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1. A study of systematic uncertainties within the MSHT PDF Framework

Author: Reader, Matthew
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract: Experimental errors are now incredibly precise, and are often dominated by the systematic uncertainties. Therefore the errors obtained in the Parton Distribution Functions that are extracted from this data will also be dominated by these experimental systematic errors, as well as the systematic errors embedded in the theoretical calculations. However, as is well known, there are often significant uncertainties in these systematic errors, and so to determine precisely the errors in the Parton Distribution Functions, we need to be thoughtful about the uncertainties in the errors themselves. In this paper, we discuss an approach where these "errors on errors" can be incorporated into a $\chi^2$ calculation, and investigate how such a model behaves and what it tells us about the resulting errors. Also we look at two data sets, ATLAS W,Z Data [arXiv:1612.03016] and the ATLAS 7 TeV Inclusive Jet Distribution Data [arXiv:1410.8857] and investigate the information that this model implies about these two data sets., Comment: 6 Pages , 7 Figures
Published: 2024

2. Multi-Criteria Decision Analysis to Develop an Efficacy-Safety Profile of Parenteral Analgesics Used in the Treatment of Postoperative Pain

Author: Schug S, Pogatzki-Zahn E, Phillips LD, Essex MN, Xia F, Reader AJ, and Pawinski R
Subjects: multi-criteria decision analysis, parenteral analgesics, postoperative pain, Medicine (General), R5-920
Abstract: Stephan Schug,1,2 Esther Pogatzki-Zahn,3 Lawrence D Phillips,4 Margaret Noyes Essex,5 Feng Xia,5 Alison J Reader,6 Robert Pawinski6 1Discipline of Anaesthesiology and Pain Medicine, Medical School, University of Western Australia, Perth, WA, Australia; 2Department of Anaesthesia and Pain Medicine, Royal Perth Hospital, Perth, WA, Australia; 3Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital of Muenster, Muenster, Germany; 4Department of Management, London School of Economics and Political Science, London, UK; 5Pfizer Inc, New York, NY, USA; 6Pfizer Ltd, Walton Oaks, Tadworth, UKCorrespondence: Margaret Noyes Essex Tel +1 212 733 8018Fax +1 646 441 5967Email margaret.essex@pfizer.comBackground: Identifying the optimal treatment in an acute postoperative setting remains a challenge. Multiple analgesic options are available, but comparing outcomes is limited by a lack of head-to-head trials. In addition, decisions based on efficacy only do not take drug safety into account. In such cases, multi-criteria decision analysis (MCDA) can be utilized to quantify and compare the efficacy and safety data of various drugs.Methodology: The efficacy-safety profiles of eight parenteral, postoperative analgesics (acetaminophen, diclofenac, ketorolac, metamizole, morphine, nefopam, parecoxib, tramadol) widely used in Europe were evaluated using an MCDA model that included 17 criteria: three for efficacy and 14 for safety. Each drug was scored on each criterion on a scale from 0 (worst) to 100 (best), according to published data and the judgment of an expert panel. A weighting process was then applied to standardize the impact of each criterion and adjust drugs’ preference scores accordingly, normalizing them on the 0– 100 scale. Sensitivity analyses were also performed, including a model in which analgesic profiles were compared when opioid sparing effect was set at a zero value for all drugs.Results: In the primary model, efficacy and safety had relative weightings of 64% and 36%, respectively. Efficacy and safety criteria with the highest values were pain relief (relative weight, 29%) and gastrointestinal effects (12%). Parecoxib received the highest overall score (93), followed by diclofenac (80), and ketorolac (75). Morphine scored the lowest (57), due to the lack of an opioid sparing effect. When opioid sparing was given a zero rating, parecoxib remained the highest scoring analgesic (93), followed by diclofenac (80), metamizole (76), and morphine (76). Parecoxib remained the most preferred analgesic in several other sensitivity analyses.Conclusion: This MCDA-based assessment suggests that parecoxib has the most favorable efficacy-safety profile among the assessed postoperative analgesics.Keywords: multi-criteria decision analysis, parenteral analgesics, postoperative pain
Published: 2020

3. Scalar enrichment and cotraces in bicategories

Author: Reader, Callum
Subjects: Mathematics - Category Theory
Abstract: It is known that every monoidal bicategory has an associated braided monoidal category of scalars. In this thesis we show that every monoidal bicategory, which is closed both monoidally and compositionally, can be enriched over the monoidal 2-category of scalar-enriched categories. This enrichment provides a number of key insights into the relationship between linear algebra and category theory. The enrichment replaces every set of 2-cells with a scalar, and we show that this replacement can be given in terms of the cotrace, first defined by Day and Street in the context of profunctors. This is analogous to the construction of the Frobenius inner product between linear maps, which is constructed in terms of the trace of linear maps. In linear algebra it is also possible to define the trace in terms of the Frobenius inner product. We show that the cotrace can be defined in terms of the enrichment, and in doing so we prove that the cotrace is an enriched version of the `categorical trace' studied by Ganter and Kapranov, and Bartlett. Thus, we unify the concept of a categorical trace with the concept of a cotrace. Finally, we study the relationship between the trace and the cotrace for compact closed bicategories. We show that the trace and cotrace have a structured relationship and share many of the properties of the linear trace including -- but not limited to -- dual invariance and linearity. Motivating examples are given throughout. We also introduce a decorated string diagram language to simplify some of the proofs., Comment: This thesis was submitted for the degree of Doctor of Philosophy of Mathematics at the University of Sheffield
Published: 2024

4. Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling

Author: Islam, Iman, Puyol-Antón, Esther, Ruijsink, Bram, Reader, Andrew J., and King, Andrew P.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Echocardiography (echo) is the first imaging modality used when assessing cardiac function. The measurement of functional biomarkers from echo relies upon the segmentation of cardiac structures and deep learning models have been proposed to automate the segmentation process. However, in order to translate these tools to widespread clinical use it is important that the segmentation models are robust to a wide variety of images (e.g. acquired from different scanners, by operators with different levels of expertise etc.). To achieve this level of robustness it is necessary that the models are trained with multiple diverse datasets. A significant challenge faced when training with multiple diverse datasets is the variation in label presence, i.e. the combined data are often partially-labelled. Adaptations of the cross entropy loss function have been proposed to deal with partially labelled data. In this paper we show that training naively with such a loss function and multiple diverse datasets can lead to a form of shortcut learning, where the model associates label presence with domain characteristics, leading to a drop in performance. To address this problem, we propose a novel label dropout scheme to break the link between domain characteristics and the presence or absence of labels. We demonstrate that label dropout improves echo segmentation Dice score by 62% and 25% on two cardiac structures when training using multiple diverse partially labelled datasets., Comment: 10 pages, 5 figures, ASMUS 2024, Held in Conjunction with MICCAI 2024
Published: 2024

5. Experimental Investigation of Thermal Performance of Novel Finned Radiators for Automotive Cooling System

Author: Singh, Hardeep, Ting, David S.-K., Reader, Graham, Eshtiaghi, Nicky, Ichiyanagi, Mitsuhisa, and Suzuki, Takashi
Published: 2024
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6. Identifying and Evaluating Upper Primary School Students' Mental Computation Strategies

Author: Mathematics Education Research Group of Australasia (MERGA), Reader, Tracey, Larkin, Kevin, and Grootenboer, Peter
Abstract: This conceptual paper discusses two frameworks, developed independently by the lead author, that will provide the conceptual foundation for the identification and evaluation of mental computation strategies students demonstrate during an upcoming research project entitled Mental Computation in Year 5. These frameworks will be used by the lead author during an intervention to investigate the application of mental computation strategies in problem solving tasks involving duration of time. It is an intended outcome of the project that the two frameworks will be useful for teachers and students in upper primary school to provide feedback regarding the teaching and learning of mental computation.
Published: 2023

7. 'I Cannot Afford Lunch': How Students' Narratives of Food Insecurity Reveal Difficulties and Coping Strategies before and during the COVID-19 Pandemic

Author: Tanzina Ahm, Jacob Shane, Rositsa Ilieva, Stacia Maher Reader, Charmaine Aleong, Caitlin Chu, Ho Yan Wong, Daniel Brusche, Karen Jiang, Arielle Edwards, Daniel Lopez, and Anita Yan
Abstract: Community college students may become more vulnerable to food insecurity during the COVID-19 pandemic, which may eventually impact their health, shape their interpretations of food insecurity and injustice within their lives, and cause them to reevaluate the support that they need from their community college. This study analyzes the food security experiences of 1,278 students during the semester before and first year of the pandemic using survey measures and narrative methodology. While students' reported levels of food insecurity did not increase after the start of the pandemic, students reported feeling less embarrassment about and greater likelihood of using food support programs. After the pandemic began, students reflected on their food security experiences in different ways. They became more likely to focus on the wellbeing of fellow students, advocate for campus food support programs, and highlight financial and nutritional problems that relate to food insecurity. They also became less likely to judge their community college for possible failures in supporting food insecure students. These changes suggested that the pandemic impacted students' understanding of food insecurity and related support programs and perhaps made students be less judgmental and more open-minded about how their college institution can support food insecure students. These findings offer implications for why and how community colleges should support food insecure students.
Published: 2024
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8. Idols of the Fragment: Barthes and Critique

Author: Reader, Simon
Published: 2024
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9. The Importance of Being Bitchy

Author: Cohen-Vrignaud, Gerard and Reader, Simon
Published: 2024

10. Aerosol delivery of SARS-CoV-2 human monoclonal antibodies in macaques limits viral replication and lung pathology.

Author: Streblow, Daniel, Hirsch, Alec, Stanton, Jeffrey, Lewis, Anne, Colgin, Lois, Hessell, Ann, Kreklywich, Craig, Smith, Jessica, Sutton, William, Chauvin, David, Woo, Jennifer, Bimber, Benjamin, LeBlanc, Cierra, Acharya, Sonia, ORoak, Brian, Sardar, Harjinder, Sajadi, Mohammad, Tehrani, Zahra, Walter, Mark, Martinez-Sobrido, Luis, Kobie, James, Reader, Rachel, Olstad, Katherine, Hobbs, Theodore, Saphire, Erica, Schendel, Sharon, Carnahan, Robert, Knoch, Jonas, Branco, Luis, Crowe, James, Van Rompay, Koen, Lovalenti, Phillip, Forthal, Donald, and Haigwood, Nancy
Subjects: Animals, Humans, SARS-CoV-2, Macaca mulatta, COVID-19, Respiratory Aerosols and Droplets, Lung, Antibodies, Viral, Virus Replication, Antibodies, Monoclonal
Abstract: Passively administered monoclonal antibodies (mAbs) given before or after viral infection can prevent or blunt disease. Here, we examine the efficacy of aerosol mAb delivery to prevent infection and disease in rhesus macaques inoculated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant via intranasal and intratracheal routes. SARS-CoV-2 human mAbs or a human mAb directed to respiratory syncytial virus (RSV) are nebulized and delivered using positive airflow via facemask to sedated macaques pre- and post-infection. Nebulized human mAbs are detectable in nasal, oropharyngeal, and bronchoalveolar lavage (BAL) samples. SARS-CoV-2 mAb treatment significantly reduces levels of SARS-CoV-2 viral RNA and infectious virus in the upper and lower respiratory tracts relative to controls. Reductions in lung and BAL virus levels correspond to reduced BAL inflammatory cytokines and lung pathology. Aerosolized antibody therapy for SARS-CoV-2 could be effective for reducing viral burden and limiting disease severity.
Published: 2023

11. Kinetic model-informed deep learning for multiplexed PET image separation

Author: Bolin Pan, Paul K. Marsden, and Andrew J. Reader
Subjects: Multiplexed PET, Kinetic modeling, Spectral analysis, Physics-informed deep learning, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Abstract Background Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair. Methods Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm. Results The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [ $$^{18}$$ 18 F]FDG+[ $$^{11}$$ 11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples. Conclusions This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data.
Published: 2024
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12. What Is Innovation?

