Search

Your search keyword '"Zhang, Hanbo"' showing total 313 results
Start Over Author "Zhang, Hanbo"
313 results on '"Zhang, Hanbo"'

1. Towards Generalist Robot Policies: What Matters in Building Vision-Language-Action Models

Author

Li, Xinghang, Li, Peiyan, Liu, Minghuan, Wang, Dong, Liu, Jirong, Kang, Bingyi, Ma, Xiao, Kong, Tao, Zhang, Hanbo, and Liu, Huaping

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Foundation Vision Language Models (VLMs) exhibit strong capabilities in multi-modal representation learning, comprehension, and reasoning. By injecting action components into the VLMs, Vision-Language-Action Models (VLAs) can be naturally formed and also show promising performance. Existing work has demonstrated the effectiveness and generalization of VLAs in multiple scenarios and tasks. Nevertheless, the transfer from VLMs to VLAs is not trivial since existing VLAs differ in their backbones, action-prediction formulations, data distributions, and training recipes. This leads to a missing piece for a systematic understanding of the design choices of VLAs. In this work, we disclose the key factors that significantly influence the performance of VLA and focus on answering three essential design choices: which backbone to select, how to formulate the VLA architectures, and when to add cross-embodiment data. The obtained results convince us firmly to explain why we need VLA and develop a new family of VLAs, RoboVLMs, which require very few manual designs and achieve a new state-of-the-art performance in three simulation tasks and real-world experiments. Through our extensive experiments, which include over 8 VLM backbones, 4 policy architectures, and over 600 distinct designed experiments, we provide a detailed guidebook for the future design of VLAs. In addition to the study, the highly flexible RoboVLMs framework, which supports easy integrations of new VLMs and free combinations of various design choices, is made public to facilitate future research. We open-source all details, including codes, models, datasets, and toolkits, along with detailed training and evaluation recipes at: robovlms.github.io., Comment: Project page: robovlms.github.io. Added limitations and future works. Fix categorization

Published
2024

2. REGNet V2: End-to-End REgion-based Grasp Detection Network for Grippers of Different Sizes in Point Clouds

Author

Zhao, Binglei, Wang, Han, Tang, Jian, Ma, Chengzhong, Zhang, Hanbo, Zhang, Jiayuan, Lan, Xuguang, and Chen, Xingyu

Subjects
Computer Science - Robotics
Abstract

Grasping has been a crucial but challenging problem in robotics for many years. One of the most important challenges is how to make grasping generalizable and robust to novel objects as well as grippers in unstructured environments. We present \regnet, a robotic grasping system that can adapt to different parallel jaws to grasp diversified objects. To support different grippers, \regnet embeds the gripper parameters into point clouds, based on which it predicts suitable grasp configurations. It includes three components: Score Network (SN), Grasp Region Network (GRN), and Refine Network (RN). In the first stage, SN is used to filter suitable points for grasping by grasp confidence scores. In the second stage, based on the selected points, GRN generates a set of grasp proposals. Finally, RN refines the grasp proposals for more accurate and robust predictions. We devise an analytic policy to choose the optimal grasp to be executed from the predicted grasp set. To train \regnet, we construct a large-scale grasp dataset containing collision-free grasp configurations using different parallel-jaw grippers. The experimental results demonstrate that \regnet with the analytic policy achieves the highest success rate of $74.98\%$ in real-world clutter scenes with $20$ objects, significantly outperforming several state-of-the-art methods, including GPD, PointNetGPD, and S4G. The code and dataset are available at https://github.com/zhaobinglei/REGNet-V2.

