintock, Majella Warnock, Paul Johnston, Linda Jude Gallagher, Clare Mc Goldrick, Moyra Mc Master, Anna Strzelecka, Rajeev Jha, Michael Kalogirou, Christine Ellis, Vinodh Krishnamurthy, Vashish Deelchand, Jon Silversides, Peter McGuigan, Kathryn Ward, Aisling O’Neill, Stephanie Finn, Barbara Phillips, Dee Mullan, Laura Oritz-Ruiz de Gordoa, Matthew Thomas, Katie Sweet, Lisa Grimmer, Rebekah Johnson, Jez Pinnell, Matt Robinson, Lisa Gledhill, Tracy Wood, Matt Morgan, Jade Cole, Helen Hill, Michelle Davies, David Antcliffe, Maie Templeton, Roceld Rojo, Phoebe Coghlan, Joanna Smee, Euan Mackay, Jon Cort, Amanda Whileman, Thomas Spencer, Nick Spittle, Vidya Kasipandian, Amit Patel, Suzanne Allibone, Roman Mary Genetu, Mohamed Ramali, Alison Ghosh, Peter Bamford, Emily London, Kathryn Cawley, Maria Faulkner, Helen Jeffrey, Tim Smith, Chris Brewer, Jane Gregory, James Limb, Amanda Cowton, Julie O’Brien, Nikitas Nikitas, Colin Wells, Liana Lankester, Mark Pulletz, Patricia Williams, Jenny Birch, Sophie Wiseman, Sarah Horton, Ana Alegria, Salah Turki, Tarek Elsefi, Nikki Crisp, Louise Allen, Iain McCullagh, Philip Robinson, Carole Hays, Maite Babio-Galan, Hannah Stevenson, Divya Khare, Meredith Pinder, Selvin Selvamoni, Amitha Gopinath, Richard Pugh, Daniel Menzies, Callum Mackay, Elizabeth Allan, Gwyneth Davies, Kathryn Puxty, Claire McCue, Susanne Cathcart, Naomi Hickey, Jane Ireland, Hakeem Yusuff, Graziella Isgro, Chris Brightling, Michelle Bourne, Michelle Craner, Malcolm Watters, Rachel Prout, Louisa Davies, Suzannah Pegler, Lynsey Kyeremeh, Gill Arbane, Karen Wilson, Linda Gomm, Federica Francia, Stephen Brett, Sonia Sousa Arias, Rebecca Elin Hall, Joanna Budd, Charlotte Small, Janine Birch, Emma Collins, Jeremy Henning, Stephen Bonner, Keith Hugill, Emanuel Cirstea, Dean Wilkinson, Michal Karlikowski, Helen Sutherland, Elva Wilhelmsen, Jane Woods, Julie North, Dhinesh Sundaran, Laszlo Hollos, Susan Coburn, Joanne Walsh, Margaret Turns, Phil Hopkins, John Smith, Harriet Noble, Maria Theresa Depante, Emma Clarey, Shondipon Laha, Mark Verlander, Alexandra Williams, Abby Huckle, Andrew Hall, Jill Cooke, Caroline Gardiner-Hill, Carolyn Maloney, Hafiz Qureshi, Neil Flint, Sarah Nicholson, Sara Southin, Andrew Nicholson, Barbara Borgatta, Ian Turner-Bone, Amie Reddy, Laura Wilding, Loku Chamara Warnapura, Ronan Agno Sathianathan, David Golden, Ciaran Hart, Jo Jones, Jonathan Bannard-Smith, Joanne Henry, Katie Birchall, Fiona Pomeroy, Rachael Quayle, Arystarch Makowski, Beata Misztal, Iram Ahmed, Thyra KyereDiabour, Kevin Naiker, Richard Stewart, Esther Mwaura, Louise Mew, Lynn Wren, Felicity Willams, Richard Innes, Patricia Doble, Joanne Hutter, Charmaine Shovelton, Benjamin Plumb, Tamas Szakmany, Vincent Hamlyn, Nancy Hawkins, Sarah Lewis, Amanda Dell, Shameer Gopal, Saibal Ganguly, Andrew Smallwood, Nichola Harris, Stella Metherell, Juan Martin Lazaro, Tabitha Newman, Simon Fletcher, Jurgens Nortje, Deirdre Fottrell-Gould, Georgina Randell, Mohsin Zaman, Einas Elmahi, Andrea Jones, Kathryn Hall, Gary Mills, Kim Ryalls, Helen Bowler, Jas Sall, Richard Bourne, Zoe Borrill, Tracey Duncan, Thomas Lamb, Joanne Shaw, Claire Fox, Jeronimo Moreno Cuesta, Kugan Xavier, Dharam Purohit, Munzir Elhassan, Dhanalakshmi Bakthavatsalam, Matthew Rowland, Paula Hutton, Archana Bashyal, Neil Davidson, Clare Hird, Manish Chhablani, Gunjan