Author: Rawlings, Bruce S., primary and Reader, Simon M., additional
Published: 2024
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13. Aristotle on Necessities and Needs

Author: Reader, Soran, primary
Published: 2024
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14. ADDITIVES TO PREVENT COATING DEFECTS CAUSED BY FLIM DEWETTING

Author: Reader, C. Jim
Subjects: Wetting -- Models, Coatings -- Composition -- Properties, Surface tension -- Analysis, Business, Chemicals, plastics and rubber industries
Abstract: Many surface defects, such as fisheyes, edge pull, and retraction are caused when the liquid film dewets after application. Application of the coating by brush, roller, or spray may effectively [...]
Published: 2024

15. Kinetic model-informed deep learning for multiplexed PET image separation

Author: Pan, Bolin, Marsden, Paul K., and Reader, Andrew J.
Published: 2024
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16. Deep learning-based PET image denoising and reconstruction: a review

Author: Hashimoto, Fumio, Onishi, Yuya, Ote, Kibo, Tashima, Hideaki, Reader, Andrew J., and Yamaya, Taiga
Published: 2024
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17. Self-Supervised and Supervised Deep Learning for PET Image Reconstruction

Author: Reader, Andrew J.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing
Abstract: A unified self-supervised and supervised deep learning framework for PET image reconstruction is presented, including deep-learned filtered backprojection (DL-FBP) for sinograms, deep-learned backproject then filter (DL-BPF) for backprojected images, and a more general mapping using a deep network in both the sinogram and image domains (DL-FBP-F). The framework allows varying amounts and types of training data, from the case of having only one single dataset to reconstruct through to the case of having numerous measured datasets, which may or may not be paired with high-quality references. For purely self-supervised mappings, no reference or ground truth data are needed. The self-supervised deep-learned reconstruction operators all use a conventional image reconstruction objective within the loss function (e.g. maximum Poisson likelihood, maximum a posteriori). If it is desired for the reconstruction networks to generalise (i.e. to need either no or minimal retraining for a new measured dataset, but to be fast, ready to reuse), then these self-supervised networks show potential even when previously trained from just one single dataset. For any given new measured dataset, finetuning is however often necessary, and of course the initial training set should ideally go beyond just one dataset if a generalisable network is sought. Example results for the purely self-supervised single-dataset case are shown, but the networks can be i) trained uniquely for any measured dataset to reconstruct from, ii) pretrained on multiple datasets and then used with no retraining for new measured data, iii) pretrained and then finetuned for new measured data, iv) optionally trained with high-quality references. The framework, with its optional inclusion of supervised learning, provides a spectrum of reconstruction approaches by making use of whatever (if any) training data quantities and types are available.
Published: 2023

18. Early‐life behavioral features are associated with chronic emesis in rhesus macaques (Macaca mulatta)

Author: Nakatani, Jamie Y, Pomerantz, Ori, Stockinger, Diane, Christe, Kari, Hopkins, Lincoln, Roberts, Jeffrey A, Reader, Rachel J, and Ardeshir, Amir
Subjects: Zoology, Biological Sciences, Prevention, Behavioral and Social Science, Basic Behavioral and Social Science, Animals, Humans, Case-Control Studies, Macaca mulatta, Quality of Life, Temperament, Vomiting, chronic emesis, early life biobehavioral temperament, rhesus macaque, Anthropology, Behavioral Science & Comparative Psychology
Abstract: Chronic emesis (CE) is a poorly understood condition in human and nonhuman primates that negatively impacts the quality of life. Early identification of risk factors for the development of CE is likely to improve the ability to manage CE cases successfully and is, therefore, desirable. Using a case-control study, we reviewed the necropsy records of the California National Primate Research Center and identified 24 animals with recorded CE, defined as five or more incidents of emesis in 1 month. A group of 89 healthy rhesus macaques (Macaca mulatta), comparable in age and percent time housed indoors, was similarly identified. Next, we investigated the association between the occurrence of CE during later stages of life after infancy and the behavioral temperament scores attained in infancy, age, sex, birth location, rearing condition, history of self-injurious behavior (SIB), and the number of lifetime sedation events. Our analysis revealed that CE was associated with degrees of temperament constructs obtained in infancy (data was available for n = 113), such as Confidence (odds ratio (OR) = 0.45, 95% CI: 0.18, 1.08, p = 0.07), Gentleness (OR = 0.47, 95% CI: 0.23, 0.96, p = 0.03), Nervousness (OR = 2.04, 95% CI: 0.98, 4.23, p = 0.05), and Vigilance (OR = 0.36, 95% CI: 015, 0.87, p = 0.02), suggesting that CE is linked to behavioral phenomenon measured in early life, long before it becomes a medical concern. Our data suggest that CE was positively correlated with a history of SIB (OR 4.26, 95% CI: 0.98, 18.47, p = 0.04). Accurate prediction of CE can then assist behavioral and colony management professionals in making informed decisions regarding the care of animals at risk of developing CE. Moreover, the novel information we reported here could have valuable implications in human medicine, where gastrointestinal distress is a common complaint affecting a person's quality of life.
Published: 2023

19. Scalar enrichment and cotraces in bicategories

Author: Reader, Callum and Willerton, Simon
Abstract: It is known that every monoidal bicategory has an associated braided monoidal category of scalars. In this thesis we show that every monoidal bicategory which is closed both monoidally and compositionally, can be enriched over the monoidal 2-category of scalar enriched categories. This enrichment provides a number of key insights into the relationship between linear algebra and category theory. The enrichment replaces every set of 2-cells with a scalar, and we show that this replacement can be given in terms of the cotrace, first defined by Day and Street in the context of profunctors. This is analogous to the construction of the Frobenius inner product between linear maps, which is constructed in terms of the trace of linear maps. In linear algebra it is also possible to define the trace in terms of the Frobenius inner product. We show that the cotrace can be defined in terms of the enrichment, and in doing so we prove that the cotrace is an enriched version of the 'categorical trace' studied by Ganter and Kapranov, and Bartlett. Thus, we unify the concept of a categorical trace with the concept of a cotrace. Finally, we study the relationship between the trace and the cotrace for compact closed bicategories. We show that the trace and cotrace have a structured relationship and share many of the properties of the linear trace including - but not limited to - dual invariance and linearity. Motivating examples are given throughout. We also introduce a decorated string diagram language to simplify some of the proofs.
Published: 2023

20. Infant rhesus macaques immunized against SARS-CoV-2 are protected against heterologous virus challenge one year later

Author: Milligan, Emma C, Olstad, Katherine, Williams, Caitlin A, Mallory, Michael, Cano, Patricio, Cross, Kaitlyn A, Munt, Jennifer E, Garrido, Carolina, Lindesmith, Lisa, Watanabe, Jennifer, Usachenko, Jodie L, Hopkins, Lincoln, Immareddy, Ramya, Lakshmanappa, Yashavanth Shaan, Elizaldi, Sonny R, Roh, Jamin W, Sammak, Rebecca L, Pollard, Rachel E, Yee, JoAnn L, Herbek, Savannah, Scobey, Trevor, Miehlke, Dieter, Fouda, Genevieve, Ferrari, Guido, Gao, Hongmei, Shen, Xiaoying, Kozlowski, Pamela A, Montefiori, David, Hudgens, Michael G, Edwards, Darin K, Carfi, Andrea, Corbett, Kizzmekia S, Graham, Barney S, Fox, Christopher B, Tomai, Mark, Iyer, Smita S, Baric, Ralph, Reader, Rachel, Dittmer, Dirk P, Van Rompay, Koen KA, Permar, Sallie R, and De Paris, Kristina
Subjects: Immunization, Emerging Infectious Diseases, Infectious Diseases, Prevention, Biodefense, Vaccine Related, Pneumonia, Pneumonia & Influenza, Lung, Biotechnology, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Prevention of disease and conditions, and promotion of well-being, 3.4 Vaccines, Infection, Good Health and Well Being, Animals, Humans, Infant, SARS-CoV-2, COVID-19 Vaccines, Macaca mulatta, COVID-19, Viral Vaccines, BNT162 Vaccine, Antibodies, Viral, Antibodies, Neutralizing, Biological Sciences, Medical and Health Sciences
Abstract: The U.S. Food and Drug Administration only gave emergency use authorization of the BNT162b2 and mRNA-1273 SARS-CoV-2 vaccines for infants 6 months and older in June 2022. Yet questions regarding the durability of vaccine efficacy, especially against emerging variants, in this age group remain. We demonstrated previously that a two-dose regimen of stabilized prefusion Washington SARS-CoV-2 S-2P spike (S) protein encoded by mRNA encapsulated in lipid nanoparticles (mRNA-LNP) or purified S-2P mixed with 3M-052, a synthetic Toll-like receptor (TLR) 7/8 agonist, in a squalene emulsion (Protein+3M-052-SE) was safe and immunogenic in infant rhesus macaques. Here, we demonstrate that broadly neutralizing and spike-binding antibodies against variants of concern (VOCs), as well as T cell responses, persisted for 12 months. At 1 year, corresponding to human toddler age, we challenged vaccinated rhesus macaques and age-matched nonvaccinated controls intranasally and intratracheally with a high dose of heterologous SARS-CoV-2 B.1.617.2 (Delta). Seven of eight control rhesus macaques exhibited severe interstitial pneumonia and high virus replication in the upper and lower respiratory tract. In contrast, vaccinated rhesus macaques had faster viral clearance with mild to no pneumonia. Neutralizing and binding antibody responses to the B.1.617.2 variant at the day of challenge correlated with lung pathology and reduced virus replication. Overall, the Protein+3M-052-SE vaccine provided superior protection to the mRNA-LNP vaccine, emphasizing opportunities for optimization of current vaccine platforms. The observed efficacy of both vaccines 1 year after vaccination supports the implementation of an early-life SARS-CoV-2 vaccine.
Published: 2023

21. Searching for the Good Life: Boundary Crossing, Co-Existence, and Conviviality

Author: Coeckelbergh, Mark and Reader, John
Published: 2023
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22. The Many Faces of Postdigital Theologies

Author: Savin-Baden, Maggi, Reader, John, and Bhatt, Ibrar
Published: 2023
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23. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Author: Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L. Barnhill, Mar Llamas-Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S. Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Reader Study Consortium, and Titus J. Brinker
Subjects: Science
Abstract: Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic.
Published: 2024
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24. Dam-Infant Rhesus Macaque Pairs to Dissect Age-Dependent Responses to SARS-CoV-2 Infection.

Author: Langel, Stephanie, Garrido, Carolina, Phan, Caroline, Travieso, Tatianna, Kirshner, Helene, DeMarco, Todd, Ma, Zhong-Min, Reader, J, Sammak, Rebecca, Shaan Lakshmanappa, Yashavanth, Roh, Jamin, Watanabe, Jennifer, Usachenko, Jodie, Immareddy, Ramya, Permar, Sallie, Van Rompay, Koen, Blasi, Maria, Pollard, Rachel, Miller, Lisa, Iyer, Smita, and Olstad, Katherine
Subjects: Animals, COVID-19, Macaca mulatta, Lung, SARS-CoV-2, Virus Replication
Abstract: The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated coronavirus disease (COVID-19) has led to a pandemic of unprecedented scale. An intriguing feature of the infection is the minimal disease in most children, a demographic at higher risk for other respiratory viral diseases. To investigate age-dependent effects of SARS-CoV-2 pathogenesis, we inoculated two rhesus macaque monkey dam-infant pairs with SARS-CoV-2 and conducted virological and transcriptomic analyses of the respiratory tract and evaluated systemic cytokine and Ab responses. Viral RNA levels in all sampled mucosal secretions were comparable across dam-infant pairs in the respiratory tract. Despite comparable viral loads, adult macaques showed higher IL-6 in serum at day 1 postinfection whereas CXCL10 was induced in all animals. Both groups mounted neutralizing Ab responses, with infants showing a more rapid induction at day 7. Transcriptome analysis of tracheal airway cells isolated at day 14 postinfection revealed significant upregulation of multiple IFN-stimulated genes in infants compared with adults. In contrast, a profibrotic transcriptomic signature with genes associated with cilia structure and function, extracellular matrix composition and metabolism, coagulation, angiogenesis, and hypoxia was induced in adults compared with infants. Our study in rhesus macaque monkey dam-infant pairs suggests age-dependent differential airway responses to SARS-CoV-2 infection and describes a model that can be used to investigate SARS-CoV-2 pathogenesis between infants and adults.
Published: 2022

25. The anticancer human mTOR inhibitor sapanisertib potently inhibits multiple Plasmodium kinases and life cycle stages.

