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204. Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer

Author

Dborin, James, Wimalaweera, Vinul, Barratt, Fergus, Ostby, Eric, O'Brien, Thomas E., and Green, Andrew G.

Subjects
Quantum Physics
Abstract

We optimise a translationally invariant, sequential quantum circuit on a superconducting quantum device to simulate the groundstate of the quantum Ising model through its quantum critical point. We further demonstrate how the dynamical quantum critical point found in quenches of this model across its quantum critical point can be simulated. Our approach avoids finite-size scaling effects by using sequential quantum circuits inspired by infinite matrix product states. We provide efficient circuits and a variety of error mitigation strategies to implement, optimise and time-evolve these states.

Published
2022
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205. Neutrino Masses and Mass Hierarchy: Evidence for the Normal Hierarchy

Author

Jimenez, Raul, Pena-Garay, Carlos, Short, Kathleen, Simpson, Fergus, and Verde, Licia

Subjects
High Energy Physics - Phenomenology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The latest cosmological constraints on the sum of neutrino masses, in combination with the latest laboratory measurements on oscillations, provide ``decisive" Bayesian evidence for the normal neutrino mass hierarchy. We show that this result holds across very different prior alternatives by exploring two extremes on the range of prior choices. In fact, while the specific numerical value for the Evidence depends on the choice of prior, the Bayesian odds remain greater than 140:1 across very different prior choices. For Majorana neutrinos this has important implications for the upper limit of the neutrino-less double beta decay half life and thus for the technology and resources needed for future double beta decay experiments., Comment: Matches accepted version to JCAP. Conclusions and results unchanged

Published
2022
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206. Being KLEVER at cosmic noon: ionised gas outflows are inconspicuous in low-mass star-forming galaxies but prominent in massive AGN hosts

Author

Concas, Alice, Maiolino, Roberto, Curti, Mirko, Hayden-Pawson, Connor, Cirasuolo, Michele, Jones, Gareth C., Mercurio, Amata, Belfiore, Francesco, Cresci, Giovanni, Cullen, Fergus, Mannucci, Filippo, Marconi, Alessandro, Cappellari, Michele, Cicone, Claudia, Peng, Yingjie, and Troncoso, Paulina

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We investigate the presence of ionised gas outflows in a sample of 141 main-sequence star-forming galaxies at $1.2 10.8$, AGN-dominated galaxies, suggesting that AGNs may be the primary drivers of these gas flows. Surprisingly, at $\log(M_\star/M_{\odot})\leq 9.6$, the observed line profiles are fully consistent with a rotating disc model, indicating that ionised gas outflows in dwarf galaxies might play a negligible role even during the peak of cosmic star-formation activity. Finally, we find that the observed mass loading factor scales with stellar mass as expected from the TNG50 cosmological simulation, but the ionised gas mass accounts for only 2$\%$ of the predicted value. This suggests that either the bulk of the outflowing mass is in other gaseous phases or the current feedback models implemented in cosmological simulations need to be revised., Comment: Accepted for publication in MNRAS, 28 pages, 20 figures

Published
2022
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207. Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance

Author

Fergus, Paul, Chalmers, Carl, Henderson, William, Roberts, Danny, and Waraich, Atif

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Pressure ulcers are a challenge for patients and healthcare professionals. In the UK, 700,000 people are affected by pressure ulcers each year. Treating them costs the National Health Service {\pounds}3.8 million every day. Their etiology is complex and multifactorial. However, evidence has shown a strong link between old age, disease-related sedentary lifestyles and unhealthy eating habits. Pressure ulcers are caused by direct skin contact with a bed or chair without frequent position changes. Urinary and faecal incontinence, diabetes, and injuries that restrict body position and nutrition are also known risk factors. Guidelines and treatments exist but their implementation and success vary across different healthcare settings. This is primarily because healthcare practitioners have a) minimal experience in dealing with pressure ulcers, and b) a general lack of understanding of pressure ulcer treatments. Poorly managed, pressure ulcers lead to severe pain, poor quality of life, and significant healthcare costs. In this paper, we report the findings of a clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the performance of a faster region-based convolutional neural network and mobile platform that categorised and documented pressure ulcers. The neural network classifies category I, II, III, and IV pressure ulcers, deep tissue injuries, and unstageable pressure ulcers. Photographs of pressure ulcers taken by district nurses are transmitted over 4/5G communications to an inferencing server for classification. Classified images are stored and reviewed to assess the model's predictions and relevance as a tool for clinical decision making and standardised reporting. The results from the study generated a mean average Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using an @.75 confidence score threshold., Comment: 12 pages, 8 figures

Published
2022

208. Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning

Author

Barratt, Fergus, Dborin, James, and Wright, Lewis

Subjects
Computer Science - Machine Learning, Condensed Matter - Strongly Correlated Electrons, Quantum Physics
Abstract

Tensor networks have demonstrated significant value for machine learning in a myriad of different applications. However, optimizing tensor networks using standard gradient descent has proven to be difficult in practice. Tensor networks suffer from initialization problems resulting in exploding or vanishing gradients and require extensive hyperparameter tuning. Efforts to overcome these problems usually depend on specific network architectures, or ad hoc prescriptions. In this paper we address the problems of initialization and hyperparameter tuning, making it possible to train tensor networks using established machine learning techniques. We introduce a `copy node' method that successfully initializes arbitrary tensor networks, in addition to a gradient based regularization technique for bond dimensions. We present numerical results that show that the combination of techniques presented here produces quantum inspired tensor network models with far fewer parameters, while improving generalization performance.

Published
2022

209. Technology Innovation and Guardrails in Elite Sport: The Future is Now

Author

Guppy, Fergus, Muniz-Pardos, Borja, Angeloudis, Konstantinos, Grivas, Gerasimos V., Pitsiladis, Asimina, Bundy, Ross, Zelenkova, Irina, Tanisawa, Kumpei, Akiyama, Hiroshi, Keramitsoglou, Iphigenia, Miller, Mike, Knopp, Melanie, Schweizer, Fabian, Luckfiel, Tobias, Ruiz, Daniel, Racinais, Sebastien, and Pitsiladis, Yannis

Published
2023
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210. Publisher Correction: Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
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212. Copy number variants as modifiers of breast cancer risk for BRCA1/BRCA2 pathogenic variant carriers

