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404 results on '"Paramsothy P"'

1. An application of continuous-variable gate synthesis to quantum simulation of classical dynamics

Author

Cochran, Sam, Stokes, James, Jayakumar, Paramsothy, and Veerapaneni, Shravan

Subjects
Quantum Physics
Abstract

Although quantum computing holds promise to accelerate a wide range of computational tasks, the quantum simulation of quantum dynamics as originally envisaged by Feynman remains the most promising candidate for achieving quantum advantage. A less explored possibility with comparably far-reaching technological applicability is the quantum simulation of classical nonlinear dynamics. Attempts to develop digital quantum algorithms based on the Koopman von Neumann formalism have met with challenges because of the necessary projection step from an infinite-dimensional Hilbert space to the finite-dimensional subspace described by a collection of qubits. This finitization produces numerical artifacts that limit solutions to very short time horizons. In this paper we review continuous-variable quantum computing (CVQC), which naturally avoids such obstacles, and a CVQC algorithm for KvN simulation of classical nonlinear dynamics is advocated. In particular, we present explicit gate synthesis for product-formula Hamiltonian simulation of anharmonic vibrational dynamics., Comment: 11 pages

Published
2024

2. Gut microbiota strain richness is species specific and affects engraftment

Author

Chen-Liaw, Alice, Aggarwala, Varun, Mogno, Ilaria, Haifer, Craig, Li, Zhihua, Eggers, Joseph, Helmus, Drew, Hart, Amy, Wehkamp, Jan, Lamousé-Smith, Esi S. N., Kerby, Robert L., Rey, Federico E., Colombel, Jean Frédéric, Kamm, Michael A., Olle, Bernat, Norman, Jason M., Menon, Rajita, Watson, Andrea R., Crossett, Emily, Terveer, Elisabeth M., Keller, Josbert J., Borody, Thomas J., Grinspan, Ari, Paramsothy, Sudarshan, Kaakoush, Nadeem O., Dubinsky, Marla C., and Faith, Jeremiah J.

Published
2024
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7. ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty

Author

Wilson, Joey, Fu, Yuewei, Friesen, Joshua, Ewen, Parker, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, and Ghaffari, Maani

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

In this paper, we develop a modular neural network for real-time {\color{black}(> 10 Hz)} semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel Convolutional Bayesian Kernel Inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a Robot Operating System (ROS) package of ConvBKI and test it on real-world perceptually challenging off-road driving data., Comment: arXiv admin note: text overlap with arXiv:2209.10663

Published
2023
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8. The phageome of patients with ulcerative colitis treated with donor fecal microbiota reveals markers associated with disease remission

Author

Marwan E. Majzoub, Sudarshan Paramsothy, Craig Haifer, Rohit Parthasarathy, Thomas J. Borody, Rupert W. Leong, Michael A. Kamm, and Nadeem O. Kaakoush

Subjects
Science
Abstract

Abstract Bacteriophages are influential within the human gut microbiota, yet they remain understudied relative to bacteria. This is a limitation of studies on fecal microbiota transplantation (FMT) where bacteriophages likely influence outcome. Here, using metagenomics, we profile phage populations - the phageome - in individuals recruited into two double-blind randomized trials of FMT in ulcerative colitis. We leverage the trial designs to observe that phage populations behave similarly to bacterial populations, showing temporal stability in health, dysbiosis in active disease, modulation by antibiotic treatment and by FMT. We identify a donor bacteriophage putatively associated with disease remission, which on genomic analysis was found integrated in a bacterium classified to Oscillospiraceae, previously isolated from a centenarian and predicted to produce vitamin B complex except B12. Our study provides an in-depth assessment of phage populations during different states and suggests that bacteriophage tracking has utility in identifying determinants of disease activity and resolution.

Published
2024
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10. Tensor-Train Compression of Discrete Element Method Simulation Data

Author

De, Saibal, Corona, Eduardo, Jayakumar, Paramsothy, and Veerapaneni, Shravan

Subjects
Mathematics - Numerical Analysis
Abstract

We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. We show that geometry-driven "tensorization" coupled with the TT decomposition (known as quantized TT) yields a hierarchical compression scheme, achieving high compression ratios for key variables in these DEM datasets.

Published
2022

11. Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Author

Wilson, Joey, Fu, Yuewei, Zhang, Arthur, Song, Jingyu, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, and Ghaffari, Maani

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

Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution layer to maximize efficency while maintaining reliability simultaneously. We apply our layer to the task of real-time 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information and incorporate semantic predictions into a global map. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, demonstrating improved latency with comparable semantic label inference results.