Phalod, Amy Kirkby, Simon Archer, Kimberley Netherton, Henrik Reschreiter, Julie Camsooksai, Sarah Patch, Sarah Jenkins, David Pogson, Steve Rose, Zoe Daly, Lutece Brimfield, Helen Claridge, Dhruv Parekh, Colin Bergin, Michelle Bates, Joanne Dasgin, Christopher McGhee, Malcolm Sim, Sophie Kennedy Hay, Steven Henderson, Mandeep-Kaur Phull, Abbas Zaidi, Tatiana Pogreban, Lace Paulyn Rosaroso, Daniel Harvey, Benjamin Lowe, Megan Meredith, Lucy Ryan, Anil Hormis, Rachel Walker, Dawn Collier, Sarah Kimpton, Susan Oakley, Kevin Rooney, Natalie Rodden, Emma Hughes, Nicola Thomson, Deborah McGlynn, Andrew Walden, Nicola Jacques, Holly Coles, Emma Tilney, Emma Vowell, Martin Schuster-Bruce, Sally Pitts, Rebecca Miln, Laura Purandare, Luke Vamplew, Michael Spivey, Sarah Bean, Karen Burt, Lorraine Moore, Christopher Day, Charly Gibson, Elizabeth Gordon, Letizia Zitter, Samantha Keenan, Evelyn Baker, Shiney Cherian, Sean Cutler, Anna Roynon-Reed, Kate Harrington, Ajay Raithatha, Kris Bauchmuller, Norfaizan Ahmad, Irina Grecu, Dawn Trodd, Jane Martin, Caroline Wrey Brown, Ana-Marie Arias, Thomas Craven, David Hope, Jo Singleton, Sarah Clark, Nicola Rae, Ingeborg Welters, David Oliver Hamilton, Karen Williams, Victoria Waugh, David Shaw, Zudin Puthucheary, Timothy Martin, Filipa Santos, Ruzena Uddin, Alastair Somerville, Kate Colette Tatham, Shaman Jhanji, Ethel Black, Arnold Dela Rosa, Ryan Howle, Redmond Tully, Andrew Drummond, Joy Dearden, Jennifer Philbin, Sheila Munt, Alain Vuylsteke, Charles Chan, Saji Victor, Ramprasad Matsa, Minerva Gellamucho, Ben Creagh-Brown, Joe Tooley, Laura Montague, Fiona De Beaux, Laetitia Bullman, Ian Kersiake, Carrie Demetriou, Sarah Mitchard, Lidia Ramos, Katie White, Phil Donnison, Maggie Johns, Ruth Casey, Lehentha Mattocks, Sarah Salisbury, Paul Dark, Andrew Claxton, Danielle McLachlan, Kathryn Slevin, Stephanie Lee, Jonathan Hulme, Sibet Joseph, Fiona Kinney, Ho Jan Senya, Aneta Oborska, Abdul Kayani, Bernard Hadebe, Rajalakshmi Orath Prabakaran, Lesley Nichols, Matt Thomas, Ruth Worner, Beverley Faulkner, Emma Gendall, Kati Hayes, Colin Hamilton-Davies, Carmen Chan, Celina Mfuko, Hakam Abbass, Vineela Mandadapu, Susannah Leaver, Daniel Forton, Kamal Patel, Elankumaran Paramasivam, Matthew Powell, Richard Gould, Elizabeth Wilby, Clare Howcroft, Dorota Banach, Ziortza Fernández de Pinedo Artaraz, Leilani Cabreros, Ian White, Maria Croft, Nicky Holland, Rita Pereira, Ahmed Zaki, David Johnson, Matthew Jackson, Hywel Garrard, Vera Juhaz, Alistair Roy, Anthony Rostron, Lindsey Woods, Sarah Cornell, Suresh Pillai, Rachel Harford, Tabitha Rees, Helen Ivatt, Ajay Sundara Raman, Miriam Davey, Kelvin Lee, Russell Barber, Manish Chablani, Farooq Brohi, Vijay Jagannathan, Michele Clark, Sarah Purvis, Bill Wetherill, Ahilanandan Dushianthan, Rebecca Cusack, Kim de Courcy-Golder, Simon Smith, Susan Jackson, Ben Attwood, Penny Parsons, Valerie Page, Xiao Bei Zhao, Deepali Oza, Jonathan Rhodes, Tom Anderson, Sheila Morris, Charlotte Xia Le Tai, Amy Thomas, Alexandra Keen, Stephen Digby, Nicholas Cowley, Laura Wild, David Southern, Harsha Reddy, Andy Campbell, Claire Watkins, Sara Smuts, Omar Touma, Nicky Barnes, Peter Alexander, Tim Felton, Susan Ferguson, Katharine Sellers, Joanne Bradley-Potts, David Yates, Isobel Birkinshaw, Kay Kell, Nicola Marshall, Lisa Carr-Knott, Charlotte Summers, and Mégarbane, Bruno

