Author: "Gerhardus, A." - Searchworks@Jio Institute Digital Library Search Results

4,596 results on '"Gerhardus, A."'

151. The Search and Seizure of Digital Evidence by Forensic Investigators in South Africa

Author: Jacobus Gerhardus Nortje and Daniel Christoffel Myburgh
Subjects: Digital forensics, digital devices, digital search and seizure, digital evidence, forensic investigation, international standards, Law in general. Comparative and uniform law. Jurisprudence, K1-7720
Abstract: The discipline of digital forensics requires a combination of skills, qualifications and knowledge in the area of forensic investigation, legal aspects and information technology. The uniqueness of digital evidence makes the adoption of traditional legal approaches problematic. Information technology terminology is currently used interchangeably without any regard to being unambiguous and consistent in relation to legal texts. Many of the information technology terms or concepts have not yet achieved legal recognition. The recognition and standardisation of terminology within a legal context are of the utmost importance to ensure that miscommunication does not occur. To provide clarity or guidance on some of the terms and concepts applicable to digital forensics and for the search and seizure of digital evidence, some of the concepts and terms are reviewed and discussed, using the Criminal Procedure Act 51 of 1977 as a point of departure. Digital evidence is often collected incorrectly and analysed ineffectively or simply overlooked due to the complexities that digital evidence poses to forensic investigators. As with any forensic science, specific regulations, guidelines, principles or procedures should be followed to meet the objectives of investigations and to ensure the accuracy and acceptance of findings. These regulations, guidelines, principles or procedures are discussed within the context of digital forensics: what processes should be followed and how these processes ensure the acceptability of digital evidence. These processes include international principles and standards such as those of the Association of Chiefs of Police Officers and the International Organisation of Standardisation. A summary is also provided of the most influential or best-recognised international (IOS) standards on digital forensics. It is concluded that the originality, reliability, integrity and admissibility of digital evidence should be maintained as follows: Data should not be changed or altered. Original evidence should not be directly examined. Forensically sound duplicates should be created. Digital forensic analyses should be performed by competent persons. Digital forensic analyses should adhere to relevant local legal requirements. Audit trails should exist consisting of all required documents and actions. The chain of custody should be protected. Processes and procedures should be proper, while recognised and accepted by the industry. If the ACPO (1997) principles and ISO/IEC 27043 and 27037 Standards are followed as a forensic framework, then digital forensic investigators should follow these standards as a legal framework.
Published: 2019
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152. Patient involvement in clinical trials: motivation and expectations differ between patients and researchers involved in a trial on urinary tract infections

Author: Imke Schilling, Heike Behrens, Claudia Hugenschmidt, Jennifer Liedtke, Guido Schmiemann, and Ansgar Gerhardus
Subjects: Clinical trial, Patient and public involvement, PPI, Patient engagement, Patient board, Motivation, Medicine, Medicine (General), R5-920
Abstract: Plain English summary Patients should be involved in the design, conduct and dissemination of research that affects them. Patient involvement leads to empowerment and enhances the quality of research. Differing motives and expectations between researchers and patients involved can hamper involvement. We wanted to learn more about patients’ and researchers’ motives and expectations in order to improve the benefits of involvement for all parties. We implemented a patient board with ten patients and five researchers for a trial on urinary tract infections (UTIs). We asked each patient and researcher about his or her motivation and expectations regarding the patient board. We found that patients’ motivations included the wish to improve the treatment of UTIs, to support patient involvement as a principle, and to enhance the benefit of others. Furthermore they were interested in learning how a patients’ board works and in exchanging with peers and scientists. In addition, a (modest) monetary incentive for involvement was welcomed.Researchers were motivated by the possibility to improve research and to contribute to the empowerment of patients. They also wanted to enhance their career opportunities, to learn more about patient involvement and to meet the increasing demand for it. Some patients expressed insecurity about their roles and tasks in the patient board. Among the researchers, some envisaged a rather passive role for themselves in the patient board while others expected to take over a more active role. Researchers emphasized that the ways and the means of communication between the researchers and the patients should be explicitly discussed. Abstract Background It has been increasingly recognized that patients should be actively involved in the design, conduct and dissemination of research. Besides empowering patients and democratizing research, involvement can enhance the quality of research and the development of equitable healthcare solutions. Differing motives and expectations between researchers and involved patients can hamper the conduct of involvement. However, little is known about patients’ and researchers’ motivations for involvement. Our aim was to study the motivation and expectations of patients and researchers towards patient and public involvement (PPI). Methods We implemented a patient board comprising ten patients and five researchers for a randomized controlled trial on the treatment of urinary tract infections (UTI). Prior to the first board meeting, we conducted telephone interviews with all researchers and patients regarding their motivation for involvement in the patient board and their expectations. The interviews were analyzed using thematic qualitative text analysis. Results Patients’ motivations included interest in improving UTI treatment, in supporting PPI, engaging for the benefit of others, exchanging with peers and scientists as well as in the methods of the board and the monetary incentive. Researchers wanted to improve research, enhance their professional development, empower patients, meet the formal demand for PPI, and learn about PPI. Regarding expectations, patients expressed insecurities about their roles, tasks and topics of discussion. They wished for an open exchange and hoped their involvement would make an impact. Researchers’ expectations for their own roles ranged between being a rather passive supporting force and active engagement in the board. The question of how to ensure the communication between the trial team and the patient board was of high importance for the researchers. Conclusions Patients’ and researchers’ motives and expectations were similar in some aspects but differed regarding agenda setting and understanding of their roles. Getting to know patients’ and researchers’ motivations and expectations at the beginning allowed us to anticipate potential conflicts or disappointments early on and to take them into consideration during the conduct of our PPI.
Published: 2019
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153. Isolated splenic hydatid disease : a rare case in rural New Zealand

Author: Bardsley, David Thomas, Bonnet, Gerhardus, and O'Grady, Michael
Published: 2022

154. Hemostasis and fibrinolysis in COVID‐19 survivors 6 months after intensive care unit discharge

Author: Anne‐Marije Hulshof, Dionne C. W. Braeken, Chahinda Ghossein‐Doha, Susanne van Santen, Jan‐Willem E. M. Sels, Gerhardus J. A. J. M. Kuiper, Iwan C. C. van der Horst, Hugo ten Cate, Bas C. T. vanBussel, Renske H. Olie, and Yvonne M. C. Henskens
Subjects: COVID‐19, fibrinolysis, follow‐up studies, hemostasis, thromboelastography, Diseases of the blood and blood-forming organs, RC633-647.5
Abstract: Abstract Background The prothrombotic phenotype has been extensively described in patients with acute coronavirus disease 2019 (COVID‐19). However, potential long‐term hemostatic abnormalities are unknown. Objective To evaluate the changes in routine hemostasis laboratory parameters and tissue‐type plasminogen activator (tPA) rotational thromboelastometry (ROTEM) 6 months after COVID‐19 intensive care unit (ICU) discharge in patients with and without venous thromboembolism (VTE) during admission. Methods Patients with COVID‐19 of the Maastricht Intensive Care COVID cohort with tPA ROTEM measurement at ICU and 6‐month follow‐up were included. TPA ROTEM is a whole blood viscoelastic assay that illustrates both clot development and fibrinolysis due to simultaneous addition of tissue factor and tPA. Analyzed ROTEM parameters include clotting time, maximum clot firmness (MCF), lysis onset time (LOT), and lysis time (LT). Results Twenty‐two patients with COVID‐19 were included and showed extensive hemostatic abnormalities before ICU discharge. TPA ROTEM MCF (75 mm [interquartile range, 68‐78]‐59 mm [49‐63]; P ≤ .001), LOT (3690 seconds [2963‐4418]‐1786 seconds [1465‐2650]; P ≤ .001), and LT (7200 seconds [6144‐7200]‐3138 seconds [2591‐4389]; P ≤ .001) normalized 6 months after ICU discharge. Of note, eight and four patients still had elevated fibrinogen and D‐dimer concentrations at follow‐up, respectively. In general, no difference in median hemostasis parameters at 6‐month follow‐up was observed between patients with (n=14) and without (n=8) VTE, although fibrinogen appeared to be lower in the VTE group (VTE–, 4.3 g/L [3.7‐4.7] vs VTE+, 3.4 g/L [3.2‐4.2]; P = .05). Conclusions Six months after COVID‐19 ICU discharge, no persisting hypercoagulable or hypofibrinolytic profile was detected by tPA ROTEM. Nevertheless, increased D‐dimer and fibrinogen concentrations persist up to 6 months in some patients, warranting further exploration of the role of hemostasis in long‐term morbidity after hospital discharge.
Published: 2021
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155. High-recall causal discovery for autocorrelated time series with latent confounders.

Author: Andreas Gerhardus and Jakob Runge
Published: 2020

156. Towards a Taxonomy of Logic Models in Systematic Reviews and Health Technology Assessments: A Priori, Staged, and Iterative Approaches

Author: Rehfuess, Eva A., Booth, Andrew, Brereton, Louise, Burns, Jacob, Gerhardus, Ansgar, Mozygemba, Kati, Oortwijn, Wija, Pfadenhauer, Lisa M., Tummers, Marcia, van de Wilt, Gert-Jan, and Rohwer, Anke
Abstract: The complexity associated with how interventions result--or fail to result--in outcomes and how context matters is increasingly recognised. Logic models provide an important tool for handling complexity, with contrasting uses in programme evaluation and evidence synthesis. To reconcile these, we developed an approach that combines the strengths of both traditions, propose a taxonomy of logic models, and provide guidance on how to choose between approaches and types of logic models in systematic reviews and health technology assessments (HTA). The taxonomy distinguishes 3 approaches (a priori, staged, and iterative) and 2 types (systems-based and process-orientated) of logic models. An a priori logic model is specified at the start of the systematic review/HTA and remains unchanged. With a staged logic model, the reviewer prespecifies several points, at which major data inputs require a subsequent version. An iterative logic model is continuously modified throughout the systematic review/HTA process. System-based logic models describe the system, in which the interaction between participants, intervention, and context takes place; process-orientated models display the causal pathways leading from the intervention to multiple outcomes. The proposed taxonomy of logic models offers an improved understanding of the advantages and limitations of logic models across the spectrum from a priori to fully iterative approaches. Choice of logic model should be informed by scope of evidence synthesis, presence/absence of clearly defined population, intervention, comparison, outcome (PICO) elements, and feasibility considerations. Applications across distinct interventions and methodological approaches will deliver good practice case studies and offer further insights on the choice and implementation of logic modelling approaches.
Published: 2018
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157. Preoperative screening for bleeding disorders: A comprehensive laboratory assessment of clinical practice

Author: Vries, Minka J., van der Meijden, Paola E., Kuiper, Gerhardus J., Nelemans, Patricia J., Wetzels, Rick J., van Oerle, René G., Lancé, Marcus D., ten Cate, Hugo, and Henskens, Yvonne M.
Published: 2018
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158. Cancelled operations: a 7-day cohort study of planned adult inpatient surgery in 245 UK National Health Service hospitals

