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1. Pattern-Recognition Processes of First-Grade Students: An Explorative Eye-Tracking Study

Author

Lukas Baumanns, Demetra Pitta-Pantazi, Eleni Demosthenous, Achim J. Lilienthal, Constantinos Christou, and Maike Schindler

Abstract

Recognizing patterns is an essential skill in early mathematics education. However, first graders often have difficulties with tasks such as extending patterns of the form ABCABC. Studies show that this pattern-recognition ability is a good predictor of later pre-algebraic skills and mathematical achievement in general, or the development of mathematical difficulties on the other hand. To be able to foster children's pattern-recognition ability, it is crucial to investigate and understand their pattern-recognition processes early on. However, only a few studies have investigated the processes used to recognize patterns and how these processes are adapted to different patterns. These studies used external observations or relied on children's self-reports, yet young students often lack the ability to properly report their strategies. This paper presents the results of an empirical study using eye-tracking technology to investigate the pattern-recognition processes of 22 first-grade students. In particular, we investigated students with and without the risk of developing mathematical difficulties. The analyses of the students' eye movements reveal that the students used four different processes to recognize patterns--a finding that refines knowledge about pattern-recognition processes from previous research. In addition, we found that for patterns with different units of repeat (i.e. ABABAB versus ABCABCABC), the pattern-recognition processes used differed significantly for students at risk of developing mathematical difficulties but not for students without such risk. Our study contributes to a better understanding of the pattern-recognition processes of first-grade students, laying the foundation for enhanced, targeted support, especially for students at risk of developing mathematical difficulties.

Published
2024
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3. Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

Author

Zhi, Zhuo, Liu, Ziquan, Elbadawi, Moe, Daneshmend, Adam, Orlu, Mine, Basit, Abdul, Demosthenous, Andreas, and Rodrigues, Miguel

Subjects
Computer Science - Machine Learning
Abstract

Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues by unleashing the potential of a transformer's in-context learning ability. Diverging from existing methods, which primarily belong to the parametric paradigm and often require sufficient training samples, our work exploits the value of the available full-modality data, offering a novel perspective on resolving the challenge. The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks. When only 1% of the training data are available, our proposed method demonstrates an average improvement of 6.1% over a recent strong baseline across various datasets and missing states. Notably, our method also reduces the performance gap between full-modality and missing-modality data compared with the baseline.

Published
2024

4. HgbNet: predicting hemoglobin level/anemia degree from EHR data

Author

Zhi, Zhuo, Elbadawi, Moe, Daneshmend, Adam, Orlu, Mine, Basit, Abdul, Demosthenous, Andreas, and Rodrigues, Miguel

Subjects
Computer Science - Machine Learning
Abstract

Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data sources for numerous medical studies. EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time series containing a significant number of missing values and irregular time intervals. To address these issues, we introduce HgbNet, a machine learning-based prediction model that emulates clinicians' decision-making processes for hemoglobin level/anemia degree prediction. The model incorporates a NanDense layer with a missing indicator to handle missing values and employs attention mechanisms to account for both local irregularity and global irregularity. We evaluate the proposed method using two real-world datasets across two use cases. In our first use case, we predict hemoglobin level/anemia degree at moment T+1 by utilizing records from moments prior to T+1. In our second use case, we integrate all historical records with additional selected test results at moment T+1 to predict hemoglobin level/anemia degree at the same moment, T+1. HgbNet outperforms the best baseline results across all datasets and use cases. These findings demonstrate the feasibility of estimating hemoglobin levels and anemia degree from EHR data, positioning HgbNet as an effective non-invasive anemia diagnosis solution that could potentially enhance the quality of life for millions of affected individuals worldwide. To our knowledge, HgbNet is the first machine learning model leveraging EHR data for hemoglobin level/anemia degree prediction.

Published
2024

11. Tranexamic acid versus placebo to prevent bleeding in patients with haematological malignancies and severe thrombocytopenia (TREATT): a randomised, double-blind, parallel, phase 3 superiority trial

