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1. TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science

Author

Sankar, Ramanakumar, Mantha, Kameswara, Fortson, Lucy, Spiers, Helen, Pengo, Thomas, Mashek, Douglas, Mo, Myat, Sanders, Mark, Christensen, Trace, Salisbury, Jeffrey, and Trouille, Laura

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

In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information. Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers. We leverage the patch-wise discriminator in our model to provide an estimate of which slices within these image cubes have poorly generalized feature representations, and correspondingly poor machine performance. These images with corresponding machine proposals would be presented to volunteers on Zooniverse for correction, leading to a drastic reduction in the volunteer effort on citizen science projects. We trained our model on ~2300 liver tissue 3D electron micrographs. Lipid droplets were segmented within these images through human annotation via the `Etch A Cell - Fat Checker' citizen science project, hosted on the Zooniverse platform. In this work, we demonstrate this framework and the selection methodology which resulted in a measured reduction in volunteer effort by more than 60%. We envision this type of joint human-machine partnership will be of great use on future Zooniverse projects., Comment: 5 pages, 1 figure, accepted for publication at HLDM '23 (ECML PKDD 2023 workshop)

Published
2023

3. A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Author

Fowler, Philip W, Wright, Carla, Spiers, Helen, Zhu, Tingting, Baeten, Elisabeth ML, Hoosdally, Sarah W, Cruz, Ana L Gibertoni, Roohi, Aysha, Kouchaki, Samaneh, Walker, Timothy M, Peto, Timothy EA, Miller, Grant, Lintott, Chris, Clifton, David, Crook, Derrick W, Walker, A Sarah, Barilar, Ivan, Battaglia, Simone, Borroni, Emanuele, Brandao, Angela Pires, Brankin, Alice, Cabibbe, Andrea Maurizio, Carter, Joshua, Cirillo, Daniela Maria, Claxton, Pauline, Clifton, David A, Cohen, Ted, Coronel, Jorge, Earle, Sarah G, Escuyer, Vincent, Ferrazoli, Lucilaine, Gao, George F, Gardy, Jennifer, Gharbia, Saheer, Ghisi, Kelen Teixeira, Ghodousi, Arash, Cruz, Ana Luıza Gibertoni, Grazian, Clara, Guthrie, Jennifer L, He, Wencong, Hoffmann, Harald, Hoosdally, Sarah J, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir Ahmed, Jarrett, Lisa, Joseph, Lavania, Jou, Ruwen, Kambli, Priti, Knaggs, Jeff, Koch, Anastasia, Kohlerschmidt, Donna, Lachapelle, Alexander S, Lalvani, Ajit, Lapierre, Simon Grandjean, Laurenson, Ian F, Letcher, Brice, Lin, Wan-Hsuan, Liu, Chunfa, Liu, Dongxin, Malone, Kerri M, Mandal, Ayan, Matias, Daniela, Meintjes, Graeme, Mendes, Flavia Freitas, Merker, Matthias, Mihalic, Marina, Millard, James, Miotto, Paolo, Mistry, Nerges, Moore, David, Dreyer, Viola, Chetty, Darren, Musser, Kimberlee A, Ngcamu, Dumisani, Nhung, Hoang Ngoc, Grandjean, Louis, Nilgiriwala, Kayzad Soli, Nimmo, Camus, Okozi, Nana, Oliveira, Rosangela Siqueira, Omar, Shaheed Vally, Paton, Nicholas, Pinhata, Juliana Maira Watanabe, Plesnik, Sara, Puyen, Zully M, Rabodoarivelo, Marie Sylvianne, Rakotosamimanana, Niaina, Rancoita, Paola MV, Rathod, Priti, Robinson, Esther, Rodger, Gillian, Rodrigues, Camilla, Rodwell, Timothy C, and Santos-Lazaro, David

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Infectious Diseases, Prevention, Antimicrobial Resistance, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Infection, Good Health and Well Being, Antitubercular Agents, Humans, Microbial Sensitivity Tests, Mycobacterium tuberculosis, Tuberculosis, Volunteers, Zooniverse Volunteer Community, CRyPTIC Consortium, M. tuberculosis, antibiotics, citizen science, clinical microbiology, infectious disease, microbiology, tuberculosis, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing.