Author: Moonesinghe, S. Ramani, Wong, Danny J.N., Harris, Steve K., Bedford, James, Boney, Oliver, Chazapis, Maria, Drake, Sharon, Farmer, Laura, Gilhooly, David, Goodwin, James, Lourtie, Jose, Wagstaff, Duncan, Grocott, Mike, Sneyd, Robert, Batchelor, Anna, Brett, Stephen, Plowright, Catherine, Shrestha, Suman, Shawyer, Richard, Ahmed, Shafi, Khondoker, Mizan, Nathanson, Mike, Cripps, Paul, Research & Audit Federation of Trainees (RAFT), Anaesthetic Audit and Research Matrix of Yorkshire (AARMY), Intensive Care & Anaesthesia Research Network of North East Trainees (INCARNNET), Merseyside Anaesthetic Group for Improving Quality (MAGIQ), Midlands East Research by Critical Care and Anaesthetic Trainees (MERCAT), North West Research and Audit Group (NWRAG), Oxford Critical Care Anaesthetic Research Enterprise (OxCCARE), Pan-London Peri-operative Audit & Research Network (PLAN), Scottish East Quality Improvement & Research Academy (SEQuoIA), SESSA Quality Improvement and Research Network (SQUARES), Severn Trainees Anaesthetic Research (STAR), South West Anaesthetic Research Matrix (SWARM), South Yorkshire Hospitals Audit and Research Collaboration (SHARC), Southcoast Peri-operative Audit and Research Collaboration (SPARC), Welsh Anaesthetic Audit Research & Engagement Network (WAAREN), West Midlands Trainee Research Anaesthesia & Intensive Care Network (WMTRAIN), Rawat, Shilpa, Sathe, Sonia, Campbell, Alison, Anandarajah, Janakan, Ball, Nicola, Bandara, Lalindra, Barnes, Dennis, Chapman, Catherine, Connor, Lynda, Cook, Amanda, Ebejer, Amanda, Evans, Samantha, Gallagher, Maire, Goff, Sarah, Halfacree, Irina, Harford, Rachel, Harris, Catherine, Jackson, Rebecca, James, Kathryn, Jones, Claire, Jones, Sharon, Malinovszky, Kathy, Mungai, Serah, Nageswaran, Hari, Perumal, Anand, Pudge, Harriet, Quinn, Leanne, Sheppard, Thomas, Smith, Trudy, Spargo, James, Storton, Sharon, Thomas, Caradog, Thomas, Charlotte, Tyrrell, Samuel, Vale, Owen, Williams, Catrin, Williams, Marie, Williams, Tom, Winfield-Young, Lewys, Worrell, Helen, Moloney, Dermot, Chuni, Chandini, Comara, Jasna, Conway, Brian, Eagle, Zara, Foster, Greg, Platt, Chris, Thorne, Alison, Hee, Wendy Lum, Armstrong, Lisa, Brennan, Albert, Burns, Justine, Busby, Chantal, Couch-Upite, Mechele, Dooks, Emma, Dunlop, Sally, Eijk, Anya, Govindaraju, Ramana, Gregory, Tamsin, Grover, Alka, Hairsine, Brigid, Hennell, Sarah, Hill, Philippa, Hollins, Thomas, Hood, Julian, Horner, Lisa, Hothersall, Helen, Ingham, Rachel, Jackson, Richard, Kitching, Amy, Kurasz, Claire, Munsie, Sarah, Nemeth, Ildiko, Paley, Carole, Premraj, Prabhakaran, Redhead, Sue, Scriven, John, Shaw, Alison, Shenton, Liz, Shiva, Hemantha, Snell, Josie, Sootheran, Lucy, Stewart, Josephine, Swinton, Frank, Totten, Claire, Varghese, Liz, Vest, Sarah, Wetherell, Ben, Hamlyn, Vincent, Tozer, James, Alce, Timothy, Bopanna, Poonam, Draper, Kevin, Emmett, Lucy, Falkner, Phillippa, Fisher, Ellie, Griffiths, Cat, Gunter, Una, Harris, Rebecca, Hashmi, Saima, Jewitt, Helen, Lodhi, Sonal, Organ, Angie, Owen, Elana, Taylor, Frances, Tritean, Maria, Wassall, Richard, Woolley, Jade, MacGregor, Mark, Atkinson, Claire, Croft, Maria, Frost, Victoria, George, Richard, Gray, Catherine, Kuttler, Anja, Lalani, Christina, Peers, Beth, Sharpe, Lisa, Skeoch, Christopher, Tarft, Hayley, Tyson, Emma, Wrigley, Martha, Qadri, Shabir, Abbas, Tahir, Barot, Niraj, Cole, Steve, Ganesh, Ritesh, Groves, Christopher, Iles, Lindsey, Petkova, Hristina, Rau, Clovis, Roberts, Jack, Shakir, Nadeem, Chaurasia, Sunil Kumar, Brown, Julia, Cunningham, Mishell, Daniels, Allison, Gannon, Emily, Haines, Annette, Johnson, Nicola, Ogle, Paul, Perumal, Srinivasan, Zeidan, Lisa, Torrance, Hew, Anwar, Sibtain, Gooneratne, Mevan, Raj, Ashok, Abbott, Tom, Ashton, Laura, Brown, Katherine, Butterfield, Emma, Fenn, Jon, Forsyth-Jones, Tim, Garth, Thomas, Gleeson, Liam, Haines, Ryan, Jacobs, Benjamin, Jyothiraj, Haren, Madhi, Shareef, Marriot, Charlotte, Martin, Rebecca, McMillan, Timothy, Menon, Arun, Mohr, Otto Ernst, Muelmenstaedt, Maria, Naeem, Hanzla, Nagendram, Myura, O'Connor, Cormac, Pang, Ching, Plummer, Kimberley, Rob, Zak, Sellers, Dan, Shaw, Michael, Smith, Christopher, Taylor, Tom, Thorat, Priya, Van Rensberg, Gerhardus, Walker, Sophie, Waqas, Farkhunda, Wicks, Peter, Wirth, Daniel, Armanious, Samuel, Allen, Kellie, Bruni, Salvatore, Burijinti Chenna, Keshava Reddy, Kaliappan, Agilan, Ocampo, Madelaine, Percuin, Adrian, Riches, Joanne, Saclot, Louie, Sevillano, Annaliza, Vertue, Mark, Knowlden, Peter, David, Beena, Gajendragadkar, Pushpaj, Gladstone, George, Hammerbeck, Henry, Iaverdino, Marina, Lilley, Carina, Linton-Willoughby, Ben, Murrell, Daniel, Patro, Shweta, Richie-Mclean, Susanna, Turnbull, Marc, Vatsala, Padmanabhan, McCourt, Killian, Black, Nick, Coary, Ciara, McCarter, Jonathan, Murphy, Alexandra, Pugh, Richard, Littler, Chris, Clements, Stephan, Bairkdar, Linda, Bennett, Gillian, Bird, Tom, Bolger, Annette, Dhar, King, Ene, Raluca, George, David, Haron, Dhania, Hobson, Lucie, Iqbal, Saba, Irvine, Lizzie, Lewis, Sion, McCallum, Roisin, O'Donovan, Benjamin, Patrick, Christopher, Perman, Christopher, Rahman, Nowfal, Roberts, Iolo, Sutcliffe, William, Towers, Tammy, Waite, Alicia, Wright, Stella, Pilsbury, Jane, Allan, Michael, Allen, Cody, Carden, Nikkita, Cotterill, Amanda, Cullis, Kerry, Eden, Daniel, Farmer, Nicola, Galloway, Andrea, Geoghegan, James, Gilani, Ahmed, Hard, Kelly, Hasan, Khalid, Horner, Christopher, Iqbal, Virginia, Lowe, Julie, Mallavalli, Ravindra, McCooty, Shanteela, McKillop, Sian, Mellers, Diane, Mukherjee, Ronan, Nicholas, Michelle, O'Hara, Chloe, Osborne, Fiona, Plunkett, Emma, Sharih, Gauhar, Westwood, John, Whapples, Annabelle, Whitehouse, Diane, Williams, Lucinda, Yasin, Bilal, Cupitt, Jason, Brown, Janette, Stoddard, Emma, Balasubramaniam, Madhu, Athanasopoulos, Paul, Bhatti, Mohammad, Blood, Laura, Buckley, Hugo, Chadwick, Julie, Cocks, Shirley, Fradkin, Deborah, Hetherington, Emily, Hill, Rebecca, Hull, Rob, Loftus, Alison, McKenna, Emma, Mcmanoman, Lucy, Milian, Lauren, Mistry, Raksha, Murthen, Lisa, Parkinson, Dave, Rhead, Kat, Sandbach, Peter, Walker, Ciara, Wheatley, Emma, Spencer, Robert, Alderson, Stephen, Bull, Christopher, Butterworth, Rachel, Cousins, Simon, Goellner, Sarah, Greasley, Mark, Lyle, Fiona, Munro, Michael, Northey, Martin, White, Stuart, Harrison, Alex, Whitney, Paul-Simon, Drake, Jeremy, Babla, Rohan, Bharti, Ash, Cole, Stephen, Crotty, Stephen, Farrow, Amy, Golhar, Anil, Indoe, Peter, Jackson, Rosada, Latif, Mariam, Leong, Melvin, Lewith, Henry, Maton-Howarth, Matthew, Millar, Victoria, Millette, Ben, Oliver, Hannah, Ralhan, Vandita, Rashid, Yasir, Rivero-Bosch, Maria, Rizvi, Sana, Saleh, Rosahn, Venkatesh, Harisg, Wilson, Kate, Nair, Ashok, Basvi, Precious, Bell, Gillian, Edwards, James, Lee, David, Luo, Guanmei, Mewies, Clare, Qureshi, Arif, Sohail, Behzad, Valap, Sachin, Hill, Stephen, Bhuwanee, Deepti, Chan, James, Dereix, John, George, Allen, Ghafoor, Tasmeen, Gold, Emma, Haigh, Daniel, Harding, David, Janardhana, Bhavia, Jayan, Jithu, King, James, Laloë, Pnt, Pearce, Lauren, Pruthi, Amit, Roscoe, Eleanor, Seharawat, Deepak, Stubbington, Mark, Vanjari, Vinayak, Venkataraman, Anantharaman, Yen, Serena, Patil, Vishal, Chan, Charles, Davenport, Garry, Featherstone, Peter, Fourie, Petrus, Hall, Christopher, Mahidik, Preeti, Masoni, Elisa, Ortu, Andrea, Patil, Anita, Pickett, Janet, Sharma, Sandeep, Goodwin, Naomi, Allen, Lucy, Anderson, Nathan, Bateson, Ethan, Bhatia, Sanchita, Churchill, Sara, Cohen, Leon, Colhoun, Patrick, Dunn, Charlotte, Durie, Carys, Elgarf, Sarah, Ferry, Jenny, Guilford, Jeremy, Halligan, Claire, Harrison, Jade, Hayes, Dominic, Holst, Ben, Hughes, Rhys, Hunt, Thomas, Jackson, John, Kohler, George, Kriger, Michael, Kulikouskaya, Svetlana, Kurani, Amit, Maden, Charlotte, Moore, Alex, Mounce, Adam, O'Donnell, Alice, Sparrow, Robert, Sutherland, Jayne, Hanison, James, Bansal, Sujesh, Greenwood, Nick, Alkholany, Mahmoud, Apple, Zoe, Asif, Anam, Bhardwaj, Neeraj, Braid, Karl, Coates, Rebecca, Czukowska, Liliana, Elliott, Lauren, Fang, Li, Greenlee, Hannah, Maria, Jashmin, Mostafa, Tarek, Patil, Amarjeet, Weigert, Andrea, Matson, Alexandra, Adamson, Christine, Barclay, Philip, Barker, Amy, Bautista, Carina, Biswas, Suman, Bull, Rhian, Carungcong, Jaime, Christoper, Fenner, Cifre, Elna, Clancy, Olivia, Friedman, Lauren, Frunza, Gabriella, Habib, Sadia, Keerthikumar, Hindusha, Kirwan, Ursula, Laycock, Helen, Leir, Sarah, Maduekwe, Victor, Martella, Carmela, Meritahti, Minna, Nundlall, Kribashnie, Oblak, Metod, Panesar, Kirat, Peters, Timothy, Pett, Eleanor, Routley, Catriona, Samani, Amee, Santhirapala, Vatsharlan, Sayan, Amrinda, Sisson, Alice, Svensson, Marie-Louise, Thankachen, Mini, Tilley, Bernadette, Topham, Charlotte, Visvendra, Sathya, Warrington, Anna, Wibberley, Helen, Spittle, Nick, Andrews, Mia, Beavis, Sarah, Bernotaitis, Daiva, Chinna, Shalini, Church, Helen, Edwards, Sian, Ford, Nicky, Hazeldine, Sarah, Hudson, James, Macey, Claire, Morgans, Matthew, Toms, Julie, Whileman, Amanda, Woodward, Jan, Wright, Stephanie, Nariani, Jaya, Clark, Sam, Duniec, Larysa, Slorach, Marc, Allan, Ashley, Burrill, Bryony, Fairless, Lynn, Francis, Nicholas, McAdam, Colin, Sheriff, Julie, McLoughlin, Thomas, Arshed, Shayan, Bird, Clare, Campbell, Debbie, Chinery, Elaine, Onya, Enoch, Perry, Ryan, Sabugueiro, Jose Miguel, Shatanda, Lydia, Sheik, Muzaffar, Jo simpson, Wong, Gabriela, Yun-Han, Chen, Wilson, Lawrence, Adshead, Duncan, Castillo, David, Faulkner, Maria, Gibson, Kim, Keogh, Tara, Michlig, Sam, Perritt, Elizabeth, Ridler, Simon, Tizzard, Kate, Troy, Anne, Rafi, Amir, Melsom, Helen, Atkinson, Vikki, Azad, Tariq, Bashliyski, Vladimir, Brown, Ellen, Clark, Sarah, Cowton, Sister