Author

Hakkaart, Christopher, Pearson, John F, Marquart, Louise, Dennis, Joe, Wiggins, George AR, Barnes, Daniel R, Robinson, Bridget A, Mace, Peter D, Aittomäki, Kristiina, Andrulis, Irene L, Arun, Banu K, Azzollini, Jacopo, Balmaña, Judith, Barkardottir, Rosa B, Belhadj, Sami, Berger, Lieke, Blok, Marinus J, Boonen, Susanne E, Borde, Julika, Bradbury, Angela R, Brunet, Joan, Buys, Saundra S, Caligo, Maria A, Campbell, Ian, Chung, Wendy K, Claes, Kathleen BM, Collonge-Rame, Marie-Agnès, Cook, Jackie, Cosgrove, Casey, Couch, Fergus J, Daly, Mary B, Dandiker, Sita, Davidson, Rosemarie, de la Hoya, Miguel, de Putter, Robin, Delnatte, Capucine, Dhawan, Mallika, Diez, Orland, Ding, Yuan Chun, Domchek, Susan M, Donaldson, Alan, Eason, Jacqueline, Easton, Douglas F, Ehrencrona, Hans, Engel, Christoph, Evans, D Gareth, Faust, Ulrike, Feliubadaló, Lidia, Fostira, Florentia, Friedman, Eitan, Frone, Megan, Frost, Debra, Garber, Judy, Gayther, Simon A, Gehrig, Andrea, Gesta, Paul, Godwin, Andrew K, Goldgar, David E, Greene, Mark H, Hahnen, Eric, Hake, Christopher R, Hamann, Ute, Hansen, Thomas VO, Hauke, Jan, Hentschel, Julia, Herold, Natalie, Honisch, Ellen, Hulick, Peter J, Imyanitov, Evgeny N, Isaacs, Claudine, Izatt, Louise, Izquierdo, Angel, Jakubowska, Anna, James, Paul A, Janavicius, Ramunas, John, Esther M, Joseph, Vijai, Karlan, Beth Y, Kemp, Zoe, Kirk, Judy, Konstantopoulou, Irene, Koudijs, Marco, Kwong, Ava, Laitman, Yael, Lalloo, Fiona, Lasset, Christine, Lautrup, Charlotte, Lazaro, Conxi, Legrand, Clémentine, Leslie, Goska, Lesueur, Fabienne, Mai, Phuong L, Manoukian, Siranoush, Mari, Véronique, Martens, John WM, McGuffog, Lesley, Mebirouk, Noura, Meindl, Alfons, Miller, Austin, and Montagna, Marco

Subjects
Human Genome, Prevention, Breast Cancer, Cancer, Genetics, Aetiology, 2.1 Biological and endogenous factors, BRCA1 Protein, BRCA2 Protein, Breast Neoplasms, DNA Copy Number Variations, Female, Genetic Predisposition to Disease, Heterozygote, Humans, RNA, Messenger, GEMO Study Collaborators, EMBRACE Collaborators, SWE-BRCA Investigators, kConFab Investigators, HEBON Investigators
Abstract

The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.

Published
2022

213. Physical activity, sedentary time and breast cancer risk: a Mendelian randomisation study

Author

Dixon-Suen, Suzanne C, Lewis, Sarah J, Martin, Richard M, English, Dallas R, Boyle, Terry, Giles, Graham G, Michailidou, Kyriaki, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Lush, Michael, Investigators, ABCTB, Ahearn, Thomas U, Ambrosone, Christine B, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J, Augustinsson, Annelie, Auvinen, Päivi, Freeman, Laura E Beane, Becher, Heiko, Beckmann, Matthias W, Behrens, Sabine, Bermisheva, Marina, Blomqvist, Carl, Bogdanova, Natalia V, Bojesen, Stig E, Bonanni, Bernardo, Brenner, Hermann, Brüning, Thomas, Buys, Saundra S, Camp, Nicola J, Campa, Daniele, Canzian, Federico, Castelao, Jose E, Cessna, Melissa H, Chang-Claude, Jenny, Chanock, Stephen J, Clarke, Christine L, Conroy, Don M, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Daly, Mary B, Devilee, Peter, Dörk, Thilo, Dwek, Miriam, Eccles, Diana M, Eliassen, A Heather, Engel, Christoph, Eriksson, Mikael, Evans, D Gareth, Fasching, Peter A, Fletcher, Olivia, Flyger, Henrik, Fritschi, Lin, Gabrielson, Marike, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A, Goldberg, Mark S, Guénel, Pascal, Gündert, Melanie, Hahnen, Eric, Haiman, Christopher A, Häberle, Lothar, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hart, Steven N, Harvie, Michelle, Hillemanns, Peter, Hollestelle, Antoinette, Hooning, Maartje J, Hoppe, Reiner, Hopper, John, Howell, Anthony, Hunter, David J, Jakubowska, Anna, Janni, Wolfgang, John, Esther M, Jung, Audrey, Kaaks, Rudolf, Keeman, Renske, Kitahara, Cari M, Koutros, Stella, Kraft, Peter, Kristensen, Vessela N, Kubelka-Sabit, Katerina, Kurian, Allison W, Lacey, James V, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Loibl, Sibylle, Lubiński, Jan, Mannermaa, Arto, and Manoochehri, Mehdi

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Health Sciences, Public Health, Oncology and Carcinogenesis, Cancer, Behavioral and Social Science, Women's Health, Genetics, Breast Cancer, Prevention, Obesity, Physical Activity, Female, Humans, Breast Neoplasms, Exercise, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Risk Factors, Sedentary Behavior, Breast Cancer Association Consortium, Breast, Physical activity, Sedentary Behaviour, Engineering, Medical and Health Sciences, Education, Sport Sciences, Clinical sciences, Sports science and exercise, Applied and developmental psychology
Abstract

ObjectivesPhysical inactivity and sedentary behaviour are associated with higher breast cancer risk in observational studies, but ascribing causality is difficult. Mendelian randomisation (MR) assesses causality by simulating randomised trial groups using genotype. We assessed whether lifelong physical activity or sedentary time, assessed using genotype, may be causally associated with breast cancer risk overall, pre/post-menopause, and by case-groups defined by tumour characteristics.MethodsWe performed two-sample inverse-variance-weighted MR using individual-level Breast Cancer Association Consortium case-control data from 130 957 European-ancestry women (69 838 invasive cases), and published UK Biobank data (n=91 105-377 234). Genetic instruments were single nucleotide polymorphisms (SNPs) associated in UK Biobank with wrist-worn accelerometer-measured overall physical activity (nsnps=5) or sedentary time (nsnps=6), or accelerometer-measured (nsnps=1) or self-reported (nsnps=5) vigorous physical activity.ResultsGreater genetically-predicted overall activity was associated with lower breast cancer overall risk (OR=0.59; 95% confidence interval (CI) 0.42 to 0.83 per-standard deviation (SD;~8 milligravities acceleration)) and for most case-groups. Genetically-predicted vigorous activity was associated with lower risk of pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87,≥3 vs. 0 self-reported days/week), with consistent estimates for most case-groups. Greater genetically-predicted sedentary time was associated with higher hormone-receptor-negative tumour risk (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), with elevated estimates for most case-groups. Results were robust to sensitivity analyses examining pleiotropy (including weighted-median-MR, MR-Egger).ConclusionOur study provides strong evidence that greater overall physical activity, greater vigorous activity, and lower sedentary time are likely to reduce breast cancer risk. More widespread adoption of active lifestyles may reduce the burden from the most common cancer in women.