Published
2022

4. A Survey of Crowdsourcing in Medical Image Analysis

Author

Ørting, Silas, Doyle, Andrew, van Hilten, Arno, Hirth, Matthias, Inel, Oana, Madan, Christopher R., Mavridis, Panagiotis, Spiers, Helen, and Cheplygina, Veronika

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain., Comment: Submitted to Human Computation

Published
2019

6. A transient search using combined human and machine classifications

Author

Wright, Darryl E., Lintott, Chris J., Smartt, Stephen J., Smith, Ken W., Fortson, Lucy, Trouille, Laura, Allen, Campbell R., Beck, Melanie, Bouslog, Mark C., Boyer, Amy, Chambers, K. C., Flewelling, Heather, Granger, Will, Magnier, Eugene A., McMaster, Adam, Miller, Grant R. M., O'Donnell, James E., Spiers, Helen, Tonry, John L., Veldthuis, Marten, Wainscoat, Richard J., Waters, Chris, Willman, Mark, Wolfenbarger, Zach, and Young, Dave R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of LSST and other large-throughput surveys., Comment: 10 pages, 9 figures, submitted to MNRAS

Published
2017
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13. Analyzing Mobility Patterns of Older Adults with Dementia Using GPS Technology

Author

Banihashemi, M. Amin, Sapkota, Shraddha, Bronskill, Susan E., Stuss, Donald T., Black, Sandra E., Bayat, Sayeh, Naglie, Gary, Rapoport, Mark, Ye, Bing, Stasiulis, Elaine, Mihailidis, Alex, Maxwell, Selena P., Cash, Meghan K., Darvesh, Sultan, Teper, Matthew Hacker, Hudon, Catherine, Vedel, Isabelle, Yang, Xin Qiang, Khanassov, Vladimir, Guseva, Elena, Lungu, Ovidiu, Goldman, Sondra, Wilchesky, Machelle, Tam, Angela, Dansereau, Christian, Itturia-Medina, Yasser, Urchs, Sebastian, Orban, Pierre, Breitner, John, Bellec, Pierre, Benhajali, Yassine, Spiers, Helen, Badhwar, AmanPreet, Trouille, Laura, Wallace, Lindsay, Theou, Olga, Godin, Judith, Rockwood, Kenneth, Andrew, Melissa, Starblanket, Danette, Bourassa, Carrie, Godard-Sebillotte, Claire, Sourial, Nadia, Rochette, Louis, Hardouin, Marine, Pelletier, Eric, Strumpf, Erin, Ruthirakuhan, Myuri, Herrmann, Nathan, Andreazza, Ana C., Gallagher, Damien, Verhoeff, Nicolaas Paul L.G., Lanctôt, Krista L., Trenaman, Shanna C., Andrew, Melissa K., Goralski, Kerry B., McLean, Allen, Morgan, Debra, Kosteniuk, Julie, O’Connell, Megan, Bayly, Melanie, Chow, Amanda Froehlich, Elliot, Valerie, Osgood, Nathaniel, Collin-Verreault, Yannik, Chouinard, Isabelle, Vogel, Jacob, Potvin, Olivier, Duchesne, Simon, Mirza, Saira Saeed, Ramirez, Joel, Lanctôt, Krista, Masellis, Mario, Kozyrev, Natalie, Albers, Shawn, Yang, Jennifer, Prado, Vania F., Rylett, R. Jane, Dekaban, Gregory A., McFall, G. Peggy, Dieumengarde, Louis, Drouin, Shannon, Camicioli, Richard, Dixon, Roger A., Adlimoghaddam, Aida, Perez, Claudia, Stortz, Greg, Djordjevic, Jelena, Snow, Wanda M., Goertzen, Andrew L, Ko, Ji Hyun, Albensi, Benedict C, Watson, Erin, Osman, Meric, Osman, Beliz Acan, O’Connell, Megan E., Kirk, Andrew, Stewart, Norma, Cammer, Allison, Innes, Anthea, Smith, Eric E., Barber, Philip A., Field, Thalia S., Frayne, Richard, Ganesh, Aravind, Hachinski, Vladimir, McCreary, Cheryl R., Pantoni, Leonardo, Sahlas, Demetrios J., Sharma, Mukul, Swartz, Richard H., Corbett, Dale, Yamin, Stéphanie, Vrkljan, Brenda, Tuokko, Holly, Sanford, Sarah, Porter, Michelle, Polgar, Jan, Myers, Anita, Moorhouse, Paige, Molnar, Frank, Mazer, Barbara, Marshall, Shawn, Gélinas, Isabelle, Crizzle, Alexandra, Byszewski, Anna, Belchior, Patricia, Bédard, Michel, Liu, Lili, Hollinda, Kara, Kaufman, David, Astell, Arlene, Daum, Christine, Stewart, Norma J., Seitz, Dallas, Holroyd-Leduc, Jayna, Daku, Jean, Hack, Tracy, Hoium, Faye, Kennett-Russill, Deb, Sauter, Kristen, Bronskill, Susan, Etches, Jacob, Guthrie, Dawn M., Williams, Nicole, Campos, Jennifer, Mick, Paul, Orange, Joseph B., Pichora-Fuller, M. Kathleen, Phillips, Natalie A., Savundranayagam, Marie, Wittich, Walter, Arsenault-Lapierre, Genevieve, Bergman, Howard, Tadley, Jaspaul, Buzatto, Adriana Zardini, Mung, Dorothea, Bajwa, Barinder, and Li, Liang