Amanda, Deane, Jill, Dent, Jean, Duckett, Karin, Duncan, Louise, Hooper, Nicholas, Johnson, Tom, Kay, Andrea, Kelly, Donna, Kent, Melanie, Knight, Jo, McCarroll, Christina, Newby, Annie, Potts, Kathryn, Roe, James, Rollins, David, Sarwar, Asia, Schofield, Lewis, Skelton, Deborah, Somanath, Sameer, Taysum, Peter, Temple, Julie, Udall, William, Varin, Jane, Venyo, Lucy, Warin, Martin, Wood, Shelly, Dhileepan, Srinivasan, Saibaba, Ravishankar Jakkala, Stewart, Emma, Warwick, Eleanor, Pemberton, Omar Alex, Garmash, Maryna, Griffiths, Rhys, Hale, Suzi, Lahloub, Fatma, Lynch, Ceri, Majeed, Abbas, Patchell, Ifan, Quinton, Jennifer, Sedghi, Babak, Wallis, Stephanie, Mansoor, Sange, Belagodu, Zakaulla, Dastur, Shirin, Fuller, Bridget, Hayes, Imogen, Keshkamat, Ashwini, Monantera, Georgia, Sandhar, Roxana, Sivakumaran, Shamini, Thomas, Megan, Torne, Manish, Turner, Sherma, Wakefield, Phillipa, Rogerson, David, Addleton, Catherine, Aju, Lisha, Appleby, Marie, Barnes, Elizabeth, Boateng, Trish, Carrick, Laura, Coulborn, Elaine, Darbyshire, Rebecca, Downes, Charlotte, Fitzgerald, Jodie, Hall, Linda, Harvey, Laura, Hassan, Amr, Holding, Kathleen, Hufton, Lianne, Humphries, Louise, Humphries, Ryan, Inman, Laurence, Issit, Nicole, Longhurst, Sarah, MacGregor, Melody, Mistry, Trusha, Mohamed, Mona, Otieno, Charlene, Read, David, Scott, Michelle, Singleton, Abbie, Smith, Jill, Sohal, Aariana, Tou, Samson, Unsworth, Vanessa, Wan, Richard, Worth, Christopher, Dobson, Richard, Buckley, Rose, Codling, Rachel, Hunter, Cain, Jackson, Jenny, Lowrie, Caroline, McMillan, Catherine, McNab, Raj, Richardson, Max, Sylvester, James, Watkin, Anna, Chambers, Jonathan, Bown, Rachel, Christie, Colin, Dukes, Stephanie, Horton, Sarah, Rees, Andrew, Williams, Patricia, Williams, Sarah, Pierson, Richard, Agarwal, Nipun, Allcock, Clare, Banga, Irmeet, Baron, Kirsty, Cole, Andrea, Dalay, Santinder, Gidda, Ranjit, Lo, Phillip, Merotra, Susan, Newton, Kathryn, Riddell, Zoe, Sonksen, Julian, Jamadarkhana, Sunil, Alcock, Chanice, Allen, Wei Lin, Baker, Pearl, Beadle, Hannah, Bramall, Jon, Chiravasuta, Radu, Chohan, Amina, Christofides, Angela, Cook, Angela, Cotta, Janet, Cruz, Carina, Gilmore, Jemma, Hills, Vicky, King, Kathryn, McClelland, Lucy, Nishimura, Sei, Nyathi, Rumyana, Panagoda, Prasan, Peacock, Louise, Shinn, Emma, Spencer, Sophie, Vianzon, Mayumi, Kapoor, Ritoo, Natarajan, Nagendra, Moore, Joanna, Amoakwa-Adu, Freda, Bahlool, Sinan, Cosier, Tracey, Driscoll, Sarah, Dulea, Claudia, Feneley, Andrew, Fletcher, Marcus, Hammad, Walid, Hazelton, Tracy, Hoyland, Kim, Katyayani, Bennur, Lloyd, Catherine, Moon, Angela, Musselwhite, Janine, Nair, Ganesh, O'Donnell, Joshua, Patankar, Pushkar, Redington, Katy, Sange, Aalia, Snazelle, Mark, Srbljak, Vanja, Stead, Richard, Tariq, Ismail, Thomas, Janine, Turney, Sharon, Chukkambotla, Srikanth, Mathew, Aji, Lie, Jason, Badsha, Zackriah, Bews, Sam, Bhandari, Kumud, Cartmell, Catherine, Cox, Greg, Desai, Hawa, Doran, Mark, Duberley, Stephen, Fitchett, Jill, Forrest, Diane, Freeman, David, Gani, Asif, Gedling, Catherine, Giles, Jessica, Goddard, Wendy, Gregson, Linda, Grimes, Yvonne, Hammond, Bev, Hartley, Janice, Hudson, Gemma, Ibrahim, Ramez, Iqbal, Nasreen, Jewers, Karen, Johnston, Brian, Kakkar, Abhishek, Koczka, Istvan, Lovell, Matt, Mahmoud, Mohamad, Melvin, Cathie, Milner, Matt, Moss, Nicky, Patel, Jeet, Perry, David, Pollard, Mike, Reed, Stephanie, Rennie, Gillian, Riley, Karen, Samuels, Sherrie, Simionescu, Ioana, Sissons, Lorna, Traynor, Stephen, Tucker, Daniel, Vetuz, Glenn, Whalley, Gillian, Marshall, Philippa, Arnold, Tim, Brookes, Jamie, Cowley, Anne, Delascasas, Ruth, Edmunds, Emma, Espitalier-Noel, Dominic, Jeevananthan, Rajeev, Law, Jenni, Mintrim, Kelly, Reid, Jack, Terry, Jackie, Verma, Manish, Zahra, Jean-Paul, Thorning, Geoff, Bailes, John, Dawson, Lindsay, Donovan, Clare, Gan, Jo Han, Jeyarajah, Kaya, Selman, Andrew, Woods, Justin, Csabi, Peter, Choo, Yin Yong, Das, Saheli, Denford, Clare, Desai, Mihir, Dimber, Rahul, Ellisy, Khaled, Griffiths, Ben, Lee, Sindy, Linnett, Vanessa, Daniels, Alex, Perkin, Sadie, Ritzema, Sister Jenny, Storey, Sister Bryony, Wood, Christine, Orme, Robert, Koh, Sock, Banks, Charis, Barker, Oliver, Beames, Susan, Brown, Aislinn, Corbett, Lucy, Dalton, James, Davis, Fiona, Edmunds, Matthew, Gatfield, Will, Gerrans, Edmund, Hickish, Tom, Hignell, Emily, Hiltout, Paula, Johnston, Helen, Miller, Sam, Mullender, Jo, O'Connell, Susan, Rosedale, Kath, Samuel, Katie, Vannahme, Milena, Baigel, Gary, Grayland, Sarah, Hawley, Victoria, McAffrey, Laura, Peggler, Suzannah, Stevens, Joe, Bidd, Heena, El-Boghdadly, Kariem, Zucco, Liana, Adelaja, Yemi, Bhattacharyya, Sanjoy, Bickmore, Elsie, Body, Suzanne, May-Ling Cheung, Jenny, Chowdhury, Priaykam, Cuddihy, Joshua, Ekwere, Ndi, John-Baptiste, Ange Lise, Kelly, Paul, Leslie, Daniel, Mon, Wint, Muzengi, Natasha, Ong, Cheng, Pattni, Nisha, Potter, Thomas, Stanton, Elena, Zaky, Maryam, Partridge, Richard, Bromhead, Helen, Evans, Peter, Griffin, Denise, Kinsella, J. Joseph, Knight, Alexander, Lewis, Rosemary Anna, Lightfoot, Heidi, Martin, Jane, Mupudzi, McDonald, Nahajski, Katherine, Oladele, Segun, Ottaway, Julia, Petley, Stephen, Sheldrake, Ian, Stewart, Alexander Michael, Stokes, Aaron, Trodd, Dawn, Varsani, Nimu, Walshe, Eleanor, Brown, Caroline Wrey, Huntley, Martin, Baldry, Natalie, Bennett, Caroline, Earl, Sophie, Guy, Joyce, Peat, Maggie, Stephenson, Lorraine, Tan, Hao Ern, Wills, Louise, Yare, Michelle, Yeung, Joyce, Antrolikar, Supriya, Atterbury, Katie, Baba, Fayaz, Bancroft, Hollie, Begum, Safia, Bellamy, Mary, Couper, Keith, Evans, Lucy, Flight, Rachel, Frankling, Catriona, Ganesh, Srikant, Gresty, Joanne, Joslyn, Ruth, Kadiri, Salma, Karimi, Aalisha Mariam, Kerr, Amy, Melody, Teresa, Moore, Faye, Morrish, Jennifer, O'Sullivan, Mary, Patel, Vanisha, Reeves, Eleanor, Robley, Richard, Sampson, Julia, Sangombe, Mirriam, Shabangu, Vusumuzi, Sheppard, Lucy, Stafford, Sam, Sutton, Peter, Taylor, Joanne, Turner, James, Velho, Rochelle, Webb, Joanne, Webber, Linda, Werpachowska, Ewa, Wilkinson-Guy, Lisa, Papageorgiou, Constandinos, Bisonoothan, Sohan, Casely, Emma, Casey, Carina, Kam, Elisa, Landers, Geraldine, Pearse, Laura, Raja, Meera, Solomon, Daniel, Stoyanov, Plamen, Talati, Chiraag, Arunachalam, Hari, Ashton, Victoria, Full, Jens, Green, Samira, Gratrix, Andrew, Abernethy, Caroline, Clarke, Abigail, Desai, Pallavbhai, Martinson, Victoria, Parekh, Ravi, Phillips, Mirain, Raina, Anju, Raut, Sarah, Smales, Chris, Smith, Neil, Ugochukwu, Ruth, Willis, Matt, Ignatova, Zhana, Gill, Stuart, Agarwal, Sunita, Barclay, Harry, Campbell, Brian, Collingborn, Maggie, Dagnan, Richard, Davies, David Steven, Dhaliwal, Randeep, Dharmarathna, Ishan, Doriaswami, Yuvraj, Evans, Hywel, Foly, Ahmed, Francis, John, Green, Tim, Hassanin, Ahmed, Havalda, Peter, Huda, Ahmad, Jones, Beryl, Khoju, Ramesh, Lackmann, Maria, Mensah, Kofi, Nwaefulu, Christopher, Schmidt, Ilona, Selvraj, Gopinath, Skarbit, Norbert, Suganthi, Obla, Zeber, Jacek, Nagaratnam, Vidhya, Mukherjee, Smitangshu, Kirby, Sue, Arnold, Glenn, Bendel, Daniel, Boynton, Claire, Carvell, James, Carver, Anthony, Clayton, Matt, Desai, Keyury, Fadhlillah, Fiqry, Georgiou, Christiana, Jayakumar, Vineetha, Jeanes, Lara, Jhugursing, Mhairi, Jose, Jasmine, Karsten, Emma, Kormendy, Flora, Lewis, Stephanie, Lloyd, Dafydd, Mautadin, Francisca, McCahill, Claire, McInerney, Susan, Morland, Laura, Morsman, Elaine, Nayee, Sunny, O'Callaghan, David, Pickford, Emily, Pratap, Bhavesh, Rao, Chandana, Shah, Alka, Smith, Stephen, Smyth, Claire, Souleimanova, Icel, Templeton, Maie, Upadhyaya, Kavita, Uren, Sophie, Voralia, Shree, Wilson, Rebecca, Kushakavsky, Vlad, Ang, Justin, Beeby, Debroah, Bell, Stephanie, Boys, Helen, Brixey, Sue, Chabo, Cathleen, Cubitt, Lynsey, Dawson, Maggie, Finch, Jenny, Lewis, Robert, Purewal, Bally, Pyae, Kaung, Ramsey, Judi, Scoones, Ben, Debreceni, Gabor, Tarin, Obaid, Zondo, Nicola, Bothma, Pieter, Carmichael, Jack, Ferreira, Teresa, Goffin, Kelly, Gurung, Sarada, Kundu, Ashish, MacLennan, Sarah, Murray, Theresa, Verma, Karan, Wilson, Graeme, Winthein, Aleinmar, Jakkampudi, Satyanarayan, Ahmed, Muneeba, Baines, Duncan, Beirnes, Charmaine, Braybrook, Chrissy, Fernandez, Olga, Gracey, Jayshree, Guha, Snehasish, Jain, Arihant, Krishnaiyan, Raghavendran, Moody, Thomas, Novaga, Jo, Tait, Frances, Tate, Jemma, Taylor, Jennifer Claire, Waldron, Molly, White, Sonia, Gupta, Anish, Botfield, Claire, Ahmed, Mohamed, Ashton, Lizzie, Bailey, Andrew Robert, Banugo, Pele, Birch, Sian, Boyle, Henry, Brown, Alice, Chen, Mark, Connolly, Lucy, Dalal, Viral, Edwards, Alexandra, Elnour, Shaima, Faccini, Timothy, Floyd, Theodore, Georgiou, Thomas, Gill, James, Hedley, Russell, Hopkins, Philip, Kumar, Roshmi, Michell, Alice, O'Shaughnessy, Rhiann Marie, O'Sullivan, Laura, Pai, Seema, Parsons, Benjamin, Peltola, Laura, Penhaligan, Beth, Place, Merate, Ramsden, Jonathan, Ratnayake, Gamunu, Shammas, Kathy, Watts, Ali, Williams, Thomas, Woodham, Valantine, Puranik, Sarang, Crooks, Jennifer, Dean, Ania, Honstvet, Chris, Hunter, Katherine, Steynberg, Sarah, Swain, Andrew, Laha, Shondipon, Bonner, Alex, Doyle, Donna, Fallon, Robert, Verlander, Mark, Williams, Alexandra, Yardley, Richard, Whiteley, Simon, Ahmed, Zaid, Child, Ossian Aukland, Black, Sheila, Calderwood, Jamie, Chapman, Andy, Collin, Robert, Dwornik, Kinga, Gallagher, Kate, Gardener, Michelle, Harrop, Gareth, Hart, Peter, Howcroft, Clare, Ilyas, Carl, Kempanna, Umakanth, Krishnamurthy, Gnanshree, Kumara, Paramesh, Loh, LI