Published
2022

214. Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Author

Grootes, Isabelle, Keeman, Renske, Blows, Fiona M, Milne, Roger L, Giles, Graham G, Swerdlow, Anthony J, Fasching, Peter A, Abubakar, Mustapha, Andrulis, Irene L, Anton-Culver, Hoda, Beckmann, Matthias W, Blomqvist, Carl, Bojesen, Stig E, Bolla, Manjeet K, Bonanni, Bernardo, Briceno, Ignacio, Burwinkel, Barbara, Camp, Nicola J, Castelao, Jose E, Choi, Ji-Yeob, Clarke, Christine L, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Devilee, Peter, Dörk, Thilo, Dunning, Alison M, Dwek, Miriam, Easton, Douglas F, Eccles, Diana M, Eriksson, Mikael, Ernst, Kristina, Evans, D Gareth, Figueroa, Jonine D, Fink, Visnja, Floris, Giuseppe, Fox, Stephen, Gabrielson, Marike, Gago-Dominguez, Manuela, García-Sáenz, José A, González-Neira, Anna, Haeberle, Lothar, Haiman, Christopher A, Hall, Per, Hamann, Ute, Harkness, Elaine F, Hartman, Mikael, Hein, Alexander, Hooning, Maartje J, Hou, Ming-Feng, Howell, Sacha J, Investigators, ABCTB, Investigators, kConFab, Ito, Hidemi, Jakubowska, Anna, Janni, Wolfgang, John, Esther M, Jung, Audrey, Kang, Daehee, Kristensen, Vessela N, Kwong, Ava, Lambrechts, Diether, Li, Jingmei, Lubiński, Jan, Manoochehri, Mehdi, Margolin, Sara, Matsuo, Keitaro, Taib, Nur Aishah Mohd, Mulligan, Anna Marie, Nevanlinna, Heli, Newman, William G, Offit, Kenneth, Osorio, Ana, Park, Sue K, Park-Simon, Tjoung-Won, Patel, Alpa V, Presneau, Nadege, Pylkäs, Katri, Rack, Brigitte, Radice, Paolo, Rennert, Gad, Romero, Atocha, Saloustros, Emmanouil, Sawyer, Elinor J, Schneeweiss, Andreas, Schochter, Fabienne, Schoemaker, Minouk J, Shen, Chen-Yang, Shibli, Rana, Sinn, Peter, Tapper, William J, Tawfiq, Essa, Teo, Soo Hwang, Teras, Lauren R, Torres, Diana, Vachon, Celine M, van Deurzen, Carolien HM, Wendt, Camilla, and Williams, Justin A

Subjects
Cancer, Breast Cancer, Breast Neoplasms, Female, Humans, Progesterone, Prognosis, Receptor, ErbB-2, Receptors, Progesterone, PREDICT Breast, breast cancer, Progesterone receptor, ABCTB Investigators, kConFab Investigators, Receptor, erbB-2, Oncology and Carcinogenesis, Public Health and Health Services, Oncology & Carcinogenesis
Abstract

BackgroundPredict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).MethodThe prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.ResultsHaving a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.ConclusionThe inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.

Published
2022

215. Getting Prepared 2019: Developmental Education Course-Taking of High School Graduates, Classes 2011-2018

Author

Minnesota Office of Higher Education, Rogness, Steve, and Fergus, Meredith

Abstract

"Getting Prepared 2019" examines Minnesota public high school graduates from the classes of 2011-2018 who enrolled in postsecondary education and those enrolled in developmental education. This report provides insight into the academic readiness of Minnesota public high school graduates. Developmental education adds to the overall cost of college for students in terms of both tuition and opportunity costs for students who spend additional time finishing developmental course requirements before starting program-specific courses. "Getting Prepared 2019" fulfills the legislative mandate (Minnesota Statute 13.32, subdivisions 3 and 6) to provide summary information about Minnesota public high school graduates who enrolled in developmental courses at Minnesota postsecondary institutions within two years of high school graduation. The report utilizes analysis resulting from linking student level data within the Minnesota Statewide Longitudinal Education Data System (SLEDS) on Minnesota public high school graduates from the Minnesota Department of Education with college enrollment data from the Minnesota Office of Higher Education and the National Student Clearinghouse. The term "college" is used to reference any type of postsecondary institution offering academic programs or vocational training.

Published
2020

216. Educating for the Future. 2020 Update

Author

Minnesota Office of Higher Education and Fergus, Meredith

Abstract

In 2015, the Minnesota Legislature enacted a state postsecondary educational attainment goal that 70% of Minnesota adults (age 25 to 44) will have attained a postsecondary certificate or degree by 2025 (Minn. Laws 2015 Chapter 69 Article 3 Sec. 6). Most importantly, the law also sets 30% and 50% educational attainment benchmarks for all races and ethnicities. Educational attainment refers to the highest level of education an individual completes. The U.S. Census measures educational attainment of a population as a percentage or count of the population that holds a postsecondary credential (certificate, associate degree, diploma, bachelor's degree, master's degree, graduate certificate, and doctoral or professional degree). Current estimates show that 63.0% of Minnesotans aged 25-44 years completed a postsecondary credential. This percentage increased slightly as compared to 2015 estimates (57.5%). This yearly report provides an update on where Minnesota is at in their state postsecondary educational attainment goal. In summary, Minnesota is making steady progress towards the 70% educational attainment goal, but attainment gaps persist among people of color and American Indians. As the state is increasingly becoming more diverse, the need for more Minnesotans to become career-ready is also increasing. Unfortunately, many American Indian, Black, Asian, and Hispanic Minnesotans are not earning credentials. Showing attainment rates by basic race groups hides differences within each group that are important. In order for Minnesota to maintain economic growth and ensure continued prosperity, the state and its colleges should engage communities of color and American Indian communities in postsecondary education. [For the 2019 report, see ED605883.]

Published
2020

218. Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence

Author

Vélez, Juliana, Castiblanco-Camacho, Paula J., Tabak, Michael A., Chalmers, Carl, Fergus, Paul, and Fieberg, John

Subjects
Computer Science - Machine Learning
Abstract

Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions. Although camera traps provide a cost-effective method for monitoring species, the time required for data processing can limit survey efficiency. Thus, the potential of Artificial Intelligence (AI), specifically Deep Learning (DL), to process camera-trap data has gained considerable attention. Using DL for these applications involves training algorithms, such as Convolutional Neural Networks (CNNs), to automatically detect objects and classify species. To overcome technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate DL in easy-to-use interfaces. We review key characteristics of four AI-powered platforms -- Wildlife Insights (WI), MegaDetector (MD), Machine Learning for Wildlife Image Classification (MLWIC2), and Conservation AI -- including data management tools and AI features. We also provide R code in an open-source GitBook, to demonstrate how users can evaluate model performance, and incorporate AI output in semi-automated workflows. We found that species classifications from WI and MLWIC2 generally had low recall values (animals that were present in the images often were not classified to the correct species). Yet, the precision of WI and MLWIC2 classifications for some species was high (i.e., when classifications were made, they were generally accurate). MD, which classifies images using broader categories (e.g., "blank" or "animal"), also performed well. Thus, we conclude that, although species classifiers were not accurate enough to automate image processing, DL could be used to improve efficiencies by accepting classifications with high confidence values for certain species or by filtering images containing blanks., Comment: 30 pages, 2 figures, 3 tables

Published
2022

219. To Impute or not to Impute? Missing Data in Treatment Effect Estimation

Author

Berrevoets, Jeroen, Imrie, Fergus, Kyono, Trent, Jordon, James, and van der Schaar, Mihaela

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

Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to the presence of an additional variable, treatment, besides the input (e.g. an individual) and the label (e.g. an outcome). The treatment variable introduces additional complexity with respect to why some variables are missing that is not fully explored by previous work. In our work we introduce mixed confounded missingness (MCM), a new missingness mechanism where some missingness determines treatment selection and other missingness is determined by treatment selection. Given MCM, we show that naively imputing all data leads to poor performing treatment effects models, as the act of imputation effectively removes information necessary to provide unbiased estimates. However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates. Our solution is selective imputation, where we use insights from MCM to inform precisely which variables should be imputed and which should not. We empirically demonstrate how various learners benefit from selective imputation compared to other solutions for missing data. We highlight that our experiments encompass both average treatment effects and conditional average treatment effects.