Author: Arendse, Lauren, Murithi, James, Qahash, Tarrick, Pasaje, Charisse, Godoy, Luiz, Dey, Sumanta, Gibhard, Liezl, Ghidelli-Disse, Sonja, Drewes, Gerard, Bantscheff, Marcus, Lafuente-Monasterio, Maria, Fienberg, Stephen, Wambua, Lynn, Gachuhi, Samuel, Coertzen, Dina, van der Watt, Mariëtte, Reader, Janette, Aswat, Ayesha, Erlank, Erica, Venter, Nelius, Mittal, Nimisha, Luth, Madeline, Ottilie, Sabine, Winzeler, Elizabeth, Koekemoer, Lizette, Birkholtz, Lyn-Marie, Niles, Jacquin, Llinás, Manuel, Fidock, David, and Chibale, Kelly
Subjects: Animals, Humans, Antimalarials, Plasmodium falciparum, MTOR Inhibitors, 1-Phosphatidylinositol 4-Kinase, Guanosine Monophosphate, Life Cycle Stages, TOR Serine-Threonine Kinases, Plasmodium, Sirolimus, Mammals
Abstract: Compounds acting on multiple targets are critical to combating antimalarial drug resistance. Here, we report that the human mammalian target of rapamycin (mTOR) inhibitor sapanisertib has potent prophylactic liver stage activity, in vitro and in vivo asexual blood stage (ABS) activity, and transmission-blocking activity against the protozoan parasite Plasmodium spp. Chemoproteomics studies revealed multiple potential Plasmodium kinase targets, and potent inhibition of Plasmodium phosphatidylinositol 4-kinase type III beta (PI4Kβ) and cyclic guanosine monophosphate-dependent protein kinase (PKG) was confirmed in vitro. Conditional knockdown of PI4Kβ in ABS cultures modulated parasite sensitivity to sapanisertib, and laboratory-generated P. falciparum sapanisertib resistance was mediated by mutations in PI4Kβ. Parasite metabolomic perturbation profiles associated with sapanisertib and other known PI4Kβ and/or PKG inhibitors revealed similarities and differences between chemotypes, potentially caused by sapanisertib targeting multiple parasite kinases. The multistage activity of sapanisertib and its in vivo antimalarial efficacy, coupled with potent inhibition of at least two promising drug targets, provides an opportunity to reposition this pyrazolopyrimidine for malaria.
Published: 2022

26. Deep learned triple-tracer multiplexed PET myocardial image separation

Author: Bolin Pan, Paul K. Marsden, and Andrew J. Reader
Subjects: multiplexed positron emission tomography, image separation, deep learning, compartmental modeling, data-driven learning, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: IntroductionIn multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling (MTCM) method requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.MethodsIn this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. A dynamic triple-tracer noisy MLEM reconstruction was used as the network input, and dynamic single-tracer noisy MLEM reconstructions were used as training labels.ResultsA simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([ 18F]FDG+ 82Rb+[ 94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and region of interest (ROI) levels.DiscussionAs compared to MTCM separation, the proposed method uses spatiotemporal information for separation, which improves the separation performance at both the voxel and ROI levels. The simulation study also demonstrates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies.
Published: 2024
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27. The Mechanical Press, 1800–1900

Author: Reader, Simon, primary
Published: 2023
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28. What the universe can do for you

Author: Reader, Rebecca
Published: 2023

29. Correction: Randomised phase II trial of weekly ixabepilone ± biweekly bevacizumab for platinum-resistant or refractory ovarian/fallopian tube/primary peritoneal cancer

Author: Roque, Dana M., Siegel, Eric R., Buza, Natalia, Bellone, Stefania, Silasi, Dan-Arin, Huang, Gloria S., Andikyan, Vaagn, Clark, Mitchell, Azodi, Masoud, Schwartz, Peter E., Rao, Gautam G., Reader, Jocelyn C., Hui, Pei, Tymon-Rosario, Joan R., Harold, Justin, Mauricio, Dennis, Zeybek, Burak, Menderes, Gulden, Altwerger, Gary, Ratner, Elena, and Santin, Alessandro D.
Published: 2024
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30. Health care utilization and behavior changes after workplace genetic testing at a large US health care system

Author: Crumpler, Nicole, Leader, Amy, Mathews, Debra, Ryan, Kerry, Spector-Bagdady, Kayte, Vogle, Alyx, Brothers, Kyle, Clayton, Ellen Wright, Deverka, Patricia, Ellis, Thomas, Goldenberg, Aaron, Mockus, Susan, Morton, Cynthia Casson, Rueter, Jens, Witham, Brett, Bessey, Ethan, Gordon, Erynn, Lee, LaTasha, Roberts, Jessica, Saidi, Fatima, Charnysh, Elizabeth, Pal, Subhamoy, Reader, Jonathan M., Uhlmann, Wendy R., McCain, Sarah, Sanghavi, Kunal, Blasco, Drew, Brandt, Rachael, Feero, William Gregory, Ferber, Rebecca, Giri, Veda N., Hendy, Katherine, Prince, Anya E.R., Lee, Charles, and Roberts, J. Scott
Published: 2024
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31. Pain Reduction of Ibuprofen Sodium Dihydrate Alone and in Combination with Acetaminophen in an Untreated Endodontic Pain Model: A Randomized, Double-blind Investigation

Author: Palya, Morgan, Chevere, Janine Matos, Drum, Melissa, Fowler, Sara, Nusstein, John, Reader, Al, and Ni, Andy
Published: 2024
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32. Aerosol delivery of SARS-CoV-2 human monoclonal antibodies in macaques limits viral replication and lung pathology

Author: Daniel N. Streblow, Alec J. Hirsch, Jeffrey J. Stanton, Anne D. Lewis, Lois Colgin, Ann J. Hessell, Craig N. Kreklywich, Jessica L. Smith, William F. Sutton, David Chauvin, Jennifer Woo, Benjamin N. Bimber, Cierra N. LeBlanc, Sonia N. Acharya, Brian J. O’Roak, Harjinder Sardar, Mohammad M. Sajadi, Zahra R. Tehrani, Mark R. Walter, Luis Martinez-Sobrido, James J. Kobie, Rachel J. Reader, Katherine J. Olstad, Theodore R. Hobbs, Erica Ollmann Saphire, Sharon L. Schendel, Robert H. Carnahan, Jonas Knoch, Luis M. Branco, James E. Crowe, Koen K. A. Van Rompay, Phillip Lovalenti, Vu Truong, Donald N. Forthal, and Nancy L. Haigwood
Subjects: Science
Abstract: Abstract Passively administered monoclonal antibodies (mAbs) given before or after viral infection can prevent or blunt disease. Here, we examine the efficacy of aerosol mAb delivery to prevent infection and disease in rhesus macaques inoculated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant via intranasal and intratracheal routes. SARS-CoV-2 human mAbs or a human mAb directed to respiratory syncytial virus (RSV) are nebulized and delivered using positive airflow via facemask to sedated macaques pre- and post-infection. Nebulized human mAbs are detectable in nasal, oropharyngeal, and bronchoalveolar lavage (BAL) samples. SARS-CoV-2 mAb treatment significantly reduces levels of SARS-CoV-2 viral RNA and infectious virus in the upper and lower respiratory tracts relative to controls. Reductions in lung and BAL virus levels correspond to reduced BAL inflammatory cytokines and lung pathology. Aerosolized antibody therapy for SARS-CoV-2 could be effective for reducing viral burden and limiting disease severity.
Published: 2023
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33. Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1

Author: M. Sigmond, J. Anstey, V. Arora, R. Digby, N. Gillett, V. Kharin, W. Merryfield, C. Reader, J. Scinocca, N. Swart, J. Virgin, C. Abraham, J. Cole, N. Lambert, W.-S. Lee, Y. Liang, E. Malinina, L. Rieger, K. von Salzen, C. Seiler, C. Seinen, A. Shao, R. Sospedra-Alfonso, L. Wang, and D. Yang
Subjects: Geology, QE1-996.5
Abstract: The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the “Analysis for Development” (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants (“p1” and “p2”) of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information.
Published: 2023
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34. Whole-Body Vibration in Oncology Rehabilitation: Perceived Benefits, Barriers, and Clinician Willingness

Author: Reader, Ben, Bernstein, Jennifer, Mersich, Kaitlyn, Strenk, Mariann L., Benedict, Jason, and Greve, Kelly
Published: 2024
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35. Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling.

Author: Iman Islam, Esther Puyol-Antón, Bram Ruijsink, Andrew J. Reader, and Andrew P. King
Published: 2024
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36. Adding 6 months of androgen deprivation therapy to postoperative radiotherapy for prostate cancer: a comparison of short-course versus no androgen deprivation therapy in the RADICALS-HD randomised controlled trial