Published
2024

3. GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation

Author

Cheang, Chi-Lam, Chen, Guangzeng, Jing, Ya, Kong, Tao, Li, Hang, Li, Yifeng, Liu, Yuxiao, Wu, Hongtao, Xu, Jiafeng, Yang, Yichu, Zhang, Hanbo, and Zhu, Minzhao

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

We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable robot manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture the dynamics of the world. This large-scale pre-training, involving 38 million video clips and over 50 billion tokens, equips GR-2 with the ability to generalize across a wide range of robotic tasks and environments during subsequent policy learning. Following this, GR-2 is fine-tuned for both video generation and action prediction using robot trajectories. It exhibits impressive multi-task learning capabilities, achieving an average success rate of 97.7% across more than 100 tasks. Moreover, GR-2 demonstrates exceptional generalization to new, previously unseen scenarios, including novel backgrounds, environments, objects, and tasks. Notably, GR-2 scales effectively with model size, underscoring its potential for continued growth and application. Project page: \url{https://gr2-manipulation.github.io}., Comment: Tech Report. Authors are listed in alphabetical order. Project page: https://gr2-manipulation.github.io

Published
2024

4. DexDiff: Towards Extrinsic Dexterity Manipulation of Ungraspable Objects in Unrestricted Environments

Author

Ma, Chengzhong, Yang, Houxue, Zhang, Hanbo, Liu, Zeyang, Zhao, Chao, Tang, Jian, Lan, Xuguang, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Grasping large and flat objects (e.g. a book or a pan) is often regarded as an ungraspable task, which poses significant challenges due to the unreachable grasping poses. Previous works leverage Extrinsic Dexterity like walls or table edges to grasp such objects. However, they are limited to task-specific policies and lack task planning to find pre-grasp conditions. This makes it difficult to adapt to various environments and extrinsic dexterity constraints. Therefore, we present DexDiff, a robust robotic manipulation method for long-horizon planning with extrinsic dexterity. Specifically, we utilize a vision-language model (VLM) to perceive the environmental state and generate high-level task plans, followed by a goal-conditioned action diffusion (GCAD) model to predict the sequence of low-level actions. This model learns the low-level policy from offline data with the cumulative reward guided by high-level planning as the goal condition, which allows for improved prediction of robot actions. Experimental results demonstrate that our method not only effectively performs ungraspable tasks but also generalizes to previously unseen objects. It outperforms baselines by a 47% higher success rate in simulation and facilitates efficient deployment and manipulation in real-world scenarios.

Published
2024

6. A prospective multicenter birth cohort in China: pregnancy health atlas

Author

Zhou, Si, Zhou, Niya, Zhang, Hanbo, Yang, Wenzhi, Liu, Qingsong, Zheng, Lianshuai, Xiang, Yuting, Zheng, Dan, Zhou, Yan, Wang, Siyi, Cheng, Danling, He, Jun, Wang, Hong, Zhang, Wenbin, Guan, Liping, Geng, Qiaoling, Zhou, Shihao, Zhai, Hongbo, Jin, Hua, Hou, Fei, Wu, Shuzhen, Gao, Jie, Yi, Jing, Sun, Luming, Wei, Fengxiang, Zhang, Jianguo, Yu, Lei, Yang, Xiao, Wang, Leilei, Zhao, Lijian, and Qi, Hongbo

Published
2024
Full Text
View/download PDF

8. SInViG: A Self-Evolving Interactive Visual Agent for Human-Robot Interaction

Author

Xu, Jie, Zhang, Hanbo, Li, Xinghang, Liu, Huaping, Lan, Xuguang, and Kong, Tao

Subjects
Computer Science - Robotics
Abstract

Linguistic ambiguity is ubiquitous in our daily lives. Previous works adopted interaction between robots and humans for language disambiguation. Nevertheless, when interactive robots are deployed in daily environments, there are significant challenges for natural human-robot interaction, stemming from complex and unpredictable visual inputs, open-ended interaction, and diverse user demands. In this paper, we present SInViG, which is a self-evolving interactive visual agent for human-robot interaction based on natural languages, aiming to resolve language ambiguity, if any, through multi-turn visual-language dialogues. It continuously and automatically learns from unlabeled images and large language models, without human intervention, to be more robust against visual and linguistic complexity. Benefiting from self-evolving, it sets new state-of-the-art on several interactive visual grounding benchmarks. Moreover, our human-robot interaction experiments show that the evolved models consistently acquire more and more preferences from human users. Besides, we also deployed our model on a Franka robot for interactive manipulation tasks. Results demonstrate that our model can follow diverse user instructions and interact naturally with humans in natural language, despite the complexity and disturbance of the environment.