Published
2022

220. Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants

Author

Richardson, Marcy E., Holdren, Megan, Brannan, Terra, de la Hoya, Miguel, Spurdle, Amanda B., Tavtigian, Sean V., Young, Colin C., Zec, Lauren, Hiraki, Susan, Anderson, Michael J., Walker, Logan C., McNulty, Shannon, Turnbull, Clare, Tischkowitz, Marc, Schon, Katherine, Slavin, Thomas, Foulkes, William D., Cline, Melissa, Monteiro, Alvaro N., Pesaran, Tina, and Couch, Fergus J.

Published
2024
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221. Optimal timing of anticoagulation after acute ischaemic stroke with atrial fibrillation (OPTIMAS): a multicentre, blinded-endpoint, phase 4, randomised controlled trial

Author

Jelley, Benjamin, Hughes, Tom, Evans, Mim, Esteban, Diego Garcia, Knibbs, Lucy, Broad, Lauren, Price, Rebecca, Griebel, Liz Hamer, Hewson, Sian, Thavanesan, Kamy, Mallon, Louise, Smith, Anna, White, Miranda, Zhang, Liqun, Clarke, Brian, Abousleiman, Youssif, Binnie, Lauren, Sim, Cai Hua, Castanheira, Margarida, Humphries, Fiona, Obarey, Sabaa, Feerick, Shez, Lee, Yee Chin, Lewis, Alex, Muhammad, Riham, Francia, Nina, Atang, Ndifreke, Banaras, Azra, Marinescu, Marilena, Ferdinand, Philip, Varquez, Resti, Ponce, Ida, Saxena, Surabhi, O'Brien, Eoin, Reyes, Juliana Delos, Mitchell-Douglas, Jennifer, Francis, Jobbin, Banerjee, Soma, Dave, Vaishali, Mashate, Sheila, Patel, Tulsi, Sekaran, Lakshmanan, Murad, Wahid, Asaipillai, Asokanathan, Sakthivel, Sethuraman, Tate, Margaret, Angus, Jane, Reid, Lisa, Fornolles, Caroline, Sundayi, Saul, Poolon, Lincy, Justin, Francis, Hunte, Sophy, Bhandari, Mohit, Kho, Jules, Cvoro, Vera, Parakramawansha, Ruwan, Couser, Mandy, Hughes, Hannah, Naqvi, Aaizza, Harkness, Kirsty, Richards, Emma, Howe, Jo, Kamara, Chris, Gardner, Jon, Bains, Harjit, Teal, Rachel, Joseph, Jeethu, Benjamin, Jithen, Al-Hussayni, Samer, Thomas, George, Robinson, Faye, Dixon, Lynn, Krishnan, Manju, Slade, Peter, Anjum, Tal, Storton, Sharon, Adie, Katja, Northcott, Keren, Morgan, Katie, Williams, Emilie, Chanashekar, Harinath, Maguire, Holly, Gabriel, Claire, Maren, Deborah, David, Hannah, Clarke, Sheron, Nagaratnam, Kiruba, Nelatur, Varun, Mannava, Neelima, Blasco, Lara, Devine, Joseph, Bathula, Rajaram, Gopi, Parvathy, Mehta, Niharika, Sreedevi Raj, Sreena, Teo, James, Sztriha, Laszio, Mah, Yee, Ankolekar, Sandeep, Sari, Beatrix, Tibajai, Maria, Morgan, Alicia, Recaman, Maria, Bayhonan, Samantha, Belo, Caroline, Finch, Sharon, Keenan, Samantha, Bowring, Angie, Shetty, Ashit, Chan, Siang, Gray, Lucy, Harrison, Thomas, Spooner, Oliver, Kinsella-Perks, Edward, Erumere, Esther, Sanders, Brittany, Sims, Don, Willmot, Mark, Littleton, Edward, Spruce, Elaine, Moody, Lisa, Sheriden, Christopher, Luxmore-Brown, Scott, Neal, Aoife, Beddows, Sophie, Tuna, Maria Assuncao, Misra, Amulya, Penn, Ruth, Mariampillai, Sonia, Anwar, Ijaz, Annamalai, Arunkumar, Whitehouse, Sarah, Shepherd, Lorna, Siddle, Elaine, Chatterjee, Kausik, Leason, Sandra, Davies, Angela, Marigold, Richard James, Frank, Sarah, Baird, Alix, Hannam-Penfold, Tomas, Inacio, Liliana, Smith, Simon, Eveson, David, Musarrat, Kashif, Khan, Shagufta, Harris, Tracy, Chowdhury, Muhibbur, Alam, Sajid, Jamieson, Elena, Anyankpele, Ebitare, Al Shalchi, Farah, Rivers, Vanessa, Bell, Stephanie, Francis, Rebecca, Beeby, Deborah, Finch, Jenny, Macleod, Mary Joan, Guzman-Gutierrez, German, Carter, Karla, Irvine, Janice, Gbadamoshi, Lukuman, Costa, Telma, Heirons, Sarah, Stoney, Hayley, Shaw, Louise, Choulerton, James, Catibog, Darwin, Sattar, Naweed, Myint, Min, Smith, Andy, Serac, Kwin, Emsley, Hedley, Sultan, Sulaiman, Gregary, Bindu, Brown, Allan, Mahmood, Afzal, Chattha, Navraj, Old, William, Pegg, Claire, Davey, Miriam, Page, Michelle, Sandhu, Banher, Phiri, Emily, Rashed, Khalid, Wilson, Elisabeth, Hindley, Esther, Board, Sarah, Antony, Sherly, Tanate, Alfonso, Davis, Michelle, Holland, Beth, Slater, Victoria, Fawcett, Michelle, England, Tim, Scott, James, Beavan, Jessica, Hedstrom, Amanda, Karunatilake, Dumin, Gillmain, Kimberley, Singh, Nishy, Hallows, Tracy, Barber, Mark, Yates, Luke, Micallef, Clayton, Esson, Derek, Meng Yu, Wai, Ming New, Benjamin Jaa, Matos, Alexandre, Burt, Clare, Cabrelli, Louise, Wilkie, Gillian, Meegada, Madana, Kirthivasan, Ramanathan, Fox, Caroline, Mead, Victoria, Lyle, Amanda, Saksena, Rajesh, Bakshi, Aashima, O'Kelly, Alison, Rehan, Jahanzeb, Ebueka, Osaretin, Cooper, Martin, Wynter, Inez, Smith, Susan, Kumar, Senthil, O'Brien, Linda, Parker, Cerrys, Parker, Emma, Khan, Numan, Patterson, Christopher, Maguire, Stuart, Quinn, Outi, Bellfield, Ruth, Behnam, Yousif, Costa, Janet, Padilla-Harris, Cheryl, Moram, Louise, Raza, Syed Abid, Tench, Helen, Sims, Tanya, McGuinness, Heather, Loosley, Ronda, Wolf-Roberts, Rebecca, Buddha, Sandeep, Salt, Irmak, Lewis, Kerry, Mavinamne, Sunanda, Ditchfield, Coleen, Dealing, Sharon, Shah, Alexander, Crossingham, Ginette, Mwadeyi, Memory, Kenton, Anthony, Omoregie, Faith, Abubakar, Saidu, Warwick, Allison, Hector, Gemma, Hassan, Ahamad, Veraque, Emelda, Farman, Michelle, Makawa, Linetty, Byrne, Anthony, Kirkham, Jackie, Blayney, Gareth, Selwyn, Jey, Kakar, Puneet, Al Khaddour, Mohammed, Dhami, Reena, Baker, Emelda, Esisi, Bernard, Clarkson, Emma, Fellowes, Dominic, Kresmir, Jergovic, Guyler, Paul, Ngo, David, Wijenayake, Indunil, Tysoe, Sharon, Galliford, Joanne, Harman, Paula, Garside, Mark, Badanahatti, Madhava, Riddell, Victoria, Gramizadeh, Gita, Dutta, Dipankar, Bajoriene, Milda, Erdogan, Hulya, Ward, Deborah, Doubal, Fergus, Samarasekera, Neshika, Risbridger, Sarah, MacRaild, Allan, Azim, Abul, Wood, Lisa, Tampset, Ruth, Shekhar, Raj, Rai, Umesh, Fuller, Tracy, Joshy, Aricsa, Nadar, Evelyn, Kini, Manohar, Ahmad, Syed, Robinson, Matthew, King, Lucia, Srinivasan, Venkatesan, Karwacka-Cichomska, Magdalena, Moore, Vicki, Smith, Kate, Kariyadil, Bincy, Kong, Kelvin, Hubbard, Kelly, Arif, Sarwat, Hasan, Muhammad, Temple, Natalie, Arcoria, Daniele, Horne, Zoey, Soe, Thandar, Wyllie, Hilary, Hacon, Christian, Sutherland, Helen, Menezes, Brian, Johnson, Venetia, Smyth, Nigel, Mehdi, Zehra, Tone, Ela, Bradley, Arian, Levell, Emma, Ekkert, Aleksandra, Mazzucco, Sara, McCafferty, Laura, Vonoven, Linda, Dewan, Suprita, Sridhar, Pagadala, Thomas, Jayne, Coetzee, Samantha, Icke, Becky, Williams, Jill, Saravanan, Narayanamoorthi, Bradley, Pamela, Gibson, Rebecca Marie, Antony, Jijimol, Ashraf, Imran, Mabuti, Jose, Kamundi, Charlotte, Patiola, Prasanna, Oakley, Naomi, Proeschel, Harold, Kelly, Debs, Longley, Wendy, Cave, Ashleigh, Ambrico, Carla, Black, Toby, Porretta, Elisa, Anthony, Alpha, Ragab, Suzanne, Dube, Judith, Kausar, Shahid, Gujjar, Abdullah, Abdullah, Mohammad, Kaur, Daljit, Gadapa, Naveen, Choudhary, Sumita, Nisar, Nabeela, Fawehinmi, Grace, Dunne, Karen, King, Sam, Kishore, Amit, Lee, Stephanie, Marsden, Tracy, Slaughter, Melanie, Cawley, Kathryn, Perez, Jane, Anderton, Peter, Soussi, Salem, Walstow, Deborah, Pugh, Rebecca, Manoj, Aravind, Fletcher, Glynn, Lopez, Paula, McCormick, Michael, Magee, Michael, Tallon, Grainne, McFarland, Denise, Cosgrove, Denise, Shinh, Naval, Metcalf, Kneale, Kostyuk, Alina, McDonald, Susan, Sayers, Sophie, Sayed, Walee, Abraham, Sam, Szabo, Gemma, Crosbie, Gareth, McIlmoyle, Jim, Fearon, Patricia, Courtney, Kerry, Tauro, Suzanne, Singh, Arun, Nair, Anand, Duberley, Stephen, Philip, Sheeba, Curley, Cath, Goddard, Wendy, Bridge, Luke, Willcoxson, Paul, Wanklyn, Peter, Owen, Jennifer, France, John, Reed, Bryony, Foulds, Angie, Richard, Bella, Parfitt, Louise, Affley, Brendan, Russo, Cristina, Dsouza, Margaret, Cruddas, Elizabeth, Hargroves, David, Rand, James, Shekar, Som, Bhat, Yaqoob, Marshall, Gail, Nash, Maxine, Ahmad, Nasar, Okoko, Blessing Oduh, Evans, Rachel, Taylor, Tegan, Dawson, Jesse, Colquhoun, Elizabeth, James, Christopher, Aguirre, Carlos, MacPhee, Catherine, Phipps, Janet, Ispoglou, Sissi, Hayes, Anne, Werring, David J, Dehbi, Hakim-Moulay, Ahmed, Norin, Arram, Liz, Best, Jonathan G, Balogun, Maryam, Bennett, Kate, Bordea, Ekaterina, Caverly, Emilia, Chau, Marisa, Cohen, Hannah, Cullen, Mairead, Doré, Caroline J, Engelter, Stefan T, Fenner, Robert, Ford, Gary A, Gill, Aneet, Hunter, Rachael, James, Martin, Jayanthi, Archana, Lip, Gregory Y H, Massingham, Sue, Murray, Macey L, Mazurczak, Iwona, Nash, Philip S, Ndoutoumou, Amalia, Norrving, Bo, Sims, Hannah, Sprigg, Nikola, Vanniyasingam, Tishok, and Freemantle, Nick