Published
2022

12. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Jannik Boos, Caspar I. van der Made, Gayatri Ramakrishnan, Eamon Coughlan, Rosanna Asselta, Britt-Sabina Löscher, Luca V.C. Valenti, Rafael de Cid, Luis Bujanda, Antonio Julià, Erola Pairo-Castineira, J. Kenneth Baillie, Sandra May, Berina Zametica, Julia Heggemann, Agustín Albillos, Jesus M. Banales, Jordi Barretina, Natalia Blay, Paolo Bonfanti, Maria Buti, Javier Fernandez, Sara Marsal, Daniele Prati, Luisa Ronzoni, Nicoletta Sacchi, Joachim L. Schultze, Olaf Riess, Andre Franke, Konrad Rawlik, David Ellinghaus, Alexander Hoischen, Axel Schmidt, Kerstin U. Ludwig, Valeria Rimoldi, Elvezia M. Paraboschi, Alessandra Bandera, Flora Peyvandi, Giacomo Grasselli, Francesco Blasi, Francesco Malvestiti, Serena Pelusi, Cristiana Bianco, Lorenzo Miano, Angela Lombardi, Pietro Invernizzi, Alessio Gerussi, Giuseppe Citerio, Andrea Biondi, Maria Grazia Valsecchi, Marina Elena Cazzaniga, Giuseppe Foti, Ilaria Beretta, Mariella D'Angiò, Laura Rachele Bettini, Xavier Farré, Susana Iraola-Guzmán, Manolis Kogevinas, Gemma Castaño-Vinyals, Koldo Garcia-Etxebarria, Beatriz Nafria, Mauro D'Amato, Adriana Palom, Colin Begg, Sara Clohisey, Charles Hinds, Peter Horby, Julian Knight, Lowell Ling, David Maslove, Danny McAuley, Johnny Millar, Hugh Montgomery, Alistair Nichol, Peter J.M. Openshaw, Alexandre C. Pereira, Chris P. Ponting, Kathy Rowan, Malcolm G. Semple, Manu Shankar-Hari, Charlotte Summers, Timothy Walsh, Latha Aravindan, Ruth Armstrong, Heather Biggs, Ceilia Boz, Adam Brown, Richard Clark, Audrey Coutts, Judy Coyle, Louise Cullum, Sukamal Das, Nicky Day, Lorna Donnelly, Esther Duncan, Angie Fawkes, Paul Fineran, Max Head Fourman, Anita Furlong, James Furniss, Bernadette Gallagher, Tammy Gilchrist, Ailsa Golightly, Fiona Griffiths, Katarzyna Hafezi, Debbie Hamilton, Ross Hendry, Andy Law, Dawn Law, Rachel Law, Sarah Law, Rebecca Lidstone-Scott, Louise Macgillivray, Alan Maclean, Hanning Mal, Sarah McCafferty, Ellie Mcmaster, Jen Meikle, Shona C. Moore, Kirstie Morrice, Lee Murphy, Sheena Murphy, Mybaya Hellen, Wilna Oosthuyzen, Chenqing Zheng, Jiantao Chen, Nick Parkinson, Trevor Paterson, Katherine Schon, Andrew Stenhouse, Mihaela Das, Maaike Swets, Helen Szoor-McElhinney, Filip Taneski, Lance Turtle, Tony Wackett, Mairi Ward, Jane Weaver, Nicola Wrobel, Marie Zechner, Gill Arbane, Aneta Bociek, Sara Campos, Neus Grau, Tim Owen Jones, Rosario Lim, Martina Marotti, Marlies Ostermann, Christopher Whitton, Zoe Alldis, Raine Astin-Chamberlain, Fatima Bibi, Jack Biddle, Sarah Blow, Matthew Bolton, Catherine Borra, Ruth Bowles, Maudrian Burton, Yasmin Choudhury, David Collier, Amber Cox, Amy Easthope, Patrizia Ebano, Stavros Fotiadis, Jana Gurasashvili, Rosslyn Halls, Pippa Hartridge, Delordson Kallon, Jamila Kassam, Ivone Lancoma-Malcolm, Maninderpal Matharu, Peter May, Oliver Mitchelmore, Tabitha Newman, Mital Patel, Jane Pheby, Irene Pinzuti, Zoe Prime, Oleksandra Prysyazhna, Julian Shiel, Melanie Taylor, Carey Tierney, Suzanne Wood, Anne Zak, Olivier Zongo, Stephen Bonner, Keith Hugill, Jessica Jones, Steven Liggett, Evie Headlam, Nageswar Bandla, Minnie Gellamucho, Michelle Davies, Christopher Thompson, Marwa Abdelrazik, Dhanalakshmi Bakthavatsalam, Munzir Elhassan, Arunkumar Ganesan, Anne Haldeos, Jeronimo Moreno-Cuesta, Dharam Purohit, Rachel Vincent, Kugan Xavier, Kumar Rohit, Frater Alasdair, Malik Saleem, Carter David, Jenkins Samuel, Zoe Lamond, Wall Alanna, Jaime Fernandez-Roman, David O. Hamilton, Emily Johnson, Brian Johnston, Maria Lopez Martinez, Suleman Mulla, David Shaw, Alicia A.C. Waite, Victoria Waugh, Ingeborg D. Welters, Karen Williams, Anna Cavazza, Maeve Cockrell, Eleanor Corcoran, Maria Depante, Clare Finney, Ellen Jerome, Mark McPhail, Monalisa Nayak, Harriet Noble, Kevin O'Reilly, Evita Pappa, Rohit Saha, Sian Saha, John Smith, Abigail Knighton, David Antcliffe, Dorota Banach, Stephen Brett, Phoebe Coghlan, Ziortza Fernandez, Anthony Gordon, Roceld Rojo, Sonia Sousa Arias, Maie Templeton, Megan Meredith, Lucy Morris, Lucy Ryan, Amy Clark, Julia Sampson, Cecilia Peters, Martin Dent, Margaret Langley, Saima Ashraf, Shuying Wei, Angela Andrew, Archana Bashyal, Neil Davidson, Paula Hutton, Stuart McKechnie, Jean Wilson, David Baptista, Rebecca Crowe, Rita Fernandes, Rosaleen Herdman-Grant, Anna Joseph, Denise O'Connor, Meryem Allen, Adam Loveridge, India McKenley, Eriko Morino, Andres Naranjo, Richard Simms, Kathryn Sollesta, Andrew Swain, Harish Venkatesh, Jacyntha Khera, Jonathan Fox, Gillian Andrew, Lucy Barclay, Marie Callaghan, Rachael Campbell, Sarah Clark, Dave Hope, Lucy Marshall, Corrienne McCulloch, Kate Briton, Jo Singleton, Sohphie Birch, Lutece Brimfield, Zoe Daly, David Pogson, Steve Rose, Ceri Battle, Elaine Brinkworth, Rachel Harford, Carl Murphy, Luke Newey, Tabitha Rees, Marie Williams, Sophie Arnold, Petra Polgarova, Katerina Stroud, Eoghan Meaney, Megan Jones, Anthony Ng, Shruti Agrawal, Nazima Pathan, Deborah White, Esther Daubney, Kay Elston, Lina Grauslyte, Musarat Hussain, Mandeep Phull, Tatiana Pogreban, Lace Rosaroso, Erika Salciute, George Franke, Joanna Wong, Aparna George, Laura Ortiz-Ruiz de Gordoa, Emily Peasgood, Claire Phillips, Michelle Bates, Jo Dasgin, Jaspret Gill, Annette Nilsson, James Scriven, Carlos Castro Delgado, Deborah Dawson, Lijun Ding, Georgia Durrant, Obiageri Ezeobu, Sarah Farnell-Ward, Abiola Harrison, Rebecca Kanu, Susannah Leaver, Elena Maccacari, Soumendu Manna, Romina Pepermans Saluzzio, Joana Queiroz, Tinashe Samakomva, Christine Sicat, Joana Texeira, Edna Fernandes Da Gloria, Ana Lisboa, John Rawlins, Jisha Mathew, Ashley Kinch, William James Hurt, Nirav Shah, Victoria Clark, Maria Thanasi, Nikki Yun, Kamal Patel, Sara Bennett, Emma Goodwin, Matthew Jackson, Alissa Kent, Clare Tibke, Wiesia Woodyatt, Ahmed Zaki, Azmerelda Abraheem, Peter Bamford, Kathryn Cawley, Charlie Dunmore, Maria Faulkner, Rumanah Girach, Helen Jeffrey, Rhianna Jones, Emily London, Imrun Nagra, Farah Nasir, Hannah Sainsbury, Clare Smedley, Tahera Patel, Matthew Smith, Srikanth Chukkambotla, Aayesha Kazi, Janice Hartley, Joseph Dykes, Muhammad Hijazi, Sarah Keith, Meherunnisa Khan, Janet Ryan-Smith, Philippa Springle, Jacqueline Thomas, Nick Truman, Samuel Saad, Dabheoc Coleman, Christopher Fine, Roseanna Matt, Bethan Gay, Jack Dalziel, Syamlan Ali, Drew Goodchild, Rhiannan Harling, Ravi Bhatterjee, Wendy Goddard, Chloe Davison, Stephen Duberly, Jeanette Hargreaves, Rachel Bolton, Miriam Davey, David Golden, Rebecca Seaman, Shiney Cherian, Sean Cutler, Anne Emma Heron, Anna Roynon-Reed, Tamas Szakmany, Gemma Williams, Owen Richards, Yusuf Cheema, Hollie Brooke, Sarah Buckley, Jose Cebrian Suarez, Ruth Charlesworth, Karen Hansson, John Norris, Alice Poole, Alastair Rose, Rajdeep Sandhu, Brendan Sloan, Elizabeth Smithson, Muthu Thirumaran, Veronica Wagstaff, Alexandra Metcalfe, Mark Brunton, Jess Caterson, Holly Coles, Matthew Frise, Sabi Gurung Rai, Nicola Jacques, Liza Keating, Emma Tilney, Shauna Bartley, Parminder Bhuie, Sian Gibson, Amanda Lyle, Fiona McNeela, Jayachandran Radhakrishnan, Alistair Hughes, Bryan Yates, Jessica Reynolds, Helen Campbell, Maria Thompsom, Steve Dodds, Stacey Duffy, Sandra Greer, Karen Shuker, Ascanio Tridente, Reena Khade, Ashok Sundar, George Tsinaslanidis, Isobel Birkinshaw, Joseph Carter, Kate Howard, Joanne Ingham, Rosie Joy, Harriet Pearson, Samantha Roche, Zoe Scott, Hollie Bancroft, Mary Bellamy, Margaret Carmody, Jacqueline Daglish, Faye Moore, Joanne Rhodes, Mirriam Sangombe, Salma Kadiri, Maria Croft, Ian White, Victoria Frost, Maia Aquino, Rajeev Jha, Vinodh Krishnamurthy, Lai Lim, Li Lim, Edward Combes, Teishel Joefield, Sonja Monnery, Valerie Beech, Sallyanne Trotman, Christine Almaden-Boyle, Pauline Austin, Louise Cabrelli, Stephen Cole, Matt Casey, Susan Chapman, Clare Whyte, Yolanda Baird, Aaron Butler, Indra Chadbourn, Linda Folkes, Heather Fox, Amy Gardner, Raquel Gomez, Gillian Hobden, Luke Hodgson, Kirsten King, Michael Margarson, Tim Martindale, Emma Meadows, Dana Raynard, Yvette Thirlwall, David Helm, Jordi Margalef, Kristine Criste, Rebecca Cusack, Kim Golder, Hannah Golding, Oliver Jones, Samantha Leggett, Michelle Male, Martyna Marani, Kirsty Prager, Toran Williams, Belinda