Lian, Morgan, James, Pipe, Jonathan, Sathiapillai, Anand, Stones, John, Wilby, Elizabeth, Shephard, Buzz, Agarwal, Manju, Bolonenkova, Tatyana, Dunlop, Lisa, Duraisamy, Karthick, Kelly, Jenna, Nurmi, Eveliina, Patel, Mitul, Perry, Jonathan, Prior, Charles, Reece-Anthony, Rosie, Rose, Bernd Oliver, Rowland, Katie, Van Ross, Jennifer, McNamara, Helen, Permall, Natasha, Robertson, Gillian, Siew, Sarah, Smith, Amy, Spearritt, Sue, Verma, Dipali, Williams, Rhys, Zaidi, Suhail, Clayton, Samantha, Goddin, James, Joy, Stuart, Malhotra, Vikram, Mindel, Samuel, Neilson, Zoe, Wijesuria, Shanelle Tharuka, Wilkins, Ryan, Mukherjee, Kirtida, Bradley, Andrew, Coetzee, Samantha, Crook, Alexandra, Emeakaroha, Eunice, Frenkiel, Marek, Hatton, Tom, Kamraj, Kootharajan, Long, Natalie, Manikundalam, Badrinath, Peiris, Muditha, Roche, Matthew, Sandhu, Banher, White, Nichola, Woods, Annette, Yarnold, Adam, Price, Nicolas, Ashe, Marion, Disney, Tom, Dixon, Caroline, Dodds, Emily, El-Sheika, Sarah, Hameed, Yasir, Jones, Rob, Kanal, Karan, Ranaweera, Sanjeewa, Ripoll, Simon, Russell, Nicki, Smith, Sally Anne, Tung, Swee Ang, Emerson, Bruce, Apps, Victoria, Austin, Mandy, Catchpole, Laura, Catley, Christine, Child, Jenny, Cranmer, Karen, Dawson, Lizzie, Farr, Beth, Gerrish, Helen, Gibson, Sian, Gunn, Jacqueline, Holland, Kim, James, Lorri, Jayasekera, Dushyanthi, Lester, Yvonne, Lyle, Amanda, McNeela, Fiona, Oborska, Aneta, Pennington, James, Perkins, Lauren, Robinson-Perrie, Hollie, Shillito, Lauren, Singizi, Bryan, Smolen, Susan, Topliffe, Joanne, Vowles, Ben, Whybro, Natalie, Wijunamai, Amon, Willsher, Lucy, Wootton, Joanne, Varma, Sandeep, Atkinson, Philip, Buckley, Sarah, Darian, Thelma, Dhamaratne, Sachini, Gould, Richard, Graham, Laura, Hirst, Charlotte, Peeling, Andrew, Prentice, Saxon, Rajasekar, Naresh, Rose, Alastair, Smith, Craig, Wilkinson, Kate, Stewart, Richard, Chevis, Chrissie, Ekpa, John, Greig, Nurse Sara, Hinch, Nurse Rebecca, Holder, Carole, Makris, Nikolaos, Moran, Nurse Louise, Padilla Harris, Nurse Cheryl, Scarletta, Nurse Diane, Smith, Jeanette, Sara-Beth Sutherland, Nurse, Turner, Joanne, Valentine, Peter, van Manen, Amelia, Walia, Nurse Sonia, White, Nurse Charlotte, Wilson, Georgina, Wren, Nurse Lynn, Wright, Nurse Francesca, Faulds, Matthew, Agius, Claire-Marie, Ahmed, Raheel, Ahmed, Sameer, Alva, Arjun, Bird, Rachael, Buckley, David, Calder, Verity, Clark, Caroline, Craven, Emily, Cullen, Katherine, Davis, John, Diamond-Fox, Sadie, Doherty, Helen, Dutta, Bhaskar, Fortune, Jonathan, Gatehouse, Alexandra, Gibson, Jane, Hay, David, Hillier, Steve, Hindmarch, Paul, James, Michael, Keshvara, Kiran, Kotur, Karuna, Lawley, Peter, Lochanie, Nimali, Macbeth, Lisa, Madhu, Yamuna, Marcangelo, Sam, McShane, Lesley, Morgan, Amy, Mowat, Stephen, Nelson, Joshua, O'Leary, Denis, Partridge, Jennifer, Pearson, Janaki, Poulton, Tom, Prasad, Sneha, Protheroe, Kay, Qureshi, Adnaan, Rangaswamy, Girish, Richardson, Victoria, Scott, Carmen, Smallshaw, Karen, Soliman, Abdelrahman, Soulsby, Graham, Wahed, Anna, Walker, Iain, Welch, Sarah, Wilson, Jessica, Winkley, Elaine, Womack, Jonathan, Mitchell, Joellene, O'Brien, Peter, Callaghan, Debbie, Clarke, Ruth, Foggo, Graeme, Gilmour, Danielle, Hickey, Stephen, Hii, Stephanie, Livingstone, Susan, McAtear, Megan, Meehan, Sharon, Milne, Gemma, Small, James, Williams, Dewi, Jardine, Catherine, White, Graham, Wright, David, Hunter, Katie, Fleming, Gillian, Marc Hastie, P.A.-A., Hunter, Catherine, Lockhart, Jennifer, Reid, Kirsten, Robb, Hilary, Harten, Johann, Crooks, Barbara A., Ratnasabapathy, Urmila, Staber, Manfred, Pettigrew, Tom, Varveris, Daphne, Brett, Michael, Appleton, Richard, Briskoe, Michael, Brown, Graeme, Brown, Stewart, Christie, Fiona, Coady, Guy, Cowan, Richard, Drysdale, Alice, D'Sylva, Stewart, Edgar, Paul, Finnie, Graeme, Gardner, John, Gupta, Shub, Hall, Colin, Hamilton, Jane, Hunter, Laura, Ikram, Umairali, Imrie, Gregor, Kerr, Euan, Kinsella, Michael, Kommer, Maya, Lafferty, Brian, Lochrin, Christopher, Lovejoy, Tom, Loy, Maria, MacLean, Aillison, MacPherson, David, Marshall, Cara, Mawhinney, Ruth, McAteer, Claire, McCaul, Prof James, McCusker, Michael, McDowall, Katherine, McGovern, Christopher, Mckie, Stewart, Morrison, Melanie, Mulla, Suleman, Neale, Elizabeth, Niciu, Cristina, Ong, Su Ying, O'Sullivan, Finbar, Parcell, Sophie, Ramsay, Sarah, Robson, Rebecca, Roy, Nithin, Samuel, Jasmine, Scotland, Gemma, Sharma, Natasha, Slade, Kate, Soosay, Alvin, Soulsby, Charlotte, Steuart, Lois, Stevenson, Sara, Sweeney, Laura, Tan, Kerwei, Taylor, Calum, Th'ng, Francesca, Thompson, Caroline, Wardlow, James, Watson, Martin, Williams, Robin, Woodier, James, Young, Simon, Robinson, David, Howes, Jacqueline, Berwick, Scott, Boyle, Andrew, Brown, Neil, Celnik, Anna, Coleman, Alistair, Elwkhiee, Mohamed, Fernandes, Mario, Hanlon, Katie, MacLean, Sarah, Morgan, Rhian, Edgar, Jonathan, Chapman, Roddy, Razouk, Khaled, Atrah, Ali, Bhat, Sunny, Bramma, Yvonne, Campbell, Euan, Campbell, Rachel, Cormack, Anna, Dalchow, Stephan, Dodd, Ammy, Ferguson, Laura, Ferraioli, Donna, Hynd, Moyra, Janeczko, Adam, Jeffrey, Sally, Kinsey, Tony, Kummur, Yuvaraj, McIntosh, Karen, McKenna, John, Mclaughlin, Maria, Millington, Ryhs, Murphy, Emma, Parker, Kris, Reevell, Tom, Robertson, Fiona, Shankey-Smith, Will, Slorach, Colum, Szelei, Tamas, Warnock, Geoff, Watson, Stuart, Alston, Peter, Geddes, Murray, McLellan, Stuart, Allen, Eireann, Birnie, Karen, Brown, Kirsteen, Critchley, Julia, Cullen, Margaret, Eros, Alex, Forbes, Callum, French, Helen, Hall, David, Hetherington, Jonathan, Kellner, Maximiliane, Kelly, Keith, Khaled, Hiba, Mantle, Damien, McLenachan, John, Midgley, Susan, Nimmo, Susan, Ordys, Bart, Phelan, Hannah, Roddam, Philip, Shaw, Louise, Smith, Carolyn, Steel, Elizabeth, Stuart, Robert, Sturrock, John, Waite, Alasdair, Waterson, Fraser, Watson, Hannah, Wood, Elizabeth, Schraag, Stefan, Boyd, Elizabeth, Jain, Surabhi, Matthews, Elaine, Rappai, Geejo, Barr, Catriona, Stewart, Michael, Christie, Sharon, Smith, Tim, Arrow, Kate, Austin, Pauline, Chapman, Susie, Little, Jonathan, Malein, William, Sadler, Amy, Younger, Rhona, Reavley, Caroline, Allan, Katy, Ashok, Vignesh, Boroojeny, Ayda Borjian, Cutts, John-Paul, Faulds, Fiona, Fernie, Mark, Gadre, Amit, Gately, Tom, Haddon, Richard, Induruwage, Lalani, King, Siobhan, Liddle, Sian, Mallett, Paul, Mehotra, Saurabh, Ali, Inthekab Mohammed, Morris, Rachel, Nunn, David, Ferraro, Rocio Ochoa, Padhi, Pulak, Prusack, Ewa, Quak, Edmund, Singleton, Georgina, So, Kin, Stolady, Daniel, Welch, Drew, Whitear, Michael, Wilkinson, Kathy, Wu, James, Yusaf, Alex, Kakodkar, Prashant, Ghoneim, Hoda Abou, Balliston, Sara, Dudgeon, Lucy, Idris, Muna Elsheikh, Karattuparambil, Juneenath, Nanda, Lohita, Raithatha, Bhavesh, Saad, Mahmood, Spimpolo, Jenny, Titinchi, Linda, Wilson, Julie, Winstone, Ulrika, Martindale, Sarah, Cameron, Ewen, Fenner, Lynn, Hartford-Beynon, Jake, Mooney, Iain, Mundy, Charlotte, Rhidian, Rhys, Scutt, Sophie, Todd, Anna, Graham, Fiona, Ashton, Amy, Cooper, Theresa, Davidson, Katherine, Eapen, Suresh, Crowther, Hannah, Hindmoor, Claire, McSkeane, Anna, Mostafa, Mahamed, Mudimadagu, Gururaj, Rice, Polly, Wilson, Antoinette, Henry, Garry, Hollister, Nigel, Hunt, Jane, Isaac, Amanda, Morris, David, Paredes-Guerra, Antonio, Pass, Katherine, Paul, Martin, Rait, Kiran, Rousseau, Guy, Shacklock, Emma, Skinner, Amanda, Stancombe, Lucia, Thomson, Laura, Nutt, Christopher, Wright, Geoffrey, Holland, Johnny, O'Neill, Orla, Sengottaiyans, Arun, Mitra, Atideb, Garg, Sanjeev, Adams, Jonathan, Aiyedun, Temitope, Ali, Hani, Biuk, Sahar, Bond, Bryany, Cheeseman, Michelle, Clayton, Mark, Cottam, Lisa-Jayne, Dent, Kathy, Evans, Elizabeth, Evans, Sandra, Goodhand, Hannah, Green, Karen, Hatton, Jonathan, Hill, Joanne, Hutchinson, Dorothy, Jacob, Shibu, Joseph, Renjith, MacNally, Laura, Mann, Jason, McTaggart, Virginia, Newton, Maria, Pearson, Sandra, Rayet, Jaspreet, Saunders, Rebecca, Saxena, Sandeep, Venkatesh, Hemamangala, Whotton, Nikita, Ezihe-Ejiofor, Ada, Barot, Hemangini, Bridger, Hayley, Elhallous, Shady, Ghafoor, Rizana, Manhas, Vitul, Patil, Tanmay, Sadavarte, Nitin, Tharmalingam, Bhamini, Pritchard, David, Bage, Jean, Higgin, Sarah, Krishnaier, Asokan, Neilly, Esther, Rayappu, Sean, Siaw, Chye, Songaile, Ilma, Standen, Peter, Suresh, Narayanan, Weston, William, Williams, Lynne, Yuchen, Jiang, Alagar, Ravi, Taylor, Adrian, Matata, Claire, McCrossan, Roopa, Robertson, Andrew, Shekar, Priya, Stoker, Wendy, Turnbull, Elizabeth, Watkinson, Clare, Worthy, Stephen, Yates, Bryan, Ignatov, Dancho, Davies, Matthew, Abdu, Ayman, Banni, Donata, Bowyer, Helen, Brooks, David, Buhmann, Ulf, Christenssen, Victoria, Collins, James, D'Sa, Aaron, Forde, Steven, Groves, Alice, Hosahalli, Jyothi, Hunt, Lauren, Koshy, George, Lee, Robyn, Martin, Susan, McKnight, Angus, Murphy, Piers, Nash, Henry, Nkanang, Bassey, Oracki, Pamela, Pauley, Tara, Pope, Alan, Samanta, Romit, Sgardelis, Panagiotis, Stocks, Tom, Vaitheeswaran, Sivaprakash, Verma, Priya, Vrazhalska, Madlena Ivanova, Weisz, Mike, Moppett, Iain, Arunachalam, Subha, Begum, Zorba, Bhangu, Sonia, Bown, Gemma, Chana, Avninder, Chhaniyara, Puja, Constable, Natalie, Delme, Luff, Fenner, Helen, Griffith, Oliver, Hackett, Stephen, Hewson, David, Inkpin, Kristofor, Jones, Richard, Kirkdale, Richard, Lyons, Ian, Mandal, Arnab, Mehrotra, Prerna, Murphy, Martin, Pearce-Smith, Alison, Priestley, Martin, Pyke, Shirley, Rasoli, Sonia, Sharma, Suneal, Squires, Gemma, Tsim, Peter, Veitch, Jonathan, Bond-Smith, Giles, Kay, Olivia, Siviter, Richard, Allen, John, Baxter, Linden, Beer, Sally, Bernardo, Mariana, Coles, Holly, Cudlip, Jo, Devries, Talitha, Duggleby, Phil, Espinosa, Alexis, Fitzgibbon, Karina, Galente, Joao, Georgiou, Domonique, Ghosh, Soumi, Hingley, Nicholas, Kingan, Ryan, Levett, Chris, Martinez, Jose, Mattin, Alex, Moore, Anna, Morrish, Samuel, Nash, Amy, Patel, Tejuswi, Roe, Cieron, Thwaites, Victoria, Wali, Anuj, Watterson, Dafydd, Charlotte