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3. Agricultural Landscape Understanding At Country-Scale

Author

Dua, Radhika, Saxena, Nikita, Agarwal, Aditi, Wilson, Alex, Singh, Gaurav, Tran, Hoang, Deshpande, Ishan, Kaur, Amandeep, Aggarwal, Gaurav, Nath, Chandan, Basu, Arnab, Batchu, Vishal, Holla, Sharath, Kurle, Bindiya, Missura, Olana, Aggarwal, Rahul, Garg, Shubhika, Shah, Nishi, Singh, Avneet, Tewari, Dinesh, Dondzik, Agata, Adsul, Bharat, Sohoni, Milind, Praveen, Asim Rama, Dangi, Aaryan, Kadivar, Lisan, Abhishek, E, Sudhansu, Niranjan, Hattekar, Kamlakar, Datar, Sameer, Chaithanya, Musty Krishna, Reddy, Anumas Ranjith, Kumar, Aashish, Tirumala, Betala Laxmi, and Talekar, Alok

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer., Comment: 34 pages, 7 tables, 15 figs

Published
2024

16. Conciseness: An Overlooked Language Task

Author

Stahlberg, Felix, Kumar, Aashish, Alberti, Chris, and Kumar, Shankar

Subjects
Computer Science - Computation and Language
Abstract

We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five human annotators, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with large neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets., Comment: EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability (TSAR)

Published
2022

20. Epidemiology of hypophosphatemia in critical illness: A multicentre, retrospective cohort study

Author

Ramanan, Mahesh, Marella, Prashanti, Young, Patrick, McIlroy, Pip, Nash, Ben, McCullough, James, Denny, Kerina J, Tallott, Mandy, Marshall, Andrea, Moore, David, White, Hayden, Sane, Sunil, Kumar, Aashish, Morrison, Lynette, Dipplesman, Pam, Taylor, Jennifer, Luke, Stephen, Paasilahti, Anni, Asimus, Ray, White, Kyle, Meyer, Jason, Hurford, Rod, Haward, Meg, Walsham, James, Bhadange, Neeraj, Stevens, Wayne, Plumpton, Kevin, Raman, Sainath, Barlow, Andrew, Tabah, Alexis, Pollock, Hamish, Baker, Stuart, Jacobs, Kylie, Attokaran, Antony G., Austin, David, Poggenpoel, Jacobus, Reoch, Josephine, Laupland, Kevin B., Edwards, Felicity, Evans, Tess, Dhanani, Jayesh, Kirrane, Marianne, Clement, Pierre, Karamujic, Nermin, Lister, Paula, Masurkar, Vikram, Murray, Lauren, Brailsford, Jane, Erbacher, Todd, Shekar, Kiran, Lavana, Jayshree, Cornmell, George, Senthuran, Siva, Whebell, Stephen, Gatton, Michelle, Tyack, Zephanie, Andrews, Robert, Keogh, Sam, Attokaran, Antony George, White, Kyle C, Doola, Ra'eesa, McIlroy, Philippa, Garrett, Peter, McCullough, James PA, and Laupland, Kevin B

Published
2024
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23. Rapid uptake of adjunctive corticosteroids for critically ill adults with septic shock following publication of ADRENAL trial. A multicenter, retrospective analysis of prescribing practices in Queensland Intensive Care Units

Author

White, Kyle C., Chaba, Anis, Meyer, Jason, Ramanan, Mahesh, Tabah, Alexis, Attokaran, Antony G., Kumar, Aashish, McCullough, James, Shekar, Kiran, Garrett, Peter, McIlroy, Philippa, Senthuran, Siva, Luke, Stephen, and Laupland, Kevin B.