Roberts, Karen Salmon, Peter Anderson, Katie Archer, Karen Austin, Caroline Davis, Alison Durie, Olivia Kelsall, Jessica Thrush, Charlie Vigurs, Laura Wild, Hannah-Louise Wood, Helen Tranter, Alison Harrison, Nicholas Cowley, Michael McAlindon, Andrew Burtenshaw, Stephen Digby, Emma Low, Aled Morgan, Naiara Cother, Tobias Rankin, Sarah Clayton, Alex McCurdy, Cecilia Ahmed, Balvinder Baines, Sarah Clamp, Julie Colley, Risna Haq, Anne Hayes, Jonathan Hulme, Samia Hussain, Sibet Joseph, Rita Kumar, Zahira Maqsood, Manjit Purewal, Leonie Benham, Zena Bradshaw, Joanna Brown, Melanie Caswell, Jason Cupitt, Sarah Melling, Stephen Preston, Nicola Slawson, Emma Stoddard, Scott Warden, Bethan Deacon, Ceri Lynch, Carla Pothecary, Lisa Roche, Gwenllian Sera Howe, Jayaprakash Singh, Keri Turner, Hannah Ellis, Natalie Stroud, Jodie Hunt, Joy Dearden, Emma Dobson, Andy Drummond, Michelle Mulcahy, Sheila Munt, Grainne O'Connor, Jennifer Philbin, Chloe Rishton, Redmond Tully, Sarah Winnard, Susanne Cathcart, Katharine Duffy, Alex Puxty, Kathryn Puxty, Lynne Turner, Jane Ireland, Gary Semple, Kate Long, Simon Whiteley, Elizabeth Wilby, Bethan Ogg, Amanda Cowton, Andrea Kay, Melanie Kent, Kathryn Potts, Ami Wilkinson, Suzanne Campbell, Ellen Brown, Julie Melville, Jay Naisbitt, Rosane Joseph, Maria Lazo, Olivia Walton, Alan Neal, Peter Alexander, Schvearn Allen, Joanne Bradley-Potts, Craig Brantwood, Jasmine Egan, Timothy Felton, Grace Padden, Luke Ward, Stuart Moss, Susannah Glasgow, Lynn Abel, Michael Brett, Brian Digby, Lisa Gemmell, James Hornsby, Patrick MacGoey, Pauline O'Neil, Richard Price, Natalie Rodden, Kevin Rooney, Radha Sundaram, Nicola Thomson, Bridget Hopkins, Laura Thrasyvoulou, Heather Willis, Martyn Clark, Martina Coulding, Edward Jude, Jacqueline McCormick, Oliver Mercer, Darsh Potla, Hafiz Rehman, Heather Savill, Victoria Turner, Charlotte Downes, Kathleen Holding, Katie Riches, Mary Hilton, Mel Hayman, Deepak Subramanian, Priya Daniel, Oluronke Adanini, Nikhil Bhatia, Maines Msiska, Rebecca Collins, Ian Clement, Bijal Patel, A. Gulati, Carole Hays, K. Webster, Anne Hudson, Andrea Webster, Elaine Stephenson, Louise McCormack, Victoria Slater, Rachel Nixon, Helen Hanson, Maggie Fearby, Sinead Kelly, Victoria Bridgett, Philip Robinson, Julie Camsooksai, Charlotte Humphrey, Sarah Jenkins, Henrik Reschreiter, Beverley Wadams, Yasmin Death, Victoria Bastion, Daphene Clarke, Beena David, Harriet Kent, Rachel Lorusso, Gamu Lubimbi, Sophie Murdoch, Melchizedek Penacerrada, Alastair Thomas, Jennifer Valentine, Ana Vochin, Retno Wulandari, Brice Djeugam, Gillian Bell, Katy English, Amro Katary, Louise Wilcox, Michelle Bruce, Karen Connolly, Tracy Duncan, Helen T-Michael, Gabriella Lindergard, Samuel Hey, Claire Fox, Jordan Alfonso, Laura Jayne Durrans, Jacinta Guerin, Bethan Blackledge, Jade Harris, Martin Hruska, Ayaa Eltayeb, Thomas Lamb, Tracey Hodgkiss, Lisa Cooper, Joanne Rothwell, Angela Allan, Felicity Anderson, Callum Kaye, Jade Liew, Jasmine Medhora, Teresa Scott, Erin Trumper, Adriana Botello, Liana Lankester, Nikitas Nikitas, Colin Wells, Bethan Stowe, Kayleigh Spencer, Craig Brandwood, Lara Smith, Katie Birchall, Laurel Kolakaluri, Deborah Baines, Anila Sukumaran, Elena Apetri, Cathrine Basikolo, Laura Catlow, Bethan Charles, Paul Dark, Reece Doonan, Alice Harvey, Daniel Horner, Karen Knowles, Stephanie Lee, Diane Lomas, Chloe Lyons, Tracy Marsden, Danielle McLaughlan, Liam McMorrow, Jessica Pendlebury, Jane Perez, Maria Poulaka, Nicola Proudfoot, Melanie Slaughter, Kathryn Slevin, Vicky Thomas, Danielle Walker, Angiy Michael, Matthew Collis, Tracey Cosier, Gemma Millen, Neil Richardson, Natasha Schumacher, Heather Weston, James Rand, Nicola Baxter, Steven Henderson, Sophie Kennedy-Hay, Christopher McParland, Laura Rooney, Malcolm Sim, Gordan McCreath, Louise Akeroyd, Shereen Bano, Matt Bromley, Lucy Gurr, Tom Lawton, James Morgan, Kirsten Sellick, Deborah Warren, Brian Wilkinson, Janet McGowan, Camilla Ledgard, Amelia Stacey, Kate Pye, Ruth Bellwood, Michael Bentley, Jeremy Bewley, Zoe Garland, Lisa Grimmer, Bethany Gumbrill, Rebekah Johnson, Katie Sweet, Denise Webster, Georgia Efford, Karen Convery, Deirdre Fottrell-Gould, Lisa Hudig, Jocelyn Keshet-Price, Georgina Randell, Katie Stammers, Maria Bokhari, Vanessa Linnett, Rachael Lucas, Wendy McCormick, Jenny Ritzema, Amanda Sanderson, Helen Wild, Anthony Rostron, Alistair Roy, Lindsey Woods, Sarah Cornell, Fiona Wakinshaw, Kimberley Rogerson, Jordan Jarmain, Robert Parker, Amie Reddy, Ian Turner-Bone, Laura Wilding, Peter Harding, Caroline Abernathy, Louise Foster, Andrew Gratrix, Vicky Martinson, Priyai Parkinson, Elizabeth Stones, Llucia Carbral-Ortega, Georgia Bercades, David Brealey, Ingrid Hass, Niall MacCallum, Gladys Martir, Eamon Raith, Anna Reyes, Deborah Smyth, Letizia Zitter, Sarah Benyon, Suzie Marriott, Linda Park, Samantha Keenan, Elizabeth Gordon, Helen Quinn, Kizzy Baines, Lenka Cagova, Adama Fofano, Lucie Garner, Helen Holcombe, Sue Mepham, Alice Michael Mitchell, Lucy Mwaura, Krithivasan Praman, Alain Vuylsteke, Julie Zamikula, Bally Purewal, Vanessa Rivers, Stephanie Bell, Hayley Blakemore, Borislava Borislavova, Beverley Faulkner, Emma Gendall, Elizabeth Goff, Kati Hayes, Matt Thomas, Ruth Worner, Kerry Smith, Deanna Stephens, Louise Mew, Esther Mwaura, Richard Stewart, Felicity Williams, Lynn Wren, Sara-Beth Sutherland, Emily Bevan, Jane Martin, Dawn Trodd, Geoff Watson, Caroline Wrey Brown, Amy Collins, Waqas Khaliq, Estefania Treus Gude, Olugbenga Akinkugbe, Alasdair Bamford, Emily Beech, Holly Belfield, Michael Bell, Charlene Davies, Gareth A.L. Jones, Tara McHugh, Hamza Meghari, Lauran O'Neill, Mark J. Peters, Samiran Ray, Ana Luisa Tomas, Iona Burn, Geraldine Hambrook, Katarina Manso, Ruth Penn, Pradeep Shanmugasundaram, Julie Tebbutt, Danielle Thornton, Jade Cole, Rhys Davies, Donna Duffin, Helen Hill, Ben Player, Emma Thomas, Angharad Williams, Denise Griffin, Nycola Muchenje, Mcdonald Mupudzi, Richard Partridge, Jo-Anna Conyngham, Rachel Thomas, Mary Wright, Maria Alvarez Corral, Reni Jacob, Cathy Jones, Craig Denmade, Sarah Beavis, Katie Dale, Rachel Gascoyne, Joanne Hawes, Kelly Pritchard, Lesley Stevenson, Amanda Whileman, Patricia Doble, Joanne Hutter, Corinne Pawley, Charmaine Shovelton, Marius Vaida, Deborah Butcher, Susie O'Sullivan, Nicola Butterworth-Cowin, Norfaizan Ahmad, Joann Barker, Kris Bauchmuller, Sarah Bird, Kay Cawthron, Kate Harrington, Yvonne Jackson, Faith Kibutu, Becky Lenagh, Shamiso Masuko, Gary H. Mills, Ajay Raithatha, Matthew Wiles, Jayne Willson, Helen Newell, Alison Lye, Lorenza Nwafor, Claire Jarman, Sarah Rowland-Jones, David Foote, Joby Cole, Roger Thompson, James Watson, Lisa Hesseldon, Irene Macharia, Luke Chetam, Jacqui Smith, Amber Ford, Samantha Anderson, Kathryn Birchall, Kay Housley, Sara Walker, Leanne Milner, Helena Hanratty, Helen Trower, Patrick Phillips, Simon Oxspring, Ben Donne, Catherine Jardine, Dewi Williams, Alasdair Hay, Rebecca Flanagan, Gareth Hughes, Scott Latham, Emma McKenna, Jennifer Anderson, Robert Hull, Kat Rhead, Carina Cruz, Natalie Pattison, Rob Charnock, Denise McFarland, Denise Cosgrove, Ashar Ahmed, Anna Morris, Srinivas Jakkula, Asifa Ali, Megan Brady, Sam Dale, Annalisa Dance, Lisa Gledhill, Jill Greig, Kathryn Hanson, Kelly Holdroyd, Marie Home, Diane Kelly, Ross Kitson, Lear Matapure, Deborah Melia, Samantha Mellor, Tonicha Nortcliffe, Jez Pinnell, Matthew Robinson, Lisa Shaw, Ryan Shaw, Lesley Thomis, Alison Wilson, Tracy Wood, Lee-Ann Bayo, Ekta Merwaha, Tahira Ishaq, Sarah Hanley, Meg Hibbert, Dariusz Tetla, Chrsitopher Woodford, Latha Durga, Gareth Kennard-Holden, Debbie Branney, Jordan Frankham, Sally Pitts, Nigel White, Shondipon Laha, Mark Verlander, Alexandra Williams, Abdelhakim Altabaibeh, Ana Alvaro, Kayleigh Gilbert, Louise Ma, Loreta Mostoles, Chetan Parmar, Kathryn Simpson, Champa Jetha, Lauren Booker, Anezka Pratley, Colene Adams, Anita Agasou, Tracie Arden, Amy Bowes, Pauline Boyle, Mandy Beekes, Heather Button, Nigel Capps, Mandy Carnahan, Anne Carter, Danielle Childs, Denise Donaldson, Kelly Hard, Fran Hurford, Yasmin Hussain, Ayesha Javaid, James Jones, Sanal Jose, Michael Leigh, Terry Martin, Helen Millward, Nichola Motherwell, Rachel Rikunenko, Jo