Author

Estcourt, Lise J, McQuilten, Zoe K, Bardy, Peter, Cole-Sinclair, Merrole, Collins, Graham P., Crispin, Philip J., Curnow, Elinor, Curnow, Jennifer, Degelia, Amber, Dyer, Claire, Friebe, Adam, Floro, Lajos, Grand, Effie, Hudson, Cara, Jones, Gail, Joseph, Joanne, Kallmeyer, Charlotte, Karakantza, Marina, Kerr, Paul, Last, Sara, Lobo-Clarke, Maria, Lumley, Matthew, McMullin, Mary F, Medd, Patrick G., Morton, Suzy M., Mumford, Andrew D., Mushkbar, Maria, Parsons, Joseph, Powter, Gillian, Sekhar, Mallika, Smith, Laura, Soutar, Richard, Stevenson, William S., Subramoniapillai, Elango, Szer, Jeff, Thomas, Helen, Waters, Neil A., Wei, Andrew H., Westerman, David A., Wexler, Sarah A., Wood, Erica M., Stanworth, Simon J., Abioye, Adrienne, Afghan, Rabia, Ai, Sylvia Ai, Akanni, Magbor, Alajangi, Rajesh, Alam, Usmaan, Al-Bubseeree, Bahaa, Alderson, Sophie, Alderson, Craig, Ali, Sayed, Ali, Kabir, Alighan, Rookmeen, Allam, Rebecca Allam, Allen, Tania, Al-Sakkaf, Wesam, Ames, Kate, Anderson, Jacqueline, Andrews, Colin, Angel, Ann-Marie, Anlya, Manuela Anlya, Ansari, Farah, Appleby, Rowan, Arnold, Claire, Asbjornsdottir, Hulda, Asfaw, Biruk, Atkins, Elissa, Atkinson, Leela, Aubrey, Clare, Ayesha, Noor, Babbola, Lola, Badcock, David, Badcock, Samuel, Baggio, Diva, Bailiff, Ben, Baines, Kizzy, Baker, Holly, Baker, Victoria, Ball, Lindsay, Ball, Martin, Balquin, Irwin, Banks, Emma, Banos, George, Barnett, Jaytee, Barrie, Claire, Barron, Claire, Barton, Rebecca, Bason, Nina, Batta, Bindu, Bautista, Dianne, Bayley, Angela, Bayly, Emma, Baynes, Fionnuala, Bazargan, Ali, Bazeley, Rachel, Beadle, Yvonne, Beardsmore, Claire, Beattie, Kate, Bedford, Caroline, Behal, Rachna, Behan, Daniel, Bejan, Lilihna, Bell, Sarah, Bell, Karen, Bell, Louise, Bell, Kaitlyn, Benjamin, Reuben, Bennett, Sam, Benson, Gary, Benson, Warwick, Bent, Cameron, Bergin, Krystal, Berry, Alex, Besenyei, Stephanie, Besley, Caroline, Betteridge, Scott, Beveridge, Leigh, Bhattacharyya, Abir, Billen, Annelies, Bilmon, Ian, Binns, Emma, Birt, Mark, Bishop, David, Blanco, Andrea, Bleby, Lisa, Blemnerhet, Richard, Blombery, Piers, Blyth, Emily, Blythe, Nicola, Boal, Lauren, Boden, Ali, Bokhari, Syed W.I., Bongetti, Elisa, Booth, Stephen, Borley, Jayne, Bowen, David, Bowers, Dawn, Boyd, Stephen, Bradley, Sarah, Bradman, Helen, Bretag, Peta, Brillante, Maria, Brockbank, Rachel, Brough, Yasmin, Brown, Ellen, Brown, Jo, Brown, Eleanor, Brown, Claire, Brown, Jenny, Brown, Susan, Browning, Joe, Brownsdon, Alex, Bruce, David, Brydon-Hill, Ruth, Buckwell, Andrea, Burgess, Dannielle, Burke, Glenda, Burley, Kate, Burney, Claire, Burns, David, Burrows, Samuel, burton, Kieran, Butler, Jason, Cambalova, Lenka, Camozzi, Maria C., Campbell, Philip, Campfield, Karen, Campion, Victoria, Cargo, Catherine, Carmona, Julia, Carney, Dennis, Casan, Joshua, Cashman, Helen, Catt, Lorraine, Cattell, Michael, Cavill, Megan, Chadbone, Rachel, Chaganti, Sridhar, Chai, Yee, Chai, Khai Li, Chang, Joshua, Chapman, Judith, Chapman, Oliver G., Chapter, Tamika, Charlton, Andrew, Chau, Celina, Chauhan, Saleena, Chavda, Nikesh, Chen, Frederick, Chen, Melody, Chen, Meng Xi, Chen, Melanie, Chen, Melissa, Cheok, Kathleen, Cheung, Mai, Chidgey, Luke, Chmielokliec, Karolina, Choi, Philip, Choi, Jae, Chok, Anne, Chopra, Ruchika, Christopherson, Louise, Chu, Vicky, Chua, Chong Chyn, Chudakou, Pavel, Chugh, Vidushi, Chung, Chi, Clark, Erin, Clarke, Peter, Clarke, Kathleen, Clay, Jennifer, Clayton, Laura, Clements, Mitch, Clemmens, Jonathan, Clifford, Ruth, Collett, Dave, Collins, Maia, Collyer, Emily, Connolly, Maureen, Cook, Mark, Coombs, Sarah, Coppell, Jason, Cornwell, Sophie, Corrigan, Claire, Coughlin, Elizabeth, Couling, Jennifer, Cousins, Tony, Cowan, Catriona, Cox, Christine, Cox, Catherine, Coyle, Luke, Craig, Emily, Creasey, Thomas, Croan, Laura, Croft, Jane, Crosbie, Nicola, Crowe, Josephine, Crowther, Helen, Crozier, Jane, Culleton, Naomi, Cullis, Jonathan, Cumming, Anita, Cummins, Michelle, Cunningham, Adam, Curley, Cameron, Curtis, Samantha, Cuthbert, Robert, Cuthill, Kirsty, Dahahayake, Dinusha A, Dang, Amy, Davies, Marc, Davies, Ceri, Dawson, Emily, Day, Tom, De Abrew, Kanchana, De Lavallade, Hugues, De