Published
2024
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31. Micrometer-thick, atomically random Si0.06Ge0.90Sn0.04 for silicon-integrated infrared optoelectronics

Author

Assali, Simone, Attiaoui, Anis, Koelling, Sebastian, Atalla, Mahmoud R. M., Kumar, Aashish, Nicolas, Jérôme, Chowdhury, Faqrul A., Lemieux-Leduc, Cédric, and Moutanabbir, Oussama

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

A true monolithic infrared photonics platform is within reach if strain and bandgap energy can be independently engineered in SiGeSn semiconductors. Herein, we investigate the structural and optoelectronic properties of a 1.5 {\mu}m-thick Si0.06Ge0.90Sn0.04 layer that is nearly lattice-matched to a Ge on Si substrate. Atomic-level studies demonstrate high crystalline quality and uniform composition and show no sign of short-range ordering and clusters. Room temperature spectroscopic ellipsometry and transmission measurements show direct bandgap absorption at 0.83 eV and a reduced indirect bandgap absorption at lower energies. Si0.06Ge0.90Sn0.04 photoconductive devices operating at room temperature exhibit dark current and spectral responsivity (1 A/W below 1.5 {\mu}m wavelengths) similar to Ge on Si devices, with the advantage of a near-infrared band gap tunable by alloy composition. These results underline the relevance of SiGeSn semiconductors in implementing a group IV material platform for silicon-integrated infrared optoelectronics.

Published
2021
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32. All-Group IV membrane room-temperature mid-infrared photodetector

Author

Atalla, Mahmoud R. M., Assali, Simone, Attiaoui, Anis, Lemieux-Leduc, Cedric, Kumar, Aashish, Abdi, Salim, and Moutanabbir, Oussama

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Strain engineering has been a ubiquitous paradigm to tailor the electronic band structure and harness the associated new or enhanced fundamental properties in semiconductors. In this regard, semiconductor membranes emerged as a versatile class of nanoscale materials to control lattice strain and engineer complex heterostructures leading to the development of a variety of innovative applications. Herein we exploit this quasi-two-dimensional platform to tune simultaneously the lattice parameter and bandgap energy in group IV GeSn semiconductor alloys. As Sn content is increased to reach a direct band gap, these semiconductors become metastable and typically compressively strained. We show that the release and transfer of GeSn membranes lead to a significant relaxation thus extending the absorption wavelength range deeper in the mid-infrared. Fully released Ge$_{0.83}$Sn$_{0.17}$ membranes were integrated on silicon and used in the fabrication of broadband photodetectors operating at room temperature with a record wavelength cutoff of 4.6 $\mu$m, without compromising the performance at shorter wavelengths down to 2.3 $\mu$m. These membrane devices are characterized by two orders of magnitude reduction in dark current as compared to devices processed from as-grown strained epitaxial layers. The latter exhibit a content-dependent, shorter wavelength cutoff in the 2.6-3.5 $\mu$m range, thus highlighting the role of lattice strain relaxation in shaping the spectral response of membrane photodetectors. This ability to engineer all-group IV transferable mid-infrared photodetectors lays the groundwork to implement scalable and flexible sensing and imaging technologies exploiting these integrative, silicon-compatible strained-relaxed GeSn membranes.

Published
2020
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35. Attack Agnostic Statistical Method for Adversarial Detection

Author

Saha, Sambuddha, Kumar, Aashish, Sahay, Pratyush, Jose, George, Kruthiventi, Srinivas, and Muralidhara, Harikrishna

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

Deep Learning based AI systems have shown great promise in various domains such as vision, audio, autonomous systems (vehicles, drones), etc. Recent research on neural networks has shown the susceptibility of deep networks to adversarial attacks - a technique of adding small perturbations to the inputs which can fool a deep network into misclassifying them. Developing defenses against such adversarial attacks is an active research area, with some approaches proposing robust models that are immune to such adversaries, while other techniques attempt to detect such adversarial inputs. In this paper, we present a novel statistical approach for adversarial detection in image classification. Our approach is based on constructing a per-class feature distribution and detecting adversaries based on comparison of features of a test image with the feature distribution of its class. For this purpose, we make use of various statistical distances such as ED (Energy Distance), MMD (Maximum Mean Discrepancy) for adversarial detection, and analyze the performance of each metric. We experimentally show that our approach achieves good adversarial detection performance on MNIST and CIFAR-10 datasets irrespective of the attack method, sample size and the degree of adversarial perturbation.