Stickley, Julie Summers, Louise Ting, Helen Tivenan, Louise Tonks, Rebecca Wilcox, Maureen Holland, Natalie Keenan, Marc Lyons, Helen Wassall, Chris Marsh, Mervin Mahenthran, Emma Carter, Thomas Kong, Helen Blackman, Ben Creagh-Brown, Sinead Donlon, Natalia Michalak-Glinska, Sheila Mtuwa, Veronika Pristopan, Armorel Salberg, Eleanor Smith, Sarah Stone, Charles Piercy, Jerik Verula, Dorota Burda, Rugia Montaser, Lesley Harden, Irving Mayangao, Cheryl Marriott, Paul Bradley, Celia Harris, Susan Anderson, Eleanor Andrews, Janine Birch, Emma Collins, Kate Hammerton, Ryan O'Leary, Michele Clark, Sarah Purvis, Russell Barber, Claire Hewitt, Annette Hilldrith, Karen Jackson-Lawrence, Sarah Shepardson, Maryanne Wills, Susan Butler, Silvia Tavares, Amy Cunningham, Julia Hindale, Sarwat Arif, Sarah Bean, Karen Burt, Michael Spivey, Carrie Demetriou, Charlotte Eckbad, Sarah Hierons, Lucy Howie, Sarah Mitchard, Lidia Ramos, Alfredo Serrano-Ruiz, Katie White, Fiona Kelly, Daniele Cristiano, Natalie Dormand, Zohreh Farzad, Mahitha Gummadi, Kamal Liyanage, Brijesh Patel, Sara Salmi, Geraldine Sloane, Vicky Thwaites, Mathew Varghese, Anelise C. Zborowski, John Allan, Tim Geary, Gordon Houston, Alistair Meikle, Peter O'Brien, Miranda Forsey, Agilan Kaliappan, Anne Nicholson, Joanne Riches, Mark Vertue, Elizabeth Allan, Kate Darlington, Ffyon Davies, Jack Easton, Sumit Kumar, Richard Lean, Daniel Menzies, Richard Pugh, Xinyi Qiu, Llinos Davies, Hannah Williams, Jeremy Scanlon, Gwyneth Davies, Callum Mackay, Joannne Lewis, Stephanie Rees, Metod Oblak, Monica Popescu, Mini Thankachen, Andrew Higham, Kerry Simpson, Jayne Craig, Rosie Baruah, Sheila Morris, Susie Ferguson, Amy Shepherd, Luke Stephen Prockter Moore, Marcela Paola Vizcaychipi, Laura Gomes de Almeida Martins, Jaime Carungcong, Inthakab Ali Mohamed Ali, Karen Beaumont, Mark Blunt, Zoe Coton, Hollie Curgenven, Mohamed Elsaadany, Kay Fernandes, Sameena Mohamed Ally, Harini Rangarajan, Varun Sarathy, Sivarupan Selvanayagam, Dave Vedage, Matthew White, Mandy Gill, Paul Paul, Valli Ratnam, Sarah Shelton, Inez Wynter, Siobhain Carmody, Valerie Joan Page, Claire Marie Beith, Karen Black, Suzanne Clements, Alan Morrison, Dominic Strachan, Margaret Taylor, Michelle Clarkson, Stuart D'Sylva, Kathryn Norman, Fiona Auld, Joanne Donnachie, Ian Edmond, Lynn Prentice, Nikole Runciman, Dario Salutous, Lesley Symon, Anne Todd, Patricia Turner, Abigail Short, Laura Sweeney, Euan Murdoch, Dhaneesha Senaratne, Michaela Hill, Thogulava Kannan, Wild Laura, Rikki Crawley, Abigail Crew, Mishell Cunningham, Allison Daniels, Laura Harrison, Susan Hope, Ken Inweregbu, Sian Jones, Nicola Lancaster, Jamie Matthews, Alice Nicholson, Gemma Wray, Helen Langton, Rachel Prout, Malcolm Watters, Catherine Novis, Anthony Barron, Ciara Collins, Sundeep Kaul, Heather Passmore, Claire Prendergast, Anna Reed, Paula Rogers, Rajvinder Shokkar, Meriel Woodruff, Hayley Middleton, Oliver Polgar, Claire Nolan, Kanta Mahay, Dawn Collier, Anil Hormis, Victoria Maynard, Cheryl Graham, Rachel Walker, Ellen Knights, Alicia Price, Alice Thomas, Chris Thorpe, Teresa Behan, Caroline Burnett, Jonathan Hatton, Elaine Heeney, Atideb Mitra, Maria Newton, Rachel Pollard, Rachael Stead, Vishal Amin, Elena Anastasescu, Vikram Anumakonda, Komala Karthik, Rizwana Kausar, Karen Reid, Jacqueline Smith, Janet Imeson-Wood, Denise Skinner, Jane Gaylard, Dee Mullan, Julie Newman, Alison Brown, Vikki Crickmore, Gabor Debreceni, Joy Wilkins, Liz Nicol, Rosie Reece-Anthony, Mark Birt, Alison Ghosh, Emma Williams, Louise Allen, Eva Beranova, Nikki Crisp, Joanne Deery, Tracy Hazelton, Alicia Knight, Carly Price, Sorrell Tilbey, Salah Turki, Sharon Turney, Joshua Cooper, Cheryl Finch, Sarah Liderth, Alison Quinn, Natalia Waddington, Tina Coventry, Susan Fowler, Michael MacMahon, Amanda McGregor, Anne Cowley, Judith Highgate, Jane Gregory, Susan O'Connell, Tim Smith, Luigi Barberis, Shameer Gopal, Nichola Harris, Victoria Lake, Stella Metherell, Elizabeth Radford, Amelia Daniel, Joanne Finn, Rajnish Saha, Nikki White, Phil Donnison, Fiona Trim, Beena Eapen, Jenny Birch, Laura Bough, Josie Goodsell, Rebecca Tutton, Patricia Williams, Sarah Williams, Barbara Winter-Goodwin, Ailstair Nichol, Kathy Brickell, Michelle Smyth, Lorna Murphy, Samantha Coetzee, Alistair Gales, Igor Otahal, Meena Raj, Craig Sell, Paula Hilltout, Jayne Evitts, Amanda Tyler, Joanne Waldron, Kate Beesley, Sarah Board, Agnieszka Kubisz-Pudelko, Alison Lewis, Jess Perry, Lucy Pippard, Di Wood, Clare Buckley, Peter Barry, Neil Flint, Patel Rekha, Dawn Hales, Lara Bunni, Claire Jennings, Monica Latif, Rebecca Marshall, Gayathri Subramanian, Peter J. McGuigan, Christopher Wasson, Stephanie Finn, Jackie Green, Erin Collins, Bernadette King, Andy Campbell, Sara Smuts, Joseph Duffield, Oliver Smith, Lewis Mallon, Watkins Claire, Liam Botfield, Joanna Butler, Catherine Dexter, Jo Fletcher, Atul Garg, Aditya Kuravi, Poonam Ranga, Emma Virgilio, Zakaula Belagodu, Bridget Fuller, Anca Gherman, Olumide Olufuwa, Remi Paramsothy, Carmel Stuart, Naomi Oakley, Charlotte Kamundi, David Tyl, Katy Collins, Pedro Silva, June Taylor, Laura King, Charlotte Coates, Maria Crowley, Phillipa Wakefield, Jane Beadle, Laura Johnson, Janet Sargeant, Madeleine Anderson, Ailbhe Brady, Rebekah Chan, Jeff Little, Shane McIvor, Helena Prady, Helen Whittle, Bijoy Mathew, Ben Attwood, Penny Parsons, Geraldine Ward, Pamela Bremmer, West Joe, Baird Tracy, Ruddy Jim, Ellie Davies, Sonia Sathe, Catherine Dennis, Alastair McGregor, Victoria Parris, Sinduya Srikaran, Anisha Sukha, Noreen Clarke, Jonathan Whiteside, Mairi Mascarenhas, Avril Donaldson, Joanna Matheson, Fiona Barrett, Marianne O'Hara, Laura Okeefe, Clare Bradley, Christine Eastgate-Jackson, Helder Filipe, Daniel Martin, Amitaa Maharajh, Sara Mingo Garcia, Glykeria Pakou, Mark De Neef, Kathy Dent, Elizabeth Horsley, Muhmmad Nauman Akhtar, Sandra Pearson, Dorota Potoczna, Sue Spencer, Melanie Clapham, Rosemary Harper, Una Poultney, Polly Rice, Rachel Mutch, Lisa Armstrong, Hayley Bates, Emma Dooks, Fiona Farquhar, Brigid Hairsine, Chantal McParland, Sophie Packham, Rehana Bi, Barney Scholefield, Lydia Ashton, Linsha George, Sophie Twiss, David Wright, Manish Chablani, Amy Kirkby, Kimberley Netherton, Kim Davies, Linda O'Brien, Zohra Omar, Emma Perkins, Tracy Lewis, Isobel Sutherland, Karen Burns, Dr Ben Chandler, Kerry Elliott, Janine Mallinson, Alison Turnbull, Prisca Gondo, Bernard Hadebe, Abdul Kayani, Bridgett Masunda, Taya Anderson, Dan Hawcutt, Laura O'Malley, Laura Rad, Naomi Rogers, Paula Saunderson, Kathryn Sian Allison, Deborah Afolabi, Jennifer Whitbread, Dawn Jones, Rachael Dore, Matthew Halkes, Pauline Mercer, Lorraine Thornton, Joy Dawson, Sweyn Garrioch, Melanie Tolson, Jonathan Aldridge, Ritoo Kapoor, David Loader, Karen Castle, Sally Humphreys, Ruth Tampsett, Katherine Mackintosh, Amanda Ayers, Wendy Harrison, Julie North, Suzanne Allibone, Roman Genetu, Vidya Kasipandian, Amit Patel, Ainhi Mac, Anthony Murphy, Parisa Mahjoob, Roonak Nazari, Lucy Worsley, Andrew Fagan, Thomas Bemand, Ethel Black, Arnold Dela Rosa, Ryan Howle, Shaman Jhanji, Ravishankar Rao Baikady, Kate Colette Tatham, Benjamin Thomas, Dina Bell, Rosalind Boyle, Katie Douglas, Lynn Glass, Emma Lee, Liz Lennon, Austin Rattray, Abigail Taylor, Rachel Anne Hughes, Helen Thomas, Alun Rees, Michaela Duskova, Janet Phipps, Suzanne Brooks, Michelle Edwards, Sheena Quaid, Ekaterina Watson, Adam Brayne, Emma Fisher, Jane Hunt, Peter Jackson, Duncan Kaye, Nicholas Love, Juliet Parkin, Victoria Tuckey, Lynne Van Koutrik, Sasha Carter, Benedict Andrew, Louise Findlay, Katie Adams, Jen Service, Alison Williams, Claire Cheyne, Anne Saunderson, Sam Moultrie, Miranda Odam, Kathryn Hall, Isheunesu Mapfunde, Charlotte Willis, Alex Lyon, Chunda Sri-Chandana, Joslan Scherewode, Lorraine Stephenson, Sarah Marsh, John Hardy, Henry Houlden, Eleanor Moncur, Ambreen Tariq, Arianna Tucci, Maria Hobrok, Ronda Loosley, Heather McGuinness, Helen Tench, Rebecca Wolf-Roberts, Val Irvine, Benjamin Shelley, Claire Gorman, Abhinav Gupta, Elizabeth Timlick, Rebecca Brady, Barry Milligan, Arianna Bellini, Jade Bryant, Anton Mayer, Amy Pickard, Nicholas Roe, Jason Sowter, Alex Howlett, Katy Fidler, Emma Tagliavini, and Kevin Donnelly