Silva, Neelaskshi, Dean, Georgina, Deane, Christopher, Demosthenous, Lisa, Desai, Amisha, Desborough, Michael, Devanny, Ian, Dhanapal, Jay, Dhani, Sundip, Di Martino, Vicky, Dickens, Emmy, DiCorleto, Carmen, Dinnett, Louise, Dirisan, Divya, Dixon, Karen, Dixon, Kiri, Doal, Inderjit, Dobivh, J, Docanto, Maria, Doecke, Helve, Donaldson, David, Donaldson, Kylee, Donohoe, Carrie, Douglas, Ashley, Doung, Stephen, Downer, Susan, D'Rozario, James, Drummond, Malcolm, Drummond, Mark, Drummond, Samantha, Drysdale, Elizabeth, D'Souza, Ross, D'Souza, Eugene, Dunn, Alex, Dutton, David, Dyson, Martin, Ediriwicurena, Kushani, Edleston, Sharon, Edwards, Dawn, Edwards, Morgan, Edwards, Anita, Eise, Nicole, Ellis, Steven, Ellis, Hayley, Elmonley, Shareef, Enstone, Rosemarie, Eordogh, Agnes, Erb, Sharon, Evans, Shannon, Evans, Megan, Ewing, Joanne, Eyre, Toby, Facey, Adam, Fammy, Mina, Farman, Jon, Farnell, Rachel, Favero, Laura, Fay, Keith, Ferguson, Karen, Fernon, Laura, Filshie, Robin, Finnegan, Damian, Fisher, Lisa, Flanagan, Asia, Fleck, Emma, Fletcher, Simon, Flora, Harpreet, Flower, Catherine, Fodor, Ioana, Foley, Heather, Folland, Emma, Folorunso, Comfort, Forbes, Molly, Fordwor, Katrina, Foster, Polly, Fox, Vanessa, Fox, Thomas, Francis, Olesya, Fryearson, Louise, Fuery, Madonna, Fung, Jiin, Furtado, Michelle, Galloway-Browne, Leanne, Gamble, Louise, Gamgee, Jeanette, Ganapathy, Arundathi, Gardner, Hayley, Gardner, Clare, Gasmelsheed, Noha, Gately, Amy, Gaynor, Lynda, Gebreid, Alex, Geffens, Ruth, George, Rachel, Gertner, Aniko, Ghebeh, Manar, Ghirardini, Emanuela, Giddings, Melainie, Gillett, Sandra, Gillett, Karen, Giri, Pratyush, Glass, Chris, Glewis, Sarah, Gooding, Sarah, Gordon, Olivia, Gordon, Joanne, Gottlieb, David, Gowda, Koushik, Gower, Elysie, Gray, Nicola, Grayer, Jo, Greaves, Elaine, Greenaway, Sally Anne, Greenfield, Graeme, Greenwood, Matthew, Gregory, Gareth, Griffin, James, Griffith, Julia, Griffith, James, Griffiths, Lindsey, Grzegrzolka, Paulina, Gu, Yisu, Guest, Jo, Guinai, Rosanna, Gullapalli, Veena, Gunolr, A., Guo, Lina, H, Wayne, Hagua, Sophia, Haile, Senait, Hall, Richard, Hamdollah-Zadeh, Maryam, Hanif, Zahra, Hanlon, Kathleen, Hann, Nicholas, Hanna, Ramez, Hannah, Guy, Hapuarachchi, Sameera, Hardman, Jacinta, Hardy, Alison, Harris, Anthony, Harris, Kylie, Harrison, Beth, Harrison, Simon, Harrison, Lea-Anne, Harrop, Sean, Harvey, Caroline, Hatcliffe, Faye, Hawking, Jo, Hawkins, Matthew, Hayden, Janet, Hayman, Michelle, Haynes, Elizabeth, Heaney, Nicholas, Hebbard, Andrew, Hempton, Jenny, Hendunneti, Sasanka, Henry, Maeve, Heywood, Jonathan, Hildyard, Catherine, Hill, Lydia, Hilldrith, Annette, Hitev, Petar, Hiwase, Smita, Hiwase, Devendra, Hoare, Chris, Hodge, Renate, Holloway, Amy, Holt, Chloe, Holton, Kelly, Homer, Lauren, Horne, Gillian, Horvath, Noemi, Hotong, Linda, Houdyk, Kristen, Houseman, Katy, Hoxhallari, Ilda, Hsu, Hannah, Hsu, Nina, Huang, Gillian, Hudson, Kerryn, Hufton, Melanie, Hughes, Timothy, Hughes, Siobhan, Hurley, Kate, Huxley, Rosie, Ibitoye, Temitope, Ibrouf, Abubaken, Inam, Farha, Indran, Tishya, Ingham, Karen, Innes, Calum, Irvine, David, Jaafar, Sarah, Jain, Manish, Jameson, Laura, Janjua, Pardeep, Jarvis, Rebecca, Jatheendran, Abirami, Javed, Abbie, Jen, Sheila, Jobanpura, Shailesh, Jobson, Irene, John, Deborah, Johns, Sophie, Johnston, Amanda, Jones, Hollie, Jones, Francesca, Joniak, Karolina, Jovanovic, Michael, Jovic, Anita, Joyce, Lauren, Judd, Andrew, Kakarlamudi, Sudhakar, Kakaroubas, Nick, Kalita, Maggie, Kam, Shirly, Kan, Julie, Kandle, P, Kanellopoulos, Alex, Kao, Chien, Kaparou, Maria, Kartsios, Charamlampos, Katsioulas, Vicki, Kaye, Russell, Keen, Katie, Kelly, Richard, Kelly, Pauline, Kelly, Donna, Kelly, Melanie, Kennedy, Glen, Kennedy, Nola, Kenny, Angela, Kenworthy, Zoe, Kerridge, Ian, Kesavan, Murali, Khafizi, Angelika, Khakwani, Muhammad, Khalid, Amna, Khamly, Kate, Khan, Anjum, Khan, Dalia, Khan, Mojid, Khan, Lubna, Khoo, Mona, Khwaja, Asim, Kim, Grace, King, Andrew, King, Vicky, King, Donna, Kinsella, Francesca, Kipp, David, Kirandeep, Pachoo, Kirui, Laura C., Kishore, Bhuvan, Knectlhi, Christopher, Knot, Amy, Knot, Armit, Ko, Cathy, Kolaric, Caitlin, Koo, Ray, Kotadia, Mary, Kothari, Jaimal, Kottaridis, Panagiotis