Yates, Anna Wilson, Gimenez, Marc, Boltres, Anita, McClean, Rebecca, Papadopoullos, Sam, Teh, Elena, Webb, Stephen, Brammar, Andrew, Humphreys, Joanne, Alibhai, Moiz, Alston, Daisy, Cohen, Jolyon, Coleman, Laura, Davies, Simon, Dervin, Dionne, Dooley, Rebecca, Drummond, Andrew, Findlay, Douglas, Fleming, Naomi, Hammoud, Yazzim, Hussain, Sanniah, Irwin, Katy, Javed, Asad, Kent, Linda, Lock, Lorraine, McSorland, Denise, Meadows, Alison, Mulligan, Paula, Nichols, Wendy, O'Callaghan, Maria, Ogor, Jonathan, Poole, Joanna, Ryder, Geoffrey, Shakoor, Akmal, Teo, Wei, Minto, Gary, Adshead, Stephen, Anderson, Helen, Carpenter, Adam, Clelland, Stuart, Datar, Priya, Elloway, Esme, Holl, Matt, Hulbert, Aidan, Humphreys, James, Hyams, Ben, McAleer, Sam, Newmarch, Mary, Notman, Holly, Patrick, Abigail, Perham, Anna, Pitman, Catherine, Reed, Fiona, Sampson, Paul, Smith, Hannah, Spinney, Sam, Toms, Nina, Warrener, Tim, Girgis, Michael, Barcraft-Barnes, Helena, Camsooksai, Julie, Colvin, Carolyn, Davies, Jack, Hamilton, Alex, Linton, Fiona, Nussbaum, Greg, Patch, Sarah, Simpson, Diane, Tbaily, Lee, Bain, James, A'Court, Alicja, Barr, Emma, Blethyn, Kate, Craig, Thomas, Cutler, Hugh, Davies, Jenny, Fry, Rebecca, Hawes, Elizabeth, Jenkins, Nicholas, Knight, Martin, Restall, David, Stubbs, Matthew, Sutherland, Angus, Swarbrick, Claire, Wilson, Robin, Winwright, Paul, Hamilton, Kevin, Adams, Anne, Allen, Orlanda, Boampomaa, Marilyn, Boniface, Anna-Marie, Coe, Robert, Daniel, Amelia, Finn, Joanne, Gill, Issac, Griffiths, Huw, Hackney, Joanna, Han, Ruth, Hare, Edward, Rintoul, Edward, Shpuza, Ervin, Smith, Ruth, Staines, Nikki, Suppiah, Puvan, White, Tracey, Pearson, Darcy, Cracknell, Ben, Farrell, Nadine, Hermanowski, Jane, Hodgson, Ruth, Hoskins, Toby, Jones, Rhiannon, Keable, Sam, Rosbergen, Melissa, Giles, Julian, Dawes, Thomas, Green, Ben Eden, Pottinger, Gail, Weller, Debbie, John, John, Burgess, Lisa, Denton, Jean, Done, Alaine, Edwards, Jayne, Garrett, Theresa, Gilla, Rajesh, Griffith-Norris, Samantha, Jones, Teresa, Linklater-Jones, Barbara, Nicholas, Claire, Owen, Sara, Perkins, Charlotte, Rowlands, Tessa, Steen, Julie, Turner, Sarah, Wales, Johanna, Woolley, Carol-Ann, Hormis, Anil, Collier, Dawn, Dixon, Kathryn, Gardner, Michael, Murray, Vicky, Soar, Harry, Walker, Rachel, Yaruk, Shifa, Yarwood, Daniel, Dill-Russell, Patrick, ruthy Arumugam, Bird, Georgina, Bleasby, Nichola, Chater-Lea, Peter, Craig, Emma, Davies, George, Foxton, Julie, Gilfillan, Helen, Jerram, Keelan, Kudsk-Iversen, Søren, Lee, Rebecca, Livesy, John, Mohabir, Amanda, Morris, Katherine, Raffles, Mike, Smith, Annika, Wang, Huiqi, Craig, James, Barratt, Nina, Bates, Andy, Bokor, Norbert, Cranshaw, Julius, Dominguez, Sergio, Fraine, Annette, Green, Richard, Molloy, Katie, Penny, Christina, Pitts, Sally, Purandare, Laura, Rabbi, Taslima, Serrano, Monica, Stoycheva, Tina, Vamplew, Luke, Williams, Steve, Berry, Julian, Adams, Benita, Bean, Sarah, Burt, Karen, Devooght-Johnson, Nicki, Hammonds, Fiona, Nicholls, Tom, Rowe, Michelle, Summers, Jessica, Ward, Olivia, Wroath, Belinda, Young, Gabbie, Matthews, Cathryn, Crosbie, Gearoid, Dunn, Alex, Eddy, Sara, Garcia, Lynda, Garwood, James, Green, Adam, Gupwell, Karin, Jones, Nicola, Marriott, Suzie, Masood, Mohammad, Mazzola, Francesca, McEvoy, Evanna, Ng, Yang, Park, Linda, Pauling, Stephanie, Potter, Alison, Renton, Caroline, Samways, Adam, Sibley, Pauline, Stead, Jenny, Thorpe, Kevin, Turner, Elizabeth, Walter, Michelle, Windsor, Kevin, Jha, Rajeev, Hooker, Nicolas, Kidel, Carlos, Abayalingam, Mayavan, Boulding, Richard, Coakley, Philip, Elahi, Noor, Helder, Filipe, Hilton, James, Joshi, Kunal, Kaur, Rupinder, Male, Victoria, Naji, Saqib, Pinto, Manuel, Rajendran, Yohinee, Save, Manjeet, Smith, Megan, Spence, Emily, Tang, Sophie, Urwin, Thomas, Wittenberg, Marc, Yan, Ruoling, Williams, Colin, Ardeshna, Arjun, Bamford, Peter, Hagyard, Sarah, Johnson, Patrick, McMonagle, Martina, Morris, Hayleigh, Murphy, Thomas, Papadopoulos, Stavros, Simpson, Emma, Smith-Williams, Jonathan, Vyapury, Vin, Juneja, Rohit, Black, Ethel, Eeles, Alex, Elliot, Justine, Jhanji, Shaman, Knoetze, Reynard, McCanny, Jamie, Oswald, Lauren, Roderick, Ellie, Gunning, Paul, Siddaiah, Narendra, Balachandar, Krishna, Bhimarasetty, Chandra, Bird, Laura, Blunt, Conny, Da Silva, Egidio, Darbyshire, Mark, Echaveznaguicni, Irene, Girgis, Youssef, Goodman, Martin, Gowda, Dinkar, Gowni, Rajashekar, Hayes, Melony, Jackson, Natalie, James, Anitha, Jayapal, Rekha, Jones, Claudette, Leung, Enid, Minhas, Simran, Mubarak, Mona, Muniyappa, Sudeshkumar, Nassa, Zehrin, Neal, Tom, Nyangoni, Kudakwashe, Panchakshariah, Suresh, Philpott, Bethany, Pinner, Craig, Pygall, Daniel, Rangaiah, Manamohan, Rao, Anil, Rea, William, Shellard, Richard, Shinner, Guy, Sutherland, Tony, Tharakan, Liza, Thomas, Sue, Travasso, Betty, Venkatesh, Pyda, Wortley, Dionne, Dickinson, Matthew, Benoliel, Sophie, Doyle, Nanci, Marriott, Cheryl, Martin, Sarah, Montague, Laura, Penhaligon, Kate, Saha, Rita, Santhirapala, Ramai, Suntharanathan, Lushani, Wakeford, Nichola, Webb, Carly, Winterbottom, Toby, Jordan, Lesley, Aldridge, Matt, Bolton-Hill, Clare, Cook, Prof Tim, Kangesan, Inthu, Kettley, Laura, Mew, Edward, Penketh, James, Quinn, Eleonore, Scholes, Sam, Uppugonduri, Sreekanth Rayalu, Allen, Lara, Lyndsay Bibb, Jill Brown, Busby, Charlotte, Katherine Cheshire, Davis, Matron Beryl, Denyer, Rebecca, Julie Edwards, Gardiner, Laura, Haque, Shamim, Icke, Julia, Jones, Katherine, Donna McIntosh, Mandy Oakley, Prasad, Linda, Victoria Roberts, Smallwood, Andrew, Tammy Smith, Stelfox, Lucy, Angela Stevens, Angela Willberry, Pratt, Oliver, Chincholkar, Mahindra, Darwin, Leanne, Dearden, Joy, Flowerdew, Adele, Harrison, Louise, Hartley, Robert, Ingham, Andrea, Jones, Ben, Lee, Stephanie, Longdon, Elizabeth, Marsden, Tracy, Meats, Kirsty, Michael, Angiy, Norwood-Green, Drew, O'Loughlin, Victoria, Owen, Holly, Patel, Manju, Perez, Jane, Whiston, Jessica, Wood, Andrea, Williams, Simon, Billingham, Chloe, Clarke, Caroline, Holmwood, Xantha, Knighton, Melba, Sands, Becky, Stevens, Paul, Strong-Sheldrake, Sophia, Taylor, Matthew, Rambhatla, Mrutyunjaya Rao, Brodier, Elizabeth, Choudhry, Ozerah, Choudhry, Usman, Fitzmaurice, Bethany, Hulme, Jonathan, Kannan, Santhana, Mak, Kay Anne, Namih, Miriam, Rajdev, Bharati, Rambhatla, Swetha, Reilley, Stuart, Rice, Rebecca, Sethia, Nalini, Smith, Katy, Wakhle, Rahul, Walmsley, Chris, Wrench, Ian, Agyemang, Michael, Bell, Elizabeth, Bird, Sarah, Briscoe, James, Cruickshanks, Andy, Drinkwater, Jake, Eggleston, Sam, Elms, Henry, Hammon, Laura, Harris, Meredith, Harrison, Christopher, Hirst, Claire, Housley, Kay, Kapoor, Krish, Kibutu, Faith, Kilner, Beverly, King, Emily, Kirk, Emily, Marshall, Esme, Mazzone, Paolo, Middle, Janet, Milner, Leanne, Mitchell, Adam, Morris, Becky, Mudd, Alexandra, Needham, Matthew, Nicholas, James, Penson, Rob, Perfitt, Joel, Pinkerton, Mark, Recto, Mayeth, Rehnstrom, Maria, Riley, Catherine, Ryan, Lorna, Smith, Jennie, Strong, Samantha, Thornley, Helen, Tookey, Bethany, West, Daniel, Whitaker, John, Willcock, Sarah, Willoughby, Shay, Wright, Jane, Jones, Paul, Abbott, Riquella, Bacon, Lisa, Godden, Christopher, Howe, David, Loughlin, Angela, Moore, Helen, Passey, Samuel, Rodgers, Rebekah, Foley, Paul, Hagan, Samantha, Marcus, Simon, Gardner, Irene, Haslam, Zena, Morrison, Moira, Obeysekera, Chai, Porter, Kim, Terrett, Helen, Henning, Jeremy, Baillie, Kirsty, Buchanan, Ella, Cain, Martyn, Colling, Kerry, Duarte, Raquel, Gibb, Chris, Hynd, Ryan, Irvine, Victoria, Lyons, Rob, Middleton, Karoline, Razaque, Usman, Sheikh, Faisal, Tabiner, Tariq, Frey, Christian, Brown, Carly, Clifford, Gayle, McKee, Madeleine, Stidolph, Beverley, White, Emert, Campbell, Bridget, Mason, Sophie, Parsons, Penny, Santana-Vaz, Natasha, Stagg, Camilla, Shah, Nirav, Barnes, Veronica, Bedoes, Penny, Clarke, Jonathan, Crichton, Robert, Das, Madhurima, Davies, Paul, Farrah, Helen, Hegarty, Aoife, Howlett, Luisa, Kleine-Brueggeney, Maren, Ma, Samson, Marsh, Veronica, McAndrew, Katherine, Mellinghoff, Johannes, Oscier, Christopher, Ryan, Christine, Smee, Elizabeth, Thurairasa, Dushanthi, Goel, Vandana, Adeney, Christopher, Behl, Sanjay, Chadwick, Karen, Coleman, Thomas, Davies, Claire, Dealing, Sharon, Elwood, Jamie, Fadden, Emma, Gillies, Stella, Kennedy, Tom, Miller, Thomas, Morton, Simon, Reilly, Rebecca, Smyth, Sue, Syratt, Thomas, Welfare, Emma, Thomas, Elizabeth, Awolesi, Jennifer, Bennett, Hannah, Bradshaw, Elizabeth, Crabtree, David, Duraman, Lauren, Hughes, Kerry, Kirk, Sarah, Leonard, Dee, Stoeter, Rachel, Parrington, Simon, Andorka, Matyas, Ang, Kingsin, Bailey, Linda, Brooks, Stephanie, Chiu, Oon, Collins, Sally, Jack, James, Knight, Harry, Lynn-Smith, Anastasia, Mackenzie, Matt, Maher, Sarah, Nimako, Louise, Sangaralingham, Siva, Sanusi, Seliat, Weller, Samantha, Willard, Elizabeth, Kulkarni, Anand, Carroll, Peter, Clark, Martyn, Coulding, Martina, Denman, Elizabeth, El Habishi, Mohyman, Haywood, Patrick, McConniffe, Jane, McCormick, Jacqueline, Melachuri, Krishnakar, Philip, Evelyn, Ridgway, Stephanie, Savill, Heather, Sharif, Kamal, Vere, Joanne, Harris, Stephen, Coombs, Alex, Darch, Flora, Doble, Patricia, Foot, Jayne, Gillett, Chris, Judd, Thomas, Moss, Alison, Pawley, Corinne, Tait, Moira, Whitcher, Alison, Wood, Natalie, Montgomery, Jane, De