Published
2019

36. Neutralising antibody potency against SARS-CoV-2 wild-type and omicron BA.1 and BA.4/5 variants in patients with inflammatory bowel disease treated with infliximab and vedolizumab after three doses of COVID-19 vaccine (CLARITY IBD): an analysis of a prospective multicentre cohort study

Author

Islam, Madiha, Croft, Nick, Cipriano, Bessie, Francia, Caroline, Khalid, Nosheen, Kingston, Ashley, Lee, Irish, Lehmann, Anouk, Naik, Kinnari, Samuels, Kevin, Plaatjies, Nicolene, Khatun, Hafiza, Bokth, Farjana, Pabriaga, Elise, Saich, Rebecca, Cousins, Hayley, Fraser, Wendy, Thomas, Rachel, Brown, Matthew, White, Benjamin, Kirkineziadis, Nikolaos, Tilley, Bernadette, Porter, Pennie, Bryant, Rachel, Robaczewska, Natalia, Muhammed, Rafeeq, Bi, Rehana, Cotter, Catherine, Grove, Jayne, Hong, Kate, Howman, Ruth, Mitchell, Monica, Clayton, Sophie, Rogers, Louise, Sultan, Sugrah, Rooney, Melanie, Cottrill, Charlotte, Singh, Salil, Dawe, Chris, Hull, Robert, Silva, Natalie, Chadwick, Julie, Robertson, Laura, Manning, Jonathan, Finlayson, Lauren, Roebuck, Allison, Dawson, Joy, Sonwalkar, Sunil, Chambers, Naomi, Robinson, Matthew, Haigh, Andrew, Matapure, Lear, Raine, Tim, George, Varun, Kapizioni, Christina, Strongili, Konstantina, Thompson, Tina, Ahmed, Mohamed, Kontos, Christos, Dawson, Claire, Bourges, Christophe, Barbutti, Isabella, Gozzard, Megan E, Hendy, Philip, Bull, Rhian, Costa, Patricia, Davey, Lisa, Hannington, Hayley, Nundlall, Kribashnie, Martins, Catarina, Avanzi, Laura, Carungcong, Jaime, Barr, Sabrina, Appleby, Richard, Johnson, Emma, Shakweh, Eathar, Phillis, Kath, Gascoyne, Rachel, Crowder, Amanda, Whileman, Amanda, London, Ian, Grounds, Jenny, Martin, Emmeline, Pajak, Susie, Price, Jude, Cawley, Kathryn, Powell, Sandra, Kearsley, Nichola, Dhar, Anjan, Brown, Ellen, Cowton, Amanda, Stamp, Kimberley, Warner, Ben, Stuart, Carmel, Lacey, Louise, de Silva, Shanika, Allcock, Clare, Harvey, Philip, Jones, Lesley, Cooke, Elise, Slater, Jayne, King, Dominic, Brooks, Johanne, Baker, Pearl, Beadle, Hannah, Cruz, Carina, Potter, Debbie, Collum, Joe, Masters, Farzana, Kumar, Aashish, Coetzee, Samantha, Peiu, Mihaela, Icke, Becky, Williams, Jill, Raj, Meena, Gaynor, Edward, Chadokufa, Sibongile, Huggett, Bonita, Meghari, Hamza, El-Khouly, Sara, Kiparissi, Fevronia, Girshab, Waffa, Russell-Walker, Lynda, Jackson, Christopher, Sidler, Sara, Claridge, Andrew, Fowler, Emily, McCafferty, Laura, Haxton, Lesley, Irving, Peter, Christodoulides, Karolina, Clifford, Angela, Dawson, Patrick, Honap, Sailish, Lim, Samuel, Luber, Raphael, Mahiouz, Karina, Meade, Susanna, Raymode, Parizade, Reynolds, Rebecca, Stanton, Anna, Tripoli, Sherill, Hare, Naomi, Odukwe, Sopuluchukwu, Balachandran, Senthuran, North, Emma, North, Jessica, Browne, Bria, Cordle, Jessica, Jameson, Ella, Siaw, Yih