D., Kuiluinathan, Gajan, Kulasekararaj, Austin, Kwan, John, Kwok, Marwan, Kwok, Phillip, Kwok, Fiona, Laane, Kristiina, Lad, Deena, Laird, Jennifer, Lam, Ada, Lane, Mary, Lanenco, Monica, Lang, Susan, Langridge, Alex, Langton, Catherine, Lannon, Michelle, Latif, Annie, Latimer, Maya, Latter, Ruth, Lau, I-Jun, Lawless, Sarah, Lawless, Theresa, Leach, Mike, Leaney, Sarah, Leary, Heather, Leavy, James, LeBlanc, Abbey, Lee, Vivienne, Lee, Edwin, Lee, Jenny, Lee, Tamara, Leischkie, Marian, Leitinger, Emma, Leon, Christopher, Leonard, Jayne, Lewis, David, Lewis, Ian, Lewis, Tania, Lim, Daniel, Littlewood, Kelly, Liu, Dara, Loh, Joanna, Lokare, Anand, Lomas, Oliver, Lovell, Richard, Lowe, Theresa, Lowry, Lisa, Lubowiecki, Marcin, Lumb, Rebecca, Lynch, Gail, Macaulay, Amanda, MacDonald, Lyndsey, MacDonald-Burn, Jill, Macmillan, Margaret, Maddock, Karen, Mahaliyana, Tomas, Mahon, Cassandra, Maidment, Alison, Maier, Susie, Mairos, Michelle, Majid, Mahseeman, Mak, Ka L, Mak, Anne, Malendrayogau, Arunthrthy, Malham, Hana, Malyon, Felicity, Mandadapu, Vineela, Mandel, Laura, Mant, Sarah, Manton, Ruth, Maouche, Nadjoua, Maqbool, Muhammad G., Marchant, Gregory, Marinho, Mariana, Marks, David, Marner, Mike, Marr, Helen, Marshall, Gillian, Martin, Siobhan, Martin, Abigail, Marzolini, Maria, Mason, Kiara, Massie, Jonathan, Masson, Rebecca, Mathavan, Vidya, Mathew, Siju, Mathie, Judith, Mattocks, Lehenta, Maybury, Bernard, Mayer, Georgina, McAlister, Chyrelle, McAllister, Jo, McConnell, Stewart, McCracken, James, McCullagh, Liz, McCulloch, Rory, Mcdermott, Christopher, Mcdonald, Kerian, McGinniss, Laura, McGurk, Fiona, McIlwain, Jessica, McIver, Kirsten, Mckay, Pam, McKenna, Lorraine, Mclornan, Donal, McMahon, Coalon, McNeice, Linda, McNeill, Susan, McNickle, Molly, McQueen, Fiona, McRae, Simon, McTaggart, Bobby, Mehew, Jenny, Mehra, Varnn, Melly, Michelle, Menichelli, Tara, Micklethwatte, Ken, Mihailescue, Loredana, Mijovic, Aleksander, Millband, Hannah, Miller, Lucy, Millien, Samuel T., Milnthorpe, James, Minson, Adrian, Molnar, Eva, Monsour, Marc, Moody, Mary, Moon, Rebecca, Moore, Sally, Moore, Katy, Morgan, Kelly, Morralley, Rebecca, Morris, Denise, Morris, Kirk, Morrison, Nicole, Moss, Merinda, Mughal, Muhammad, Muir, Paul, Mukkath, David, Mulla, Aasiyu, Mulligan, Stephen, Mullings, Joanne, Mulqueen, Angela, Muluey, Caitlin, Murdoch, Sarah, Murrani, Sura, Murthy, Vidhya, Musngi, Jimmy, Mustafa, Nadreen, Mynes, Tracey, Nalpantidis, Anastasios, Nandurkar, Harshal, Nardone, Linda, Nasari, Latifa, Nash, Monica, Naylor, Georgina, Ngu, Loretta, Nguethina, Melissa, Nguyen, John, Nguyen, Joseph, Nichol, Wendy, Nicholls, Emma, Nicole, Catherine S., Nicolson, Phillip, Nielson, David, Nikolousis, Emmanouil, Nix, Georgina, Njoku, Rita, Norman, Jane, Norman, Amy, Norris, Phoebe, North, Daniel, Norwood, Megan, Notcheva, Gaynor, Novitzky-Basso, Igor, Nyaboko, Joseph, Nygren, Maria, Obu, Ingrid, O'Connell, Siobhan, O'Connor, Jody, O'Kelly, Deanna, O'Niell, Aideen, Ony, Jeremy, Oo, Kathy, Oo, April, Oppermann, Anne, Orr, Ruth, O'Sullivan, Mary, Page, Jennifer, Palfreyman, Emma, Paneesha, Shankaranarayana, Panicker, Shyam, Parbutt, Catherine, Parigi, Elesha, Paris, Gemma, Parker, Tracey, Parnell, Caroline, Parrish, Christopher, Parsons, Alex, Pasat, Mioara, Patel, Natasha, Patel, Vijay, Patel, Pooja, Patel, Chaya, Pati, Nalini, Patterson, Andrea, Paul, Lauren, Payet, Danielle, Payne, Elspeth, Peachey, Victoria, Pearson, Amanda, Peniket, Andy, Percy, Laura, Pereyra, Millicent, Pervaiz, Omer, Phalod, Gunjan