Subjects
SARS-CoV-2, host genetics, toll-like receptor 7, targeted sequencing, rare variants, variant collapsing analysis, Genetics, QH426-470
Abstract

Summary: Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (

Published
2024
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13. Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling

Author

Marwan E. Majzoub, Laurence D. W. Luu, Craig Haifer, Sudarshan Paramsothy, Thomas J. Borody, Rupert W. Leong, Torsten Thomas, and Nadeem O. Kaakoush

Subjects
metabolic modelling, microbiome, human, environmental, metagenomics, Microbiology, QR1-502
Abstract

ABSTRACT Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.

Published
2024
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14. Profiling the colonic mucosal response to fecal microbiota transplantation identifies a role for GBP5 in colitis in humans and mice

Author

Laurence D. W. Luu, Abhimanu Pandey, Sudarshan Paramsothy, Chinh Ngo, Natalia Castaño-Rodríguez, Cheng Liu, Michael A. Kamm, Thomas J. Borody, Si Ming Man, and Nadeem O. Kaakoush

Subjects
Science
Abstract

Abstract Host molecular responses to fecal microbiota transplantation (FMT) in ulcerative colitis are not well understood. Here, we profile the human colonic mucosal transcriptome prior to and following FMT or placebo to identify molecules regulated during disease remission. FMT alters the transcriptome above the effect of placebo (n = 75 vs 3 genes, q

Published
2024
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15. MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments

Author

Wilson, Joey, Song, Jingyu, Fu, Yuewei, Zhang, Arthur, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, and Ghaffari, Maani

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

This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions or traces. We create SSC baselines from state-of-the-art open source networks and construct a benchmark real-time dense local semantic mapping algorithm, MotionSC, by leveraging recent 3D deep learning architectures to enhance SSC with temporal information. Our network shows that the proposed data set can quantify and supervise accurate scene completion in the presence of dynamic objects, which can lead to the development of improved dynamic mapping algorithms. All software is available at https://github.com/UMich-CURLY/3DMapping.

Published
2022

17. Continuous-variable optimization with neural network quantum states

Author

Zhang, Yabin, Gorsich, David, Jayakumar, Paramsothy, and Veerapaneni, Shravan

Subjects
Quantum Physics, Mathematics - Optimization and Control
Abstract

Inspired by proposals for continuous-variable quantum approximate optimization (CV-QAOA), we investigate the utility of continuous-variable neural network quantum states (CV-NQS) for performing continuous optimization, focusing on the ground state optimization of the classical antiferromagnetic rotor model. Numerical experiments conducted using variational Monte Carlo with CV-NQS indicate that although the non-local algorithm succeeds in finding ground states competitive with the local gradient search methods, the proposal suffers from unfavorable scaling. A number of proposed extensions are put forward which may help alleviate the scaling difficulty.

Published
2021

18. Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference

Author

Unnikrishnan, Aishwarya, Wilson, Joey, Gan, Lu, Capodieci, Andrew, Jayakumar, Paramsothy, Barton, Kira, and Ghaffari, Maani

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

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.

Published
2021

19. Enhanced denitrification driven by a novel iron-carbon coupled primary cell: chemical and mixotrophic denitrification

Author

Ruofan Wu, Paramsothy Jeyakumar, Nanthi Bolan, Xu Zhai, Hailong Wang, Minghui Pan, Jiapan Lian, Liping Cheng, Jiangzhou Li, Minghei Hou, Yonghe Cui, Xiaoe Yang, and Kuai Dai

Subjects
Multipathway denitrification, Iron and carbon coupled-galvanic cell, Nitrate removal, Electron transfer capability, Environmental sciences, GE1-350, Agriculture
Abstract

Abstract Iron-carbon micro-electrolysis system is a promising method for promoting electron transfer in nitrate removal. However, many traditional approaches involving simple physical mixing inevitably suffered from the confined iron-carbon contact area and short validity period, leading to the overuse of iron. Here, a ceramsite-loaded microscale zero-valent iron (mZVI) and acidified carbon (AC) coupled-galvanic cell (CMC) was designed to support chemical, autotrophic and heterotrophic denitrification. Long-term experiments were conducted to monitor the nitrogen removal performance of denitrification reactors filled with CMC and thus optimized the denitrification performance by improving fabrication parameters and various operating conditions. The denitrification contributions test showed that the chemical denitrification pathway contributed most to nitrate removal (57.3%), followed by autotrophic (24.6%) and heterotrophic denitrification pathways (18.1%). The microbial analysis confirmed the significant aggregation of related denitrifying bacteria in the reactors, while AC promoted the expression of relevant nitrogen metabolism genes because of accelerated uptake and utilization of iron complexes. Meanwhile, the electrochemical analysis revealed a significantly improved electron transfer capacity of AC compared to pristine carbon. Overall, our study demonstrated the application of a novel mZVI-AC coupled material for effective nitrate removal and revealed the potential impact of CMC in the multipathway denitrification process. Graphical Abstract

Published
2024
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21. A Workload Adaptive Haptic Shared Control Scheme for Semi-Autonomous Driving

Author

Luo, Ruikun, Weng, Yifan, Wang, Yifan, Jayakumar, Paramsothy, Brudnak, Mark J., Paul, Victor, Desaraju, Vishnu R., Stein, Jeffrey L., Ersal, Tulga, and Yang, X. Jessie

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Robotics
Abstract

Haptic shared control is used to manage the control authority allocation between a human and an autonomous agent in semi-autonomous driving. Existing haptic shared control schemes, however, do not take full consideration of the human agent. To fill this research gap, this study presents a haptic shared control scheme that adapts to a human operator's workload, eyes on road and input torque in real-time. We conducted human-in-the-loop experiments with 24 participants. In the experiment, a human operator and an autonomy module for navigation shared the control of a simulated notional High Mobility Multipurpose Wheeled Vehicle (HMMWV) at a fixed speed. At the same time, the human operator performed a target detection task for surveillance. The autonomy could be either adaptive or non-adaptive to the above-mentioned human factors. Results indicate that the adaptive haptic control scheme resulted in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort.

Published
2020

22. An Active Learning Framework for Constructing High-fidelity Mobility Maps

Author

Marple, Gary R., Gorsich, David, Jayakumar, Paramsothy, and Veerapaneni, Shravan

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A mobility map, which provides maximum achievable speed on a given terrain, is essential for path planning of autonomous ground vehicles in off-road settings. While physics-based simulations play a central role in creating next-generation, high-fidelity mobility maps, they are cumbersome and expensive. For instance, a typical simulation can take weeks to run on a supercomputer and each map requires thousands of such simulations. Recent work at the U.S. Army CCDC Ground Vehicle Systems Center has shown that trained machine learning classifiers can greatly improve the efficiency of this process. However, deciding which simulations to run in order to train the classifier efficiently is still an open problem. According to PAC learning theory, data that can be separated by a classifier is expected to require $\mathcal{O}(1/\epsilon)$ randomly selected points (simulations) to train the classifier with error less than $\epsilon$. In this paper, building on existing algorithms, we introduce an active learning paradigm that substantially reduces the number of simulations needed to train a machine learning classifier without sacrificing accuracy. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling.