Harn, Manzano, Lane, Segal, Jonathan, Al-Bakir, Ibrahim, Khakoo, Imran, Portukhay, Sofiya, Thoua, Nora, Davidson, Katherine, Miah, Jagrul, Canclini, Lisa, Hall, Alex, Furreed, Hassina, Mitchell-Inwang, Christine, Hayes, Melony, Myers, Sally, Talbot, Alison, Turnbull, Jack, Whitehead, Emma, Stamp, Katie, Pattinson, Alison, Mathew, Verghese, Sherris, Leanne, Wilcox, Julie, Ramachandran, Sankaranarayanan, Robertson, Hayley, Harvey, Angela, Hicks, Lucy, Byrne, Tara-Marie, Cabreros, Leilani, Downing-Wood, Hannah, Hunter, Sophie, Saifuddin, Mohammad Aamir, Prabhudev, Hemanth, Balarajah, Sharmili, Krucznski, Jan, Driva, Kalliopi, D'Mello, Andrea, Shah, Parith, Castro-Seoane, Rocio, Ibraheim, Hajir, Constable, Laura E, Lo, Jonathan W, Torkizadeh, Melissa, Hermangild, Sherine K, Sutherland, Helen, Wilhelmsen, Elva, Mackintosh, Katherine, Verma, Ajay M, Sebastian, Juliemol, Peerally, Mohammad Farhad, Guerdette, Anne-marie, Coburn, Susan, Novem lam, Ching Yee, Durrant, Donna, Schaefer, Belinda, Serna, Solange, Shahzad, Muhammad, Kent, Alexandra, Choong, Lee Meng, Pantaloni, Benedetta, Ravdas, Pantelis, Vadamalayan, Babu, Foley, Stephen, Arnold, Becky, Heeley, Cheryl, Lovegrove, Wayne, Sowton, Donna, Allsop, Lynne, Gregory, Heidi, Gill, Mandy, Holmes, Megan, Balan, Valeria, Smith, Susan, Turner, Sarah, Smith, Philip J, Steel, Alan, Bretland, Giovanna, King, Sarah, Lofthouse, Martina, Rigby, Lindsey, Subramanian, Sreedhar, Tyrer, David, Martin, Kate, Probert, Christopher, Kamperidis, Nikolaos, Adedoyin, Temi, Baden, Manisha, Brown, Jeannette, Chacko, Feba, Young, Lisa, Cicchetti, Michela, Saifuddin, Mohammad, Yesupatham, Priya, Gowda, Rohit, Williams, Maureen, Kemp, Karen, Akhand, Rima, Gray, Glaxy, John, Anu, John, Maya, Mohammed, Tasnim, Sathe, Diamond, Jones, Natasha, Soren, Jennifer, Sprakes, Michael, Burton, Julie, Kane, Patricia, Lupton, Stephanie, Bartholomew, Jacqueline, Denis, Elizabeth, Daniels, Alison, MacFaul, George, Scaletta, Diane, Siamia, Loria, Williams, Felicity, Green, Chloe, Ver, Zeljka, Lamb, Chris, Doona, Mary, Hogg, Ashleigh, Jeffrey, Lesley, King, Andrew, Speight, R Alexander, Doyle, Jennifer, Owen, Ruth, Haworth, Jenny, Patterson, Linda, Varnaulasingam, Vithusa, Mowat, Craig, Rice, Debbie, MacFarlane, Susan, MacLeod, Anne, Mohammed, Samera, Murray, Shona, Elliott, Anne, Anne Morris, Mary, Coke, Louise, Hindle, Grace, Kolokouri, Eirini, Wright, Catherine, Lee, Claire, Ward, Nicola, Dann, Adele, Lockett, Melanie, Cranfield, Charlotte, Jennings, Louise, Srivastava, Ankur, Ward, Lana, Jeynes, Nouf, Ranga, Poonam, Rajasekhar, Praveen, Gallagher, Lisa, Ward, Jill, Basnett, Rae, Murphy, Judy, Parking, Lauren, Lawson, Emma, Short, Stacey, Devadason, David, Moran, Gordon, Khan, Neelam, Tarr, Lauren, Olivia, Charmaine, Warbarton, Samantha, Kelly, Sian, Limdi, Jimmy, Goulden, Kay, Javed, Asad, McKenzie, Lauren, Melville, Julie, Liu, Eleanor, Sabine, Joseph, Jacob, Patricia, McSorland, Denise, Schofield, Nick, Cornwall, Lisa, Quirke, James, Crook, Emma, Turner, Anne, Bhandari, Pradeep, Baker-Moffatt, Michelle, Dash, Joanne, Le