Subjects
Abstracts, CCNA Investigator Abstracts, I. Masters Students, III. Postdoctoral Fellows, II. MD, MD/PhD, or PhD Students
Published
2019

14. Influence of Intracerebral Hemorrhage Location on Incidence, Characteristics, and Outcome: Population-Based Study

Author

Samarasekera, Neshika, Fonville, Arthur, Lerpiniere, Christine, Farrall, Andrew J., Wardlaw, Joanna M., White, Philip M., Smith, Colin, Al-Shahi Salman, Rustam, Addison, Anne, Ahmad, Kate, Alhadad, Syed, Andrews, Peter, Bisset, Elaine, Bodkin, Peter, Bouhaidar, Ralph, Brennan, Paul, Campbell, Brian, Chandran, Siddharthan, Cook, Helen, Davenport, Richard, Dennis, Martin, Derry, Chris, Dodds, Katrina, Doubal, Fergus, Duncan, Susan, Elder, Andrew, Fitzpatrick, Mike, Foley, Peter, Fouyas, Ioannis, Ghosh, Sudipto, Gibson, Rod, Gordon, Claire, Grant, Robin, Hewett, Russell, Hughes, Fiona, Hughes, Mark, Hunt, David, Hunter, Neil, Ironside, James, Liaquat, Imran, Josephson, Colin, Kamat, Anant, Kealley, Susan, Keir, Sarah, Kerr, Gillian, Kerrigan, Simon, Keston, Peter, King, Matthew, Knight, Richard, Macdonald, Elizabeth, Mackay, Graham, Macleod, Donald, Macleod, Malcolm, Maguire, Conor, Makin, Steven, Mathews, Ashok, Maxwell, Fiona, McClellan, Stuart, Millar, Tracey, Morris, Zoe, Morse, Tim, Mumford, Colin, Murray, Katherine, Myles, Lynn, Nimmo, Graham, Ng, Yi, Pal, Suvankar, Rannikmae, Kristiina, Rhodes, Jonathan, Ross, Jerard, Russell, Tim, Sandercock, Peter, Sellar, Robin, Shanmuganathan, Mano, Shekhar, Himanshu, Simms, Henry, Sittampalam, Mara, Soleiman, Hamza, Spiers, Helen, Statham, Patrick, Stavrinos, Neo, Stone, Jon, Stuart, Joyce, Sudlow, Cathie, Summers, David, Taylor, Pat, Torgersen, Antonia, van Dijke, Margarethe, Walker, Robert, Weller, Belinda, Whiteley, William, Whittle, Ian, Will, Robert, Young, Wendy, Anderson, Judith, Broadbent, Seona, Butler, Laura, Caesar, Dave, Cantley, Patricia, Carter, Jonathan, Clegg, Gareth, Coull, Andrew, Crosswaite, Alastair, Dear, James, Dummer, Simon, Duncan, Fiona, Elder-Gracie, Trish, Enright, Kate, Fitzgerald, Tom, Fothergill, Jane, Frier, Brian, Grant, David, Gray, Alasdair, Hart, Simon, Henderson, Robin, Jaap, Alan, Leigh-Smith, Simon, Jones, Michael, Masson, Moyra, McCallum, Lynn, McKechnie, Martin, McKillop, Graham, Mead, Gillian, Morley, Wendy, Morrow, Billie, Morrow, Frank, Murchison, Jon, Murphy, Ross, Ng, Jasmine, Ogundipe, Olayinka, Patel, Dilip, Pollock, Alison, Reed, Matthew, Roberts, Geraint, Selvarajah, Johann, Smith, Randy, Stirling, Claire, Turner, Neil, Wilson, Matthew, Yordanov, Stanko, Bell, Nicola, Chambers, Sarah, Dewar, Sandra, Farquhar, Donald, Harmouche, Ali, Jacob, Ashok, Jackson, Katherine, Knox, Anne, McCafferty, Jon, Moultrie, Sam, Munang, Latana, Noble, Donald, Ramsay, Scott, Spence, Linda, Threlfall, Bethany, Williams, Adrian, Wilson, James, Fitzgerald, Alastair, Jamieson, Andrew, Lange, Peter, McIntosh, Andrew, Morrison, Lewis, and Todd, Iain