Subjects
[SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, General Medicine, [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, Writing Committee for the REMAP-CAP Investigators, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.TOX] Life Sciences [q-bio]/Toxicology, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.TOX]Life Sciences [q-bio]/Toxicology, General & Internal Medicine, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.MHEP.PSR] Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, 11 Medical and Health Sciences
Abstract

Importance Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures The primary end point was organ support–free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned –1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support–free days were 0 (IQR, –1 to 15), 0 (IQR, –1 to 13), and 0 (–1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support–free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support–free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions.

Published
2020
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15. Clinical decision support systems in addiction and concurrent disorders: A systematic review and meta-analysis.

Author

Tai AMY, Kim JJ, Schmeckenbecher J, Kitchin V, Wang J, Kazemi A, Masoudi R, Fadakar H, Iorfino F, and Krausz RM

Subjects
Humans, Patient Satisfaction, Mental Disorders therapy, Decision Support Systems, Clinical, Substance-Related Disorders therapy
Abstract

Introduction: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders., Methods: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials., Results: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS., Discussion: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models., Conclusion: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Published
2024
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16. Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

Author

Kazemi A, Boyd M, Choi F, Tai AMY, Tsang VW, To T, Kim J, Jang K, Shams F, Schreiter S, Cabanis M, and Krausz RM

Abstract

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality., (©Alireza Kazemi, Marisha Boyd, Fiona Choi, Andy Man Yeung Tai, Vivian WL Tsang, Tam To, Jane Kim, Kerry Jang, Farhud Shams, Stefanie Schreiter, Maurice Cabanis, Reinhard Michael Krausz. Originally published in JMIR Formative Research (https://formative.jmir.org), 11.03.2024.)

Published
2024
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17. A Call for an Evidence-Based Strategy Against the Overdose Crisis.

Author

Krausz RM, Westenberg JN, Tai AMY, Fadakar H, Seethapathy V, Mathew N, Azar P, Phillips A, Schütz CG, Choi F, Vogel M, Cabanis M, Meyer M, Jang K, and Ignaszewski M

Subjects
Humans, Analgesics, Opioid therapeutic use, Drug Overdose prevention & control, Opioid-Related Disorders drug therapy
Abstract

Competing Interests: Declaration of Conflicting InterestsMV has received consultation and speaker fees from Camurus. AP holds a US patent entitled “Tetrahydroprotoberbine Compounds and Uses Thereof “ in the Treatment of Neurological, Psychiatric and Neurodegenerative Diseases (United States, US20150306092) and holds shares in Resilience Biosciences Inc., Canada, focused on tetrahydroprotoberberine drug development. MI declares receiving honoraria for presenting (BCCSU, WCAF, Indivior), receiving VCHRI Team Grant (Fentanyl Cohort Study), receiving CSAM grant (Development of Stigma Series), being awarded the UBC/VGH Foundation BMO Capital Markets Innovators Challenge (Clinical Application of a Compact, Quantitative, and Inexpensive Opioid Detector), receiving research stipend (BCCfE The Hope to Health Research & Innovation Centre) and participating in a regional consult meeting for Otsuka and Lundbeck. The other authors declare no conflict of interest.

Published
2024
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18. Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression.