D, Pham, Anh, Pho, Jason, Pickard, Keir, Pidcock, Michael, Piggin, Anna, Pishyar, Yalda, Pocock, Abigail, Pol, Ranjendres, Polzella, Paolo, Poolan, Sonia, Portingale, Vicki, Posnett, Claire, Potluri, Sandeep, Potter, Victoria, Pratt, Guy, Prodger, Catherine, Pueblo, Andres, Puliyayil, Anish, Puvanakumar, Pratheepan, Qadri, Abdul, Quach, Hang, Quinn, Michael, Rafferty, Mark, Rahman, Marzia, Raj, Kavita, Raj, Sonia, Rajendran, Ramina, Ramanan, Radha, Ramasamy, Karthik, Rampotas, Alexandros, Ranchhod, Natasha, Rashid, Sabia, Ratanjee, Sunita, Rathore, Gurpreet, Ratnasingam, Sumita, Rayat, Manjit, Rayner, Michael, Reddell-Denton, Rebecca, Redding, Nicola, Reddy, Udaya, Rehman, Atique, Rice, Carol, Riches, Iwona, Rider, Thomas, Riley, John, Rinaldi, Ciro, Roberts, Kayleigh, Roberts, Andrew, Robertson, Bryony, Robertson, Peter, Robinson, Dan, Robinson, Rebecca, Robjohns, Emma, Robledo, Laura, Rodrigues, Ana, Rofe, Chris, Roff, Bridie, Rogers, Rachel, Rolt, Jill, Rooney, Carmela, Rose, Kathy, Rose, Hannah, Ross, David, Rouf, Shahara, Rourke, Claire, Routledge, David, Ruggiero, Janet, Rule, Simon, Rumsey, Richard, Sagge, Cherry, Saldhana, Helen, Salisbury, Richard, Salisbury, Sarah, Salvaris, Ross, Sanders, Kay, Sangombe, Mirriam, Sanigorska, Anna, Santos, Kristine, Sarkis, Taylah, Sarma, Anita, Saunders, Natalie, Schmidt, Kara, Schmidtmann, Anja, Schumacher, Ann, Scorer, Tom Scorer, Scott, Asleigh, Seath, Ingrid, Sejman, Frances, Selim, Adrian, Shamim, Nadia, Shan, Jocelyn, Shanmuganathan, Naranie, Shanmugaranjan, Shaminie, Sharpe, Michelle, Sharpley, Faye, Shaw, Emma, Sheath, Cara, Sheehy, Oonagh, Shen, Vivian, Sherbide, Solomon, Sheridan, Mathew, Sheridan, Jane, Sheridon, Matthew, Shields, Tracy, Sim, Hau V, Sim, Shirlene, Sims, Matt, Singaraveloo, Lydia, Singh, Gurcharan, Singh, Jasmine, Sladesal, Shree, Sloan, Andrew, Slobodian, Peter, Smith, Sophie, Smith, Sarit, Smith, Claire, Smith, Alastair, Smith, Neil, Snowden, Katherine, Solis, Joel, Somios, Denise, Soo, Jade, Spanevello, Michelle, Spaulding, Madeleie, Spence, Laura, Spillane, Liz, Spiteri, Alisha, Sprigg, Naomi, Springett, Sally, Stafford, Lynn, Stainthorp, Katherine, Stark, Kate, Steeden, Louise, Stephen, Ella, Stephenson, Aisling, Stewart, Andrew, Stewart, Orla, Stobie, Emma, Stokes, Chelsea, Streater, Jacqui, Suddens, Charlie-Marie, Suntharalingam, Surenthini, Surana, Narinder, Sutherland, Robyn, Sutherland, Antony, Sutton, David, Sweeney, Connor, Sweet, Reilly, Szucs, Aniko P, Taheri, Leila E., Tailor, Hinesh, Tam, Constantine, Tambakis, George, Tamplin, Mary, Tan, Chee, Tan, Sui, Tan, Joanne, Tan, Zhi, Taran, Tatiana, Tarpey, Fiona, Taseka, Angela, Tasker, Suzy, Tatarczuch, Maciej, Tayabali, Sarrah, Taylor, Hannah, Taylor, Robert, Taylor, Melaine, Taylor-Moore, Ella, Teasdale, Lesley, Tebbet, Elizabeth, Tedjasepstra, Aditya, Tedjaseputra, Aditya, Tepkumkun, Oummy, Terpstra, Andrew, Thomas, Wayne, Thomas, Shanice, Thompson, Rachel, Thornton, Thomas, Thorp, Bronwyn, Thrift, Moi Yap, Thwaites, Phillipa, Timbres, Jasmine, Tindall, Lauren, Tiong, Ing Soo, Tippler, Nicole, Todd, Tony, Todd, Shirley, Toghill, Neil, Tomlinson, Eve, Tooth, Jacinta, Topp, M., Trail, Nicola, Tran, Nguyen, Tran, Elizabeth, Tran, Vi, Treder, Bona, Tribbeck, Michelle, Trochowski, Siobhan, Truslove, Maria, Tse, Tsun, Tseu, Bing, Tucker, David, Turner, Kelly, Turner, Dianne, Turner, Herleen, Turner, Gillian, Twohig, Julie, Tylee, Thomas, Uhe, Micheleine, Underhill, Lauren, V, Joanne, Van der Vliet, Georgina, Van Tonder, Tina, VanderWeyden, Carrie, Varghese, Jerry, Vaughan, Lachlan, Veale, David, Vickaryyous, Nicky, Vince, Kathryn, Von Welligh, Jacoba, Vora, Sona, Wadehra, Karan, Walker, Rebecca, Walker, Stephen, Wallace, Roslyn, Wallniosve, Stephanie, Wallwork, S., Walmsley, Zoe, Walters, Fiona, Wang, Joyce, Wang, Angela, Wang, Chen, Wanyika, Mercy, Warcel, Dana, Wardrobe, Katrina, Warnes, Kristian, Waterhouse, Christopher, Waterworth, Adam, Watson, Caroline, Watson, Edmund, Watts, Emily, Weaver, Emma, Weber, Nicholas, Webley, Kaytie, Welford, Anna, Wells, Matt, Westbury, Sarah, Westcott, Jackie, Western, Robyn, Weston, Julia, White, Jessica, White, Phillipa, Whitehead, Anna, Whitehouse, James, Wieringa, Samantha, Willan, John, Williams, Sandra, Williams, Bethany, Williamson, Stephanie, Willoughby, Brett, Wilmot, Gail, Wilmott, Rosalind, Wilson, Joanna, Wilson, Emma, Wilson, Suzy, Wilson, Heather, Wilson, Caroline, Wilson, Tanya, Wilton, Margaret, Wiltshire, Paula, Wincup, Joanne, Wolf, Julia, Wong, Henna, Wong, Cyndi, Wong, Daniel, Wong, Jonathan, Wong, Shi Qin, Wood, Sarah, Wood, Henry, Wooding, Jackie, Woolley, Kelly, Wright, Myles, Wynn-Williams, Roland, Yannakou, Costas, Yeoh, Zhi Han, Yeung, David, Young, Agnes, Yuen, Flora, Yuen, Agnes, Zaja, Oliver, Zhang, Xiao-Yin, and Zhang, Mei