Published
2015
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15. A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates.

Author

Fowler, Philip W., Wright, Carla, Spiers, Helen, Tingting Zhu, Baeten, Elisabeth M. L., Hoosdally, Sarah W., Gibertoni Cruz, Ana L., Roohi, Aysha, Kouchaki, Samaneh, Walker, Timothy M., Peto, Timothy E. A., Miller, Grant, Lintott, Chris, Clifton, David, Crook, Derrick W., and Walker, A. Sarah

Published
2022
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17. A Survey of Crowdsourcing in Medical Image Analysis

Author

Doyle, Andrew, Hirth, Matthias, Madan, Christopher R, Mavridis, Dom Panagiotis, Spiers, Helen, Cheplygina, Veronika, van Hilten, Arno, and Inel, Oana

Abstract

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that a technique that is well established in a number of disciplines, including astronomy, ecology and meteorology for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches and challenges and provide recommendations to researchers implementing crowdsourcing for medical imaging tasks. Finally, we discuss future opportunities for development within this emerging domain.

Published
2020

18. A Survey of Crowdsourcing in Medical Image Analysis

Author

Ørting, Silas Nyboe, primary, Doyle, Andrew, additional, Van Hilten, Arno, additional, Hirth, Matthias, additional, Inel, Oana, additional, Madan, Christopher R, additional, Mavridis, Panagiotis, additional, Spiers, Helen, additional, and Cheplygina, Veronika, additional

Published
2020
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19. Selected rapporteur summaries from the XX world congress of psychiatric genetics, Hamburg, Germany, october 14–18, 2012

Author

Anderson-Schmidt, Heike, Beltcheva, Olga, Brandon, Mariko D., Byrne, Enda M., Diehl, Eric J., Duncan, Laramie, Gonzalez, Suzanne D., Hannon, Eilis, Kantojärvi, Katri, Karagiannidis, Iordanis, Kos, Mark Z., Kotyuk, Eszter, Laufer, Benjamin I., Mantha, Katarzyna, McGregor, Nathaniel W., Meier, Sandra, Nieratschker, Vanessa, Spiers, Helen, Squassina, Alessio, Thakur, Geeta A., Tiwari, Yash, Viswanath, Biju, Way, Michael J., Wong, Cybele C.P., OʼShea, Anne, and DeLisi, Lynn E.

Published
2013
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20. Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Author

Spiers, Helen, primary, Songhurst, Harry, additional, Nightingale, Luke, additional, de Folter, Joost, additional, Hutchings, Roger, additional, Peddie, Christopher J, additional, Weston, Anne, additional, Strange, Amy, additional, Hindmarsh, Steve, additional, Lintott, Chris, additional, Collinson, Lucy M, additional, and Jones, Martin L, additional

Published
2020
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23. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations.