Published
2020

23. Neural network based terramechanics modeling and estimation for deformable terrains

Author

Dallas, James, Cole, Michael P., Jayakumar, Paramsothy, and Ersal, Tulga

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

In this work, a neural network based terramechanics model and terrain estimator are presented with an outlook for optimal control applications such as model predictive control. Recognizing the limitations of the state-of-the-art terramechanics models in terms of operating conditions, computational cost, and continuous differentiability for gradient-based optimization, an efficient and twice continuously differentiable terramechanics model is developed using neural networks for dynamic operations on deformable terrains. It is demonstrated that the neural network terramechanics model is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model. Furthermore, the neural network terramechanics model is implemented within a terrain estimator and it is shown that using this model the estimator converges within around 2% of the true terrain parameter. Finally, with model predictive control applications in mind, which typically rely on bicycle models for their predictions, it is demonstrated that utilizing the estimated terrain parameter can reduce prediction errors of a bicycle model by orders of magnitude. The result is an efficient, dynamic, twice continuously differentiable terramechanics model and estimator that has inherent advantages for implementation in model predictive control as compared to previously established models.

Published
2020

24. NLOptControl: A modeling language for solving optimal control problems

Author

Febbo, Huckleberry, Jayakumar, Paramsothy, Stein, Jeffrey L., and Ersal, Tulga

Subjects
Computer Science - Mathematical Software, Mathematics - Optimization and Control
Abstract

Current direct-collocation-based optimal control software is either easy to use or fast, but not both. This is a major limitation for users that are trying to formulate complex optimal control problems (OCPs) for use in on-line applications. This paper introduces NLOptControl, an open-source modeling language that allows users to both easily formulate and quickly solve nonlinear OCPs using direct-collocation methods. To achieve these attributes, NLOptControl (1) is written in an efficient, dynamically-typed computing language called Julia, (2) extends an optimization modeling language called JuMP to provide a natural algebraic syntax for modeling nonlinear OCPs; and (3) uses reverse automatic differentiation with the acrylic-coloring method to exploit sparsity in the Hessian matrix. This work explores the novel design features of NLOptControl and compares its syntax and speed to those of PROPT. The syntax comparisons shows that NLOptControl models OCPs more concisely than PROPT. The speeds of various collocation methods within PROPT and NLOptControl are benchmarked over a range of collocation points using performance profiles; overall, NLOptControl's single, two, and four interval pseudospectral methods are roughly $14$, $26$, and $36$ times faster than PROPT's, respectively. NLOptControl is well-suited to improve existing off-line and on-line control systems and to engender new ones.

Published
2020

25. Effects of Thiopurine Withdrawal on Vedolizumab-Treated Patients With Ulcerative Colitis: A Randomized Controlled Trial.

Author

Pudipeddi, Aviv, Paramsothy, Sudarshan, Kariyawasam, Viraj, Paramsothy, Ramesh, Ghaly, Simon, Haifer, Craig, An, Yoon-Kyo, Begun, Jakob, Connor, Susan J., Corte, Crispin, Ward, Mark G., De Cruz, Peter, Lan-San Fung, Caroline, Redmond, Diane, Chan, Webber, Mourad, Fadi, Kermeen, Melissa, and Leong, Rupert W.

Abstract

The impact of thiopurine de-escalation while on vedolizumab versus continuing thiopurine therapy in ulcerative colitis (UC) is unclear. We aimed to determine the effect of thiopurine withdrawal for patients with UC in remission on vedolizumab. This multicenter randomized controlled trial recruited UC patients on vedolizumab 300 mg intravenously every 8 weeks and a thiopurine. Patients in steroid-free clinical remission for ≥6 months and endoscopic remission/improvement (Mayo endoscopic subscore ≤1) were randomized 2:1 to withdraw or continue thiopurine. Primary outcome was comparing week 48 vedolizumab trough concentrations. Secondary outcomes were clinical relapse (partial Mayo score ≥3 and fecal calprotectin >150 μg/g or increase in Mayo endoscopic subscore ≥1 from baseline), fecal calprotectin remission (<150 μg/g), C-reactive protein remission (<5 mg/L), centrally read endoscopic remission (Mayo endoscopic subscore = 0), histologic remission (Nancy index = 0), histo-endoscopic remission, and adverse events. In total, 62 patients were randomized to continue (n = 20) or withdraw (n = 42) thiopurine. At week 48, vedolizumab trough concentrations were not significantly different between continue and withdrawal groups (14.7 μg/mL, interquartile rate [IQR], 12.3–18.5 μg/mL versus 15.9 μg/mL, IQR, 10.1–22.7 μg/mL, respectively, P = 0.36). The continue group had significantly higher fecal calprotectin remission (95.0%, 19/20 versus 71.4%, 30/42; P =.03), histologic remission (80.0%, 16/20 versus 48.6%, 18/37; P =.02), and histo-endoscopic remission (75.0%, 15/20 versus 32.4%, 12/37; P =.002) than the withdrawal group. Histologic activity (hazard ratio [HR], 15.5; 95% confidence interval [CI], 1.6–146.5; P =.02) and prior anti-tumor necrosis factor exposure (HR, 6.5; 95% CI, 1.3–33.8; P =.03) predicted clinical relapse after thiopurine withdrawal. Thiopurine withdrawal did not affect vedolizumab trough concentrations. However, it may increase fecal calprotectin, histologic, and histo-endoscopic activity. Histologic activity and prior anti-tumor necrosis factor exposure may predict disease relapse on thiopurine withdrawal for patients using vedolizumab for UC. Australian and New Zealand Trial Registry, number ACTRN12618000812291. [Display omitted] [ABSTRACT FROM AUTHOR]

Published
2024
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26. Real-time trajectory planning for automated vehicle safety and performance in dynamic environments

Author

Febbo, Huckleberry, Jayakumar, Paramsothy, Stein, Jeffrey L., and Ersal, Tulga

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. In particular, a parametric study relating execution horizon and obstacle speed, indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon ($\leq0.375\;s$) and the obstacles are moving slowly ($\leq2.11\frac{m}{s}$). However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety by a factor of $6.73$ ($p=2.2\times10^{-16}$) without, in most cases, increasing the solve-times. Overall, the results indicate that (1) safe trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above, unless a static or low-speed environment permits a less comprehensive planner; and (2) NLOptControl can solve the formulation in real-time.

Published
2020

27. Online terrain estimation for autonomous vehicles on deformable terrains

Author

Dallas, James, Jain, Kshitij, Dong, Zheng, Cole, Michael P., Jayakumar, Paramsothy, and Ersal, Tulga

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order surrogate model is able to accurately replicate the forces predicted by the SCM while reducing the computation cost by an order of magnitude. This surrogate model is then utilized in a unscented Kalman filter to estimate the sinkage exponent. Simulations suggest this parameter can be estimated within 4% of its true value for clay and sandy loam terrains. It is also shown that utilizing this estimated parameter can reduce the prediction errors of the future vehicle states by orders of magnitude, which could assist with achieving more robust model-predictive autonomous navigation strategies.

Published
2019
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29. Remediation technologies for neonicotinoids in contaminated environments: Current state and future prospects

Author

Jing Wei, Xiaoyu Wang, Chen Tu, Tao Long, Yuanqing Bu, Hailong Wang, Paramsothy Jeyakumar, Jinlin Jiang, and Shaopo Deng

Subjects
Neonicotinoid, Remediation, Photocatalysis, Advanced oxidation processes, Bacterial degradation, Phytoremediation, Environmental sciences, GE1-350
Abstract

Neonicotinoids (NEOs) are synthetic insecticides with broad-spectrum insecticidal activity and outstanding efficacy. However, their extensive use and persistence in the environment have resulted in the accumulation and biomagnification of NEOs, posing significant risks to non-target organisms and humans. This review provides a summary of research history, advancements, and highlighted topics in NEOs remediation technologies and mechanisms. Various remediation approaches have been developed, including physiochemical, microbial, and phytoremediation, with microbial and physicochemical remediation being the most extensively studied. Recent advances in physiochemical remediation have led to the development of innovative adsorbents, photocatalysts, and optimized treatment processes. High-efficiency degrading strains with well-characterized metabolic pathways have been successfully isolated and cultured for microbial remediation, while many plant species have shown great potential for phytoremediation. However, significant challenges and gaps remain in this field. Future research should prioritize isolating, domesticating or engineering high efficiency, broad-spectrum microbial strains for NEO degradation, as well as developing synergistic remediation techniques to enhance removal efficiency on multiple NEOs with varying concentrations in different environmental media. Furthermore, a shift from pipe-end treatment to pollution prevention strategies is needed, including the development of green and economically efficient alternatives such as biological insecticides. Integrated remediation technologies and case-specific strategies that can be applied to practical remediation projects need to be developed, along with clarifying NEO degradation mechanisms to improve remediation efficiency. The successful implementation of these strategies will help reduce the negative impact of NEOs on the environment and human health.