Published
2024
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223. Prescription of potentially inappropriate medications after an intensive care unit stay for acute respiratory failure

Author

Crane, Julia, Hoenig, Benjamin, Karamourtopoulos, Maria, Larson, Julia, Licht, Andre De Souza, Londoño, Isabel, Toksoz-Exley, Andrew, Turnbull, Alison, Akhlaghi, Narjes, Aloori, Swetha, Caraker, Elise, Cherukuri, Sai Phani Sree, Kadiri, Naga Preethi, Koneru, Mounica, Kota, Pooja, Lakhmalla, Mounika, Lee, Emma Maelian, Mahmoud, Mazin Ali, Malik, Albahi, Nikooie, Roozbeh, Roberts, Darin, Singu, Sriharsha, Beesley, Sarah, Hopkins, Ramona O., Armbruster, Brent, Aston, Valerie, Brown, Katie, Daw, Austin, Fergus, Melissa, Hirshberg, Ellie, Kumar, Naresh, Smith, Rilee, High, Craig, Beck, Emily, Abel, Rebecca, Hays, Margaret, Mogan, Susan, Roth, Megan, Bose, Somnath, Groat, Danielle, Stollings, Joanna L., Barney, Patrick, Dinglas, Victor D., Goodspeed, Valerie M., Carmichael, Harris, Mir-Kasimov, Mustafa, Jackson, James C., Needham, Dale M., Brown, Samuel M., and Sevin, Carla M.