Poidevin, Alison, Downe, Hayley, Bombeo, Lucille, Blackman, Helen, Smith, Rebecca, Wiles, Alan, Bloxham, Hannah, Dias, Jose, Nadar, Evelyn, Curgenven, Hollie, Gilham, Ellie, Macdonald, Jonathan, Finan, Shona, McMeeken, Faye, Mahmood, Misbah, Shields, Stephanie, Seenan, John Paul, DeSilva, Des, Malkakorpi, Susanna, Carson, Rachel, Lawrence, Holly, Boateng, Ofori, Kpodo, Felix, Whiteoak, Simon, Edger-Earley, Kelli, Vamplew, Luke, Samways, Joanna, Roffe, Sue, Ingram, Sarah, James, Joel, Botfield, Sharon, Hammonds, Fiona, James, Clare, Berry, Zoe, Aspinall, Gemma, Hawkins, Sarah, Parkinson, Marian, Gardner-Thorpe, Helen, Marriott, Suzie, Redstone, Clare, Windak, Halina, Adam, Ana-Marie, Mabb, Hannah, Stevenson, Emma, Record, Jessica, Murray, Charles, Diaba, Cynthia, Joseph, Fexy, Pakou, Glykeria, Gleeson, Yvonne, Nunag, Annalyn, Berrill, James, Stroud, Natalie, Pothecary, Carla, Roche, Lisa, Turner, Keri, Deering, Lisa, Israel, Lynda, Baker, Evelyn, Nash, Maxine, Fagbemi, Andrew, Jennings, Felicia, Mayor, Imelda, Wilson, Jill, Wheeler, Alice, Phillips, Nicola, Gordon, John, Levell, Emma, Zagalo, Silvia, Hoad, Ina, Anil, Bindu, Russell, Richard, Henderson, Paul, Millar, Margaret, Alexakis, Christopher, Michalak, Natalia, Marriott, Cheryl, Stone, Sarah, Pristopan, Veronika, Saunders, John, Burton, Helen, Cambridge, Vanessa, Clark, Tonia, Ekblad, Charlotte, Hierons, Sarah, Katebe, Joyce, Saunsbury, Emma, Perry, Rachel, Brookes, Matthew, Davies, Kathryn, Green, Marie, Plumbe, Ann, Ormerod, Clare, Christensen, Helen, Howlett, Hannah, Keen, Anne, Ogor, Jonathan, Greenhaigh, Marie, Knowles, Karen, Yin, Shanzi, Poulaka, Maria, Anthony, Alpha, Newitt, Emily, Trim, Fiona, Casey, Ruth, Seymour, Katherine, Reed, Catherine, Joy, Lijo, Fogden, Edward, Russell, Kalisha, Hussain, Samia, Phillips, Anne, Abdulla, Muaad, Butterworth, Jeff, Adams, Colene, Carnahan, Mandy, Buckingham, Elizabeth, Childs, Danielle, Magness, Alison, Stickley, Jo, Motherwell, Nichola, Tonks, Louise, Gibson, Hannah, Wistance, Kate, Thomas, Caradog, Brinkworth, Elaine, Connor, Lynda, Cook, Amanda, Rees, Tabitha, Harford, Rachel, Farley, Sean, Jones, Marie, Wesley, Emma, Moss, Alison, Lucas, Jacob, Lorimer, Claire, Oleary, Maria, Dixon, Maxine, Goodchild, Fiona, Twenlow, Rebecca, Pawley, Corinne, Ramadas, Arvind, Tregonning, Julie, Okeke, Olaku, Jackson, Wendy, Koumoutsos, Ioannis, George, Viji, Kunhunny, Swapna, Laverick, Sophie, Anderson, Isla, Smith, Sophie, Joyce, Joan, Janarthan, Sarala, Patel, Kamal, Ali, Mariam, Mhandu, Hilda, Rana, Aleem, Spears, Katherine, Teixeira, Joana, Mencias, Mark, Seaward, Abigail, Sousa, Jessica, Said, Nooria, Soomaroo, Mark, Raspa, Valentina, Tacouri, Asha, Reps, Nicholas, Martin, Rebecca, Samakomva, Tinashe, Selinger, Christian, Carbonell, Jenelyn, Onovira, Felicia, Quartey, Doris, L'Anson, Alice, Ashworth, Andrew, Bailey, Jessica, Dunn, Angie, Bespaloi, Gjuzel, Rasalan, Harold, Mahmood, Zahid, Campbell, Racheal, Marsh, Liane, Coughlan, Tricia, Jafar, Wisam, Marrs, Janet, McPheat, Christopher, Rahman, Monira, Davies, Sarah, Habibi, Ruth, Jessup-Dunton, Ellen, Joefield, Teishel, Layug, Reina, Patel, Vinod, Vere, Joanne, Turner, Victoria, Kilroy, Susan, Coulding, Martina, Clark, Martyn, McCormick, Jacqueline, Nisar, Attiya, Walker, Gareth, Atkins, Stacey, Growdon, Jasmine, George, Becky, McNeill, Charlotte, Reed, Bryony, Foulds, Angela, Marshall, Catherine, Allison, Michele, Dillon, Briony, Cooney, Rachel, Bennett, Lillie, Bowlas, Louise, Shariff, Sharafaath, Fraser, Aileen, Punnette, Dwayne, Lambert, Rebecca, Bishop-Hurman, Charlotte, Undrell, Elizabeth, Belfield, Katherine, Din, Said, Addleton, Catherine, Appleby, Marie, Brown, Johanna, Holding, Kathleen, Fraser, Catherine, Birkenshaw, Janice, Williams, Jodie, Maulion, Kamille, Lane, Meg, Kravale, Arita, Smith, Claud, Hooper, Patricia, deCaestecker, John, Watchorn, Olivia, Clarke, Ellie, Hayward, Chris, Inniss, Susan, Pritchard, Lucy, Rudge, Karen, Carney, Amanda, Griffee, Sarah, Andreyev, Jervoise, Babu, Sathish, Hayhurst, Caroline, Lockwood, Carol, Osborne, Lynn, Roper, Amanda, Warner, Karen, Hindle, Julia, Lawrence, Tara, Netherton, Kimberley, Watt, Caroline, Szymiczek, Kinga, Mehta, Shameer, Bell, James, Blad, William, Whitley, Lisa, Jastrub, Roman, Durai, Dhamaraj, Baker, Mark, John Sivamurugan, Elizabeth, Evans, Mim, Cummings, Fraser, Harris, Clare, Jones, Amy, Krauze, Liga, Rahmany, Sohail, Earl, Michelle, Vowles, Jenny, Torokwa, Audrey, Petrova, Mirela, Procter, Andrew, Stanley, Jo, Silvamoniz, Claudia, Bettey, Marion, Wahid, Amar, Morrison, Zoe, Thomas-Turner, Rhian, Yendle, Louise, Muller, Jennifer, Mitchell, Marcus, Kirkwood, John, Barnes, Anna, Chaudhary, Rakesh, Claridge, Melanie, Ellis, Chiara, Kemp, Cheryl, Tobi, Ogwa, Milton, Jentus, Johnston, Emma, Oblak, Metod, Winpenny, Carmen, Svensson, Marie-Louise, Godden, Jo, Ashhar, Marium, Alexander, Debbie, Covil, Kate, Derikx, Lauranne, Siakavellas, Sryros, Baxter, Helen, Robertson, Scott, Smith, Linda, Poulose, Beena, Colemam, Anne, Balint, Margareta, Rhys-Jones, Gareth, Watters, Helen, Begg, Susan, Grosalcalde, Beatriz, Coyle, Judy, Johns, Kerrie, Hughes, Rachel, Phipps, Janet, Taylor, Abigail, MacPhee, Catherine, Brooks, Suzanne, John, Jolene, Edwards, Michelle, Smith, Katie, Howard, Linda, Wood, Dianne, Muddu, Ajay, Barman, Laura, Mallinson, Janine, Neale, Tania, Ionita, Diana, Elliot, Kerry, Turnball, Alison, Thomas, Iola, Thomas, Alice, Andrews, Kelly, Sutton, Jonathon, Mulvaney Jones, Caroline, Roberts, Julia, Bishop, Jeannie, Liu, Zhigang, Le, Kaixing, Zhou, Xin, Alexander, James L, Lin, Simeng, Bewshea, Claire, Chanchlani, Neil, Nice, Rachel, McDonald, Timothy J, Lamb, Christopher A, Sebastian, Shaji, Kok, Klaartje, Lees, Charlie W, Hart, Ailsa L, Pollok, Richard C, Boyton, Rosemary J, Altmann, Daniel M, Pollock, Katrina M, Goodhand, James R, Kennedy, Nicholas A, Ahmad, Tariq, and Powell, Nick