Published
2024

9. Towards Unified Interactive Visual Grounding in The Wild

Author

Xu, Jie, Zhang, Hanbo, Si, Qingyi, Li, Yifeng, Lan, Xuguang, and Kong, Tao

Subjects
Computer Science - Robotics
Abstract

Interactive visual grounding in Human-Robot Interaction (HRI) is challenging yet practical due to the inevitable ambiguity in natural languages. It requires robots to disambiguate the user input by active information gathering. Previous approaches often rely on predefined templates to ask disambiguation questions, resulting in performance reduction in realistic interactive scenarios. In this paper, we propose TiO, an end-to-end system for interactive visual grounding in human-robot interaction. Benefiting from a unified formulation of visual dialogue and grounding, our method can be trained on a joint of extensive public data, and show superior generality to diversified and challenging open-world scenarios. In the experiments, we validate TiO on GuessWhat?! and InViG benchmarks, setting new state-of-the-art performance by a clear margin. Moreover, we conduct HRI experiments on the carefully selected 150 challenging scenes as well as real-robot platforms. Results show that our method demonstrates superior generality to diversified visual and language inputs with a high success rate. Codes and demos are available at https://github.com/jxu124/TiO., Comment: Accepted to ICRA 2024

Published
2024

10. Vision-Language Foundation Models as Effective Robot Imitators

Author

Li, Xinghang, Liu, Minghuan, Zhang, Hanbo, Yu, Cunjun, Xu, Jie, Wu, Hongtao, Cheang, Chilam, Jing, Ya, Zhang, Weinan, Liu, Huaping, Li, Hang, and Kong, Tao

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

Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data. To this end, we derive a simple and novel vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo. Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step vision-language comprehension, models sequential history information with an explicit policy head, and is slightly fine-tuned by imitation learning only on language-conditioned manipulation datasets. Such a decomposition provides RoboFlamingo the flexibility for open-loop control and deployment on low-performance platforms. By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control. Our extensive experimental results also reveal several interesting conclusions regarding the behavior of different pre-trained VLMs on manipulation tasks. We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy., Comment: Fix typos. Project page: https://roboflamingo.github.io

Published
2023

11. InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions

Author

Zhang, Hanbo, Xu, Jie, Mo, Yuchen, and Kong, Tao

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, \invig, for interactive visual grounding under language ambiguity. Our dataset comprises over 520K images accompanied by open-ended goal-oriented disambiguation dialogues, encompassing millions of object instances and corresponding question-answer pairs. Leveraging the \invig dataset, we conduct extensive studies and propose a set of baseline solutions for end-to-end interactive visual disambiguation and grounding, achieving a 45.6\% success rate during validation. To the best of our knowledge, the \invig dataset is the first large-scale dataset for resolving open-ended interactive visual grounding, presenting a practical yet highly challenging benchmark for ambiguity-aware HRI. Codes and datasets are available at: \href{https://openivg.github.io}{https://openivg.github.io}., Comment: 8 pages, 9 figures, 3 tables, under review

Published
2023

13. What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?

Author

Zeng, Yan, Zhang, Hanbo, Zheng, Jiani, Xia, Jiangnan, Wei, Guoqiang, Wei, Yang, Zhang, Yuchen, and Kong, Tao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field. To address this issue, this paper presents a systematic and comprehensive study, quantitatively and qualitatively, on training such models. We implement over 20 variants with controlled settings. Concretely, for network structures, we compare different LLM backbones and model designs. For training data, we investigate the impact of data and sampling strategies. For instructions, we explore the influence of diversified prompts on the instruction-following ability of the trained models. For benchmarks, we contribute the first, to our best knowledge, comprehensive evaluation set including both image and video tasks through crowd-sourcing. Based on our findings, we present Lynx, which performs the most accurate multi-modal understanding while keeping the best multi-modal generation ability compared to existing open-sourced GPT4-style models., Comment: 32 pages