Published
2024
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226. Circulating inflammatory and immune response proteins and endometrial cancer risk: a nested case-control study and Mendelian randomization analyses

Author

Wang, Sabrina E., Viallon, Vivian, Lee, Matthew, Dimou, Niki, Hamilton, Fergus, Biessy, Carine, O'Mara, Tracy, Kyrgiou, Maria, Crosbie, Emma J., Truong, Therese, Severi, Gianluca, Kaaks, Rudolf, Fortner, Renée Turzanski, Schulze, Matthias B., Bendinelli, Benedetta, Sabina, Sieri, Tumino, Rosario, Sacerdote, Carlotta, Panico, Salvatore, Crous-Bou, Marta, Sánchez, Maria-Jose, Aizpurua, Amaia, Palacios, Daniel Rodriguez, Guevara, Marcela, Travis, Ruth C., Tsilidis, Konstantinos K., Heath, Alicia, Yarmolinsky, James, Rinaldi, Sabina, Gunter, Marc J., and Dossus, Laure

Published
2024
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227. Organization and Structures for Detection and Monitoring of CKD Across World Countries and Regions: Observational Data From a Global Survey

Author

Tungsanga, Somkanya, Fung, Winston, Okpechi, Ikechi G., Ye, Feng, Ghimire, Anukul, Li, Philip Kam-Tao, Shlipak, Michael G., Tummalapalli, Sri Lekha, Arruebo, Silvia, Caskey, Fergus J., Damster, Sandrine, Donner, Jo-Ann, Jha, Vivekanand, Levin, Adeera, Saad, Syed, Tonelli, Marcello, Bello, Aminu K., and Johnson, David W.

Published
2024
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228. Incidence of endometrial cancer in BRCA mutation carriers

Author

Kotsopoulos, Joanne, Lubinski, Jan, Huzarski, Tomasz, Bychkovsky, Brittany L., Moller, Pal, Kim, Raymond H., Tung, Nadine, Eisen, Andrea, Foulkes, William, Singer, Christian F., Aeilts, Amber, Neuhausen, Susan L., Bordeleau, Louise, Karlan, Beth, Fruscio, Robert, Eng, Charis, Olopade, Olufunmilayo, Zakalik, Dana, Couch, Fergus, y Cajal, Teresa Ramon, Sun, Ping, Gronwald, Jacek, and Narod, Steven A.

Published
2024
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236. Contributors

Author

Agirman, Gulistan, primary, Berkers, Celia, additional, Brummer, Robert J., additional, Caspani, Giorgia, additional, Clarke, Gerard, additional, Collins, James M., additional, Cryan, John F., additional, Dawson, S.L., additional, Delzenne, Nathalie M., additional, de Vos, Willem M., additional, Dilger, Ryan N., additional, Dinan, Timothy G., additional, Donovan, Sharon M., additional, Elford, Joshua D., additional, Fighera, Michele Rechia, additional, Forsgård, Richard, additional, Freire Royes, Luiz Fernando, additional, Ganda-Mall, John Peter, additional, Gareau, Mélanie G., additional, Ghomi, R.H., additional, Ghosh, Tarini Shankar, additional, Godinho, Douglas Buchmann, additional, Golden, Rebecca K., additional, Green, M., additional, Harvey, Michael, additional, Horn, J., additional, Hsiao, Elaine Y., additional, Huynh, Kevin, additional, Jacka, F.N., additional, Joung, Sangyun, additional, Khan, Naiman A., additional, Kraneveld, Aletta D., additional, Luczynski, Pauline, additional, Lynch, Caoimhe M.K., additional, Markidi, Anastasia, additional, Marques, Tatiana Milena, additional, Mayer, E.A., additional, Mayer, D.E., additional, McMath, Arden, additional, McVey Neufeld, Karen-Anne, additional, Mills, S., additional, Murphy, A.B., additional, Murray, N., additional, Nagpal, Jatin, additional, Nemani, K., additional, Neyrinck, Audrey M., additional, O'Connor, K., additional, O'Mahony, Siobhain M., additional, O'Toole, Paul W., additional, Perez-Pardo, Paula, additional, Quigley, Eamonn M.M., additional, Randolph, E., additional, Ratcliffe, E.M., additional, Rodriguez, Julie, additional, Ross, R.P., additional, Schellekens, Harriët, additional, Schneider, Melinda, additional, Shanahan, Fergus, additional, Stanton, C., additional, Sutkus, Loretta T., additional, Swann, Jonathan, additional, Travica, N., additional, Wall, Rebecca, additional, and Wright, Gillian M., additional

Published
2024
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238. Health care utilization in young adults with childhood physical disabilities: a nationally representative prospective cohort study.

Author

Fergus, Kirkpatrick B, Zambeli-Ljepović, Alan, Hampson, Lindsay A, Copp, Hillary L, and Nagata, Jason M

Subjects
Humans, Longitudinal Studies, Prospective Studies, Adolescent, Adult, Child, Disabled Persons, Health Services Accessibility, Patient Acceptance of Health Care, Female, Male, Young Adult, Disability, Emergency department, Health care transition, Health care utilization, Health disparity, Hospitalization, Clinical Research, Rehabilitation, Health Services, Prevention, Pediatric, Behavioral and Social Science, Depression, Brain Disorders, Mental Health, 8.1 Organisation and delivery of services, Health and social care services research, Mental health, Good Health and Well Being, Paediatrics and Reproductive Medicine, Pediatrics
Abstract

BackgroundYoung people with physical disabilities face barriers to accessing health care; however, few studies have followed adolescents with physical disabilities longitudinally through the transition of care into adulthood. The objective of this study was to investigate differences in health care utilization between adolescents with physical disabilities and those without during the transition period from adolescent to adult care.MethodsWe utilized the National Longitudinal Study of Adolescent to Adult Health, a prospective cohort study following adolescents ages 11-18 at baseline (1994-1995) through adulthood. Baseline physical disability status was defined as difficulty using limbs, using assistive devices or braces, or having an artificial limb; controls met none of these criteria. Health care utilization outcomes were measured seven years after baseline (ages 18-26). These included yearly physical check-ups, unmet health care needs, and utilization of last-resort medical care, such as emergency departments, inpatient hospital wards, and inpatient mental health facilities. Multiple logistic regression models were used to predict health care utilization, controlling for age, sex, race/ethnicity, insurance status, and history of depression.ResultsThirteen thousand four hundred thirty-six participants met inclusion criteria, including 4.2% with a physical disability and 95.8% without. Half (50%) of the sample were women, and the average age at baseline was 15.9 years (SE = 0.12). In logistic regression models, those with a disability had higher odds of unmet health care needs in the past year (Odds Ratio (OR) 1.41 95% CI 1.07-1.87), two or more emergency department visits in the past five years (OR 1.34 95% CI 1.06-1.70), and any hospitalizations in the past five years (OR 1.36 95% CI 1.07-1.72). No statistically significant differences in preventive yearly check-ups or admission to mental health facilities were noted.ConclusionsYoung adults with physical disabilities are at higher risk of having unmet health care needs and using last-resort health care services compared to their non-disabled peers.