Author

Shams F, Tai AMY, Kim J, Boyd M, Meyer M, Kazemi A, and Krausz RM

Abstract

Background: Substance use disorders affect 36 million people globally, but only a small proportion of them receive the necessary treatment. E-health interventions have been developed to address this issue by improving access to substance use treatment. However, concerns about participant engagement and adherence to these interventions remain. This review aimed to evaluate adherence to e-health interventions targeting substance use and identify hypothesized predictors of adherence., Methods: A systematic review of literature published between 2009 and 2020 was conducted, and data on adherence measures and hypothesized predictors were extracted. Meta-analysis and meta-regression were used to analyze the data. The two adherence measures were (a) the mean proportion of modules completed across the intervention groups and (b) the proportion of participants that completed all modules. Four meta-regression models assessed each covariate including guidance, blended treatment, intervention duration and recruitment strategy., Results: The overall pooled adherence rate was 0.60 (95%-CI: 0.52-0.67) for the mean proportion of modules completed across 30 intervention arms and 0.47 (95%-CI: 0.35-0.59) for the proportion of participants that completed all modules across 9 intervention arms. Guidance, blended treatment, and recruitment were significant predictors of adherence, while treatment duration was not., Conclusion: The study suggests that more research is needed to identify predictors of adherence, in order to determine specific aspects that contribute to better exposure to intervention content. Reporting adherence and predictors in future studies can lead to improved meta-analyses and the development of more engaging interventions. Identifying predictors can aid in designing effective interventions for substance use disorders, with important implications for e-health interventions targeting substance use., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Published
2023
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19. Egyptian Students Open to Digital Mental Health Care: Cross-Sectional Survey.

Author

Mamdouh M, Tai AMY, Westenberg JN, Shams F, Jang K, Badawy A, Elsawi H, and Krausz M

Abstract

Background: In Egypt, the shortage of mental health services, particularly for adolescents and young adults, is apparent. Electronic mental health (EMH) has been proposed as a solution to bridge the gap and better address the needs of young people. However, EMH is new to Egypt and its acceptability among target populations is crucial to its implementation and success., Objective: The objective of this study is to explore the interest of Egyptian youth in EMH, identify perceived barriers to EMH, and inform the design of EMH tools to best address the needs of youth., Methods: A web-based cross-sectional survey was distributed among medical students at Tanta University in Egypt. Chi-square and one-way analysis of variance tests were performed for inferential analyses using a significance level of .05., Results: Of the 707 individuals who completed the survey (90.9% response rate), 60.5% (428) were female, 62% (438) lived in urban and suburban areas, and the mean age of the sample was 20.5 (SD 1.8) years. The vast majority of participants (522/707, 73.8%) had already used the internet to find information about mental health problems, but the information was unsatisfactory for about half of them (386/707, 54.6%). Almost all students reported that they would prefer web-based therapy if EMH were available through a trustworthy national web-based platform for youth mental health (601/707, 85%). Students believed that emotional difficulties, social support, and coping strategies were the main topics that EMH should help with. The most common perceived barriers for EMH use in Egypt were concerns about privacy (382/707, 54%) and a lack of technology literacy and unfamiliarity with EMH (352/707, 50%)., Conclusions: EMH is a promising strategy for addressing gaps in the mental health care for young people. To construct and implement a digital system of care that addresses the unique needs and preferences of youth, adolescents and young adults should be involved in the co-development and design., (©Mostafa Mamdouh, Andy Man Yeung Tai, Jean Nicolas Westenberg, Farhud Shams, Kerry Jang, Adel Badawy, Houssam Elsawi, Michael Krausz. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.03.2022.)

Published
2022
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20. Treatment approaches and outcome trajectories for youth with high-risk opioid use: A narrative review.

Author

Westenberg JN, Tai AMY, Elsner J, Kamel MM, Wong JSH, Azar P, Vo DX, Moore E, Mathew N, Seethapathy V, Choi F, Vogel M, and Krausz RM

Subjects
Adolescent, Adult, Cost-Benefit Analysis, Humans, Treatment Outcome, Analgesics, Opioid adverse effects
Abstract

Aim: First use of opioids often happens in adolescence and an increasing number of opioid overdoses are being reported among youth. The purpose of this narrative review was to present the treatment approaches for youth with high-risk opioid use, determine whether the literature supports the use of opioid agonist treatment among youth and identify evidence for better treatment outcomes in the younger population., Methods: A search of the literature on PubMed using MeSH terms specific to youth, opioid use and treatment approaches generated 1436 references. Following a screening process, 137 papers were found to be relevant to the treatment of high-risk opioid use among youth. After full-text review, 19 eligible studies were included: four randomized controlled trials, nine observational studies and six reviews., Results: Research for the different treatment options among youth is limited. The available evidence shows better outcomes in terms of retention in care and cost-effectiveness for opioid agonist treatment than abstinence-based comparisons. Integrating psychosocial interventions into the continuum of care for youth can be an effective way of addressing comorbid psychiatric conditions and emotional drivers of substance use, leading to improved treatment trajectories., Conclusions: From the limited findings, there is no evidence to deny youth with high-risk opioid use the same treatment options available to adults. A combination of pharmacological and youth-specific psychosocial interventions is required to maximize retention and survival. There is an urgent need for more research to inform clinical strategies toward appropriate treatment goals for such vulnerable individuals., (© 2021 John Wiley & Sons Australia, Ltd.)