Author: Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, Parmar, Max, Patterson, Paul, Pickering, Holly, Pugh, Cheryl, Rauchenberger, Mary, Ribeiro, Helena, Roach, Carol, Seray-Wurie, Fatimah, Shaji, Preetha, Sims, Hannah, Spittle, Ben, Sydes, Matthew, Tappenden, Nancy, Tsang, Lilian, Uscinska, Barbara, Brown, Teddy, Casey, Sue, Davidson, Cathy, Dellar, Conor, Fletcher, Mandy, Meyer, Ralph, Murphy, Karen, Parulekar, Wendy, Richardson, Harriet, Richardson, Karen, Whelan, Kate, Bissett, Donald, Bonner-Shand, Shelagh, Grant, Judith, Lynch, Rory, MacDonald, Graham, McWilliam, Marie, Moir, Rachel, Rodwell, Sue, Shearer, Kirsty, Smith, Margaret, Binns, Louise, Briggs, Maxine, Brown, Simon, Casanova, Nathalie, Clark, Katy, Hartley, Jasmine, Henry, Ann, Henson, Helen, Hill, Pipa, Jeyasanger, Ganesan, McAlister, Judy, Parkinson, Sharron, Shaw, Alison, Alcock, Christopher, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Hogbin, Emma, Hyde, Katherine, Kavanagh, Roisin, Ngumo, Alice, Padilla-Harris, Cheryl, Panakis, Niki, Pwint, Thinn, Sabharwal, Ami, Stammers, Tracey, Stone, Helena, Varley, Gail, Weaver, Andrew, Wynn, Hazel, Allan, Elaine, Ansari, Jawaher, Cannon, Philip, Carruthers, Ross, Coubrough, Claudia, Cowan, Chloe, Glen, Hilary, Graham, Janet, Hardie, Maryon, Love, Clare, Macleod, Nicholas, Mahmood, Rana, McClements, Jane, McGlynn, Brian, McKernan, Margaret, Mcleod, Nick, Othman, Aqilah, Ross, Kristy, Roxburgh, Patricia, Templeton, Maureen, Tufail, Aisha, Turley, Claudia, Wilson, Mark, Ali, Zulfiqer, Andrews, Kelly, Burns, Rebecca, Butterworth, Caryl, Coventry, Thomas, Evans, Angela, Jones, Gareth, Lewis, Sion, Oommen, Nikhil, Owen, Susan, Saxton, Wendy, Tapping, Sister Hayley, Thomas, Dianne, Thomas, Rachel, Walker, Beth, Williams, Rachel, Collard, Susan, Ford, Victoria, Goodhind, Frances, Holbrook, Becky, Kershaw, Elizabeth, Ley, Samantha, Martinez, Maria, McCormack, Anne, Sheehan, Denise, Toy, Elizabeth, Van-Koutrik, Lynne, Varughese, Mohini, Aitken, Katherine, Barlow, David, Beattie, Louise, Bownie-Mukumbu, Godfrey, Bryan, Rachel, Corner, Victoria, Edwards, Abigail, Fazekasne Fulep, Adrienn, Guerrero-Urbano, Teresa, Gwilt, Julie, Happle, Liz, Jackman, Dee, Krysta, Beata, Paisey, Sangeeta, Shaffer, Richard, Smith, Jackie, Westley, Rosalyne, Yusof, Hilawati, Appleby, Rowan, Avis, Joanne, Beresford, Mark, Brydon-Hill, Ruth, Cox, Christine, Curtis, Samantha, Elwell, Christine, Frim, Olivera, Hamilton, Beatrice, Hudson, Zoe, Jenner, Abigail, Mantle, Mark, Mason, Jessica, Masson, Susan, McFarlane, Jonathan, Newman, Hugh, Pocock, Abigail, Robertson, Bryony, Tomlinson, Eve, Wassall, Rebecca, Williams (Née Allen), Sister Tania, Brown, Grace, Clarke, Peter, Cole, Aiden, Cousins, Jane, Gilchrist, Gail, Hagan, Chris, Hanna, Emma, Harney, Jackie, Hetherington, Stacey, Houghton, Fionnuala, Hynds, Sharon, Jain, Suneil, Johnston, Ruth, Keane, Patrick, Lyons, Ciara, McAleese, Jonathan, McAllister, Jo, McKenna, Karen, Mitchell, Darren, Murray, Stacey, O'Sullivan, Joe, Rajan, Nambi, Shum, Poh Lin, Stewart, David, Stranex, Stephen, Totten, Grace, Chandrashekara, Madhura, Drew, Ellen, Drew, Mary (Ellen), Fletcher, Julie, Gamble, Tina, Lokes, Rachel, Schumacher, Ann, Shetty, Kavitha, Sohanpal, Sundip, Whitehouse, James, Zarkar, Anjali, Ford, Daniel, Glaholm, John, Lenton, Daniel, O'Beney, Rachael, Taylor, Helen, Thomas, Helen, Chetiyawardana, Samantha, Doherty, A, El-Modir, Ahmed, Gunn, Melanie, James, Nicholas, Kedge, Liz, Mannan, Aliyah, Marange, Josephine, Morrison, Andrew, Okubanjo, Maryanne, Olaniyi-Oduntan, Darlletta, Peterkin, Zhane, Preece, George, Qureshi, Zayn, Ross, Liliam, Samra, Tarandip, Southgate, Elizabeth, Stephens, Rebekah, Stevenson, Robert, Watson-Jones, Phillip, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Charnley, Natalie, Desai, Parth, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thomas, Jacqueline, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Dawson, Louise, Ditchfield, Julie, Al-Samarraie, Amir, Bolger, Annette, Davies, Llinos, Flavin, Aileen, Heron, Jane, Hughes, Faye, Jackson, Kim, Lewis, Joanne, Manley, Rachel, Rutter, Charley-Anne, Smith, Nick, Srinivasan, Vaiku, Stockport, Jane, Thomas, Heather, Toussi, H, Wood, Cathryn, Archer, Simon, Fletcher, Joanne, Ginnelly, Helen, Kirkby, Amy, Knight (n. Sherburn), Victoria, Lawrence nee Palmer, Tara, Mashegede, Beverley, Metcalf, Karen, Panades, Miguel, Sreenivasan, Thiagarajan, Szymiczek, Kinga, Thomas, Isobel, Young, Anita, Broadley, Eve, Brock, Sue, Davies, Joseph, Jones, Stephanie, McCully, Ruby, Naraine, Nicky, Preece, Kate, Purandare, Laura, Rabbi, Taslima, Savage, Sarah, Vamplew, Luke, Bamford, Linda, Benton, Richard, Cotterill, Lauren, Inman, Hayley, Karsera, Louise, Kay, Anne Marie, Kenyon, Lucille, Lazarte, Jannika, McNulty, Dawn, Moore, Eleanor, Nallathambi, Chandran, Naylor, Deirdre, Owen, Lisa, Parikh, Shefali, Pendrill, Amy, Price, Joanne, Robertshaw, Helen, Rodda, Sree, Sewell, Jane, Teo, Mark, Waldron, Eleanor, Bloomfield, David, Blythe, Tracy, Brown, Joanne, Carr, Philippa, Chambers, Carla, Dlodlo, Jessica, Frattaroli, Paul, Gilbert, Duncan, Hart, Andrew, Hodges, Samantha, Hooper, Simon, Hunter, Catherine, Lactao, Ranee, Leitao, Madalena, McKinna, Fiona, Nikapota, Ashok, Peterson, Jane, Robinson, Angus, Tremlett, Jean, Webb, Andrew, Wilkins, Marie, Young, Bobbie, Bahl, Amit, Baxter, Matt, Bowers, Dawn, Challipalli, Amarnath, Coe, Marc, Cowley, Sue, Gillatt, David, Griffiths, Dorothy, Henson (nee Savidge), Verity, Hilman, Serena, Hollister, Robert, Kisanga, Mary, Lee, Vivienne, Lowe, Amelia, Macefield, Rhiannon, Pegler, Ruth, Robertson, Peter, Saldanha, Helen, Simmonds, Mary, Sims, Laura, Wheeler, Lucie, Wilson, Paula, Woods, Martin, Yarrow, Sue, Zelley, Sarah, Abd Alazeez, Mohamed, Alderton, Kate, Brain, Carol, Chilcott, Helen, Clarke, Samantha, Cobos-Arrivabene, Marta, Corderoy, Helen, Cousins, Rebecca, Garner, Victoria, Herbert, Chris, Johnson, Lyndsey, Jones, Kathryn, Kemp, Gail, Kirkby, Sarah, Kirkpatrick, Suriya, Koupparis, Anthony, Manning, Courtney, Oxley, Jon, Perry, Emily, Persad, Raj, Phipps, Charlotte, Richmond-Russell, Kerry, Rowe, Edward, Shelton, Luke, Shiridzinomwa, Constance, Smith, Carolyn, Treasure, Ann, Wyatt, Clare, Beesley, Katherine, Benson, Richard, Couch, Ellie, Foxwell, Sue, Goss, Vanessa, Harnwell, Juliette, Hughes-Davies, Luke, Joslin, Vicky, Laubscher, Angelique, Mazhar, Danish, Patterson, Helen, Raj, Sanjay, Ramanujachar, Ramya, Rimmer, Yvonne, Robinson, Nicola, Russell, Simon, Stone, Matthew, Thomas, Robert, Treeby, Jo, Vickery, Kathryn, Woodward, Cathryn, Ahmed, Ilyas, Appleby, Bonny, Brulinski, Patryk, Buckley, Julie, Catt, Natalie, Coppins, Helen, Crawford, Sister Denise, Davies, Julie-Ann, Drakeley, Susan, Edwards, Albert, Evans, Clary, Galante, Joao, Gladwell, Louise, Gough, Jessica, Hargreaves, Carolyn, Hegarty, Gemma, Kehoe, Laura, Larkins, Rachel, Lipsham, Margaret, Little, Jessica, Malde, Rohit, Middleton, Sharon, Mikropoulos, Christos, Mirza, Arafat, Mithal, Natasha, Nathan, Kannon, Raman, Rakesh, Rendall, Alice, Robinson, Karen, Ryan, Rachel, Sim, Van, Swann, Michelle, Tasker, Lorraine, Taylor, Katy, Thomas, Carys, Trigonis, Ioannis, Walker, Karen, Williams, Joanne, Williamson, Elizabeth, Wood, Marian, Wood, Pauline, Zurakovsky, Hilary, Bois (nee Harris), Elizabeth, Chan, Hin Fan, Chikopela, Loveness, Clark, Helen, Clements, Colette, Holliday, Samantha, Hughes, Owen, Jones, Clare, Kynaston, Howard, Mason, Malcolm, Narahari, Krishna, Pearse, Kevin, Staffurth, John, Austin, Mandy, Barnett, Christian, Camburn, Sister Tracey, Dawson, Elizabeth, Gibson, Sian, Hamid, Abdel, Kancherla, Kiran, Leone, Priscilla, Perkins, Lauren, Ramsay, Valerie, Singizi, Bryan, Allen, Julie, Anderson, Susan, Andrews (nee Moore), Eleanor, Bakawala, Rehana, Beazer, Sarah, Binks, Colin, Bowen, Jo, Broomfield, Barbara, Cook, Audrey, Crossley, Lin, Ford, Chris, Forkes, Janet, Hall, Julia, Healey-Mariano, Jennifer, Jenkins, Peter, Johnson, Nigel, Jonnada, Sai, Owen, Roger, Pratten, Elaine, Sayers, Rachel, Shimell, Chris, Skelton, Amy, Tan, Matthew, Trigg-Hogarth, Kate, Wronski, Sue, Aldous, Mary, Archer, Denise, Eccleson, Helen, Gallimore, Elizabeth, Green, Sue, Ibrahim, Azman, Jones, Carys, Prince, Jude, Chrystal, Melanie, Din, Omar, Firth, Alexandra, Ford, Nicky, Kirkbride, Peter, Martindale, John, Redfearn, Alison, Smedley (nee McCabe), Janine, Smith, Lucy, Sorice, Vittoria, Stevenson, Lesley, Toms, Julie, Wood, Sister Kim, Barke, Nyssa, Basu, Devy, Dewar, Lorna, Driscoll, Celine, Hunting, Liz, Kumar, Muthar, Mabelin, Louies, Sizer, Bruce, Thorogood, Lucy, Askew, Sarah, Beech, Darren, Broom, Jane, Duley, Emma, Firth, Elizabeth, Gould, Aaron, Hocking, Christopher, Johns, Louise, Kent, Emma, Madine, John, Partridge, Kerena, Penhaligon, Corrine, Pentecost, Catherine, Pollard, Kay, Poultney, Melissa, Pynsent, William, Skewes, Johanna, Steele, Anita, Thomson, Alastair H, Topps, Alice, Townley, Luke, Wheatley, Duncan, Brown, Maggie, Chan, Andrew, Connor, Marie, Elwell, Sue, Geraghty, Kerry, Griffiths, Theresa, Hayward, Lesley, Humber, Caroline, Khan, Yakhub, Lake, Sister Judith, Laverick, Rosaleen, Mansell, Gemma, Mislang, Albert, Ngwenya, Su, Panesar, Pritpal, Sanders, Kay, Simmons, Elaine, Sinfield, Melanie, Stanley, Laura, Steventon, Emily, Stockdale, Andrew, Sunny, Manju, Thind, Manreet, Tranter, Fiona, Tsalic, Medy, Worlding, Jane, Adamson, Vanessa, Bennion, Carole, Butler-Barnes, Jenny, Hough, Adele, Hough, Chris, Hughes, Joanne, Irwin, P, Javle, P, Logue, John, Mansfield, Carolyn, Smith, Rachel, Sumner, Lesley, Tomlinson, Annabel, Tran, Anna, Walker, Caroline, Wylie, James, Chakraborti, Prabir, Das, Prantik, Ennis, Dawn, Marriott, Sue, Muthukumar, Dakshinamoorthy, Pattu, Pugazhenthi, Ward, Colin, Alzouebi, Mymoona, Burlace, Barbara, Ferguson, Catherine, Hooton, Georgia, Jeffcutt, Nicole, Neal, Amy, Pezaro, Carmel, Sivoglo, Virgil, Taylor, Jennifer, Wilkinson, Nicola, Afzal, Naveed, Anderson, Beverley, Andrews, Stephen, Bakker, Piet, Breakspear, Sally, Cornaby, Andrew, Crellin, Perric, Gibbins, Andrew, Gibbins, Jackie, Glen, Tracy, Goodsell, Josie, Horton, Sarah, Love, Sally, Masters, Benjamin, Rees, Andrew, Sharpe, Simon, Taylor, Kate, Wignall, Suzy, Harrow, Sister Kath, Jones, Lesley, Kanyi, Karen, Keng-Koh, Pek, McGarry, Karen, Ramachandra, Prakash, Watts, Angela, Baumber, Shelley, Howard, Joanna, Jones-Skipper, Kay, Manetta, Caroline, McCrisken, Lauren, Taylor, Jo-Anne, Williams, Amanda, Clark, Alison, Clarke, Maria, Egan, Lisa, Fiddes, Kathleen, Forman, Susan, Gardiner, Fiona, Jeffrey, David, Mariappan, Param, Mayne, Barbara, McLaren, Duncan, Rogers, Brian, Woods, Catherine, Young, Douglas, Anderson, John, Baines, Kizzy, Crundwell, M, Davey, Elizabeth, Lawless, Theresa, Matkins, Ellen, McGinley, Shannon, McGrath, John, Piper, Jane, Roantree, Alison, Seath, Ingrid, Srinivasan, Rajaguru, Stott, Mark, Timmings, Katie, Webb, Claire, Al-hasso, Abdulla, Barwell, Lorraine, Dodds, David, Finnegan, Niall, Griffen, Ailsa, Jones, Rob, Lawless, Claire, MacMillan, Antonia, Russell, Martin, Sadozye, Azmat, Shirley, Sian, Sidek, Norma, Wallace, Jan, Akhtar, Naeem, Bell, Karen, Humpreys, Louise, McWilliam, Donna, Moody, Emma, O'Neil, Rebecca, Peck, Suzannah, Auladin, Raidah, Bird, Stephanie, Bissa, Caterina, Dabbs, Marianne, Di