Author

Spiers, Helen, Songhurst, Harry, Nightingale, Luke, Folter, Joost, Hutchings, Roger, Peddie, Christopher J., Weston, Anne, Strange, Amy, Hindmarsh, Steve, Lintott, Chris, Collinson, Lucy M., and Jones, Martin L.

Subjects
*TRAINING of volunteers, *DEEP learning, *ELECTRON microscopy, *SCANNING electron microscopy, *MACHINE learning, *NUCLEAR membranes
Abstract

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high‐quality ground‐truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high‐quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data. [ABSTRACT FROM AUTHOR]

Published
2021
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24. Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model

Author

Spiers, Helen, Hannon, Eilis, Wells, Sara, Williams, Brenda, Fernandes, Cathy, and Mill, Jonathan

Subjects
Aging, Ageing, DNA methylation, Cross-tissue, Epigenetics, Inbred mouse, Developmental Biology
Abstract

Epigenetic disruption has been implicated in many diseases of aging, and age-associated DNA methylation changes at specific genomic loci in humans are strongly correlated with chronological age. The aim of this study was to explore the specificity of selected age-associated differentially methylated positions (aDMPs) identified in human epidemiological studies by quantifying DNA methylation across multiple tissues in homologous regions of the murine genome. We selected four high-confidence aDMPs (located in the vicinity of the ELOVL2, GLRA1, MYOD1 and PDE4C genes) and quantified DNA methylation across these regions in four tissues (blood, lung, cerebellum and hippocampus) from male and female C57BL/6J mice, ranging in age from fetal (embryonic day 17) to 630 days. We observed tissue-specific age-associated changes in DNA methylation that was directionally consistent with those observed in humans. These findings lend further support to the notion that changes in DNA methylation are associated with chronological age and suggest that these processes are often conserved across tissues and between mammalian species. Our data highlight the relevance of utilizing model systems, in which environmental and genetic influences can be carefully controlled, for the further study of these phenomena.

Published
2016
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29. TRANSCRIPTOMIC STUDIES OF THE HUMAN PRENATAL BRAIN

Author

O'Brien, Heath, primary, Hannon, Eilis, additional, Spiers, Helen, additional, Jeffries, Aaron, additional, McLaughlin, Gemma, additional, Davies, William, additional, Anney, Richard, additional, Hill, Matthew, additional, O'Donovan, Michael, additional, Mill, Jon, additional, and Bray, Nick, additional

Published
2019
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33. A transient search using combined human and machine classifications

Author

Wright, Darryl E., primary, Lintott, Chris J., additional, Smartt, Stephen J., additional, Smith, Ken W., additional, Fortson, Lucy, additional, Trouille, Laura, additional, Allen, Campbell R., additional, Beck, Melanie, additional, Bouslog, Mark C., additional, Boyer, Amy, additional, Chambers, K. C., additional, Flewelling, Heather, additional, Granger, Will, additional, Magnier, Eugene A., additional, McMaster, Adam, additional, Miller, Grant R. M., additional, O'Donnell, James E., additional, Simmons, Brooke, additional, Spiers, Helen, additional, Tonry, John L., additional, Veldthuis, Marten, additional, Wainscoat, Richard J., additional, Waters, Chris, additional, Willman, Mark, additional, Wolfenbarger, Zach, additional, and Young, Dave R., additional

Published
2017
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34. Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions

Author

Viana, Joana, primary, Hannon, Eilis, additional, Dempster, Emma, additional, Pidsley, Ruth, additional, Macdonald, Ruby, additional, Knox, Olivia, additional, Spiers, Helen, additional, Troakes, Claire, additional, Al-Saraj, Safa, additional, Turecki, Gustavo, additional, Schalkwyk, Leonard C, additional, and Mill, Jonathan, additional

Published
2016
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39. Indigenous participation in health sciences education