Published
2022
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21. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

Author

Tai AMY, Albuquerque A, Carmona NE, Subramanieapillai M, Cha DS, Sheko M, Lee Y, Mansur R, and McIntyre RS

Subjects
Alzheimer Disease diagnosis, Alzheimer Disease therapy, Artificial Intelligence, Data Mining methods, Decision Trees, Depression therapy, Humans, Mental Disorders therapy, Models, Biological, Precision Medicine methods, Schizophrenia diagnosis, Schizophrenia therapy, Machine Learning, Mental Disorders diagnosis, Psychiatry methods
Abstract

Introduction: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems., Methods: Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper., Results: Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics., Conclusions: Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Published
2019
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22. High-throughput loss-of-heterozygosity study of chromosome 3p in lung cancer using single-nucleotide polymorphism markers.

Author

Tai AL, Mak W, Ng PK, Chua DT, Ng MY, Fu L, Chu KK, Fang Y, Qiang Song Y, Chen M, Zhang M, Sham PC, and Guan XY

Subjects
Genes, Tumor Suppressor, Genotype, Humans, Nucleic Acid Hybridization, Polymorphism, Single Nucleotide, Reproducibility of Results, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Carcinoma, Non-Small-Cell Lung genetics, Chromosomes, Human, Pair 3 genetics, Loss of Heterozygosity, Lung Neoplasms genetics
Abstract

Loss of DNA copy number at the short arm of chromosome 3 is one of the most common genetic changes in human lung cancer, suggesting the existence of one or more tumor suppressor genes (TSG) at 3p. To identify most frequently deleted regions and candidate TSGs within these regions, a recently developed single-nucleotide polymorphism (SNP)-mass spectrometry-genotyping (SMSG) technology was applied to investigate the loss of heterozygosity (LOH) in 30 primary non-small-cell lung cancers. A total of 386 SNP markers that spanned a region of 70 Mb at 3p, from 3pter to 3p14.1, were selected for LOH analysis. The average intermarker distance in the present study is approximately 180 kb. Several frequently deleted regions, including 3p26.3, 3p25.3, 3p24.1, 3p23, and 3p21.1, were found. Several candidate TSGs within these frequently detected LOH regions have been found, including APG7L at 3p25.3, CLASP2 at 3p23, and CACNA2D3 at 3p21.1. This study also showed that SMSG technology is a very useful approach to rapidly define the minimal deleted region and to identify target TSGs in a given cancer.

Published
2006
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23. Establishment and characterization of a human non-small cell lung cancer cell line.

Author

Tai AL, Fang Y, Sham JS, Deng W, Hu L, Xie D, Tsao GS, Cheung AL, and Guan XY

Subjects
Aged, Carcinoma, Non-Small-Cell Lung enzymology, Carcinoma, Non-Small-Cell Lung genetics, Chromosome Aberrations, Chromosomes, Human, Pair 12 genetics, Chromosomes, Human, Pair 3 genetics, Humans, In Situ Hybridization, Fluorescence, Lung Neoplasms enzymology, Lung Neoplasms genetics, Male, Nucleic Acid Hybridization, Spectral Karyotyping, Telomerase metabolism, Tumor Cells, Cultured, Carcinoma, Non-Small-Cell Lung pathology, Lung Neoplasms pathology
Abstract

Lung cancer is one of the most common causes of cancer death worldwide. Although many efforts have been made to explore the mechanisms involved in the development of lung cancer, the genetic events involved in the pathogenesis of lung cancer are still unclear. For a better mechanistic scope of study, a well-established cellular model is essential. We report the establishment of a squamous cell carcinoma (SCC) cell line of human lung, SCC-37. Chromosomal abnormalities and global genomic alterations of SCC-37 were studied by spectral karyotyping (SKY) and comparative genomic hybridization (CGH), respectively. Results showed that SCC-37 was a hypodiploid with complex chromosomal rearrangements. Some of the alterations, such as the gain of 1q25-qter in SCC-37, have been correlated to the tumor recurrence of non-small cell lung cancer (NSCLC). Other interesting findings include the amplification of 3q25-qter and 12q13, suggesting the existence of important oncogenes in the amplicons. This cell line may thus provide a useful cellular resource for studying the pathogenesis of SCC of the lung in the future.

Published
2005

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