Maria, Fabio, Goh, Chee, Harding, Joshua, Jamieson, Ceri, Jones, Jules, Khaksar, Sara, Laing, Robert, Matthews, Laura, Medisetti, Aruna, Molony-Oates, Barbara, Money-Kyrle, Julian, Nobes, Jenny, Rabsztyn, Weronika, Sidi, Frances, Smale, Daisy May, Stone, Sarah, Tindall, Annie, Woods, Jane, Wu, Min, Daniel, Amelia, Holmes, Evelyn, Kellaway, Joanne, Lewis, Amanda, Light, Teresa, Melcher, Lucinda, Robinson, Lily, Staines, Nikki, Carini, Serrafina, Cooper, Sophie, Evans, Melanie, Follows, Josh, Grant, Warren, Hedges, Andy, Hughes, Claire, Jones (Birch), Janine, King, Lisa, Lees, Laura, Mercer, Lily, Myles, Sophie, Roberts, Zara, Williamson, Nicola, Abbassi, Sara, Alonzi, Roberto, Anyamene, Nicola, Bici, Julia, Collins, Lucy, Dickson, Jeanette, Garrido-Perez, Sandra, Hoskin, Peter, Hughes, Robert, Jenkins, Suzanne, Kowalewska, Paulina, Milner, Jessica, Mitchell, Lesley, Ostler, Peter, Rashid, Aamna, Stapleton, Stephanie, Bashir, Faheem, Bone, Linzi, Clark, Diane, Dixit, Sanjay, Horne, Rhian, Jones, Dawn, Palmer, Sarah, Plowman, Kristian, Rawlings, Julie, Simms, Matthew, Addison, Anglise, Arnott, Seonaid, Borgaonkar, Sudhir, Brown, Sandra, Campbell, Audrey, Colligan, Steve, Cormack, Margaret, Doughty, Victoria, Kelly, Kay, Lister, Debbie, Macdonald, Alison, Macgregor, Carol, Mackay, Rachel, Madeleine, Jude, McNally, Morag, McPhail, Neil, Neville, Sean, Nicholls, Alison, Papakostidi, Aristoula, Simpson, Georgina, Sinclair, Glenda, Skene, Anna, Telfer, Sheena, Urquhart, Zoe, Whillis, David, Etheridge, Charlotte, Ford, Amanda, Ridley, Paul, Scrase, Christopher, Upson, Susan, Venkitaraman, Ramachandran, Capaldi, Lisa, Churn, Mark, Higgins, Linda, Knott, Helen, Stringer (pr. Davis), Sally, Taylor, Jacob, Tranter, Helen, Wollaston, Julie, Brogan (nee Roddie), Stephanie, Erskine, Susan, Hamill, Maureen, Prentice, Lynn, Symon, Lesley, Teahan, Seamus, Todd, Anne, Tweedle, James, Young, Sally, Austin (nee Glover), Gemma, Boon, Ian, Bottomley, David, Breckin, Edmund, Clarke, Jude, Coyle, Catherine, Davies, Emily, Goulding, James, Kiltie, Anne, Loughrey, Carmel, Murad, Fatima, Pratt, Claire, Shuttleworth, Pam, Smith, Beccy, Smith, Gill, Branson (née Dineen), Amy, Christmas, Sallyanne, Cooke, Jill, Griffiths, Leyshon, Kent, Christopher, King, Amy, Kockelbergh, Roger, Potterton, Janet, Speed, Lesley, Vasanthan, Subramanian, Walker, Julia, Baria, Karin, Bateman, Samantha, Coombs, Sarah, Cunningham, Amy, Dahar, Nazeer, Daruwala, Pallon, Francis, Olesya, Hall, Jane, Hoare, Kathryn, Key, Claire, Lockwood, Carol, Mark, Ian, Newton, Rachel, Sloan, Andrew, Bermingham, Nicola, Dean, Lynsey, Malik, Zafar, Pilkington, Pauline, Robson, Peter, Rogers, Heather, Rogers, Thomas, Treherne, Katy, Bourke, Amie, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Falconer, Alison, Ford, Amy, Harinarayanan, Sathish, Hassan, Nebah, Hornzee, Gillian, Magwaro, Sophia, McInerney, Sue, Pratap, Bhavesh, Rezkallah, Sarah, Sadare (Olaleye), Ibiyemi, Stancliffe, May, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Ahamadali, Sheefa, Arsanious, Nasr, Balogun, Shaki, Batish, Cheryl, Campbell, Yvonne, Crowley, Claire, Dunne, Emma, Ferreira, Miguel, Haldeos, Anne, Hijab, Adham, Huddart, Robert, John, Babbin, Michaelidou, Andriana, Nawrozzadeh, Mohammed, Pach, Jackie, Patel, Priyanka, Payne, Ann, Perry, Matthew, Serra, Maria, Springall, Christine, Thompson, Jane, Thomson, Sister Jane, Tree, Alison, Aya, Viviana, Beaney, Ronald, Bower, Lorna, Conway, Lisa-Jane, Donaghey, Sally, Gordon, Hussain, Grau, Isabel, Harris, Sarah, Hogan, Vesna, Hore, Brendan, Hughes, Simon, Jones, Katie, Kuenzig, Greg, Morris, Stephen, Nunney, Dawn, Rashid, Jessica, Reynolds, Philip, Rooprai, Bali, Mangar, Stephen, Azirar, Hanna, Caumont, Tom, Gupta, Nishi, Hill, Judy, Karp, Stephen, Kullane, Sagal, Macavoy, Tina, Neave, Farhad, Newby, Jackie, Van Someren, Chloe, Anderson, Naomi, Annan, Lynda, Cooke, Juniebel, Douch, Emma, Fawcitt, Sara, Gore, Alexandra, Grieve, Sylvia, Jarvis, Claire, McCadden, Angela, Needleman, Sarah, Pigott, Katherine, Powell, Hannah, Ramtohul, Kaliyanee, Rosenfelder, Nicola, Vilarino-Varela, Maria, Ali, Jennyfa, Ali, Maryam, Banda, Laillah-Crystal, Bott, Trevor, Brown, Emmanuel, Bryant, Hanna, Carlino, Giulia, Grant, Cordelia, Hogan, Holly, Khoo, Vincent, Mojidra, Chintan, Montebello, Joseph, Morrison, Jennifer, Olabanji, Matthew, Pillai, Sijy, Siu, Bernard, Stafferton, Ruth, Storrs, Sarah, Townsend-Thorn, Debra, Van As, Nicholas, Vijayakumar, Vijitha, Welsh, Liam, Dearnaley, David, Gao, Annie, Horwich, Alan, Lewington, Val, Marshall, John, Murphy, Louise, Parker, Chris, Beaton, Kirsty, Bech-Nielsen, Nanette, Bolton, Olivia, Conibear, John, Dekaj, Fatjon, Hillman, Paul, Jayachandran, Resmi, Kaliski, Alexandre, Kiff, Janet, Nicholson, Alastair, Nixon, Jude, Oladimeji, Janet, Riches, Oscar, Smith, Dan, Tipples, Karen, Wells, Paula, Anderson, Chris, Daly, Deirdre, Dover, Serena, Gilmartin, Claire, Golden, Sophie, Gregg, Jane, Issa, Rami, Oni, Titilayo, Patel, Chandni, Quarrell, Mark, Rolfe, Debbie, Tighe, Helen, Toledo, Jesusa, Tuazon, Juel, Varro, Robert, Bohra, Vikram, Caballes, Mark, Custins, Laura, Ivie, Stephanie, Joshi, Manisha, Liau, Joy, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Tippins, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hines, John, Holden, Simon, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Roberts, Verity, Taylor, Henry, Tribe, Lisa, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Wilson, Jim, Branagan, Jenny, Dell, Gillian, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Jampana, Raj, Jastrzebska, Patrycja, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Ruane, Sarah, Sabbagh, Ahmad, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Davies, Joe, Downs, Nichola, Gillespie, Sally, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Gibbins, Kate, Martin, Jackie, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Hodgkinson, Sheila, Kilmartin, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Wade, Lynne, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Chahal, Rohit, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Kanaga-Sundaram, Subramanian, Littler, Stephen, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chettle, Judith, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Walsh, Donna, Webb, Kerrie, Williams, Jo, Dymond, Harvey, Eleftheriadis, Symeon, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Cooke, Peter, Grant, Anna, Homer, David, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Cleary, Kristy, Davies, Sue, Gauntlett, Monica, Gupta, Kamalnayan, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Tyler, Jayne, White, Ann, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Cserbane, Anita, Fernandez, Claire, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Protheroe, Andrew, Trewsmith, Neil, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, Spillane, Maria, Sullivan, Frank, Teh, Chiaw Woon, Wright, Ann, Harving, Niels, Hejlesen, Finn, Kempel, Mette Moe, Steffensen, Kirsten, Bentzen, Lise, Borre, Michael, Buus, Simon, Fode, Kirsten, Kaa Bach, Birgit, Lassen, Yasmin, Laursen, Vibeke, Lemng Kruse, Helle, Lucakova, Slavka, Nørmark Iversen, Helle, Brasso, Klaus, Christensen, Anne Juel, Gruschy, Lisa, Klovgaard, Karina, Lunau, Anne Sofie, Meidhal Petersen, Peter, Moller Christensen, Trine, Poder, Lone Lund, Ribers, Kristine, Stilling, Sine, Thim, Stine, Torin Lehmann, Anna, Vandborg, Maria, Christensen, Tine, Kokholm, Kathrine Friser, Lindberg, Henriette, Nybom, Kirstine, Rønnengart, Eva, Saxe, Charlotte, Baird, Noelle, Braun, Michael W, Cheung, Arthur, Fourt, Monica, Jackson, Cathy, Jepson, Debbie, Johnson, Arlissa, Karvat, Anand, Kwan, Winkle, McIndoe, Sue, Mitchell, Donna, Sandhu, Sandeep, Serpanchy, Rosanne, Wu, Helen, Beausoleil, Rachelle, Crawford, Melanie, DiMarco, Christine, Ding, Ian, Gladwish, Adam, MacIsaac, Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, Claudia, Hodder, June, Hollenhorst, Helmut, Hubley, Lynn, Joseph, Paul K, Little, Erin, MacDonald, Angela, MacIsaac, Kathy, MacVicar, Jennifer, McCracken, Jillian, Moffatt, Emily, Nieforth, Joan, Patil, Nikhilesh, Rijt, Kelsey van der, Roberts, Victoria, Rose, Tina, Rutledge, Robert DH, Salyzyn, Camryn, Simpson, Robin, Sutherland, Donna, Walker, Heather, Wilke, Derek, Yeo, Tanya, Yunace, Lorrie, Zinck, Connie, Bier, Bianca, Bucci, Catherine, Corbett, Thomas B, Dayes, Ian S, Eady, Robin, Gudelis, Susan O, Hill, Elaine, Kinrade, Yvonne, Laughlin, Anne, Lukka, Himu, Makepeace, Barbara, Marshall, Lynn, Nielsen, Linda, Oliverio, Carmela, Patel, Malti Behn, Tsakirdis, Thedoros, Wong, Jason W T, Allen, Kirsten, Bachand, Francois, Carefoot, Layton, Cotts, Shannon, Creelman, Brianna, Crook, Juanita, GeorgeChayka, Danielle, Halperin, Ross, Hartt, Nancy, Hayes, Jill A, Kim, David, Kim, TuongVan, Kronstal, Flo, Lee, Bernard, Lew, Tanya, McClelland, Marie, Opitz, Debbie, Overend, Aldyn, Reed, Melanie, Watson, Michaella, Wilkie, Karen, Winn, Carol, Yanchuk, Janet, Bonner, Scott, Brander, Mike, Brundage, Michael, Carlson, Cindy, Edwards, Jackie, ElKerdawy, Hala, Harpell, Rhonda, Hartman, Carrie, Kalyvas, Maria, KirbyBello, Denise, Kumar, Vikaash, Leach, Deborah, MacVicar, Karen, Mahmud, Aamer, Maize, Christine, Moelker, Yvonne, Murano, Abby, Paul, Nancy, Ross, Ashley, RossSmith, Marleen, Shenfield, Carey, Spencer, Craig, Tennant, Virginia, Vermette, Tracy, Willing, Stephanie, Zaza, Khaled O, Zeltser, Fred, Gearing, Jennifer, Girolametto, Carla, Gopaul, Darindra, Hamilton, Ronald, Hendel, Debra, Janke, Susan, Knackstedt, Jochen, Kuk, Joda, Morrison, MaryBeth, Mumin, Anissa, Nemirov, Stephanie, Rachakonda, Ramana, Shantz, Brenda S, Siddiqui, Atif, Thallury, Sheeba, Ahmad, Belal, Bailey, Laura, Bauman, Glenn, Camara, Mary Jane, Corkery, Danylle, DSouza, David, DiLullo, Elenor, Eichenberger, Lois, Francis, Peggy, Grant, Susan, Gratton, Albert, Gurzanski, Christina, Hare, Jennifer A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, Zwarych, Darlene, Parker, Chris C, Clarke, Noel W, Cook, Adrian D, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Bower, Lorna C, Bottomley, David M, Branagan, Jennifer, Chung, Peter W M, Goh, Chee L, Malone, Shawn C, Morris, Stephen L, Ong, Aldrich D, Tarver, Kathryn L, Tree, Alison C, Wylie, James P, Zarkar, Anjali M, Parulekar, Wendy R, Parmar, Mahesh K B, and Sydes, Matthew R
Published: 2024
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37. Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial

Author: Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, Parmar, Max, Patterson, Paul, Pickering, Holly, Pugh, Cheryl, Rauchenberger, Mary, Ribeiro, Helena, Roach, Carol, Seray-Wurie, Fatimah, Shaji, Preetha, Sims, Hannah, Spittle, Ben, Sydes, Matthew, Tappenden, Nancy, Tsang, Lilian, Uscinska, Barbara, Brown, Teddy, Casey, Sue, Davidson, Cathy, Dellar, Conor, Fletcher, Mandy, Meyer, Ralph, Murphy, Karen, Parulekar, Wendy, Richardson, Harriet, Richardson, Karen, Whelan, Kate, Bissett, Donald, Bonner-Shand, Shelagh, Grant, Judith, Lynch, Rory, MacDonald, Graham, McWilliam, Marie, Moir, Rachel, Rodwell, Sue, Shearer, Kirsty, Smith, Margaret, Binns, Louise, Briggs, Maxine, Brown, Simon, Casanova, Nathalie, Clark, Katy, Hartley, Jasmine, Henry, Ann, Henson, Helen, Hill, Pipa, Jeyasanger, Ganesan, McAlister, Judy, Parkinson, Sharron, Shaw, Alison, Alcock, Christopher, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Hogbin, Emma, Hyde, Katherine, Kavanagh, Roisin, Ngumo, Alice, Padilla-Harris, Cheryl, Panakis, Niki, Pwint, Thinn, Sabharwal, Ami, Stammers, Tracey, Stone, Helena, Varley, Gail, Weaver, Andrew, Wynn, Hazel, Allan, Elaine, Ansari, Jawaher, Cannon, Philip, Carruthers, Ross, Coubrough, Claudia, Cowan, Chloe, Glen, Hilary, Graham, Janet, Hardie, Maryon, Love, Clare, Macleod, Nicholas, Mahmood, Rana, McClements, Jane, McGlynn, Brian, McKernan, Margaret, Mcleod, Nick, Othman, Aqilah, Ross, Kristy, Roxburgh, Patricia, Templeton, Maureen, Tufail, Aisha, Turley, Claudia, Wilson, Mark, Ali, Zulfiqer, Andrews, Kelly, Burns, Rebecca, Butterworth, Caryl, Coventry, Thomas, Evans, Angela, Jones, Gareth, Lewis, Sion, Oommen, Nikhil, Owen, Susan, Saxton, Wendy, Tapping, Sister Hayley, Thomas, Dianne, Thomas, Rachel, Walker, Beth, Williams, Rachel, Collard, Susan, Ford, Victoria, Goodhind, Frances, Holbrook, Becky, Kershaw, Elizabeth, Ley, Samantha, Martinez, Maria, McCormack, Anne, Sheehan, Denise, Toy, Elizabeth, Van-Koutrik, Lynne, Varughese, Mohini, Aitken, Katherine, Barlow, David, Beattie, Louise, Bownie-Mukumbu, Godfrey, Bryan, Rachel, Corner, Victoria, Edwards, Abigail, Fazekasne Fulep, Adrienn, Guerrero-Urbano, Teresa, Gwilt, Julie, Happle, Liz, Jackman, Dee, Krysta, Beata, Paisey, Sangeeta, Shaffer, Richard, Smith, Jackie, Westley, Rosalyne, Yusof, Hilawati, Appleby, Rowan, Avis, Joanne, Beresford, Mark, Brydon-Hill, Ruth, Cox, Christine, Curtis, Samantha, Elwell, Christine, Frim, Olivera, Hamilton, Beatrice, Hudson, Zoe, Jenner, Abigail, Mantle, Mark, Mason, Jessica, Masson, Susan, McFarlane, Jonathan, Newman, Hugh, Pocock, Abigail, Robertson, Bryony, Tomlinson, Eve, Wassall, Rebecca, Williams (Née Allen), Sister Tania, Brown, Grace, Clarke, Peter, Cole, Aiden, Cousins, Jane, Gilchrist, Gail, Hagan, Chris, Hanna, Emma, Harney, Jackie, Hetherington, Stacey, Houghton, Fionnuala, Hynds, Sharon, Jain, Suneil, Johnston, Ruth, Keane, Patrick, Lyons, Ciara, McAleese, Jonathan, McAllister, Jo, McKenna, Karen, Mitchell, Darren, Murray, Stacey, O'Sullivan, Joe, Rajan, Nambi, Shum, Poh Lin, Stewart, David, Stranex, Stephen, Totten, Grace, Chandrashekara, Madhura, Drew, Ellen, Drew, Mary (Ellen), Fletcher, Julie, Gamble, Tina, Lokes, Rachel, Schumacher, Ann, Shetty, Kavitha, Sohanpal, Sundip, Whitehouse, James, Zarkar, Anjali, Ford, Daniel, Glaholm, John, Lenton, Daniel, O'Beney, Rachael, Taylor, Helen, Thomas, Helen, Chetiyawardana, Samantha, Doherty, A, El-Modir, Ahmed, Gunn, Melanie, James, Nicholas, Kedge, Liz, Mannan, Aliyah, Marange, Josephine, Morrison, Andrew, Okubanjo, Maryanne, Olaniyi-Oduntan, Darlletta, Peterkin, Zhane, Preece, George, Qureshi, Zayn, Ross, Liliam, Samra, Tarandip, Southgate, Elizabeth, Stephens, Rebekah, Stevenson, Robert, Watson-Jones, Phillip, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Charnley, Natalie, Desai, Parth, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thomas, Jacqueline, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Dawson, Louise, Ditchfield, Julie, Al-Samarraie, Amir, Bolger, Annette, Davies, Llinos, Flavin, Aileen, Heron, Jane, Hughes, Faye, Jackson, Kim, Lewis, Joanne, Manley, Rachel, Rutter, Charley-Anne, Smith, Nick, Srinivasan, Vaiku, Stockport, Jane, Thomas, Heather, Toussi, H, Wood, Cathryn, Archer, Simon, Fletcher, Joanne, Ginnelly, Helen, Kirkby, Amy, Knight (n. Sherburn), Victoria, Lawrence nee Palmer, Tara, Mashegede, Beverley, Metcalf, Karen, Panades, Miguel, Sreenivasan, Thiagarajan, Szymiczek, Kinga, Thomas, Isobel, Young, Anita, Broadley, Eve, Brock, Sue, Davies, Joseph, Jones, Stephanie, McCully, Ruby, Naraine, Nicky, Preece, Kate, Purandare, Laura, Rabbi, Taslima, Savage, Sarah, Vamplew, Luke, Bamford, Linda, Benton, Richard, Cotterill, Lauren, Inman, Hayley, Karsera, Louise, Kay, Anne Marie, Kenyon, Lucille, Lazarte, Jannika, McNulty, Dawn, Moore, Eleanor, Nallathambi, Chandran, Naylor, Deirdre, Owen, Lisa, Parikh, Shefali, Pendrill, Amy, Price, Joanne, Robertshaw, Helen, Rodda, Sree, Sewell, Jane, Teo, Mark, Waldron, Eleanor, Bloomfield, David, Blythe, Tracy, Brown, Joanne, Carr, Philippa, Chambers, Carla, Dlodlo, Jessica, Frattaroli, Paul, Gilbert, Duncan, Hart, Andrew, Hodges, Samantha, Hooper, Simon, Hunter, Catherine, Lactao, Ranee, Leitao, Madalena, McKinna, Fiona, Nikapota, Ashok, Peterson, Jane, Robinson, Angus, Tremlett, Jean, Webb, Andrew, Wilkins, Marie, Young, Bobbie, Bahl, Amit, Baxter, Matt, Bowers, Dawn, Challipalli, Amarnath, Coe, Marc, Cowley, Sue, Gillatt, David, Griffiths, Dorothy, Henson (nee Savidge), Verity, Hilman, Serena, Hollister, Robert, Kisanga, Mary, Lee, Vivienne, Lowe, Amelia, Macefield, Rhiannon, Pegler, Ruth, Robertson, Peter, Saldanha, Helen, Simmonds, Mary, Sims, Laura, Wheeler, Lucie, Wilson, Paula, Woods, Martin, Yarrow, Sue, Zelley, Sarah, Abd Alazeez, Mohamed, Alderton, Kate, Brain, Carol, Chilcott, Helen, Clarke, Samantha, Cobos-Arrivabene, Marta, Corderoy, Helen, Cousins, Rebecca, Garner, Victoria, Herbert, Chris, Johnson, Lyndsey, Jones, Kathryn, Kemp, Gail, Kirkby, Sarah, Kirkpatrick, Suriya, Koupparis, Anthony, Manning, Courtney, Oxley, Jon, Perry, Emily, Persad, Raj, Phipps, Charlotte, Richmond-Russell, Kerry, Rowe, Edward, Shelton, Luke, Shiridzinomwa, Constance, Smith, Carolyn, Treasure, Ann, Wyatt, Clare, Beesley, Katherine, Benson, Richard, Couch, Ellie, Foxwell, Sue, Goss, Vanessa, Harnwell, Juliette, Hughes-Davies, Luke, Joslin, Vicky, Laubscher, Angelique, Mazhar, Danish, Patterson, Helen, Raj, Sanjay, Ramanujachar, Ramya, Rimmer, Yvonne, Robinson, Nicola, Russell, Simon, Stone, Matthew, Thomas, Robert, Treeby, Jo, Vickery, Kathryn, Woodward, Cathryn, Ahmed, Ilyas, Appleby, Bonny, Brulinski, Patryk, Buckley, Julie, Catt, Natalie, Coppins, Helen, Crawford, Sister Denise, Davies, Julie-Ann, Drakeley, Susan, Edwards, Albert, Evans, Clary, Galante, Joao, Gladwell, Louise, Gough, Jessica, Hargreaves, Carolyn, Hegarty, Gemma, Kehoe, Laura, Larkins, Rachel, Lipsham, Margaret, Little, Jessica, Malde, Rohit, Middleton, Sharon, Mikropoulos, Christos, Mirza, Arafat, Mithal, Natasha, Nathan, Kannon, Raman, Rakesh, Rendall, Alice, Robinson, Karen, Ryan, Rachel, Sim, Van, Swann, Michelle, Tasker, Lorraine, Taylor, Katy, Thomas, Carys, Trigonis, Ioannis, Walker, Karen, Williams, Joanne, Williamson, Elizabeth, Wood, Marian, Wood, Pauline, Zurakovsky, Hilary, Bois (nee Harris), Elizabeth, Chan, Hin Fan, Chikopela, Loveness, Clark, Helen, Clements, Colette, Holliday, Samantha, Hughes, Owen, Jones, Clare, Kynaston, Howard, Mason, Malcolm, Narahari, Krishna, Pearse, Kevin, Staffurth, John, Austin, Mandy, Barnett, Christian, Camburn, Sister Tracey, Dawson, Elizabeth, Gibson, Sian, Hamid, Abdel, Kancherla, Kiran, Leone, Priscilla, Perkins, Lauren, Ramsay, Valerie, Singizi, Bryan, Allen, Julie, Anderson, Susan, Andrews (nee Moore), Eleanor, Bakawala, Rehana, Beazer, Sarah, Binks, Colin, Bowen, Jo, Broomfield, Barbara, Cook, Audrey, Crossley, Lin, Ford, Chris, Forkes, Janet, Hall, Julia, Healey-Mariano, Jennifer, Jenkins, Peter, Johnson, Nigel, Jonnada, Sai, Owen, Roger, Pratten, Elaine, Sayers, Rachel, Shimell, Chris, Skelton, Amy, Tan, Matthew, Trigg-Hogarth, Kate, Wronski, Sue, Aldous, Mary, Archer, Denise, Eccleson, Helen, Gallimore, Elizabeth, Green, Sue, Ibrahim, Azman, Jones, Carys, Prince, Jude, Chrystal, Melanie, Din, Omar, Firth, Alexandra, Ford, Nicky, Kirkbride, Peter, Martindale, John, Redfearn, Alison, Smedley (nee McCabe), Janine, Smith, Lucy, Sorice, Vittoria, Stevenson, Lesley, Toms, Julie, Wood, Sister Kim, Barke, Nyssa, Basu, Devy, Dewar, Lorna, Driscoll, Celine, Hunting, Liz, Kumar, Muthar, Mabelin, Louies, Muthu Kumar, Dakshinamoorthy, Sizer, Bruce, Thorogood, Lucy, Askew, Sarah, Beech, Darren, Broom, Jane, Duley, Emma, Firth, Elizabeth, Gould, Aaron, Hocking, Christopher, Johns, Louise, Kent, Emma, Madine, John, Partridge, Kerena, Penhaligon, Corrine, Pentecost, Catherine, Pollard, Kay, Poultney, Melissa, Pynsent, William, Skewes, Johanna, Steele, Anita, Thomson, Alastair H, Topps, Alice, Townley, Luke, Wheatley, Duncan, Brown, Maggie, Chan, Andrew, Connor, Marie, Elwell, Sue, Geraghty, Kerry, Griffiths, Theresa, Hayward, Lesley, Humber, Caroline, Khan, Yakhub, Lake, Sister Judith, Laverick, Rosaleen, Mansell, Gemma, Mislang, Albert, Ngwenya, Su, Panesar, Pritpal, Sanders, Kay, Simmons, Elaine, Sinfield, Melanie, Stanley, Laura, Steventon, Emily, Stockdale, Andrew, Sunny, Manju, Thind, Manreet, Tranter, Fiona, Tsalic, Medy, Worlding, Jane, Adamson, Vanessa, Bennion, Carole, Butler-Barnes, Jenny, Hough, Adele, Hough, Chris, Hughes, Joanne, Irwin, P, Javle, P, Logue, John, Mansfield, Carolyn, Smith, Rachel, Sumner, Lesley, Tomlinson, Annabel, Tran, Anna, Walker, Caroline, Wylie, James, Chakraborti, Prabir, Das, Prantik, Ennis, Dawn, Marriott, Sue, Pattu, Pugazhenthi, Ward, Colin, Alzouebi, Mymoona, Burlace, Barbara, Ferguson, Catherine, Hooton, Georgia, Jeffcutt, Nicole, Neal, Amy, Pezaro, Carmel, Sivoglo, Virgil, Taylor, Jennifer, Wilkinson, Nicola, Afzal, Naveed, Anderson, Beverley, Andrews, Stephen, Bakker, Piet, Breakspear, Sally, Cornaby, Andrew, Crellin, Perric, Gibbins, Andrew, Gibbins, Jackie, Glen, Tracy, Goodsell, Josie, Horton, Sarah, Love, Sally, Masters, Benjamin, Rees, Andrew, Sharpe, Simon, Taylor, Kate, Wignall, Suzy, Harrow, Sister Kath, Jones, Lesley, Kanyi, Karen, Keng-Koh, Pek, McGarry, Karen, Ramachandra, Prakash, Watts, Angela, Baumber, Shelley, Howard, Joanna, Jones-Skipper, Kay, Manetta, Caroline, McCrisken, Lauren, Taylor, Jo-Anne, Williams, Amanda, Clark, Alison, Clarke, Maria, Egan, Lisa, Fiddes, Kathleen, Forman, Susan, Gardiner, Fiona, Jeffrey, David, Mariappan, Param, Mayne, Barbara, McLaren, Duncan, Rogers, Brian, Woods, Catherine, Young, Douglas, Anderson, John, Baines, Kizzy, Crundwell, M, Davey, Elizabeth, Lawless, Theresa, Matkins, Ellen, McGinley, Shannon, McGrath, John, Piper, Jane, Roantree, Alison, Seath, Ingrid, Srinivasan, Rajaguru, Stott, Mark, Timmings, Katie, Webb, Claire, Al-hasso, Abdulla, Barwell, Lorraine, Dodds, David, Finnegan, Niall, Griffen, Ailsa, Jones, Rob, Lawless, Claire, MacMillan, Antonia, Russell, Martin, Sadozye, Azmat, Shirley, Sian, Sidek, Norma, Wallace, Jan, Akhtar, Naeem, Bell, Karen, Humpreys, Louise, McWilliam, Donna, Moody, Emma, O'Neil, Rebecca, Peck, Suzannah, Auladin, Raidah, Bird, Stephanie, Bissa, Caterina, Dabbs, Marianne, Di