Published
2023
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44. Unraveling the role of cytochrome P450 enzymes in oleanane triterpenoid biosynthesis in arjuna tree.

Author

Srivastava, Gaurav, Vyas, Poonam, Kumar, Aashish, Singh, Anamika, Bhargav, Pravesh, Dinday, Sandeep, and Ghosh, Sumit

Abstract

SUMMARY: Triterpenoids (C30‐isoprenoids) represent a major group of natural products with various physiological functions in plants. Triterpenoids and their derivatives have medicinal uses owing to diverse bioactivities. Arjuna (Terminalia arjuna) tree bark accumulates highly oxygenated β‐amyrin‐derived oleanane triterpenoids (e.g., arjunic acid, arjungenin, and arjunolic acid) with cardioprotective roles. However, biosynthetic routes and enzymes remain poorly understood. We mined the arjuna transcriptome and conducted cytochrome P450 monooxygenase (P450) assays using Saccharomyces cerevisiae and Nicotiana benthamiana to identify six P450s and two P450 reductases for oxidative modifications of oleanane triterpenoids. P450 assays using oleananes revealed a greater substrate promiscuity of C‐2α and C‐23 hydroxylases/oxidases than C‐28 oxidases. CYP716A233 and CYP716A432 catalyzed β‐amyrin/erythrodiol C‐28 oxidation to produce oleanolic acid. C‐2α hydroxylases (CYP716C88 and CYP716C89) converted oleanolic acid and hederagenin to maslinic acid and arjunolic acid. CYP716C89 also hydroxylated erythrodiol and oleanolic aldehyde. However, CYP714E107a and CYP714E107b catalyzed oleanolic acid/maslinic acid/arjunic acid, C‐23 hydroxylation to form hederagenin, arjunolic acid and arjungenin, and hederagenin C‐23 oxidation to produce gypsogenic acid, but at a lower rate than oleanolic acid C‐23 hydroxylation. Overall, P450 substrate selectivity suggested that C‐28 oxidation is the first P450‐catalyzed oxidative modification in the arjuna triterpenoid pathway. However, the pathway might branch thereafter through C‐2α/C‐23 hydroxylation of oleanolic acid. Taken together, these results provided new insights into substrate range of P450s and unraveled biosynthetic routes of triterpenoids in arjuna. Moreover, complete elucidation and reconstruction of arjunolic acid pathway in S. cerevisiae and N. benthamiana suggested the utility of arjuna P450s in heterologous production of cardioprotective compounds. Significance Statement: Arjuna tree bark contains bioactive triterpenoids that received considerable attention as cardioprotective compounds, but the biosynthetic enzymes remained largely unknown. In this work, transcriptome mining, metabolite analysis, and enzyme biochemical characterization led to identification of six cytochrome P450 monooxygenases (CYP716A233, CYP716A432, CYP716C88, CYP716C89, CYP714E107a, and CYP714E107b) and two cognate P450 reductases (TaCPR1 and TaCPR2) that catalyzed sequential C‐2α, C‐23, and C‐28 oxidations of oleanane scaffolds for the biosynthesis of arjunic acid, arjungenin, and arjunolic acid. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Study and Analysis of Various Load Balancing Techniques for Software-Defined Network (A Systematic Survey)