Published
2023
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31. Tensor Train accelerated solvers for nonsmooth rigid body dynamics

Author

Corona, Eduardo, Gorsich, David, Jayakumar, Paramsothy, and Veerapaneni, Shravan

Subjects
Mathematics - Numerical Analysis
Abstract

In the last two decades, increased need for high-fidelity simulations of the time evolution and propagation of forces in granular media has spurred renewed interest in discrete element method (DEM) modeling of frictional contact. Force penalty methods, while economic and accessible, introduce artificial stiffness, requiring small time steps to retain numerical stability. Optimization-based methods, which enforce contacts geometrically through complementarity constraints, allow the use of larger time steps at the expense of solving a nonlinear complementarity problem (NCP) each time step. We review the latest efforts to produce solvers for this NCP, focusing on its relaxation to a cone complementarity problem (CCP) and solution via an equivalent quadratic optimization problem with conic constraints. We distinguish between linearly convergent first order methods and second order methods, which gain quadratic convergence and more robust performance at the expense of the solution of large sparse linear systems. We propose a novel acceleration for the solution of Newton step linear systems in second order methods using low-rank compression based fast direct solvers. We use the Quantized Tensor Train (QTT) decomposition to produce efficient approximate representations of the system matrix and its inverse. This provides a robust framework to accelerate its solution in a direct or a preconditioned iterative method. In a number of numerical tests, we demonstrate that this approach displays sublinear scaling of precomputation costs, may be efficiently updated across Newton iterations as well as across time steps, and leads to a fast, optimal complexity solution of the Newton step. This allows our method to gain an order of magnitude speedups over state-of-the-art preconditioning techniques for moderate to large-scale systems, mitigating the computational bottleneck of second order methods., Comment: Submitted to the Journal Applied Mechanics Reviews (ASME) (invited article)

Published
2018

32. Effects of biochar in combination with varied N inputs on grain yield, N uptake, NH3 volatilization, and N2O emission in paddy soil

Author

Zhenghua Yi, Paramsothy Jeyakumar, Chengcheng Yin, and Haijun Sun

Subjects
biochar, N fertilizer, rice yield, NH3 volatilization, N2O emission, Microbiology, QR1-502
Abstract

Biochar application can improve crop yield, reduce ammonia (NH3) volatilization and nitrous oxide (N2O) emission from farmland. We here conducted a pot experiment to compare the effects of biochar application on rice yield, nitrogen (N) uptake, NH3 and N2O losses in paddy soil with low, medium, and high N inputs at 160 kg/ha, 200 kg/ha and 240 kg/ha, respectively. The results showed that: (1) Biochar significantly increased the rice grain yield at medium (200 kg/ha) and high (240 kg/ha) N inputs by 56.4 and 70.5%, respectively. The way to increase yield was to increase the rice N uptake, rice panicle number per pot and 1,000 grain weight by 78.5–96.5%, 6–16% and 4.4–6.1%, respectively; (2) Under low (160 kg/ha) N input, adding biochar effectively reduced the NH3 volatilization by 31.6% in rice season. The decreases of pH value and NH4+-N content in surface water, and the increases of the abundance of NH4+-N oxidizing archaea and bacteria (AOA and AOB) communities contributed to the reduction of NH3 volatilization following the biochar application; (3) Under same N input levels, the total N2O emission in rice season decreased by 43.3–73.9% after biochar addition. The decreases of nirK and nirS gene abundances but the increases of nosZ gene abundance are the main mechanisms for biochar application to reduce N2O emissions. Based on the results of the current study, adding biochar at medium (200 kg/ha) N level (N200 + BC) is the best treatment to synchronically reduce NH3 and N2O losses, improve grain yield, and reduce fertilizer application in rice production system.

Published
2023
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34. Biochar Derived from Urban Green Waste Can Enhance the Removal of Cd from Water and Reduce Soil Cd Bioavailability

Author

Xiang Li, Paramsothy Jeyakumar, Nanthi Bolan, Lianxi Huang, Muhammad Saqib Rashid, Zhongzhen Liu, Lan Wei, and Hailong Wang

Subjects
urban green waste, adsorption, biochar immobilization, Cd, soil remediation, Chemical technology, TP1-1185
Abstract

The beneficial utilization of potentially increasing urban green waste (UGW) is critical for sustainable urban development in China. In this study, UGW was pyrolyzed at different temperatures, and the resulting biochar was used to amend Cd-contaminated soils to grow cabbage. Our results showed that the Cd adsorption capacity of UGW-biochar was positively correlated with the surface area, O/C, and (O+N)/C value of biochar. Furthermore, UGW-biochar was incorporated into three Cd-contaminated soils, including one acidic soil and two neutral soils, to assess its impact on the availability of Cd. The most substantial reduction in the concentration of available Cd was observed in the acidic soil, of the three tested soils. In the neutral soils, a more substantial reduction was found in the heavily Cd-contaminated soil compared to the lightly Cd-contaminated soil. UGW-biochar amendments to the three Cd-contaminated soils resulted in an increase in the cabbage biomass in acidic soil, whereas in neutral soils, it increased in lightly contaminated soils but decreased in heavily contaminated soils. Additionally, the Cd bioaccumulation factor (BCF), translocation factor (TF), and removal efficiency (RE), as impacted by the biochar application, were calculated in the lightly Cd-contaminated soil–cabbage system. The BCF decreased from 5.84 to 3.80 as the dosage of the UGW-biochar increased from 0% to 3%, indicating that the UGW-biochar immobilized Cd and reduced its bioaccumulation in cabbage roots. Based on our investigations, UGW-biochar effectively immobilizes Cd by reducing its mobility and bioavailability in a lightly contaminated environment matrix.

Published
2023
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35. Fragile X Syndrome-Associated Emergency Department Visits in the United States, 2006-2011

Author

Do, Thuy Quynh N., Riley, Catharine, Paramsothy, Pangaja, Ouyang, Lijing, Bolen, Julie, and Grosse, Scott D.

Abstract

Using national data, we examined emergency department (ED) encounters during 2006-2011 for which a diagnosis code for fragile X syndrome (FXS) was present (n = 7,217). Almost half of ED visits coded for FXS resulted in hospitalization, which is much higher than for ED visits not coded for FXS. ED visits among females coded for FXS were slightly more likely to result in hospitalization. These findings underscore the importance of surveillance systems that could accurately identify individuals with FXS, track healthcare utilization and co-occurring conditions, and monitor quality of care in order to improve care and reduce FXS-associated morbidity.

Published
2020
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38. Dynamic Semantic Occupancy Mapping Using 3D Scene Flow and Closed-Form Bayesian Inference

Author

Aishwarya Unnikrishnan, Joey Wilson, Lu Gan, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, and Maani Ghaffari

Subjects
Bayesian inference, computer vision, mapping, semantic scene understanding, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.

Published
2022
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39. Types of Myocardial Infarction Among Human Immunodeficiency Virus–Infected Individuals in the United States

Author

Crane, Heidi M, Paramsothy, Pathmaja, Drozd, Daniel R, Nance, Robin M, Delaney, JA Chris, Heckbert, Susan R, Budoff, Matthew J, Burkholder, Greer A, Willig, James H, Mugavero, Michael J, Mathews, William C, Crane, Paul K, Moore, Richard D, Eron, Joseph J, Napravnik, Sonia, Hunt, Peter W, Geng, Elvin, Hsue, Priscilla, Rodriguez, Carla, Peter, Inga, Barnes, Greg S, McReynolds, Justin, Lober, William B, Crothers, Kristina, Feinstein, Mathew, Grunfeld, Carl, Saag, Michael S, and Kitahata, Mari M

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Infectious Diseases, Heart Disease - Coronary Heart Disease, Cardiovascular, Sexually Transmitted Infections, HIV/AIDS, Hematology, Heart Disease, Clinical Research, Prevention, Infection, Good Health and Well Being, Adult, Coronary Angiography, Electrocardiography, Female, Follow-Up Studies, HIV, HIV Infections, Humans, Incidence, Male, Middle Aged, Myocardial Infarction, Retrospective Studies, Risk Assessment, Risk Factors, Survival Rate, Time Factors, United States, Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) Cohort, Cardiovascular medicine and haematology
Abstract

ImportanceThe Second Universal Definition of Myocardial Infarction (MI) divides MIs into different types. Type 1 MIs result spontaneously from instability of atherosclerotic plaque, whereas type 2 MIs occur in the setting of a mismatch between oxygen demand and supply, as with severe hypotension. Type 2 MIs are uncommon in the general population, but their frequency in human immunodeficiency virus (HIV)-infected individuals is unknown.ObjectivesTo characterize MIs, including type; identify causes of type 2 MIs; and compare demographic and clinical characteristics among HIV-infected individuals with type 1 vs type 2 MIs.Design, setting, and participantsThis longitudinal study identified potential MIs among patients with HIV receiving clinical care at 6 US sites from January 1, 1996, to March 1, 2014, using diagnoses and cardiac biomarkers recorded in the centralized data repository. Sites assembled deidentified packets, including physician notes and electrocardiograms, procedures, and clinical laboratory tests. Two physician experts adjudicated each event, categorizing each definite or probable MI as type 1 or type 2 and identifying the causes of type 2 MI.Main outcomes and measuresThe number and proportion of type 1 vs type 2 MIs, demographic and clinical characteristics among those with type 1 vs type 2 MIs, and the causes of type 2 MIs.ResultsAmong 571 patients (median age, 49 years [interquartile range, 43-55 years]; 430 men and 141 women) with definite or probable MIs, 288 MIs (50.4%) were type 2 and 283 (49.6%) were type 1. In analyses of type 1 MIs, 79 patients who underwent cardiac interventions, such as coronary artery bypass graft surgery, were also included, totaling 362 patients. Sepsis or bacteremia (100 [34.7%]) and recent use of cocaine or other illicit drugs (39 [13.5%]) were the most common causes of type 2 MIs. A higher proportion of patients with type 2 MIs were younger than 40 years (47 of 288 [16.3%] vs 32 of 362 [8.8%]) and had lower current CD4 cell counts (median, 230 vs 383 cells/µL), lipid levels (mean [SD] total cholesterol level, 167 [63] vs 190 [54] mg/dL, and mean (SD) Framingham risk scores (8% [7%] vs 10% [8%]) than those with type 1 MIs or who underwent cardiac interventions.Conclusions and relevanceApproximately half of all MIs among HIV-infected individuals were type 2 MIs caused by heterogeneous clinical conditions, including sepsis or bacteremia and recent use of cocaine or other illicit drugs. Demographic characteristics and cardiovascular risk factors among those with type 1 and type 2 MIs differed, suggesting the need to specifically consider type among HIV-infected individuals to further understand MI outcomes and to guide prevention and treatment.