Published
2022

239. Bilateral Oophorectomy and the Risk of Breast Cancer in BRCA1 Mutation Carriers: A Reappraisal.

Author

Kotsopoulos, Joanne, Lubinski, Jan, Gronwald, Jacek, Menkiszak, Janusz, McCuaig, Jeanna, Metcalfe, Kelly, Foulkes, William D, Neuhausen, Susan L, Sun, Sophie, Karlan, Beth Y, Eisen, Andrea, Tung, Nadine, Olopade, Olufunmilayo I, Couch, Fergus J, Huzarski, Tomasz, Senter, Leigha, Bordeleau, Louise, Singer, Christian F, Eng, Charis, Fruscio, Robert, Pal, Tuya, Sun, Ping, and Narod, Steven A

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Oncology and Carcinogenesis, Breast Cancer, Cancer, Rare Diseases, Aging, Prevention, Patient Safety, Ovarian Cancer, Clinical Research, Adult, BRCA1 Protein, Breast Neoplasms, Female, Humans, Mutation, Odds Ratio, Ovarian Neoplasms, Ovariectomy, Risk, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundThe lack of consensus on whether bilateral oophorectomy impacts risk of developing breast cancer among BRCA1 mutation carriers might be attributed to various biases, specifically, cancer-induced testing bias due to inclusion of prevalent cases. We conducted two complementary matched case-control analyses to evaluate the association of oophorectomy and BRCA1 breast cancer.MethodsA research questionnaire was administered every two years to collect information on exposures and disease. In the first analysis, we limited the study to prevalent breast cancer cases (diagnosed prior to study entry; n = 2,962) who were matched to controls on year of birth and country of residence (n = 4,358). In the second approach, we limited to 330 incident cases (diagnosed in the follow-up period) and 1,548 matched controls. Conditional logistic regression was used to estimate the adjusted odds ratios (OR) and 95% confidence intervals (CI) of invasive breast cancer.ResultsIn the first approach, there was a significant inverse association between oophorectomy and the risk of developing breast cancer [OR = 0.43; 95% confidence interval (CI), 0.34-0.55; P < 00001]. In the second approach, there was no association between oophorectomy and risk (OR = 1.21; 95% CI, 0.87-1.70; P = 0.26).ConclusionsThe inclusion of women with a personal history of breast cancer prior to ascertainment likely impacts upon the association of oophorectomy and BRCA1 breast cancer risk.ImpactOophorectomy is unlikely a determinant of breast cancer risk in BRCA1 mutation carriers but should be offered at age 35 to reduce the risk of ovarian and fallopian tube cancer.

Published
2022

240. Bacterial Strain–Dependent Dissociation of Cell Recruitment and Cell-to-Cell Spread in Early M. tuberculosis Infection

Author

Zha, B Shoshana, Desvignes, Ludovic, Fergus, Tawania J, Cornelius, Amber, Cheng, Tan-Yun, Moody, D Branch, and Ernst, Joel D

Subjects
Biodefense, Tuberculosis, Vaccine Related, Lung, Infectious Diseases, Emerging Infectious Diseases, Rare Diseases, Prevention, 2.2 Factors relating to the physical environment, Aetiology, 2.1 Biological and endogenous factors, Infection, Inflammatory and immune system, Good Health and Well Being, Animals, Immunity, Innate, Macrophages, Alveolar, Mice, Mycobacterium tuberculosis, innate response, T cell priming, Beijing strain, T cell activation, dendritic cells, innate immunity, macrophage, strain diversity, tuberculosis, Microbiology
Abstract

In the initial stage of respiratory infection, Mycobacterium tuberculosis traverses from alveolar macrophages to phenotypically diverse monocyte-derived phagocytes and neutrophils in the lung parenchyma. Here, we compare the in vivo kinetics of early bacterial growth and cell-to-cell spread of two strains of M. tuberculosis: a lineage 2 strain, 4334, and the widely studied lineage 4 strain H37Rv. Using flow cytometry, live cell sorting of phenotypic subsets, and quantitation of bacteria in cells of the distinct subsets, we found that 4334 induces less leukocyte influx into the lungs but demonstrates earlier population expansion and cell-to-cell spread. The earlier spread of 4334 to recruited cells, including monocyte-derived dendritic cells, is accompanied by earlier and greater magnitude of CD4+ T cell activation. The results provide evidence that strain-specific differences in interactions with lung leukocytes can shape adaptive immune responses in vivo. IMPORTANCE Tuberculosis is a leading infectious disease killer worldwide and is caused by Mycobacterium tuberculosis. After exposure to M. tuberculosis, outcomes range from apparent elimination to active disease. Early innate immune responses may contribute to differences in outcomes, yet it is not known how bacterial strains alter the early dynamics of innate immune and T cell responses. We infected mice with distinct strains of M. tuberculosis and discovered striking differences in innate cellular recruitment, cell-to-cell spread of bacteria in the lungs, and kinetics of initiation of antigen-specific CD4 T cell responses. We also found that M. tuberculosis can spread beyond alveolar macrophages even before a large influx of inflammatory cells. These results provide evidence that distinct strains of M. tuberculosis can exhibit differential kinetics in cell-to-cell spread which is not directly linked to early recruitment of phagocytes but is subsequently linked to adaptive immune responses.

Published
2022

241. Young Adult Healthcare Exposure and Future Opioid Misuse: A Prospective Cohort Study

Author

Fergus, Kirkpatrick B, Schwab, Marisa E, Butler, Christi, Cattle, Chloe J, Breyer, Benjamin N, Copp, Hillary L, and Nagata, Jason M

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Public Health, Health Sciences, Clinical Sciences, Substance Misuse, Prescription Drug Abuse, Clinical Research, Pediatric Research Initiative, Brain Disorders, Pediatric, Health Services, Behavioral and Social Science, Prevention, 8.1 Organisation and delivery of services, Health and social care services research, Mental health, Good Health and Well Being, Adolescent, Adult, Analgesics, Opioid, Cohort Studies, Humans, Longitudinal Studies, Opioid-Related Disorders, Practice Patterns, Physicians', Prescription Drug Misuse, Prospective Studies, Young Adult, Medical and Health Sciences, Education, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionOutpatient opioid prescribing is associated with opioid misuse in young adults, but the longitudinal association between general healthcare exposure and opioid misuse has not been explored. The objective of this study is to examine the association between healthcare exposure in young adulthood and future opioid misuse.MethodsData were drawn from the National Longitudinal Study of Adolescent to Adult Health (2001-2018) and analyzed in 2021. Healthcare exposure (i.e., inpatient hospitalization and visits to the clinic, emergency department, mental-health facility, or dentist) between individuals aged 18 and 26 years was the primary independent variable; only patients who did not report opioid misuse at baseline were included. Opioid misuse was defined as using prescription painkillers without a doctor's permission and was measured 17 years after exposure. Multivariable logistic regression was used to examine any associations with opioid misuse (ages 33-43 years).ResultsA total of 8,225 young adults with a mean baseline age of 21.8 (SE=0.12) years met inclusion criteria. Approximately 13.7% reported new opioid misuse at follow-up. Those reporting opioid misuse at follow-up were more likely to be White, lack a college education, or report depression. Those exposed to inpatient hospitalization, emergency departments, or mental-health facilities had an increased risk of future opioid misuse.ConclusionsIn young adults reporting no opioid misuse at baseline, healthcare exposure was associated with an increased risk of opioid misuse later in adulthood in this large, national cohort. Physicians encounter this at-risk population daily, reinforcing the importance of responsible prescribing practices and the need for targeted screening, patient education, and intervention efforts in the healthcare setting.