Author

Kumar, Aashish, Anand, Darpan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tiwari, Shailesh, editor, Suryani, Erma, editor, Ng, Andrew Keong, editor, Mishra, K. K., editor, and Singh, Nitin, editor

Published
2021
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48. On the Security Weaknesses in Password-Based Anonymous Authentication Scheme for E-Health Care

Author

Ali, Rifaqat, Chandrakar, Preeti, Kumar, Aashish, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Santosh Kumar, editor, Samanta, Sourav, editor, Dey, Nilanjan, editor, and Kumar, Rajesh, editor

Published
2020
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50. Associations between Late Lactate Clearance and Clinical Outcomes in Adults with Hyperlactataemia in the Setting of Diabetic Ketoacidosis.

Author

Kumar, Aashish, Anstey, Christopher, Doola, Ra'eesa, Mcllroy, Philippa, Whebell, Stephen, Shekar, Kiran, Attokaran, Antony, Marella, Prashanti, White, Kyle, Luke, Stephen, Tabah, Alexis, Laupland, Kevin, and Ramanan, Mahesh

Subjects
*APACHE (Disease classification system), *DIABETIC acidosis, *LENGTH of stay in hospitals, *INTENSIVE care units, *HYPERLACTATEMIA
Abstract

Objective: This study aimed to determine the associations between lactate clearance in hyperlactataemic patients with diabetic ketoacidosis (DKA) and intensive care unit (ICU), hospital length of stay (LOS), and case-fatality. Methods: A retrospective, multicentre, cohort study of adult patients admitted to ICU with hyperlactataemia and a primary diagnosis of DKA from twelve sites in Queensland, Australia was conducted utilising pre-existing datasets that were linked for research purposes. The patients were divided into early and late lactate clearance groups; the early lactate clearance group included patients whose lactate returned to <2.0 mmol/L within 12 h, and the remainder were classified as late lactate clearance group. Results: The final dataset included 511 patients, 427 in the early lactate clearance group and 84 in the late lactate clearance group. Late lactate clearance was associated with increasing ICU LOS (β = +15.82, 95% CI +0.05 to +31.59, p < 0.049), increasing hospital LOS (β = +7.24, 95% CI +0.11 to 14.37, p = 0.048) and increasing Acute Physiology and Chronic Health Evaluation(APACHE) III score (ICU LOS outcome variable β = +1.05, 95% CI +0.88 to +1.22, p < 0.001; hospital LOS outcome variable β = +3.40, 95% CI +2.22 to 4.57, p < 0.001). Hospital case-fatality was not significantly different (2.2% in the early clearance group vs. 1.7% in the late clearance group, p = 0.496). Conclusions: In hyperlactataemic patients with DKA, late lactate clearance was associated with a statistically significant increase in both ICU and hospital LOS, though the clinical significance in both is minor. [ABSTRACT FROM AUTHOR]

Published
2024
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