Maria, Fabio, Goh, Chee, Harding, Joshua, Jamieson, Ceri, Jones, Jules, Khaksar, Sara, Laing, Robert, Matthews, Laura, Medisetti, Aruna, Molony-Oates, Barbara, Money-Kyrle, Julian, Nobes, Jenny, Rabsztyn, Weronika, Sidi, Frances, Smale, Daisy May, Stone, Sarah, Tindall, Annie, Woods, Jane, Wu, Min, Daniel, Amelia, Holmes, Evelyn, Kellaway, Joanne, Lewis, Amanda, Light, Teresa, Melcher, Lucinda, Robinson, Lily, Staines, Nikki, Carini, Serrafina, Cooper, Sophie, Evans, Melanie, Follows, Josh, Grant, Warren, Hedges, Andy, Hughes, Claire, Jones (Birch), Janine, King, Lisa, Lees, Laura, Mercer, Lily, Myles, Sophie, Roberts, Zara, Williamson, Nicola, Abbassi, Sara, Alonzi, Roberto, Anyamene, Nicola, Bici, Julia, Collins, Lucy, Dickson, Jeanette, Garrido-Perez, Sandra, Hoskin, Peter, Hughes, Robert, Jenkins, Suzanne, Kowalewska, Paulina, Milner, Jessica, Mitchell, Lesley, Ostler, Peter, Rashid, Aamna, Stapleton, Stephanie, Bashir, Faheem, Bone, Linzi, Clark, Diane, Dixit, Sanjay, Horne, Rhian, Jones, Dawn, Palmer, Sarah, Plowman, Kristian, Rawlings, Julie, Simms, Matthew, Addison, Anglise, Arnott, Seonaid, Borgaonkar, Sudhir, Brown, Sandra, Campbell, Audrey, Colligan, Steve, Cormack, Margaret, Doughty, Victoria, Kelly, Kay, Lister, Debbie, Macdonald, Alison, Macgregor, Carol, Mackay, Rachel, Madeleine, Jude, McNally, Morag, McPhail, Neil, Neville, Sean, Nicholls, Alison, Papakostidi, Aristoula, Simpson, Georgina, Sinclair, Glenda, Skene, Anna, Telfer, Sheena, Urquhart, Zoe, Whillis, David, Etheridge, Charlotte, Ford, Amanda, Ridley, Paul, Scrase, Christopher, Upson, Susan, Venkitaraman, Ramachandran, Capaldi, Lisa, Churn, Mark, Higgins, Linda, Knott, Helen, Stringer (pr. Davis), Sally, Taylor, Jacob, Tranter, Helen, Wollaston, Julie, Brogan (nee Roddie), Stephanie, Erskine, Susan, Hamill, Maureen, Prentice, Lynn, Symon, Lesley, Teahan, Seamus, Todd, Anne, Tweedle, James, Young, Sally, Austin (nee Glover), Gemma, Boon, Ian, Bottomley, David, Breckin, Edmund, Clarke, Jude, Coyle, Catherine, Davies, Emily, Goulding, James, Kiltie, Anne, Loughrey, Carmel, Murad, Fatima, Pratt, Claire, Shuttleworth, Pam, Smith, Beccy, Smith, Gill, Branson (née Dineen), Amy, Christmas, Sallyanne, Cooke, Jill, Griffiths, Leyshon, Kent, Christopher, King, Amy, Kockelbergh, Roger, Potterton, Janet, Speed, Lesley, Vasanthan, Subramanian, Walker, Julia, Baria, Karin, Bateman, Samantha, Coombs, Sarah, Cunningham, Amy, Dahar, Nazeer, Daruwala, Pallon, Francis, Olesya, Hall, Jane, Hoare, Kathryn, Key, Claire, Lockwood, Carol, Mark, Ian, Newton, Rachel, Sloan, Andrew, Bermingham, Nicola, Dean, Lynsey, Malik, Zafar, Pilkington, Pauline, Robson, Peter, Rogers, Heather, Rogers, Thomas, Treherne, Katy, Bourke, Amie, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Falconer, Alison, Ford, Amy, Harinarayanan, Sathish, Hassan, Nebah, Hornzee, Gillian, Magwaro, Sophia, McInerney, Sue, Pratap, Bhavesh, Rezkallah, Sarah, Sadare (Olaleye), Ibiyemi, Stancliffe, May, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Ahamadali, Sheefa, Arsanious, Nasr, Balogun, Shaki, Batish, Cheryl, Campbell, Yvonne, Crowley, Claire, Dunne, Emma, Ferreira, Miguel, Haldeos, Anne, Hijab, Adham, Huddart, Robert, John, Babbin, Michaelidou, Andriana, Nawrozzadeh, Mohammed, Pach, Jackie, Patel, Priyanka, Payne, Ann, Perry, Matthew, Serra, Maria, Springall, Christine, Thompson, Jane, Thomson, Sister Jane, Tree, Alison, Aya, Viviana, Beaney, Ronald, Bower, Lorna, Conway, Lisa-Jane, Donaghey, Sally, Gordon, Hussain, Grau, Isabel, Harris, Sarah, Hogan, Vesna, Hore, Brendan, Hughes, Simon, Jones, Katie, Kuenzig, Greg, Morris, Stephen, Nunney, Dawn, Rashid, Jessica, Reynolds, Philip, Rooprai, Bali, Mangar, Stephen, Azirar, Hanna, Caumont, Tom, Gupta, Nishi, Hill, Judy, Karp, Stephen, Kullane, Sagal, Macavoy, Tina, Neave, Farhad, Newby, Jackie, Van Someren, Chloe, Anderson, Naomi, Annan, Lynda, Cooke, Juniebel, Douch, Emma, Fawcitt, Sara, Gore, Alexandra, Grieve, Sylvia, Jarvis, Claire, McCadden, Angela, Needleman, Sarah, Pigott, Katherine, Powell, Hannah, Ramtohul, Kaliyanee, Rosenfelder, Nicola, Vilarino-Varela, Maria, Ali, Jennyfa, Ali, Maryam, Banda, Laillah-Crystal, Bott, Trevor, Brown, Emmanuel, Bryant, Hanna, Carlino, Giulia, Grant, Cordelia, Hogan, Holly, Khoo, Vincent, Mojidra, Chintan, Montebello, Joseph, Morrison, Jennifer, Olabanji, Matthew, Pillai, Sijy, Siu, Bernard, Stafferton, Ruth, Storrs, Sarah, Townsend-Thorn, Debra, Van As, Nicholas, Vijayakumar, Vijitha, Welsh, Liam, Dearnaley, David, Gao, Annie, Horwich, Alan, Lewington, Val, Marshall, John, Murphy, Louise, Parker, Chris, Beaton, Kirsty, Bech-Nielsen, Nanette, Bolton, Olivia, Conibear, John, Dekaj, Fatjon, Hillman, Paul, Jayachandran, Resmi, Kaliski, Alexandre, Kiff, Janet, Nicholson, Alastair, Nixon, Jude, Oladimeji, Janet, Riches, Oscar, Smith, Dan, Tipples, Karen, Wells, Paula, Anderson, Chris, Daly, Deirdre, Dover, Serena, Gilmartin, Claire, Golden, Sophie, Gregg, Jane, Issa, Rami, Oni, Titilayo, Patel, Chandni, Quarrell, Mark, Rolfe, Debbie, Tighe, Helen, Toledo, Jesusa, Tuazon, Juel, Varro, Robert, Bohra, Vikram, Caballes, Mark, Custins, Laura, Ivie, Stephanie, Joshi, Manisha, Liau, Joy, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Tippins, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hines, John, Holden, Simon, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Roberts, Verity, Taylor, Henry, Tribe, Lisa, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Wilson, Jim, Branagan, Jenny, Dell, Gillian, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Jampana, Raj, Jastrzebska, Patrycja, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Ruane, Sarah, Sabbagh, Ahmad, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Davies, Joe, Downs, Nichola, Gillespie, Sally, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Gibbins, Kate, Martin, Jackie, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Hodgkinson, Sheila, Kilmartin, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Wade, Lynne, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Chahal, Rohit, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Kanaga-Sundaram, Subramanian, Littler, Stephen, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chettle, Judith, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Walsh, Donna, Webb, Kerrie, Williams, Jo, Dymond, Harvey, Eleftheriadis, Symeon, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Cooke, Peter, Grant, Anna, Homer, David, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Cleary, Kristy, Davies, Sue, Gauntlett, Monica, Gupta, Kamalnayan, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Tyler, Jayne, White, Ann, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Cserbane, Anita, Fernandez, Claire, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Protheroe, Andrew, Trewsmith, Neil, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, Spillane, Maria, Sullivan, Frank, Teh, Chiaw Woon, Wright, Ann, Harving, Niels, Hejlesen, Finn, Kempel, Mette Moe, Steffensen, Kirsten, Bentzen, Lise, Borre, Michael, Buus, Simon, Fode, Kirsten, Kaa Bach, Birgit, Lassen, Yasmin, Laursen, Vibeke, Lemng Kruse, Helle, Lucakova, Slavka, Nørmark Iversen, Helle, Brasso, Klaus, Christensen, Anne Juel, Gruschy, Lisa, Klovgaard, Karina, Lunau, Anne Sofie, Meidhal Petersen, Peter, Moller Christensen, Trine, Poder, Lone Lund, Ribers, Kristine, Stilling, Sine, Thim, Stine, Torin Lehmann, Anna, Vandborg, Maria, Christensen, Tine, Kokholm, Kathrine Friser, Lindberg, Henriette, Nybom, Kirstine, Rønnengart, Eva, Saxe, Charlotte, Baird, Noelle, Braun, Michael W, Cheung, Arthur, Fourt, Monica, Jackson, Cathy, Jepson, Debbie, Johnson, Arlissa, Karvat, Anand, Kwan, Winkle, McIndoe, Sue, Mitchell, Donna, Sandhu, Sandeep, Serpanchy, Rosanne, Wu, Helen, Beausoleil, Rachelle, Crawford, Melanie, DiMarco, Christine, Ding, Ian, Gladwish, Adam, MacIsaac, Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, Claudia, Hodder, June, Hollenhorst, Helmut, Hubley, Lynn, Joseph, Paul K, Little, Erin, MacDonald, Angela, MacIsaac, Kathy, MacVicar, Jennifer, McCracken, Jillian, Moffatt, Emily, Nieforth, Joan, Patil, Nikhilesh, Rijt, Kelsey van der, Roberts, Victoria, Rose, Tina, Rutledge, Robert DH, Salyzyn, Camryn, Simpson, Robin, Sutherland, Donna, Walker, Heather, Wilke, Derek, Yeo, Tanya, Yunace, Lorrie, Zinck, Connie, Bier, Bianca, Bucci, Catherine, Corbett, Thomas B, Dayes, Ian S, Eady, Robin, Gudelis, Susan O, Hill, Elaine, Kinrade, Yvonne, Laughlin, Anne, Lukka, Himu, Makepeace, Barbara, Marshall, Lynn, Nielsen, Linda, Oliverio, Carmela, Patel, Malti Behn, Tsakirdis, Thedoros, Wong, Jason W T, Allen, Kirsten, Bachand, Francois, Carefoot, Layton, Cotts, Shannon, Creelman, Brianna, Crook, Juanita, GeorgeChayka, Danielle, Halperin, Ross, Hartt, Nancy, Hayes, Jill A, Kim, David, Kim, TuongVan, Kronstal, Flo, Lee, Bernard, Lew, Tanya, McClelland, Marie, Opitz, Debbie, Overend, Aldyn, Reed, Melanie, Watson, Michaella, Wilkie, Karen, Winn, Carol, Yanchuk, Janet, Bonner, Scott, Brander, Mike, Brundage, Michael, Carlson, Cindy, Edwards, Jackie, ElKerdawy, Hala, Harpell, Rhonda, Hartman, Carrie, Kalyvas, Maria, KirbyBello, Denise, Kumar, Vikaash, Leach, Deborah, MacVicar, Karen, Mahmud, Aamer, Maize, Christine, Moelker, Yvonne, Murano, Abby, Paul, Nancy, Ross, Ashley, RossSmith, Marleen, Shenfield, Carey, Spencer, Craig, Tennant, Virginia, Vermette, Tracy, Willing, Stephanie, Zaza, Khaled O, Zeltser, Fred, Gearing, Jennifer, Girolametto, Carla, Gopaul, Darindra, Hamilton, Ronald, Hendel, Debra, Janke, Susan, Knackstedt, Jochen, Kuk, Joda, Morrison, MaryBeth, Mumin, Anissa, Nemirov, Stephanie, Rachakonda, Ramana, Shantz, Brenda S, Siddiqui, Atif, Thallury, Sheeba, Ahmad, Belal, Bailey, Laura, Bauman, Glenn, Camara, Mary Jane, Corkery, Danylle, DSouza, David, DiLullo, Elenor, Eichenberger, Lois, Francis, Peggy, Grant, Susan, Gratton, Albert, Gurzanski, Christina, Hare, Jennifer A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, Zwarych, Darlene, Parker, Chris C, Cook, Adrian D, Clarke, Noel W, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Pugh, Cheryl A, Bower, Lorna C, Bottomley, David M, Chung, Peter W M, Ford, Vicky, Goh, Chee L, Heath, Catherine M, James, Nicholas D, Kim-Sing, Charmaine, Kodavatiganti, Ravi, Malone, Shawn C, Morris, Stephen L, Ong, Aldrich D, Parulekar, Wendy R, Parmar, Mahesh K B, and Sydes, Matthew R
Published: 2024
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38. Exploring the modulatory influence on the antimalarial activity of amodiaquine using scaffold hybridisation with ferrocene integration