Published
2025
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12. Therapeutic strategies and treatment sequencing in patients with chronic lymphocytic leukemia: An international study of ERIC, the European Research Initiative on CLL

Author

Thomas Chatzikonstantinou, Lydia Scarfò, Eva Minga, Georgios Karakatsoulis, Dimitra Chamou, Jana Kotaskova, Gloria Iacoboni, Christos Demosthenous, Elisa Albi, Miguel Alcoceba, Salem Al‐Shemari, Thérèse Aurran‐Schleinitz, Francesca Bacchiarri, Sofia Chatzileontiadou, Rosa Collado, Zadie Davis, Marcos Daniel deDeus Santos, Maria Dimou, Elena Dmitrieva, David Donaldson, Gimena Dos Santos, Barbara Dreta, Maria Efstathopoulou, Shaimaa El‐Ashwah, Alicia Enrico, Andrzej Frygier, Sara Galimberti, Andrea Galitzia, Eva Gimeno, Valerio Guarente, Romain Guieze, Sean Harrop, Eleftheria Hatzimichael, Yair Herishanu, José‐Ángel Hernández‐Rivas, Ozren Jaksic, Elżbieta Kalicińska, Kamel Laribi, Volkan Karakus, Arnon P. Kater, Bonnie Kho, Maria Kislova, Εliana Konstantinou, Maya Koren‐Michowitz, Ioannis Kotsianidis, Zuzana Kubova, Jorge Labrador, Deepesh Lad, Luca Laurenti, Thomas Longval, Alberto Lopez‐Garcia, Juan Marquet, Stanislava Maslejova, Carlota Mayor‐Bastida, Biljana Mihaljevic, Ivana Milosevic, Fatima Miras, Riccardo Moia, Marta Morawska, Uttam K. Nath, Almudena Navarro‐Bailón, Jacopo Olivieri, Irina Panovska‐Stavridis, Maria Papaioannou, Cheyenne Pierie, Anna Puiggros, Gianluigi Reda, Gian M. Rigolin, Rosa Ruchlemer, Mattia Schipani, Annett Schiwitza, Yandong Shen, Tereza Shokralla, Martin Simkovic, Svetlana Smirnova, Dina S. A. Soliman, Stephan Stilgenbauer, Tamar Tadmor, Kristina Tomic, Eric Tse, Theodoros Vassilakopoulos, Andrea Visentin, Candida Vitale, George Vrachiolias, Vojin Vukovic, Renata Walewska, Zhenshu Xu, Munci Yagci, Lucrecia Yañez, Mohamed Yassin, Jana Zuchnicka, David Oscier, Alessandro Gozzetti, Panagiotis Panagiotidis, Francesc Bosch, Paolo Sportoletti, Blanca Espinet, Gerassimos A. Pangalis, Viola M. Popov, Stephen Mulligan, Maria Angelopoulou, Fatih Demirkan, Tomas Papajík, Bella Biderman, Roberta Murru, Marta Coscia, Constantine Tam, Antonio Cuneo, Gianluca Gaidano, Rainer Claus, Niki Stavroyianni, Livio Trentin, Darko Antic, Lukas Smolej, Olga B. Kalashnikova, Mark Catherwood, Martin Spacek, Sarka Pospisilova, Michael Doubek, Eugene Nikitin, Anastasia Chatzidimitriou, Paolo Ghia, and Kostas Stamatopoulos

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Published
2024
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15. Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting

Author

Zamani, Majid, Okreghe, Christian, and Demosthenous, Andreas

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels {\sigma}_N between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.

Published
2022

21. Continual Learning on the Edge with TensorFlow Lite

Author

Demosthenous, Giorgos and Vassiliades, Vassilis

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

Deploying sophisticated deep learning models on embedded devices with the purpose of solving real-world problems is a struggle using today's technology. Privacy and data limitations, network connection issues, and the need for fast model adaptation are some of the challenges that constitute today's approaches unfit for many applications on the edge and make real-time on-device training a necessity. Google is currently working on tackling these challenges by embedding an experimental transfer learning API to their TensorFlow Lite, machine learning library. In this paper, we show that although transfer learning is a good first step for on-device model training, it suffers from catastrophic forgetting when faced with more realistic scenarios. We present this issue by testing a simple transfer learning model on the CORe50 benchmark as well as by demonstrating its limitations directly on an Android application we developed. In addition, we expand the TensorFlow Lite library to include continual learning capabilities, by integrating a simple replay approach into the head of the current transfer learning model. We test our continual learning model on the CORe50 benchmark to show that it tackles catastrophic forgetting, and we demonstrate its ability to continually learn, even under non-ideal conditions, using the application we developed. Finally, we open-source the code of our Android application to enable developers to integrate continual learning to their own smartphone applications, as well as to facilitate further development of continual learning functionality into the TensorFlow Lite environment., Comment: 8 pages, 9 figures

Published
2021

22. The Use of Collaborative Assignment in Online Learning Environments: The Case of Higher Education

Author

Demosthenous, George, Panaoura, Areti, and Eteokleous, Nikleia

Abstract

Many studies investigated the use of collaboration at conventional teaching environment in different educational levels. The present study examines students' behavior during a collaborative assignment in an online learning environment in higher education. Data were collected by graduate students who were attending a course at a distance learning master degree program in Special Education. The developed group dynamics and students' beliefs about their role during the activity were "revealed" by their reactions to the discussion forum, their private e-mails to the instructor, their activity at the platform and their contribution on the assignment, for which the wiki tool was used. Results indicated that although students were at the same time in-service or pre-service teachers at primary and secondary education and they were expected to implement group work at their teaching, they had low self-efficacy beliefs in using it as students in online learning environment. Their major difficulties were related with their fear about possible negative consequences concerning their marks due to other members' behavior and the lack of experiences. Suggestions on how the group work and the collaborative assignment can be integrated more effectively in an online learning environment in higher education courses are discussed.

Published
2020

23. Open-Label Randomized Controlled Study of Ciprofloxacin vs Rifaximin as Neutropenia Prophylaxis in Allogeneic Hematopoietic Stem Cell Transplantation

Author

Gavriilaki, Eleni, Mallouri, Despina, Laspa, Evgenia, Papakonstantinou, Anna, Lazaridou, Andriana, Varelas, Christos, Baldoumi, Eirini, Giannakopoulou, Angeliki, Demosthenous, Christos, Vardi, Anna, Bousiou, Zoi, Batsis, Ioannis, Gkioula, Georgia, Anagnostopoulos, Achilles, and Sakellari, Ioanna

Published
2024
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33. Vaccination delays maedi-visna lentivirus infection in a naturally-infected sheep flock

Author

Gudnadóttir Margrét, Demosthenous Andreas, and Hadjisavvas Theophanis

Subjects
Lentivirus, Maedi-Visna, Vaccine-trial, Challenge by natural infection, Veterinary medicine, SF600-1100
Abstract