Selincourt, Gabrielle, Fitzell, Pauline, Flower, Katie, Horan, Kathleen, Hulance, Bianca, Mercer, Pauline, Shaw, Sarah, Webber, Robin, Kakkar, Manish, Joachim, Suganthi, Aggarwal, Seetal, Ali, Amr, Ang, Boon, Bhardwa, Vishnu, Deamer, Wendy, Filby, Lorna, Fletcher, Jo, Fombon, Felix, Gawley, Shannon, Gosavi, Chandrakant, Hindle, Julia, Lawrence, Tara, Maher, Robert, Moore, Samantha, Netherton, Kimberley, Orasanu, Teodora, Poimenidi, Evangelia, Potter, Louise, Ranasinghe, Niyesa, Ribeiro, Lester, Ruth, Thoy, Butler, Susie, Sutton, Tim, Thomas, Issy, Veluvolu, Divya, Walker, Thomas, Wariyapola, Charindri, Zaheer, Lail, Luoma, Valpuri, Ferguson, Catriona, Stewart, Adrienne, Bapat, Smita, Bose, Gahan, Bugo, John, Campbell, Marta, Cervi, Elizabeth, Ciechanowicz, Sarah, Coathup, Rachel, El-Wahab, Niveen, Frith, Claire, Gill, Preea, Gray, Martin, Luoma, Astri, Mehta, Sachin, Murali, Mayur, Mussad, Asya, Parker, Laura, Patch, Josh, Perella, Paolo, Robson, Emily, Setty, Thunga, Vieira, Filipe, Wall, Josh, Witby, Jon, Zghaibe, Wael, Zhang, Sheldon, Merron, Stephen, Abano, Nenette, Barnett, Loretta, Barry, Adrian, Birks, Pamela, Brade, Judith, Butler, Adrian, Carpio, Racquel, Colclough, Viv, Eaton, Katrina, Edwards, Carol, Emery, Lisa, Finney, Kay, Gallagher, Susan, Ghatge, Satyajeet, Grocott, Jeanette, Hall, Amanda, Hardwick, Alison, Harrison, Anne, Hendy, Susan, Hiden, Joanne, James, Alex, Jeganath, Vijay, Kumar, Rahul, Lord, Stephen, Lovatt, Rachel, Lyjko, Susan, Maguire, Holly, Marsden, Mia, Mehta, Venu, Mishra, Rajeev, Mittal, Rohit, Moores, Andrew, Nalwaya, Pramod, Omeara, Matthew, Orosz, Sandor, Patel, Nehal, Pattison, Nicola, Ponce, Ida, Puttapa, Ashok, Rajamanickam, Tamilselvan, Rao, Nagaraj, Remegoso, Alda, Rhodes, Rochelle, Rooney, Angela, Sadler, Emma, Singh, Permendra, Soon, Wai, Tomlins, Simon, Umbarje, Kanchan, Vallabhaneni, Murali, Varquez, Resti, Wain, Jill, White, Sorana, Willetts, Elizabeth, Williams, Benedict, Wood, Andrew, Washington, Stephen, Anipindi, Sujata, Aston, Jennifer, Bradley-Potts, Joanne, Butler, Ian, Cunningham, Jennifer, Duncan, Alastair, Garrard, Hywel, Haslam, Daniel, Kirk, Danielle, McCormick, Richard, McEvoy, Michael, Mistry, Sara, Ormandy, Danielle, Sharp, Tracy, Song, Andrew, Williamson, Georgina, Young, Francis, Tasker, Laura, Bailey, Elizabeth, Baker, Roisin, Bamford, Amy, Bergin, Colin, Blackburn, Julia, Bodo, Kinga, Carrera, Ronald, Cooper, Lauren, Despy, Liesl, Ellis, Karen, Foster, Morgan, Garner, Sharon, Harkett, Lyndon, Harkett, Samantha, Ip, Peter, Lay, Frances, Liu, Hon Sum, Mee, Laura, Nazir, Samir, Osbourne-Wylde, Jacob, Patel, Krupali, Pope, Martin, Reeves, Emma, Sahni, Melanie, Small, Charlotte, Spooner, Brendon, Spruce, Elaine, Whitehouse, Arlo, Wharton, Nicholas, Alexander, Rachel, Ashford, Amy, Aspinall, Rebecca, Bateman, Richard, Bell, Matthew, Bennett-Britton, Jennifer, Bewley, Jeremy, Blanshard, Hannah, Bourdeaux, Christopher, Brook, Peter, Cain, Helen, Cameron, Claire, Cassells, Naomi, Chauhan, Nilesh, Clarke, Adrian, Cockroft, Melanie, Cohen, Alan, Collins, Joanna, Cominos, Timothy, Craven, Rachael, Davies, Ian, Dolling, Sarah, Dowse, Claire, Driver, Kate, Duffen, Adam, Dunn, Sarah-Jane, Earnshaw, Charlotte, Forrest, Frances, Freshwater-Turner, Dan, Gardner, Issie, Gibbins, Matthew, Gibbison, Ben, Gleeson, Suzanne, Gould, Timothy, Govier, Matthew, Gupta, Benjamin, Hadfield, John, Harvey, Nicola, Henderson, Sophia, Herbert, Lara, Hillier, James, Hood, Elizabeth, Howes, Ben, Howes, Helen, Hughes, Sian, Jackson, Kathryn, Jacobs, Toby, Jenkins, Emma, Jenkins, Ian, Johnstone, Alistair, Jones, Lydia, Jones, Rebecca, Joshi, Natasha, Kamalanathan, Kajan, Kellie, Neil, Kidd, Lawrence, Kinsella, Mike (Stephen), Knight, Thomas, Leslie, Rebecca, Linter, Stephen, Lovell, Tim, Martin, Matthew, McHugh, Ursula, McIndoe, Andrew, McKendry, Rachel, Middleditch, Alexander, Miles, Edward, Milne, Lesley, Molyneux, Mat, Muchatuta, Neil, Murphy, Ruth, O'Higgins, Fran, Oglesby, Kieran, Pachucki, Marcin, Paoloni, Claudia, Patel, Nishita, Patton, Catherine (Katie), Paulich, Skylar, Pearson, Annabel, Pickering, Anthony, Platt, Martin, Pope, Charlie, Pryn, Steve, Ranjan, Amit, Rasburn, Neil, Ray, Andrew, Rivers, Jonathan, Rooney, Kieron, Ryder, Ian, Sanders, Sarah, Sasi-Kumar, Kavita, Scott, Liam, Scrutton, Mark, Searles, Chloe, Self, James, Shah, Sanjoy, Sheils, Mark, Shipway, Toby, Short, Heather, Simmonds, Lauren, Smith, Daniela, Soodan, Anjali, Steeds, Charlotte, Sweet, Katie, Syme, Phoebe, Thomas, Matthew J., Tomkins, Sally, Underwood, Susan, Wagstaff, Adrian, Watson, Paul, Webster, Denise, Welham, Katie, Whaley, Anne, Williams, Helen, Wilson, Hannah, Winton, Anoushka, Woodward, Thomas, Worthington, Jeanie, Younie, Stuart, Bradbury, Carol, Auldin, Mohammad, Bosworth, Kate, Choksey, Falguni, Dudkowsky, Bernice, Elton, John, Evans, Gail, Grove, Charlotte, Ingram, Clare, Kelly, Andrew, Lax, Peter, Lynes, Elena, Madden, George, Mistry, Vinesh, Molitor, Timothy, Morgan, Catherine, Morris, Natalie, Pallister, Emily, Patel, Pooja, Ragothaman, Vijay, Ramm, Katie, Robinson, Ben, Sharma, Meghna, Shuttleworth, Daniel, Vaidyanath, Chandrashekhar, Warren, Jen, Wong, Rachel, Lau, Gary, Conway, Hannah, Majeed, Adeel, Parmar, Jaina, Rana, Meenal, Yusuff, Hakeem, Rimmer, Corinne, Abdelrhamen, Wael, Allison, Kathryn, Burns, Karen, Cahill, Nichola, Clark, Jon, Donaldson, Hannah, Heseltine, Nick, Mackenzie, Nicola, McArthur, Carol, Moreland, Neil, Shohelly, Sharmin, Thatcher, Hilary, Tran, Steven, Walker, Catriona, Al-Rawi, Samar, Wakatsuki, Mai, Aarvold, Alice, Addy, Melissa, Ashfield, Georgina, Beauchamp, Nigel, Belcher, Alex, Bennett, Tom, Burgess, Victoria, Burton, Andy, Campbell, Matthew, Cruikshank, Ross, Cumpstey, Andy, Davies, Rhys, Ford, Chris, Ford, Renee, Hawkins, Lesley, Hendra, Tim, Huttley, Thomas, Jolly, Erica, Kalcheva, Nevena, Kennedy, Charlie, Lees, Jessica, Lowe, Richard, Martindale, Tim, McCormick, Sarah, McPhee, Hannah, Millinchamp, Fran, Muddanyake, Rahul, Osorio, Eduardo, Patel, Kiran, Pestell, Samuel, Phillips, Steve, Plumb, Jamie, Pratt, Thomas, Quartermain, Bert, Reeves, Rebecca, Robin, Sophie, Scully, Thecla, Sehgal, Anamika, Seifalan, Said, Shuttleworth, Suzanne, Siddique, Omar, Sleap, Esme, Smith, Victoria, Snooks, Richard, Solanki, Virginia, Timmons, Christina, Trimble, Alice, Tunney, Doug, Walker, Kat, Wee, Naomi, Wigley, James, Wilkins, Huw, Wong, Hanna, Wood, Laura, Yogya, Dinithi, Young, Ruth, Shanbhag, Sumant, Ameer, Farrukh, Finnity, Jonathan, Haddock, James, Hunt, Alex, Kuravi, Aditya, Lenehen, Fionnuala, McOwat, Lindsay, Naqvi, Asad, Richardson, Lisa, Siddons, Victoria, Charters, Seema, Jumani, Deepa, Bradley, Peter, Bubb, Laura, Chan, Rebekah, Connell, Lynne, Crone, Rachel, Davis, Hannah, Defeyter, Pascal, Evans, Alison, Howard, Ramdas, Prady, Helena, Rogers, Natalie, Roughley, Lindsay, Whittle, Helen, Zeinali, Daniel, Cherian, Vinanti, Latif, Muhammad Usman, Bajwa, Maqsood, Guy, Jonny, Guz, Krzysztos, Iftikhar, Nauman, Iwanicka, Beata, Khan, Mujeeb, Love, Anna-Marie, Montgomery, Celia, Mulvany, Stephen, O'Kane, Emma, O'Kane, Sinead, Siddiqui, Mansoor, Small, Cathal, Subhani, Shaik, Dana, Emily, Kennedy, Richard, Amey, Isobel, Ashton, Hilary, Baird, Yolanda, Bushell, Caroline, Chadbourn, Indra, Cohen, Merle, Finlay, Emma, Gilbert, Jeanette, Helm, David, Lewis, Carla, Macallan, Jenny, Margalef, Jordi, Martins, Nic, Metiu, Gabi, Moore, Sally, Rose, Hannah, Standley, Tom, Page, Valerie, Bakonyi, Tomas, Ben-Nathan, Simon, Claridge, Melanie, Evans, Jackie, Gardiner, Harriet, Kantsedikas, Ilya, Lachana, Ann, Makker, Ratna, Markwell, Karen, Merrill, Colin, Mukerjee, Prasun, O'Donohoe, Lynn, Oglesby, Fiona, Osbourne, Laura, Parker, Beena, Passaro, Emelia, Russell, Katherine, Shah, Sneh, Sundayi, Saul, Walker, Elaine, Zhao, Xiao, Hosdurga, Guru, Cotterill, Donna, Crowther, Nicola, Dymond, Harvey, Ratcliffe-Law, Thomas, Saunders, Glenn, Saunders, Thomas, Gopal, Vijayakumar, Ahmed, Sajjad, Ali, Abdalla, Crawford, Sarah, De'Ath, Clare, Humphreys, Sally, Swanevelder, Carin, Yap, Yeng, Silk, Jane, Adeniba, Rashidat, Armstrong, Richard, Egole, Osi, Fan, Karen, Gilbert, Kayleigh, Gould, Laura, Hickman, John, Long, Fei, Pahary, Sheik, Reichman, Michelle, Simpson, Kathryn, Singaravelu, Suresh, Ahmed, Dabeeruddeen, Brailsford, Fiona, Bridge, Laura, Collis, Lauren, Jacob, Reni, Jones, Cathy, Kirrage, Amanda, Lowe, Jessica, Mullen, Joanne, Prenter, Andrew, Sarao, Sarbpreet, Sharpley, David, Wankhade, Kavita, Persad, Cindy, Burtenshaw, Andrew, Blyth, Kate, Corcoran, Felicity, Davis, Caroline, Dolan, Rachel, Hussain, Nageena, Pearson, Emma, Pierson, Anna, Poyntz, Victoria, Thrush, Jessica, Wollaston, Alex, Wollaston, Julie, Clements, Paul, Biswas, Arindam, Clayton, Sarah, Clayton, Thomas, Cochrane, Naomi, Duell, Kirsty, Evans, James, Gaskell, Mark, Heaton, Dianne, Heaton, Linzi, Hill, Claire, Killen, Ruth, Latham, Sandra, McIntosh, Gemma, Moore, Chris, Parkinson, Val, Pemberton, Nicola, Robinson, Matthew, Sawyer, Alastair, Sykes, Isabelle, Taylor, Tracey, Tierney, Caroline, van de Snepscheut-Jones, Katja, Wade, Emily, Wahid, Mohammed, Wang, Henry, Williams, Claire, Wu, Dominic, Yadagiri, Manjula, Zabauski, Dmitry, Troth, Laura, Andrews, Eleanor, Bains, Ranjit, Beard, Laura, Birch, Janine, Britton, Rachael, Salmon, Mike, Vassell, Elizabeth, Kubisz-Pudelko, Agnieszka, Allen, Jamie, Allison, Joanna, Baird, Andrew, Gregory, Rob, Luckhurst, Hannah, Mairs, Rebecca, Marshall, Ben, Pippard, Lucy, Pitt-Kerby, Tressy, Savva, Andrew, Walkden, Graham, Weegenaar, Celly, Chandler, Ben, Kanakaraj, Muthuraj, Chamberlain, Andy, Dyer, Simon, Elliott, Kerry, Forshaw, Greg, Furness, Rosie, Gibson, Andy, Howe, Laura, Kent, Sarah, Rodgers, Alicia, Straughan, Ben, Temlett, Emma, Whitwell, John, Wong, D.J.N., Harris, S.K., and Moonesinghe, S.R.
Published: 2018
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159. Characterization of causal ancestral graphs for time series with latent confounders.