Published
2022

244. Field theory of charge sharpening in symmetric monitored quantum circuits

Author

Barratt, Fergus, Agrawal, Utkarsh, Gopalakrishnan, Sarang, Huse, David A., Vasseur, Romain, and Potter, Andrew C.

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons
Abstract

Monitored quantum circuits (MRCs) exhibit a measurement-induced phase transition between area-law and volume-law entanglement scaling. MRCs with a conserved charge additionally exhibit two distinct volume-law entangled phases that cannot be characterized by equilibrium notions of symmetry-breaking or topological order, but rather by the non-equilibrium dynamics and steady-state distribution of charge fluctuations. These include a charge-fuzzy phase in which charge information is rapidly scrambled leading to slowly decaying spatial fluctuations of charge in the steady state, and a charge-sharp phase in which measurements collapse quantum fluctuations of charge without destroying the volume-law entanglement of neutral degrees of freedom. By taking a continuous-time, weak-measurement limit, we construct a controlled replica field theory description of these phases and their intervening charge-sharpening transition in one spatial dimension. We find that the charge fuzzy phase is a critical phase with continuously evolving critical exponents that terminates in a modified Kosterlitz-Thouless transition to the short-range correlated charge-sharp phase. We numerically corroborate these scaling predictions also hold for discrete-time projective-measurement circuit models using large-scale matrix-product state simulations, and discuss generalizations to higher dimensions., Comment: 5+8 pages, 3 figures

Published
2021
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245. Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Author

Fyles, Martyn, Vihta, Karina-Doris, Sudre, Carole H, Long, Harry, Das, Rajenki, Jay, Caroline, Wingfield, Tom, Cumming, Fergus, Green, William, Hadjipantelis, Pantelis, Kirk, Joni, Steves, Claire J, Ourselin, Sebastien, Medley, Graham F, Fearon, Elizabeth, and House, Thomas

Subjects
Statistics - Applications, Quantitative Biology - Populations and Evolution, 62P10
Abstract

Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes., Comment: 60 pages; 29 figures

Published
2021

246. Explaining Latent Representations with a Corpus of Examples

Author

Crabbé, Jonathan, Qian, Zhaozhi, Imrie, Fergus, and van der Schaar, Mihaela

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

Modern machine learning models are complicated. Most of them rely on convoluted latent representations of their input to issue a prediction. To achieve greater transparency than a black-box that connects inputs to predictions, it is necessary to gain a deeper understanding of these latent representations. To that aim, we propose SimplEx: a user-centred method that provides example-based explanations with reference to a freely selected set of examples, called the corpus. SimplEx uses the corpus to improve the user's understanding of the latent space with post-hoc explanations answering two questions: (1) Which corpus examples explain the prediction issued for a given test example? (2) What features of these corpus examples are relevant for the model to relate them to the test example? SimplEx provides an answer by reconstructing the test latent representation as a mixture of corpus latent representations. Further, we propose a novel approach, the Integrated Jacobian, that allows SimplEx to make explicit the contribution of each corpus feature in the mixture. Through experiments on tasks ranging from mortality prediction to image classification, we demonstrate that these decompositions are robust and accurate. With illustrative use cases in medicine, we show that SimplEx empowers the user by highlighting relevant patterns in the corpus that explain model representations. Moreover, we demonstrate how the freedom in choosing the corpus allows the user to have personalized explanations in terms of examples that are meaningful for them., Comment: Presented at the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021)

Published
2021

247. Validating Gaussian Process Models with Simulation-Based Calibration

Author

Mcleod, John and Simpson, Fergus

Subjects
Computer Science - Machine Learning
Abstract

Gaussian process priors are a popular choice for Bayesian analysis of regression problems. However, the implementation of these models can be complex, and ensuring that the implementation is correct can be challenging. In this paper we introduce Gaussian process simulation-based calibration, a procedure for validating the implementation of Gaussian process models and demonstrate the efficacy of this procedure in identifying a bug in existing code. We also present a novel application of this procedure to identify when marginalisation of the model hyperparameters is necessary., Comment: 2 pages, 1 figure

Published
2021

248. Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection

Author

Givnan, Sean, Chalmers, Carl, Fergus, Paul, Ortega, Sandra, and Whalley, Tom

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies. The approach extracts key features from signals representing known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system were green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.

Published
2021

249. The KLEVER survey: Nitrogen abundances at $z\sim$2 and probing the existence of a fundamental nitrogen relation

Author

Hayden-Pawson, Connor, Curti, Mirko, Maiolino, Roberto, Cirasuolo, Michele, Belfiore, Francesco, Cappellari, Michele, Concas, Alice, Cresci, Giovanni, Cullen, Fergus, Kobayashi, Chiaki, Mannucci, Filippo, Marconi, Alessandro, Meneghetti, Massimo, Mercurio, Amata, Peng, Yingjie, Swinbank, Mark, and Vincenzo, Fiorenzo

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present a comparison of the nitrogen-to-oxygen ratio (N/O) in 37 high-redshift galaxies at $z\sim$2 taken from the KMOS Lensed Emission Lines and VElocity Review (KLEVER) Survey with a comparison sample of local galaxies, taken from the Sloan Digital Sky Survey (SDSS). The KLEVER sample shows only a mild enrichment in N/O of $+$0.1 dex when compared to local galaxies at a given gas-phase metallicity (O/H), but shows a depletion in N/O of $-$0.36 dex when compared at a fixed stellar mass (M$_*$). We find a strong anti-correlation in local galaxies between N/O and SFR in the M$_*$-N/O plane, similar to the anti-correlation between O/H and SFR found in the mass-metallicity relation (MZR). We use this anti-correlation to construct a fundamental nitrogen relation (FNR), analogous to the fundamental metallicity relation (FMR). We find that KLEVER galaxies are consistent with both the FMR and the FNR. This suggests that the depletion of N/O in high-$z$ galaxies when considered at a fixed M$_*$ is driven by the redshift-evolution of the mass-metallicity relation in combination with a near redshift-invariant N/O-O/H relation. Furthermore, the existence of an fundamental nitrogen relation suggests that the mechanisms governing the fundamental metallicity relation must be probed by not only O/H, but also N/O, suggesting pure-pristine gas inflows are not the primary driver of the FMR, and other properties such as variations in galaxy age and star formation efficiency must be important., Comment: 23 pages, 11 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society

Published
2021
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250. A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties

Author

Lin, En-Tzu, Hayes, Fergus, Lamb, Gavin P., Heng, Ik Siong, Kong, Albert K. H., Williams, Michael J., Saha, Surojit, and Veitch, John

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a $\sim$90$\times$ speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future., Comment: 9 pages, 4 figures, accepted to the special issue of Universe, "Waiting for GODOT -- Present and Future of Multi-Messenger Astronomy"

Published
2021
Full Text
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