Abstract Background The Maedi-Visna (MV) lentivirus causes two slowly progressive eventually fatal diseases of sheep, Maedi, a progressive interstitial pneumonia, and Visna, a progressive demyelinating disease of the central nervous system. Other lentiviruses also cause fatal slow infections in their natural hosts, e.g. the HIV virus in humans. Results of experimental vaccination against any lentivirus where vaccinees are challenged by natural routes, may therefore be of general interest. From 1991–1998 experiments with formalin-inactivated whole Maedi-Visna virus vaccine were carried out in the Department of Microbiology at the University of Iceland. Western Blot tests showed good immune response to all major proteins of the virus. When aluminium hydroxide was added to the vaccine all vaccinees developed neutralizing antibodies to the vaccine strain at titers 1/8 – 1/256. After housing 5 twin pairs, one twin in each pair vaccinated, the other unvaccinated, with infected sheep for 4 years, all the unvaccinated twins became infected, but only 2 of their vaccinated siblings as confirmed by virus cultivation experiments on tissues from their lungs spleens lymph nodes and choroid plexuses. Results One twin in each of 40 female twin pairs, born into a Maedi-Visna-infected sheep flock and kept under natural farming conditions in Cyprus, was vaccinated at birth, 3 weeks and 3 months, with formalin-inactivated whole Maedi-Visna lentivirus vaccine adjuvanted with aluminium hydroxide. 17 mothers of the twins were seronegative, 13 seroconverting and 10 had old infection. Of 17 vaccinees born to seronegative mothers 9 were uninfected at 28 months, but only 2 of their unvaccinated siblings. Of 13 unvaccinated twins born to seroconverting mothers, 12 caught infection during their first 10 weeks, but only 4 of their vaccinated siblings. Vaccination had no effects on 10 vaccinees born to mothers with long-standing Maedi-Visna infections and broad andibody response at birth of their lambs. Conclusion Compared with their unvaccinated siblings, natural infection was delayed in significant number of vaccinated twins born by seronegative and seroconverting mothers and vaccinated at birth, 3 weeks and 3 months with formalin inactivated whole MV vaccine adjuvanted with aluminium hydroxide. Maternal antibodies interfered with vaccination so early in life if the mother had old infection.

Published
2013
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36. Nurturing Mathematical Creativity for the Concept of Arithmetic Mean in a Technologically Enhanced 'Personalised Mathematics and Mathematics Inquiry' Learning Environment

Author

Pitta-Pantazi, D., Christou, C., Demosthenous, E., Pittalis, M., and Chimoni, M.

Abstract

Mathematical creativity is considered beneficial for learning mathematics. However, research studies that provide empirical evidence of students' mathematical creativity in regular mathematics classrooms are relatively scarce. This study explores the idea of mini-creativity (mini-c), which is defined as the creative processes involved in the construction of personal knowledge and understanding. The aim of the study is the identification and characterization of mini-c instances when students are first introduced to the arithmetic mean concept. In total, 97 Grade 6 students participated in a technologically enhanced instructional program which was developed according to the principles of the theoretical framework 'personalised mathematics and mathematics inquiry' (PMMI). The analysis of the results suggests that the learning environment supported the development of mini-c. From students' worksheets and classroom observations, three categories of mini-c instances were identified and characterized, as follows: (a) building insight and abstractness of a mathematical concept; (b) creating, manipulating, and connecting representations; and (c) expressing flexible thinking.

Published
2022
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37. An optogenetic cell therapy to restore control of target muscles in an aggressive mouse model of amyotrophic lateral sclerosis

Author

J Barney Bryson, Alexandra Kourgiantaki, Dai Jiang, Andreas Demosthenous, and Linda Greensmith

Subjects
regenerative medicine, motor neurons, optogenetics, Amyotrophic Lateral Sclerosis, paralysis, cell transplant, Medicine, Science, Biology (General), QH301-705.5
Abstract

Breakdown of neuromuscular junctions (NMJs) is an early pathological hallmark of amyotrophic lateral sclerosis (ALS) that blocks neuromuscular transmission, leading to muscle weakness, paralysis and, ultimately, premature death. Currently, no therapies exist that can prevent progressive motor neuron degeneration, muscle denervation, or paralysis in ALS. Here, we report important advances in the development of an optogenetic, neural replacement strategy that can effectively restore innervation of severely affected skeletal muscles in the aggressive SOD1G93A mouse model of ALS, thus providing an interface to selectively control the function of targeted muscles using optical stimulation. We also identify a specific approach to confer complete survival of allogeneic replacement motor neurons. Furthermore, we demonstrate that an optical stimulation training paradigm can prevent atrophy of reinnervated muscle fibers and results in a tenfold increase in optically evoked contractile force. Together, these advances pave the way for an assistive therapy that could benefit all ALS patients.

Published
2024
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38. Other malignancies in the history of CLL: an international multicenter study conducted by ERIC, the European Research Initiative on CLL, in HARMONY