Author

Spiers, Helen

Abstract

At a time when health professionals struggle to address severely inadequate social conditions which are reflected in the poor health of Indigenous peoples from remote and rural Australia, Indigenous students are receiving confused messages from national politicians concerning their level of support available. This study identifies ways in which Western tertiary institutions may better appreciate those elements of the Indigenous students’ learning environment considered critical for success. This study initially describes the institutional support structures and applied strategies currently considered effective and culturally appropriate to assist Aboriginal and Torres Strait Islander students in the Northern Territory. In attempting to investigate whether these solutions to the acknowledged problem of low retention rates and course completions in tertiary study are supported by the students themselves, the study records the perceptions of a sample of Indigenous students enrolled in three Northern Territory institutions across both the Vocational Education and Training (VET) and Higher Education (HE) undergraduate and post-graduate health sciences courses. With its methodology based within the Interpretive Paradigm, the study utilises qualitative research orientations, including semi-structured interviews and focus group discussions, and acknowledges the ethical and political dimensions of the research. The resultant data provides insights into the many complex issues impacting on Indigenous Health Science students, many of whom are employed as community Health Workers. The research identifies that the informal learning generated by their unique experiences results in the students reaching a hybrid cultural space within the Western tertiary environment in which they can be an individual without loss of cultural identity. Amongst the findings it was learnt that institutions need to further foster relationships with the family and community connections of the students. This study reveals a Model of Support recommending that increased institutional Cultural Sensitivity involving the simultaneous development of Cultural Nurturing and Cultural Connections is required.

Published
2010
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43. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci

Author

Hannon, Eilis, Spiers, Helen, Viana, Joana, Pidsley, Ruth, Burrage, Joe, Murphy, Therese M, Troakes, Claire, Turecki, Gustavo, O'Donovan, Michael C, Schalkwyk, Leonard C, Bray, Nicholas J, and Mill, Jonathan

Abstract

We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n = 166) of human fetal brain samples spanning 56–166 d post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs were primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and showed substantial overlap with genetic variants that were also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum), we found that most fetal brain mQTLs were developmentally stable, although a subset was characterized by fetal-specific effects. Fetal brain mQTLs were enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we found that mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.

Published
2016
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47. Rapid Down-Regulation of Glucocorticoid Receptor Gene Expression in the Dentate Gyrus after Acute Stress in vivo: Role of DNA Methylation and MicroRNA Activity.

Author

Mifsud KR, Saunderson EA, Spiers H, Carter SD, Trollope AF, Mill J, and Reul JM

Subjects
Acute Disease, Animals, DNA (Cytosine-5-)-Methyltransferases genetics, DNA Methyltransferase 3A, Down-Regulation, Gene Expression, Male, Mifepristone administration & dosage, Promoter Regions, Genetic, RNA, Messenger metabolism, Rats, Rats, Wistar, Receptors, Glucocorticoid antagonists & inhibitors, DNA Methylation, Dentate Gyrus metabolism, MicroRNAs genetics, Receptors, Glucocorticoid genetics, Stress, Psychological genetics
Abstract

Background: Although glucocorticoid receptors (GRs) in the hippocampus play a vital role in the regulation of physiological and behavioural responses to stress, the regulation of receptor expression remains unclear. This work investigates the molecular mechanisms underpinning stress-induced changes in hippocampal GR mRNA levels in vivo., Methods: Male Wistar rats were killed either under baseline conditions or after forced swim stress (FSS; 15 min in 25°C water). Rat hippocampi were micro-dissected (for mRNA, microRNA, and DNA methylation analysis) or frozen whole (for chromatin immunoprecipitation). In an additional experiment, rats were pre-treated with RU486 (a GR antagonist) or vehicle., Results: FSS evoked a dentate gyrus-specific reduction in GR mRNA levels. This was related to an increased DNMT3a protein association with a discreet region of the Nr3c1 (GR gene) promoter, shown here to undergo increased DNA methylation after FSS. FSS also caused a time-dependent increase in the expression of miR-124a, a microRNA known to reduce GR mRNA expression, which was inversely correlated with a reduction in GR mRNA levels 30 min after FSS. FSS did not affect GR binding to a putative negative glucocorticoid response element within the Nr3c1 gene., Conclusions: Acute stress results in decreased GR mRNA expression specifically in the dentate gyrus. Our results indicate that a complex interplay of multiple molecular mechanisms - including increased DNA methylation of discrete CpG residues within the Nr3c1 gene, most likely facilitated by DNMT3a, and increased expression of miR-124a - could be responsible for these changes., (© 2016 S. Karger AG, Basel.)

Published
2017
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