Author: Andreas Gerhardus
Published: 2021

160. Structured methodology review identified seven (RETREAT) criteria for selecting qualitative evidence synthesis approaches

Author: Booth, Andrew, Noyes, Jane, Flemming, Kate, Gerhardus, Ansgar, Wahlster, Philip, van der Wilt, Gert Jan, Mozygemba, Kati, Refolo, Pietro, Sacchini, Dario, Tummers, Marcia, and Rehfuess, Eva
Published: 2018
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161. The impact of employee remuneration inequalities on employee productivity in the South African workplace

Author: Gerhardus van Zyl
Subjects: remuneration gap, fixed-panel data estimations, the sen index, employee productivity, international standard classification of occupations (isco-88), additive multivariate linear model, Economics as a science, HB71-74
Abstract: Orientation: This article is part of an ongoing research project on various aspects of employee productivity in the South African workplace. Research purpose: The aim of this article is to determine firm-based employee productivity impacts as a result of employee remuneration inequalities (excess-remuneration and under-remuneration) in the South African workplace. Motivation for the study: The study focuses on understanding the impact and magnitude of employee remuneration inequalities on employee productivity in a unionised South African workplace. Research design: The article adopts two distinct estimation models. The aim of the additive multivariate linear estimation model is to determine the sign and the significance of the impact of both under- and excess-remuneration levels on employee productivity when employee characteristics such as levels of training, work experience and managerial involvement are considered. The second model is a fixed-effect panel data estimation where the full sample set of the relevant firm-based data is used. The aim of the panel data estimations is to estimate the robustness of the additive multivariate linear estimates. The manufacturing industry of Gauteng has been chosen as the case study, given the importance of this industry, in the gross geographical product of Gauteng province and the availability of firm-based data. Main findings: Estimation results indicate a strong and significant negative impact of under-remuneration on employee productivity levels. Excess-remuneration levels have a small positive impact on employee productivity levels. Practical/managerial implications: The estimations indicate the necessity to eliminate remuneration inequalities and opt for equalised remuneration structures for similar occupations in the market to enhance employee productivity levels. Contribution/value-added: The study contributes to our understanding of the impact of remuneration inequalities for similar occupations on employee productivity.
Published: 2021
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162. Extracorporeal oxygenation and COVID-19 epidemic: is the membrane fail-safe to cross contamination?

Author: Squiccimarro, Enrico, Rociola, Ruggiero, Haumann, Renard Gerhardus, Grasso, Salvatore, Lorusso, Roberto, and Paparella, Domenico
Published: 2020
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163. What Do We Know about Social and Non-Social Factors Influencing the Pathway from Cognitive Health to Dementia? A Systematic Review of Reviews

Author: Marta Lenart-Bugla, Mateusz Łuc, Marcin Pawłowski, Dorota Szcześniak, Imke Seifert, Henrik Wiegelmann, Ansgar Gerhardus, Karin Wolf-Ostermann, Etiënne A. J. A. Rouwette, M. Arfan Ikram, Henry Brodaty, Yun-Hee Jeon, Jane Maddock, Anna Marseglia, René J. F. Melis, Suraj Samtani, Hui-Xin Wang, Anna-Karin Welmer, Myrra Vernooij-Dassen, and Joanna Rymaszewska
Subjects: dementia, cognitive function, risk factors, protective factors, social factors, cognitive decline, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: The heterogeneous and multi-factorial nature of dementia requires the consideration of all health aspects when predicting the risk of its development and planning strategies for its prevention. This systematic review of reviews provides a comprehensive synthesis of those factors associated with cognition in the context of dementia, identifying the role of social aspects and evidencing knowledge gaps in this area of research. Systematic reviews and meta-analyses from 2009–2021 were searched for within Medline, PsycINFO, CINAHL Complete, Cochrane, and Epistemonikos. Reviewers independently screened, reviewed, and assessed the records, following the PRISMA-2020 guidelines. From 314 included studies, 624 cognitive-related factors were identified, most of them risk factors (61.2%), mainly belonging to the group of ‘somatic comorbidities’ (cardiovascular disease and diabetes) and ‘genetic predispositions’. The protective factors (20%) were mainly related to lifestyle, pointing to the Mediterranean diet, regular physical activity, and cognitively stimulating activities. Social factors constituted 9.6% of all identified factors. Research on biological and medical factors dominates the reviewed literature. Greater social support and frequent contact may confer some protection against cognitive decline and dementia by delaying its onset or reducing the overall risk; however, overall, our findings are inconsistent. Further research is needed in the fields of lifestyle, psychology, social health, and the protective factors against cognitive decline and dementia.
Published: 2022
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164. Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids’ System via a Novel Reinforcement Learning Approach

Author: Kang Li, Gerhardus P. Hancke, Xiangpeng Xie, Huifeng Zhang, Chunxia Dou, and Dong Yue
Subjects: Mathematical optimization, Computer Networks and Communications, Intersection (set theory), Computer science, Control (management), Boundary (topology), Fuzzy control system, Multi-objective optimization, Computer Science Applications, Artificial Intelligence, Economic cost, Convergence (routing), Reinforcement learning, Software
Abstract: With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.
Published: 2023
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165. Monetary value of human capital disclosure for predicting future business earnings

Author: Mpho D. Magau, Gerhard Roodt, and Gerhardus van Zyl
Subjects: human capital, human capital disclosure, human resource management, intellectual capital, organisational performance, Personnel management. Employment management, HF5549-5549.5
Abstract: Orientation: Human capital (HC) is a key dimension of intellectual capital (IC) that creates shareholder value when integrated with structural capital (SC) and relational capital (RC). The disclosure of HC reveals how the human resource (HR) practices support business growth. Research purpose: The primary focus of this study was to investigate the extent of how human capital disclosure (HCD) could predict organisational performance (expected future earnings) of the Johannesburg Stock Exchange (JSE)-listed companies. Motivation for the study: Human capital disclosure reduces information asymmetry, predicts future business performance and improves the investors’ buy-sell-hold decisions. Research approach/design and method: A causal, comparative design was applied quantitatively in a cross-sectional survey of 150 top- and low-performing JSE-listed companies sampled based on their market capitalisation. A self-constructed disclosure index with a seven-point scoring system was developed to examine the extent of disclosure across 81 items. Subsequently, multiple regression analysis was computed to test the relationship between the dependent and independent variables. Main findings: Human capital disclosure was associated with both the market capitalisation and book value. Overall, no mediation of human resource risk disclosure (HRRD) against organisational performance was found. Lastly, information disclosed on gender diversity yielded the interaction effect with both structural capital disclosure (SCD) and relational capital disclosure (RCD) but not with HCD. Practical/managerial implications: Human capital disclosure identifies key HR practices that drive organisational performance and create shareholder value. This information can potentially improve benchmarking and support investor decision-making. Contribution/value-add: The study provides a novel contribution to IC theory by demonstrating how HCD can be used to identify HR practices that leverage the monetary value of HC for business improvement.
Published: 2021
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166. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity

Author: Sachs, Norman, de Ligt, Joep, Kopper, Oded, Gogola, Ewa, Bounova, Gergana, Weeber, Fleur, Balgobind, Anjali Vanita, Wind, Karin, Gracanin, Ana, Begthel, Harry, Korving, Jeroen, van Boxtel, Ruben, Duarte, Alexandra Alves, Lelieveld, Daphne, van Hoeck, Arne, Ernst, Robert Frans, Blokzijl, Francis, Nijman, Isaac Johannes, Hoogstraat, Marlous, van de Ven, Marieke, Egan, David Anthony, Zinzalla, Vittoria, Moll, Jurgen, Boj, Sylvia Fernandez, Voest, Emile Eugene, Wessels, Lodewyk, van Diest, Paul Joannes, Rottenberg, Sven, Vries, Robert Gerhardus Jacob, Cuppen, Edwin, and Clevers, Hans
Published: 2018
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167. Development of a surface plasmon resonance sensor for coupling to capillary electrophoresis allowing affinity assessment of protein mixture components

Author: Domínguez-Vega, Elena, Haselberg, Rob, Iperen, Dick van, Kool, Jeroen, Somsen, Govert W., and de Jong, Gerhardus J.
Published: 2018
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168. Establishment of Pancreatic Organoids from Normal Tissue and Tumors

Author: Else Driehuis, Ana Gracanin, Robert Gerhardus Jacob Vries, Hans Clevers, and Sylvia Fernández Boj
Subjects: Cancer, Organoids, Stem Cells, Science (General), Q1-390
Abstract: Summary: Establishment of patient-derived adult stem cell-based pancreatic (tumor) organoids was first described in 2015. Since then, multiple laboratories have demonstrated the robustness of this method. We recently described the generation of a pancreatic cancer biobank containing 30 well-characterized tumor organoid models. Here, we describe the applied methods in detail. Use of tumor-selective media prevents contamination with normal cells. Generated organoids can be cryopreserved and can serve as a living biobank of pancreatic cancer for biomarker identification and drug screening.For complete information on the generation and use of this protocol, please refer to Driehuis et al. (2019).
Published: 2020
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169. The impact of new production technology on employee productivity in the South African workplace

Author: Gerhardus van Zyl
Subjects: technology, fixed-panel data estimations, components of new technology, employee productivity, international standard classification of occupations (ioco-88), manufacturing industry, Economics as a science, HB71-74
Abstract: Orientation: This study is part of an ongoing research project on various aspects of employee productivity in the South African workplace. Research purpose: The aim of this article was to determine the firm-based employee productivity impacts because of the acquisition and introduction of new production technologies in the South African workplace. Motivation for the study: The study focusses on understanding the impact of various components of new production technology on employee productivity for different age groups and skill levels. Research approach/design and method: The study adopts two distinct estimation processes. The aim of the first set of estimations was to construct a technology index. Secondly, fixed-panel data estimations determine the percentage change in employee productivity based on technology-to-employee ratio, technology index, different technology components, different age groups and different skill levels. The manufacturing industry of Gauteng is the focus of this case study. Main findings: Estimation results indicate, in general, positive employee productivity effects, superior employee productivity gains for the 35- to 55-year employee age group and greater employee productivity gains for the lower-skilled employee segment. Practical/managerial implications: The estimation rankings of new production technology for the improvement of employee productivity, are of the utmost importance when deciding on the acquisition and introduction of different kinds of new production technologies in the workplace. Contribution/value-add: The study contributes to our understanding on how the acquisition of new production technologies could affect employee productivity at the workplace.
Published: 2020
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170. Kinetic and thermodynamic characterisation of HIV-protease inhibitors against E35D↑G↑S mutant in the South African HIV-1 subtype C protease

Author: Sibusiso Maseko, Eden Padayachee, Siyabonga Maphumulo, Thavendran Govender, Yasien Sayed, Glenn Maguire, Johnson Lin, Tricia Naicker, Sooraj Baijnath, and Kruger Hendrik Gerhardus
Subjects: protease, hiv, mutant, inhibitor, thermodynamics, Therapeutics. Pharmacology, RM1-950
Abstract: Herein, we report the effect of nine FDA approved protease inhibitor drugs against a new HIV-1 subtype C mutant protease, E35D↑G↑S. The mutant has five mutations, E35D, two insertions, position 36 (G and S), and D60E. Kinetics, inhibition constants, vitality, Gibbs free binding energies are reported. The variant showed a decreased affinity for substrate and low catalytic efficiency compared to the wild type. There was a significant decrease in the binding of seven FDA approved protease inhibitors against the mutant (p
Published: 2019
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171. Supporting capacity management decisions in healthcare using data-driven process simulation.