Author

Chatzikonstantinou, Thomas, Scarfò, Lydia, Karakatsoulis, Georgios, Minga, Eva, Chamou, Dimitra, Iacoboni, Gloria, Kotaskova, Jana, Demosthenous, Christos, Smolej, Lukas, Mulligan, Stephen, Alcoceba, Miguel, Al-Shemari, Salem, Aurran-Schleinitz, Thérèse, Bacchiarri, Francesca, Bellido, Mar, Bijou, Fontanet, Calleja, Anne, Medina, Angeles, Khan, Mehreen Ali, Cassin, Ramona, Chatzileontiadou, Sofia, Collado, Rosa, Christian, Amy, Davis, Zadie, Dimou, Maria, Donaldson, David, Santos, Gimena Dos, Dreta, Barbara, Efstathopoulou, Maria, El-Ashwah, Shaimaa, Enrico, Alicia, Fresa, Alberto, Galimberti, Sara, Galitzia, Andrea, García-Serra, Rocío, Gimeno, Eva, González-Gascón-y-Marín, Isabel, Gozzetti, Alessandro, Guarente, Valerio, Guieze, Romain, Gogia, Ajay, Gupta, Ritu, Harrop, Sean, Hatzimichael, Eleftheria, Herishanu, Yair, Hernández-Rivas, José-Ángel, Inchiappa, Luca, Jaksic, Ozren, Janssen, Susanne, Kalicińska, Elżbieta, Laribi, Kamel, Karakus, Volkan, Kater, Arnon P., Kho, Bonnie, Kislova, Maria, Konstantinou, Eliana, Koren-Michowitz, Maya, Kotsianidis, Ioannis, Kreitman, Robert J., Labrador, Jorge, Lad, Deepesh, Levin, Mark-David, Levy, Ilana, Longval, Thomas, Lopez-Garcia, Alberto, Marquet, Juan, Martin-Rodríguez, Lucia, Maynadié, Marc, Maslejova, Stanislava, Mayor-Bastida, Carlota, Mihaljevic, Biljana, Milosevic, Ivana, Miras, Fatima, Moia, Riccardo, Morawska, Marta, Murru, Roberta, Nath, Uttam Kumar, Navarro-Bailón, Almudena, Oliveira, Ana C., Olivieri, Jacopo, Oscier, David, Panovska-Stavridis, Irina, Papaioannou, Maria, Papajík, Tomas, Kubova, Zuzana, Phumphukhieo, Punyarat, Pierie, Cheyenne, Puiggros, Anna, Rani, Lata, Reda, Gianluigi, Rigolin, Gian Matteo, Ruchlemer, Rosa, Daniel de Deus Santos, Marcos, Schipani, Mattia, Schiwitza, Annett, Shen, Yandong, Simkovic, Martin, Smirnova, Svetlana, Abdelrahman Soliman, Dina Sameh, Spacek, Martin, Tadmor, Tamar, Tomic, Kristina, Tse, Eric, Vassilakopoulos, Theodoros, Visentin, Andrea, Vitale, Candida, von Tresckow, Julia, Vrachiolias, George, Vukovic, Vojin, Walewska, Renata, Wasik-Szczepanek, Ewa, Xu, Zhenshu, Yagci, Munci, Yañez, Lucrecia, Yassin, Mohamed, Zuchnicka, Jana, Angelopoulou, Maria, Antic, Darko, Biderman, Bella, Catherwood, Mark, Claus, Rainer, Coscia, Marta, Cuneo, Antonio, Demirkan, Fatih, Espinet, Blanca, Gaidano, Gianluca, Kalashnikova, Olga B., Laurenti, Luca, Nikitin, Eugene, Pangalis, Gerassimos A., Panagiotidis, Panagiotis, Popov, Viola Maria, Pospisilova, Sarka, Sportoletti, Paolo, Stavroyianni, Niki, Tam, Constantine, Trentin, Livio, Chatzidimitriou, Anastasia, Bosch, Francesc, Doubek, Michael, Ghia, Paolo, and Stamatopoulos, Kostas

Published
2023
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46. Neural Stimulation Hardware for the Selective Intrafascicular Modulation of the Vagus Nerve

Author

I. Strauss, F. Agnesi, C. Zinno, A. Giannotti, A. Dushpanova, V. Casieri, D. Terlizzi, F. Bernini, K. Gabisonia, Y. Wu, D. Jiang, V. Paggi, S. Lacour, F. Recchia, A. Demosthenous, V. Lionetti, and S. Micera

Subjects
Vagus nerve stimulation, intraneural stimulation, neural stimulation, neurostimulator, cardiac functions, electrically evoked action potentials, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

The neural stimulation of the vagus nerve is able to modulate various functions of the parasympathetic response in different organs. The stimulation of the vagus nerve is a promising approach to treating inflammatory diseases, obesity, diabetes, heart failure, and hypertension. The complexity of the vagus nerve requires highly selective stimulation, allowing the modulation of target-specific organs without side effects. Here, we address this issue by adapting a neural stimulator and developing an intraneural electrode for the particular modulation of the vagus nerve. The neurostimulator parameters such as amplitude, pulse width, and pulse shape were modulated. Single-, and multi-channel stimulation was performed at different amplitudes. For the first time, a polyimide thin-film neural electrode was designed for the specific stimulation of the vagus nerve. In vivo experiments were performed in the adult minipig to validate to elicit electrically evoked action potentials and to modulate physiological functions, validating the spatial selectivity of intraneural stimulation. Electrochemical tests of the electrode and the neurostimulator showed that the stimulation hardware was working correctly. Stimulating the porcine vagus nerve resulted in spatially selective modulation of the vagus nerve. ECAP belonging to alpha and beta fibers could be distinguished during single- and multi-channel stimulation. We have shown that the here presented system is able to activate the vagus nerve and can therefore modulate the heart rate, diastolic pressure, and systolic pressure. The here presented system may be used to restore the cardiac loop after denervation by implementing biomimetic stimulation patterns. Presented methods may be used to develop intraneural electrodes adapted for various applications.

Published
2023
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47. A Förster Resonance Energy Transfer (FRET)-Based Immune Assay for the Detection of Microcystin-LR in Drinking Water

Author

Alessandro Capo, Angela Pennacchio, Concetta Montagnese, Antonis Hadjiantonis, Panayiota Demosthenous, Alessandro Giusti, Maria Staiano, Sabato D’Auria, and Antonio Varriale

Subjects
biosensors, Cyanobacteria bloom, microcystin, FRET, fluorescence spectroscopy, Chemical technology, TP1-1185
Abstract

Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena, Planktothrix, and Nostoc produce and release toxins called microcystins (MCs) into the water. MCs can have severe effects on human and animal health following their ingestion and inhalation. The MC structure is composed of a constant region (composed of five amino acid residues) and a variable region (composed of two amino acid residues). When the MC variable region is composed of arginine and leucine, it is named MC-LR. The most-common methods used to detect the presence of MC-LR in water are chromatographic-based methods (HPLC, LC/MS, GC/MS) and immunological-based methods (ELISA). In this work, we developed a new competitive Förster resonance energy transfer (FRET) assay to detect the presence of traces of MC-LR in water. Monoclonal antibody anti-MC-LR and MC-LR conjugated with bovine serum albumin (BSA) were labeled with the near-infrared fluorophores CF568 and CF647, respectively. Steady-state fluorescence measurements were performed to investigate the energy transfer process between anti-MC-LR 568 and MC-LR BSA 647 upon their interaction. Since the presence of unlabeled MC-LR competes with the labeled one, a lower efficiency of FRET process can be observed in the presence of an increasing amount of unlabeled MC-LR. The limit of detection (LoD) of the FRET assay is found to be 0.245 nM (0.245 µg/L). This value is lower than the provisional limit established by the World Health Organization (WHO) for quantifying the presence of MC-LR in drinking water.

Published
2024
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