Published
2017

40. Duration of the menopausal transition is longer in women with young age at onset

Author

Paramsothy, Pangaja, Harlow, Siobán D, Nan, Bin, Greendale, Gail A, Santoro, Nanette, Crawford, Sybil L, Gold, Ellen B, Tepper, Ping G, and Randolph, John F

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Minority Health, Contraception/Reproduction, Women's Health, Tobacco, Aging, Tobacco Smoke and Health, Estrogen, Reproductive health and childbirth, Adult, Black or African American, Age of Onset, Asian, Female, Humans, Menopause, Menstrual Cycle, Middle Aged, Time Factors, United States, White People, Ethnicity, Menopausal transition, Obesity, Race, Smoking, Medical and Health Sciences, Obstetrics & Reproductive Medicine, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

ObjectiveThe menopausal transition (MT) is a critical period associated with physiologic changes that influence women's long-term health and longevity. Information is, however, limited regarding factors that influence age at the onset of the MT and its duration (ie, time from MT onset to the final menstrual period).MethodsWe analyzed data for 1,145 women from four sites of the Study of Women's Health Across the Nation who participated in the menstrual calendar substudy, had the start of the MT identified, and had no missing covariate information. Participants included from four racial/ethnic groups: African American, white, Chinese, and Japanese. Women completed daily menstrual calendars from 1996 to 2006 and questions on hormone therapy use monthly. Baseline measures included education, economic strain, and menstrual cycle characteristics. Annual measures included height, weight, and smoking status. Cox proportional hazards models were used to analyze the data.ResultsThe adjusted median duration of the MT ranged from 4.37 years among the oldest age-at-onset quartile to 8.57 years among the youngest age-at-onset quartile (P

Published
2017

41. Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing

Author

Jordan Ewing, Thomas Oommen, Jobin Thomas, Anush Kasaragod, Richard Dobson, Colin Brooks, Paramsothy Jayakumar, Michael Cole, and Tulga Ersal

Subjects
machine learning, deep learning, remote sensing, hyperspectral imaging, thermal imaging, terramechanics, Chemical technology, TP1-1185
Abstract

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.

Published
2023
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42. Intravenous Versus Subcutaneous Infliximab in Inflammatory Bowel Disease: A Systematic Review and Meta-analysis.

Author

Chetwood, John David, Tran, Yvonne, Subramanian, Sreedhar, Smith, Philip J, Iborra, Marisa, Buisson, Anthony, Paramsothy, Sudarshan, and Leong, Rupert W

Abstract

Background Subcutaneous [SC] infliximab may provide multiple benefits over intravenous [IV] formulations. However, studies for efficacy and safety in inflammatory bowel disease [IBD] have been constrained by small sizes that limit the interpretation of outcomes, particularly for subgroups potentially at high risk of disease relapse. Methods We conducted a systematic review and random-effects meta-analysis up to January 2023, to evaluate the change in clinical remission after transitioning from IV to SC infliximab in patients with IBD in clinical remission. The primary outcome was measured using the relative risk for meta-analysis. Results We identified 15 studies of patients established ≥ 3 months on IV infliximab, consisting of 1371 patients and 840 patient-years of follow-up. There was no loss of clinical remission in the IBD cohort overall, Crohn's disease [CD], or perianal CD [ p  = 0.55 and p  = 0.11 at 9–12 months, and p  = 0.50 at 6 months, respectively]. Neither prior IV dose [≤ 10 mg/kg 6-weekly] [ p  = 0.48] nor IBD disease subtype was associated with an increased clinical relapse rate at 6 months (p  = 0.48 and p  = 0.45 [UC vs CD], respectively). Conclusion Changing patients established on IV infliximab to an SC formulation is associated with a high ongoing clinical remission and a low adverse event rate. Furthermore, there are no signals for adverse outcomes among different IBD disease subtypes, nor in those on escalated IV infliximab dosing schedules up to 10 mg/kg 6-weekly. These data should provide patients and clinicians alike with confidence in SC infliximab use in IBD. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Vedolizumab has longer persistence than infliximab as a first-line biological agent but not as a second-line biological agent in moderate-to-severe ulcerative colitis: real-world registry data from the Persistence Australian National IBD Cohort (PANIC) study

Author

Aviv Pudipeddi, Yanna Ko, Sudarshan Paramsothy, and Rupert W. Leong

Subjects
Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Background: The choice between infliximab (IFX) and vedolizumab (VED) as a first-line biological agent in moderate-to-severe ulcerative colitis (UC) can be difficult. Second-line vedolizumab (VED) efficacy may decline following prior infliximab (IFX) treatment failure in UC patients. However, it is not known whether second-line IFX efficacy declines after failure of first-line VED. Aims: We aimed to compare first-line and second-line persistence of IFX and VED, in particular whether second-line IFX persistence declines after failure of first-line VED. Methods: Persistence of IFX and VED was analysed from the Australian Pharmaceutical Benefits Scheme registry data as either first- or second-line treatment in UC. Propensity score matching (1:1) was conducted in the comparison of first-line treatments. Cox proportional hazard regression analysis was used to identify significant predictors and expressed as a hazard ratio (HR and 95% CI). Results: There were 420 subjects with moderate-to-severe UC who received either first-line IFX ( n = 251) or VED ( n = 169), with 774 patient-years of follow-up. First-line VED had significantly longer persistence than first-line IFX (>50.2 versus 22.2 months, p = 0.001). Fifty-three subjects failed first-line IFX and swapped to second-line VED (IFX→VED group). Twenty-two subjects failed first-line VED group and swapped to second-line IFX (VED→IFX group). First-line VED persistence was significantly longer than second-line VED (>50.2 versus 32.0 months, p = 0.03), but first-line IFX persistence was not statistically significantly different to second-line IFX (27.6 months versus > 38.6 months, p = 0.30). Immunomodulator co-therapy was significantly associated with a lower risk of nonpersistence of first-line VED (HR: 0.55, 95% CI: 0.33–0.89, p = 0.02) and IFX (HR: 0.63,95%CI: 0.33–0.92, p = 0.02). Conclusion: VED had a significantly longer persistence than IFX as first-line biological agent but does not disadvantage second-line IFX use in moderate-to-severe UC. VED after IFX is associated with significantly poorer persistence. VED, therefore, should be considered as the first-line biological agent of choice in UC.

Published
2022
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47. Australia IBD Microbiome (AIM) Study: protocol for a multicentre longitudinal prospective cohort study

Author

Nan Wu, Sudarshan Paramsothy, Simon Ghaly, Ramesh Paramsothy, Astrid-Jane Williams, Crispin Corte, and Steven Leach

Subjects
Medicine
Abstract

Introduction Crohn’s disease and ulcerative colitis are common chronic idiopathic inflammatory bowel diseases (IBD), which cause considerable morbidity. Although the precise mechanisms of disease remain unclear, evidence implicates a strong multidirectional interplay between diet, environmental factors, genetic determinants/immune perturbations and the gut microbiota. IBD can be brought into remission using a number of medications, which act by suppressing the immune response. However, none of the available medications address any of the underlying potential mechanisms. As we understand more about how the microbiota drives inflammation, much interest has focused on identifying microbial signals/triggers in the search for effective therapeutic targets. We describe the establishment of the Australian IBD Microbiota (AIM) Study, Australia’s first longitudinal IBD bioresource, which will identify and correlate longitudinal microbial and metagenomics signals to disease activity as evaluated by validated clinical instruments, patient-reported surveys, as well as biomarkers. The AIM Study will also gather extensive demographic, clinical, lifestyle and dietary data known to influence microbial composition in order to generate a more complete understanding of the interplay between patients with IBD and their microbiota.Methods The AIM Study is an Australian multicentre longitudinal prospective cohort study, which will enrol 1000 participants; 500 patients with IBD and 500 healthy controls over a 5-year period. Assessment occurs at 3 monthly intervals over a 24-month period. At each assessment oral and faecal samples are self-collected along with patient-reported outcome measures, with clinical data also collected at baseline, 12 and 24 months. Intestinal tissue will be sampled whenever a colonoscopy is performed. Dietary intake, general health and psychological state will be assessed using validated self-report questionnaires. Samples will undergo metagenomic, transcriptomic, proteomic, metabolomic and culturomic analyses. Omics data will be integrated with clinical data to identify predictive biomarkers of response to therapy, disease behaviour and environmental factors in patients with IBD.Ethics and dissemination Ethical approval for this study has been obtained from the South Eastern Sydney Local Health District Research Ethics Committee (HREC 2019/ETH11443). Findings will be reported at national and international gastroenterology meetings and published in peer-reviewed journals.Trial registration number ACTRN12619000911190.

Published
2021
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