Published
2023

20. Robotic Grasping from Classical to Modern: A Survey

Author

Zhang, Hanbo, Tang, Jian, Sun, Shiguang, and Lan, Xuguang

Subjects
Computer Science - Robotics
Abstract

Robotic Grasping has always been an active topic in robotics since grasping is one of the fundamental but most challenging skills of robots. It demands the coordination of robotic perception, planning, and control for robustness and intelligence. However, current solutions are still far behind humans, especially when confronting unstructured scenarios. In this paper, we survey the advances of robotic grasping, starting from the classical formulations and solutions to the modern ones. By reviewing the history of robotic grasping, we want to provide a complete view of this community, and perhaps inspire the combination and fusion of different ideas, which we think would be helpful to touch and explore the essence of robotic grasping problems. In detail, we firstly give an overview of the analytic methods for robotic grasping. After that, we provide a discussion on the recent state-of-the-art data-driven grasping approaches rising in recent years. With the development of computer vision, semantic grasping is being widely investigated and can be the basis of intelligent manipulation and skill learning for autonomous robotic systems in the future. Therefore, in our survey, we also briefly review the recent progress in this topic. Finally, we discuss the open problems and the future research directions that may be important for the human-level robustness, autonomy, and intelligence of robots.

Published
2022

24. Density-based Curriculum for Multi-goal Reinforcement Learning with Sparse Rewards

Author

Yang, Deyu, Zhang, Hanbo, Lan, Xuguang, and Ding, Jishiyu

Subjects
Computer Science - Robotics
Abstract

Multi-goal reinforcement learning (RL) aims to qualify the agent to accomplish multi-goal tasks, which is of great importance in learning scalable robotic manipulation skills. However, reward engineering always requires strenuous efforts in multi-goal RL. Moreover, it will introduce inevitable bias causing the suboptimality of the final policy. The sparse reward provides a simple yet efficient way to overcome such limits. Nevertheless, it harms the exploration efficiency and even hinders the policy from convergence. In this paper, we propose a density-based curriculum learning method for efficient exploration with sparse rewards and better generalization to desired goal distribution. Intuitively, our method encourages the robot to gradually broaden the frontier of its ability along the directions to cover the entire desired goal space as much and quickly as possible. To further improve data efficiency and generality, we augment the goals and transitions within the allowed region during training. Finally, We evaluate our method on diversified variants of benchmark manipulation tasks that are challenging for existing methods. Empirical results show that our method outperforms the state-of-the-art baselines in terms of both data efficiency and success rate., Comment: 8 pages, 7 figures

Published
2021

27. 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
Full Text
View/download PDF

28. 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
Full Text
View/download PDF

29. INVIGORATE: Interactive Visual Grounding and Grasping in Clutter

Author

Zhang, Hanbo, Lu, Yunfan, Yu, Cunjun, Hsu, David, Lan, Xuguang, and Zheng, Nanning

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

This paper presents INVIGORATE, a robot system that interacts with human through natural language and grasps a specified object in clutter. The objects may occlude, obstruct, or even stack on top of one another. INVIGORATE embodies several challenges: (i) infer the target object among other occluding objects, from input language expressions and RGB images, (ii) infer object blocking relationships (OBRs) from the images, and (iii) synthesize a multi-step plan to ask questions that disambiguate the target object and to grasp it successfully. We train separate neural networks for object detection, for visual grounding, for question generation, and for OBR detection and grasping. They allow for unrestricted object categories and language expressions, subject to the training datasets. However, errors in visual perception and ambiguity in human languages are inevitable and negatively impact the robot's performance. To overcome these uncertainties, we build a partially observable Markov decision process (POMDP) that integrates the learned neural network modules. Through approximate POMDP planning, the robot tracks the history of observations and asks disambiguation questions in order to achieve a near-optimal sequence of actions that identify and grasp the target object. INVIGORATE combines the benefits of model-based POMDP planning and data-driven deep learning. Preliminary experiments with INVIGORATE on a Fetch robot show significant benefits of this integrated approach to object grasping in clutter with natural language interactions. A demonstration video is available at https://youtu.be/zYakh80SGcU., Comment: 12 pages, full version