Author: Gerhardus van Hulzen, Niels Martin, Benoît Depaire, and Geert Souverijns
Published: 2022
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172. Health technology assessment of public health interventions: an analysis of characteristics and comparison of methods—study protocol

Author: Tim Mathes, Gerald Willms, Stephanie Polus, Constance Stegbauer, Melanie Messer, Corinna Klingler, Heidi Ehrenreich, Dea Niebuhr, Georg Marckmann, Ansgar Gerhardus, and Dawid Pieper
Subjects: Public health, Health promotion, Health technology assessment, Methodology review, Medicine
Abstract: Abstract Background Conducting a health technology assessment (HTA) of public health interventions (PHIs) poses some challenges. PHIs are often complex interventions, which affect the number and degree of interactions of the aspects to be assessed. Randomized controlled trials on PHIs are rare as they are difficult to conduct because of ethical or feasibility issues. The aim of this study is to provide an overview of the methodological characteristics and to compare the applied assessment methods in HTAs on PHIs. Methods We will systematically search HTA agencies for HTAs on PHIs published between 2012 and 2016. We will identify the HTAs by screening the webpages of members of international HTA organizations. One reviewer will screen the list of HTAs on the webpages of members of international HTA organization, and a second review will double-check the excluded records. For this methodological review, we define a PHI as a population-based intervention on health promotion or for primary prevention of chronic or non-chronic diseases. Only full HTA reports will be included. At maximum, we will include a sample of 100 HTAs. In the case that we identify more than 100 relevant HTAs, we will perform a random selection. We will extract data on effectiveness, safety and economic as well as on social, cultural, ethical and legal aspects in a priori piloted standardized tables. We will not assess the risk of bias as we focus on exploring methodological features. Data extraction will be performed by one reviewer and verified by a second. We will synthesize data using tables and in a structured narrative way. Discussion Our analysis will provide a comprehensive and current overview of methods applied in HTAs on PHIs. We will discuss approaches that may be promising to overcome the challenges of evaluating PHIs.
Published: 2018
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173. Integration und Kooperation aus gesundheitswissenschaftlicher Sicht

Author: Koch, Jennifer, Schmiemann, Guido, Gerhardus, Ansgar, Luthe, Ernst-Wilhelm, Series editor, Weatherly, John N., Series editor, Brandhorst, Andreas, editor, and Hildebrandt, Helmut, editor
Published: 2017
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174. South African Industry and Geographic Dynamics Perspective on the Link Between Employee Diversity Attributes and Employee Productivity

Author: Van Zyl, Gerhardus, primary
Published: 2023
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175. Discovering causal relations and equations from data

Author: Camps-Valls, Gustau, primary, Gerhardus, Andreas, additional, Ninad, Urmi, additional, Varando, Gherardo, additional, Martius, Georg, additional, Balaguer-Ballester, Emili, additional, Vinuesa, Ricardo, additional, Diaz, Emiliano, additional, Zanna, Laure, additional, and Runge, Jakob, additional
Published: 2023
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176. Wissenschaft und Forschung im und für den Öffentlichen Gesundheitsdienst in Deutschland

Author: Gerhardus, Ansgar, primary, Grill, Eva, additional, and Rexroth, Ute, additional
Published: 2023
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177. Long-term dietary shifts in a generalist predator, the wolverine (Gulo gulo)

Author: Massé, Clément F., primary, Hiltunen, Tamara A., additional, Lansink, Gerhardus M. J., additional, Holmala, Katja, additional, Isomursu, Marja, additional, Kojola, Ilpo, additional, Aspi, Jouni, additional, and Welker, Jeffery M., additional
Published: 2023
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178. Clinical preparedness programme as perceived by first‐year diagnostic radiography students in South Africa

Author: Thomas, Heidi, primary and Koch, Gerhardus George Visser, additional
Published: 2023
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179. Developing and Evaluating Digital Public Health Interventions: The Digital Public Health Framework (Preprint)

Author: Jahnel, Universität Bremen, primary, Pan, Chen-Chia, additional, Pedros Barnils, Núria, additional, Muellmann, Saskia, additional, Freye, Merle, additional, Dassow, Hans-Henrik, additional, Lange, Oliver, additional, Reinschluessel, Anke V., additional, Rogowski, Wolf, additional, and Gerhardus, Ansgar, additional
Published: 2023
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180. A renewed glance at the Palearctic golden eagle: genetic variation in space and time

Author: Karabanina, Ekaterina, primary, Lansink, Gerhardus, additional, Ponnikas, Suvi, additional, and Kvist, Laura, additional
Published: 2023
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181. Ammonium oxidizing bacterial populations in South African activated sludge wastewater treatment plants

Author: Welz, Pamela J., primary, Thobejane, Mfundisi P., additional, and van Blerk, Gerhardus N., additional
Published: 2023
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182. Optimal Management of Cardiac Surgery Patients Using Direct Oral Anticoagulants: Recommendations for Clinical Practice

Author: Heuts, Samuel, primary, Ceulemans, Angelique, additional, Kuiper, Gerhardus J A J M, additional, Schreiber, Jan U, additional, van Varik, Bernard J, additional, Olie, Renske H, additional, Ten Cate, Hugo, additional, Maessen, Jos G, additional, Milojevic, Milan, additional, and Maesen, Bart, additional
Published: 2023
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183. Epidemiology and Time-Loss Shoulder Injuries in Professional South African Rugby Players: A Prospective Study That Focuses on Real-Time Collision Data during a Tackle

Author: Louwrens, Jan Gerhardus, primary, Jansen van Rensburg, Audrey, additional, Viljoen, Carel T., additional, Hendricks, Sharief, additional, Botha, Tanita, additional, and Janse van Rensburg, Dina C. (Christa), additional
Published: 2023
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184. Theories used for Social Prescribing in studies - a scoping review

Author: Evers, S, primary, Husk, K, additional, Napierala, H, additional, Klose, L, additional, and Gerhardus, A, additional
Published: 2023
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185. Fully compatible and ultra-sensitive micellar electrokinetic chromatography-tandem mass spectrometry using sheathless porous-tip interfacing

Author: Moreno-González, David, Haselberg, Rob, Gámiz-Gracia, Laura, García-Campaña, Ana M., de Jong, Gerhardus J., and Somsen, Govert W.
Published: 2017
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186. Benefits of reducing the size of the navel-end opening in ‘Navel’ sweet oranges (Citrus sinensis)

Author: Stander, Ockert P.J., Gilbert, M.J., Moore, S.D., Kirkman, W., and Schutte, Gerhardus C.
Published: 2017
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187. Computational insights for predicting the binding and selectivity of peptidomimetic plasmepsin IV inhibitors against cathepsin D

Author: Lucas Sousa Martins, Hendrik Gerhardus Kruger, Tricia Naicker, Cláudio Nahum Alves, Jerônimo Lameira, and José Rogério Araújo Silva
Subjects: General Chemical Engineering, General Chemistry
Abstract: Free energy calculations explain the selectivity of peptidomimetic inhibitors to PlmIV and CatD enzymes. Insights for the development of novel potent and selective PlmIV inhibitors are provided.
Published: 2023
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188. The effect of crop rotation and tillage practice on residue decomposition and wheat performance in the Western Cape, South Africa

Author: Johannes Stephanus Theron, Gerhardus Johannes van Coller, Lindy Joy Rose, Johan Labuschagne, and Pieter Andreas Swanepoel
Subjects: Ecology, Soil Science, Plant Science
Published: 2023
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189. Economic Valuation of Urban Green Spaces across a Socioeconomic Gradient: A South African Case Study

Author: Louis Gerhardus Lategan, Zene Steynberg, Elizelle Juanee Cilliers, and Sarel Stephanus Cilliers
Subjects: green infrastructure, environmental justice, property values, proximity principle, socioeconomic status, Global South, Agriculture
Abstract: Urban green spaces (UGSs) may present economic contributions through increases in proximate property values, encapsulated in the proximity principle (PP). More data on the PP is required from the Global South, where the quality and equitable distribution of UGSs are important considerations. This paper investigates the PP in Potchefstroom, South Africa following a quantitative approach, by statistically analyzing municipal property valuations in three districts differentiated according to their socioeconomic status (SES). Districts are divided into sample areas where three zones are demarcated according to their proximity to a UGS. The results show that property valuations are generally higher for properties in closer proximity to UGSs in lower- and higher-income samples, but are lower in middle-income areas. Neighborhood characteristics and SES, UGS amenity and maintenance, ecosystem services and disservices, domestic garden area and residential property size may be connected to the confirmation or rejection of the PP. The rejection of the PP in middle-income areas indicates a need to improve public UGSs as amenity destinations. The results confirming the PP in low-income areas could incentivize expenditures to improve UGS area and quality to increase the willingness to pay for proximity to such spaces and, reciprocally, increase revenue from municipal property taxes.
Published: 2022
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190. Interactive Data-Driven Business Process Simulation (Extended Abstract).

Author: Gerhardus van Hulzen
Published: 2020

191. Who PACS a Punch? The Role of the Picture Archiving and Communication System/Radiology Information System (PACS/RIS) in Quantifying Experiential Learning in Radiology Residency.

Author: Abraham Gerhardus Wilhelmus Greyling and Richard D. Pitcher
Published: 2018
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192. Extracorporeal Oxygenation and Coronavirus Disease 2019 Epidemic: Is the Membrane Fail-Safe to Cross Contamination?

Author: Squiccimarro, Enrico, Rociola, Ruggiero, Haumann, Renard Gerhardus, Grasso, Salvatore, Lorusso, Roberto, and Paparella, Domenico
Published: 2020
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193. Radial Artery Cannulation in Young Children: Comment

Author: Kuiper, Gerhardus J. A. J. M.
Published: 2020
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194. Series: Clinical Epidemiology in South Africa. Paper 3: Logic models help make sense of complexity in systematic reviews and health technology assessments

Author: Rohwer, Anke, Pfadenhauer, Lisa, Burns, Jacob, Brereton, Louise, Gerhardus, Ansgar, Booth, Andrew, Oortwijn, Wija, and Rehfuess, Eva
Published: 2017
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195. The perceived impact of family physicians on the district health system in South Africa: a cross-sectional survey

Author: Klaus B. von Pressentin, Robert J. Mash, Laurel Baldwin-Ragaven, Roelf Petrus Gerhardus Botha, Indiran Govender, Wilhelm Johannes Steinberg, and Tonya M. Esterhuizen
Subjects: Family physicians, District health system, Perceived impact, Primary health care, Cross-sectional study, Medicine (General), R5-920
Abstract: Abstract Background Evidence from first world contexts support the notion that strong primary health care teams contain family physicians (FPs). African leaders are looking for evidence from their own context. The roles and scope of practice of FPs are also contextually defined. The South African family medicine discipline has agreed on six roles. These roles were incorporated into a family physician impact assessment tool, previously validated in the Western Cape Province. Methods A cross-sectional study design was used to assess the perceived impact of family physicians across seven South African provinces. All FPs working in the district health system (DHS) of these seven provinces were invited to participate. Sixteen respondents (including the FP) per enrolled FP were asked to complete the validated 360-degree assessment tool. Results A total number of 52 FPs enrolled for the survey (a response rate of 56.5%) with a total number of 542 respondents. The mean number of respondents per FP was 10.4 (SD = 3.9). The perceived impact made by FPs was high for five of the six roles. Co-workers rated their FP’s impact across all six roles as higher, compared to the other doctors at the same facility. The perceived beneficial impact was experienced equally across the whole study setting, with no significant differences when comparing location (rural vs. metropolitan), facility type or training model (graduation before and ≥ 2011). Conclusions The findings support the need to increase the deployment of family physicians in the DHS and to increase the number being trained as per the national position paper.
Published: 2018
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196. Unsteady Linearisation of Bed Shear Stress for Idealised Storm Surge Modelling

Author: Pieter C. Roos, Giordano Lipari, Chris Pitzalis, Koen R. G. Reef, Gerhardus H. P. Campmans, and Suzanne J. M. H. Hulscher
Subjects: storm surge, channel model, bottom friction, linearisation, frequency domain, Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581
Abstract: The modelling of time-varying shallow flows, such as tides and storm surges, is complicated by the nonlinear dependency of bed shear stress on flow speed. For tidal flows, Lorentz’s linearisation circumvents nonlinearity by specifying a (steady) friction coefficient r based on a tide-averaged criterion of energy equivalence. However, this approach is not suitable for phenomena with episodic and irregular forcings such as storm surges. Here, we studied the implications of applying Lorentz’s energy criterion in an instantaneous sense, so that an unsteady friction coefficient r(t) adjusts to the temporal development of natural wind-driven flows. This new bed-stress parametrisation was implemented in an idealised model of a single channel, forced by time-varying signals of wind stress (acting over the entire domain) and surface elevation (at the channel mouth). The solution method combines analytical solutions of the cross-sectionally averaged linearised shallow-water equations, obtained in the frequency domain, with an iterative procedure to determine r(t). Model results, compared with a reference finite-difference solution retaining the quadratic bed shear stress, show that this new approach accurately captures the qualitative and quantitative aspects of the surge dynamics (height and timing of surge peaks, sloshing, friction-induced tide-surge interaction) for both synthetic and realistic wind forcings.
Published: 2021
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197. Modelling multiple cycles of static and dynamic recrystallisation using a fully implicit isotropic material model based on dislocation density

Author: Jansen van Rensburg, Gerhardus J., Kok, Schalk, and Wilke, Daniel N.
Published: 2018
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198. Simultaneous estimation of boundary conditions and material model parameters

Author: van Rensburg, Gerhardus J. Jansen, Kok, Schalk, and Wilke, Daniel N.
Published: 2018
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199. Chiral capillary electrophoresis with UV-excited fluorescence detection for the enantioselective analysis of 9-fluorenylmethoxycarbonyl-derivatized amino acids

Author: Prior, Amir, Coliva, Giulia, de Jong, Gerhardus J., and Somsen, Govert W.
Published: 2018
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200. Finanzierung der Gesundheitsforschung in Deutschland

Author: Rieks, Stephan and Gerhardus, Ansgar
Published: 2018
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