Author: Mbaba, Mziyanda, Golding, Taryn M., Omondi, Reinner O., Mohunlal, Roxanne, Egan, Timothy J., Reader, Janette, Birkholtz, Lyn-Marie, and Smith, Gregory S.
Published: 2024
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39. Efficacy of the TuttleNumbNow Intraosseous Method for Pulpal Anesthesia in the Mandibular First Molar: A Prospective, Randomized, Crossover Study

Author: Dolphin, Tyler, Fowler, Sara, Drum, Melissa, Nusstein, John, Reader, Al, and Draper, John
Published: 2024
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40. Consistent ecosystem service bundles emerge across global mountain, island and delta systems

Author: Oliver Reader, M., Eppinga, Maarten B., de Boer, Hugo J., Petchey, Owen L., and Santos, Maria J.
Published: 2024
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41. P864: Employees’ views and healthcare utilization following employer-sponsored pharmacogenomic testing at a large US healthcare system

Author: Elizabeth Charnysh, Subhamoy Pal, Jonathan Reader, Sarah McCain, Katherine Hendy, Kunal Sanghavi, Wendy Uhlmann, William Feero, Anya Prince, Rachael Brandt, Rebecca Ferber, Veda Giri, Charles Lee, and J. Scott Roberts
Subjects: Genetics, QH426-470, Medicine
Published: 2024
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42. Semi-Synthetic Analogues of Cryptolepine as a Potential Source of Sustainable Drugs for the Treatment of Malaria, Human African Trypanosomiasis, and Cancer.

Author: Abacha, Yabalu, Forkuo, Arnold, Gbedema, Stephen, Mittal, Nimisha, Ottilie, Sabine, Rocamora, Frances, Winzeler, Elizabeth, van Schalkwyk, Donelly, Kelly, John, Taylor, Martin, Reader, Janette, Birkholtz, Lyn-Marie, Lisgarten, David, Cockcroft, Jeremy, Lisgarten, John, Palmer, Rex, Talbert, Rosemary, Shnyder, Steven, and Wright, Colin
Subjects: Plasmodium falciparum, Plasmodium knowlesi, Trypanosoma brucei, halogenation of cryptolepine, ovarian cancer, sustainable pharmaceuticals
Abstract: The prospect of eradicating malaria continues to be challenging in the face of increasing parasite resistance to antimalarial drugs so that novel antimalarials active against asexual, sexual, and liver-stage malaria parasites are urgently needed. In addition, new antimalarials need to be affordable and available to those most in need and, bearing in mind climate change, should ideally be sustainable. The West African climbing shrub Cryptolepis sanguinolenta is used traditionally for the treatment of malaria; its principal alkaloid, cryptolepine (1), has been shown to have antimalarial properties, and the synthetic analogue 2,7-dibromocryptolepine (2) is of interest as a lead toward new antimalarial agents. Cryptolepine (1) was isolated using a two-step Soxhlet extraction of C. sanguinolenta roots, followed by crystallization (yield 0.8% calculated as a base with respect to the dried roots). Semi-synthetic 7-bromo- (3), 7, 9-dibromo- (4), 7-iodo- (5), and 7, 9-dibromocryptolepine (6) were obtained in excellent yields by reaction of 1 with N-bromo- or N-iodosuccinimide in trifluoroacetic acid as a solvent. All compounds were active against Plasmodia in vitro, but 6 showed the most selective profile with respect to Hep G2 cells: P. falciparum (chloroquine-resistant strain K1), IC50 = 0.25 µM, SI = 113; late stage, gametocytes, IC50 = 2.2 µM, SI = 13; liver stage, P. berghei sporozoites IC50 = 6.13 µM, SI = 4.6. Compounds 3-6 were also active against the emerging zoonotic species P. knowlesi with 5 being the most potent (IC50 = 0.11 µM). In addition, 3-6 potently inhibited T. brucei in vitro at nM concentrations and good selectivity with 6 again being the most selective (IC50 = 59 nM, SI = 478). These compounds were also cytotoxic to wild-type ovarian cancer cells as well as adriamycin-resistant and, except for 5, cisplatin-resistant ovarian cancer cells. In an acute oral toxicity test in mice, 3-6 did not exhibit toxic effects at doses of up to 100 mg/kg/dose × 3 consecutive days. This study demonstrates that C. sanguinolenta may be utilized as a sustainable source of novel compounds that may lead to the development of novel agents for the treatment of malaria, African trypanosomiasis, and cancer.
Published: 2022

43. Early post-infection treatment of SARS-CoV-2 infected macaques with human convalescent plasma with high neutralizing activity had no antiviral effects but moderately reduced lung inflammation

Author: Van Rompay, Koen KA, Olstad, Katherine J, Sammak, Rebecca L, Dutra, Joseph, Watanabe, Jennifer K, Usachenko, Jodie L, Immareddy, Ramya, Roh, Jamin W, Verma, Anil, Lakshmanappa, Yashavanth Shaan, Schmidt, Brian A, Di Germanio, Clara, Rizvi, Nabeela, Liu, Hongwei, Ma, Zhong-Min, Stone, Mars, Simmons, Graham, Dumont, Larry J, Allen, A Mark, Lockwood, Sarah, Pollard, Rachel E, de Assis, Rafael Ramiro, Yee, JoAnn L, Nham, Peter B, Ardeshir, Amir, Deere, Jesse D, Jain, Aarti, Felgner, Philip L, Coffey, Lark L, Iyer, Smita S, Hartigan-O’Connor, Dennis J, Busch, Michael P, and Reader, J Rachel
Subjects: Medical Microbiology, Biomedical and Clinical Sciences, Immunology, Pneumonia & Influenza, Infectious Diseases, Coronaviruses Therapeutics and Interventions, Immunization, Emerging Infectious Diseases, Coronaviruses, Lung, Infection, Good Health and Well Being, Animals, Antibodies, Neutralizing, Antiviral Agents, COVID-19, Humans, Immunization, Passive, Macaca mulatta, RNA, Viral, SARS-CoV-2, COVID-19 Serotherapy, Microbiology, Virology, Medical microbiology
Abstract: Early in the SARS-CoV-2 pandemic, there was a high level of optimism based on observational studies and small controlled trials that treating hospitalized patients with convalescent plasma from COVID-19 survivors (CCP) would be an important immunotherapy. However, as more data from controlled trials became available, the results became disappointing, with at best moderate evidence of efficacy when CCP with high titers of neutralizing antibodies was used early in infection. To better understand the potential therapeutic efficacy of CCP, and to further validate SARS-CoV-2 infection of macaques as a reliable animal model for testing such strategies, we inoculated 12 adult rhesus macaques with SARS-CoV-2 by intratracheal and intranasal routes. One day later, 8 animals were infused with pooled human CCP with a high titer of neutralizing antibodies (RVPN NT50 value of 3,003), while 4 control animals received normal human plasma. Animals were monitored for 7 days. Animals treated with CCP had detectable but low levels of antiviral antibodies after infusion. In comparison to the control animals, CCP-treated animals had similar levels of viral RNA in upper and lower respiratory tract secretions, similar detection of viral RNA in lung tissues by in situ hybridization, but lower amounts of infectious virus in the lungs. CCP-treated animals had a moderate, but statistically significant reduction in interstitial pneumonia, as measured by comprehensive lung histology. Thus overall, therapeutic benefits of CCP were marginal and inferior to results obtained earlier with monoclonal antibodies in this animal model. By highlighting strengths and weaknesses, data of this study can help to further optimize nonhuman primate models to provide proof-of-concept of intervention strategies, and guide the future use of convalescent plasma against SARS-CoV-2 and potentially other newly emerging respiratory viruses.
Published: 2022

44. Twists, Turns and Changing Directions

Author: Reader, Ian, primary
Published: 2023
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45. Patient Identification of Diagnostic Safety Blindspots and Participation in “Good Catches” Through Shared Visit Notes

Author: BELL, SIGALL K., BOURGEOIS, FABIENNE, DONG, JOE, GILLESPIE, ALEX, NGO, LONG H., READER, TOM W., THOMAS, ERIC J., and DESROCHES, CATHERINE M.
Published: 2022

46. DISCOVERING ANCIENT AND VETERAN TREES IN ENGLAND

Author: Nolan, Victoria, Gilbert, Francis, Reed, Tom, and Reader, Tom
Published: 2022

47. Cytogenetics and FISH in Precision Molecular Pathology of Aggressive B-Cell Lymphomas

Author: Ning, Yi, Reader, Jocelyn, Allen, Timothy C., Series Editor, Crane, Genevieve M., editor, and Loghavi, Sanam, editor
Published: 2023
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48. Postdigital Research and Human Agency

Author: Reader, John, Jandrić, Petar, Series Editor, Escaño González, Carlos, Editorial Board Member, Ford, Derek R., Editorial Board Member, Hayes, Sarah, Editorial Board Member, Kerres, Michael, Editorial Board Member, Knox, Jeremy, Editorial Board Member, Peters, Michael A., Editorial Board Member, Tesar, Marek, Editorial Board Member, and MacKenzie, Alison, editor
Published: 2023
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49. The Habitat Characteristics of Banteng (Bos Javanicus D’alton, 1832) in Pringtali Feeding Ground, Meru Betiri National Park, East Java

Author: Siddiq, Arif Mohammad, Sulistiyowati, Hari, Reader, Tom, Ma, Wanshu, Series Editor, Lelono, Asmoro, editor, Akbar Bahar, Muhammad, editor, Wathon, Syubanul, editor, Senjarini, Kartika, editor, Ginanjar Arip, Asep, editor, Putrasetya, Ramdhan, editor, Andika, Beny, editor, Ayu Sukma, Nadhea, editor, and Sugiharto, Bambang, Editor-in-Chief
Published: 2023
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50. Access to Drinking Water, Food Security and Adequate Housing: Challenges for Engineering, Past, Present and Future

Author: Reader, Graham T., Ting, David S.-K., editor, and Vasel-Be-Hagh, Ahmad, editor
Published: 2023
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