Published
2021

30. REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter

Author

Zhang, Hanbo, Yang, Deyu, Wang, Han, Zhao, Binglei, Lan, Xuguang, Ding, Jishiyu, and Zheng, Nanning

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Despite the impressive progress achieved in robotic grasping, robots are not skilled in sophisticated tasks (e.g. search and grasp a specified target in clutter). Such tasks involve not only grasping but the comprehensive perception of the world (e.g. the object relationships). Recently, encouraging results demonstrate that it is possible to understand high-level concepts by learning. However, such algorithms are usually data-intensive, and the lack of data severely limits their performance. In this paper, we present a new dataset named REGRAD for the learning of relationships among objects and grasps. We collect the annotations of object poses, segmentations, grasps, and relationships for the target-driven relational grasping tasks. Our dataset is collected in both forms of 2D images and 3D point clouds. Moreover, since all the data are generated automatically, it is free to import new objects for data generation. We also released a real-world validation dataset to evaluate the sim-to-real performance of models trained on REGRAD. Finally, we conducted a series of experiments, showing that the models trained on REGRAD could generalize well to the realistic scenarios, in terms of both relationship and grasp detection. Our dataset and code could be found at: https://github.com/poisonwine/REGRAD

Published
2021

35. REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds

Author

Zhao, Binglei, Zhang, Hanbo, Lan, Xuguang, Wang, Haoyu, Tian, Zhiqiang, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy observations. This paper presents an end-to-end grasp detection network taking one single-view point cloud as input to tackle the problem. Our network includes three stages: Score Network (SN), Grasp Region Network (GRN), and Refine Network (RN). Specifically, SN regresses point grasp confidence and selects positive points with high confidence. Then GRN conducts grasp proposal prediction on the selected positive points. RN generates more accurate grasps by refining proposals predicted by GRN. To further improve the performance, we propose a grasp anchor mechanism, in which grasp anchors with assigned gripper orientations are introduced to generate grasp proposals. Experiments demonstrate that REGNet achieves a success rate of 79.34% and a completion rate of 96% in real-world clutter, which significantly outperforms several state-of-the-art point-cloud based methods, including GPD, PointNetGPD, and S4G. The code is available at https://github.com/zhaobinglei/REGNet_for_3D_Grasping., Comment: Accepted to ICRA 2021

Published
2020

36. Hindsight Trust Region Policy Optimization

Author

Zhang, Hanbo, Bai, Site, Lan, Xuguang, Hsu, David, and Zheng, Nanning

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

Reinforcement Learning(RL) with sparse rewards is a major challenge. We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards. Hindsight refers to the algorithm's ability to learn from information across goals, including ones not intended for the current task. HTRPO leverages two main ideas. It introduces QKL, a quadratic approximation to the KL divergence constraint on the trust region, leading to reduced variance in KL divergence estimation and improved stability in policy update. It also presents Hindsight Goal Filtering(HGF) to select conductive hindsight goals. In experiments, we evaluate HTRPO in various sparse reward tasks, including simple benchmarks, image-based Atari games, and simulated robot control. Ablation studies indicate that QKL and HGF contribute greatly to learning stability and high performance. Comparison results show that in all tasks, HTRPO consistently outperforms both TRPO and HPG, a state-of-the-art algorithm for RL with sparse rewards., Comment: Accepted by IJCAI 2021

Published
2019

39. A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes

Author

Zhang, Hanbo, Lan, Xuguang, Bai, Site, Wan, Lipeng, Yang, Chenjie, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open problem, because there are two main challenges for autonomous robots: (1)it is a comprehensive task to know what and how to grasp; (2)it is hard to deal with the situations in which the target is hidden or covered by other objects. In this paper, we propose a multi-task convolutional neural network for autonomous robotic grasping, which can help the robot find the target, make the plan for grasping and finally grasp the target step by step in object stacking scenes. We integrate vision-based robotic grasping detection and visual manipulation relationship reasoning in one single deep network and build the autonomous robotic grasping system. Experimental results demonstrate that with our model, Baxter robot can autonomously grasp the target with a success rate of 90.6%, 71.9% and 59.4% in object cluttered scenes, familiar stacking scenes and complex stacking scenes respectively., Comment: Submitted to IROS2019

Published
2018

40. A Real-time Robotic Grasp Approach with Oriented Anchor Box

Author

Zhang, Hanbo, Zhou, Xinwen, Lan, Xuguang, Li, Jin, Tian, Zhiqiang, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Grasp is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust and real-time robotic grasp approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the Orientation Anchor Box Mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any priors. With oriented anchor boxes, the grasps can be predicted more accurately and efficiently. Besides, to accelerate the network training and further improve the performance of angle regression, Angle Matching is proposed during training instead of Jaccard Index Matching. Five-fold cross validation results demonstrate that our proposed algorithm achieves an accuracy of 98.8% and 97.8% in image-wise split and object-wise split respectively, and the speed of our detection algorithm is 67 FPS with GTX 1080Ti, outperforming all the current state-of-the-art grasp detection algorithms on Cornell Dataset both in speed and accuracy. Robotic experiments demonstrate the robustness and generalization ability in unseen objects and real-world environment, with the average success rate of 90.0% and 84.2% of familiar things and unseen things respectively on Baxter robot platform., Comment: arXiv admin note: text overlap with arXiv:1803.02209

Published
2018

41. ROI-based Robotic Grasp Detection for Object Overlapping Scenes

Author

Zhang, Hanbo, Lan, Xuguang, Bai, Site, Zhou, Xinwen, Tian, Zhiqiang, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively., Comment: Submitted to IROS 2019

Published
2018

42. Fully Convolutional Grasp Detection Network with Oriented Anchor Box

Author

Zhou, Xinwen, Lan, Xuguang, Zhang, Hanbo, Tian, Zhiqiang, Zhang, Yang, and Zheng, Nanning

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi-grasp predictor. The feature extractor is a deep convolutional neural network. The multi-grasp predictor regresses grasp rectangles from predefined oriented rectangles, called oriented anchor boxes, and classifies the rectangles into graspable and ungraspable. On the standard Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on image-wise split and object-wise split respectively, and outperforms the latest state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise split., Comment: 8 pages 6 figures

Published
2018

44. Visual Manipulation Relationship Network

Author

Zhang, Hanbo, Lan, Xuguang, Zhou, Xinwen, Tian, Zhiqiang, Zhang, Yang, and Zheng, Nanning

Subjects
Computer Science - Robotics
Abstract

Robotic grasping detection is one of the most important fields in robotics, in which great progress has been made recent years with the help of convolutional neural network (CNN). However, including multiple objects in one scene can invalidate the existing CNN-based grasping detection algorithms, because manipulation relationships among objects are not considered, which are required to guide the robot to grasp things in the right order. This paper presents a new CNN architecture called Visual Manipulation Relationship Network (VMRN) to help robot detect targets and predict the manipulation relationships in real time. To implement end-to-end training and meet real-time requirements in robot tasks, we propose the Object Pairing Pooling Layer (OP2L) to help to predict all manipulation relationships in one forward process. Moreover, in order to train VMRN, we collect a dataset named Visual Manipulation Relationship Dataset (VMRD) consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image, which is labeled by the manipulation relationship tree. The experimental results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the real-time requirements in robot tasks.

Published
2018

45. Role of the Foliar Endophyte Colletotrichum in the Resistance of Invasive Ageratina adenophora to Disease and Abiotic Stress.

Author

Yang, Ailing, Li, Yuxuan, Zeng, Zhaoying, and Zhang, Hanbo

Subjects
PLANT invasions, PHYTOPATHOGENIC microorganisms, PLANT defenses, ABIOTIC stress, INVASIVE plants, ENDOPHYTES
Abstract

Plant-associated fungi often drive plant invasion success by increasing host growth, disease resistance, and tolerance to environmental stress. A high abundance of Colletotrichum asymptomatically accumulated in the leaves of Ageratina adenophora. In this study, we aimed to clarify whether three genetically distinct endophytic Colletotrichum isolates (AX39, AX115, and AX198) activate invasive plant defenses against disease and environmental stress. We observed that, in the absence of pathogen attack and environmental stress, the foliar endophyte Colletotrichum reduced photosynthesis-related physiological indicators (i.e., chlorophyll content and soluble sugar content), increased resistance-related indicators (i.e., total phenolic (TP) and peroxidase (POD) activity), and decreased the biomass of A. adenophora. However, endophytic Colletotrichum strains exhibit positive effects on resistance to certain foliar pathogen attacks. Strains AX39 and AX115 promoted but AX198 attenuated the pathogenic effects of pathogen strains G56 and Y122 (members of Mesophoma ageratinae). In contrast, AX39 and AX115 weakened, but AX198 had no effect on, the pathogenic effect of the pathogen strain S188 (Mesophoma speciosa; Didymellaceae family). We also found that endophytes increase the biomass of A. adenophora under drought or nutrient stress. Strain AX198 significantly increased stem length and chlorophyll content under drought stress. Strain AX198 significantly increased the aboveground dry weight, AX115 increased the stem length, and AX39 significantly increased the chlorophyll content under nutrient stress. Our results revealed that there are certain positive effects of foliar Colletotrichum endophytes on A. adenophora in response to biotic and abiotic stresses, which may be beneficial for its invasion. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

46. Vibration Reduction Performance of a Novel Harmonic Frequency Damping Device for Damping Pad Floating Slab Track.

Author

Chen, Zhaowei, Zhang, Hanbo, Pu, Qianhua, Xu, Pengfei, and Chen, Zhihui

Subjects
FINITE element method, TENSILE tests, DYNAMIC models, URBAN transit systems, RUBBER
Abstract

The stability of the damping pad floating slab track (DPFST) plays a critical role in the operational safety and passenger comfort of urban rail transit systems and represents a significant technical challenge. This paper introduces a novel harmonic frequency damping device (HFDD) with preload characteristics to enhance DPFST stability. First, the rubber damping pad's constitutive relationship is determined using uniaxial tensile tests and the Mooney–Rivlin model. Next, a vehicle–track coupled dynamic model and a finite element model of the DPFST with HFDD are developed. Finally, the effects of HFDD installation and parameter adjustments on the DPFS's modal and dynamic responses are examined. Results show that the HFDD effectively adjusts the DPFS's natural frequency and suppresses its acceleration and displacement. Increasing HFDD stiffness from 0 to 10 kN/mm raises the DPFS's natural frequency by up to 7.58 Hz. Within the stiffness and damping ranges of 0–20 kN/mm and 0–100 kN·s/m, respectively, the HFDD significantly reduces DPFS vibration, with maximum reductions in acceleration of 45.64% and 64.24% and in displacement of 47.55% and 39.06%. However, beyond these ranges, further increases in stiffness and damping substantially reduce the HFDD's vibration suppression effectiveness and excessively high values are impractical for engineering use. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

49. Research on Downscaling and Correction of TRMM Data in Central China

Author

Zhang, Hanbo, Dou, Shiqing, Xu, Yong, Zhang, Nan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zu, Qiaohong, editor, Tang, Yong, editor, and Mladenović, Vladimir, editor

Published
2021
Full Text
View/download PDF

50. Temporal and Spatial Changes of Ecosystem Health in Guangdong Province in Recent 11 Years

Author

Zhang, Nan, Xu, Yong, Dou, Shiqing, Jing, Juanli, Zhang, Hanbo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zu, Qiaohong, editor, Tang, Yong, editor, and Mladenović, Vladimir, editor

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results