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10. Comparison of differences in cohort (forwards) and case control (backwards) methodological approaches for validation of the Hypotension Prediction Index.

Author

Davies, Simon J, Sessler, Daniel I, Jian, Zhongping, Fleming, Neal W, Mythen, Monty, Maheshwari, Kamal, Veelo, Denise P, Vlaar, Alexander Pj, Settels, Jos, Scheeren, Thomas, van der Ster, Bjp, Sander, Michael, Cannesson, Maxime, and Hatib, Feras

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Anesthesiology, Clinical sciences
Abstract

BackgroundThe Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiological changes that may lead to hypotension. The original validation used a case control (backwards) analysis that has been suggested to be biased. We therefore conducted a cohort (forwards) analysis and compared this to the original validation technique.MethodsWe conducted a retrospective analysis of data from previously reported studies. All data were analysed identically with 2 different methodologies and receiver operating characteristic curves (ROC) constructed. Both backwards and forwards analyses were performed to examine differences in area under the ROC for HPI and other haemodynamic variables to predict a MAP < 65mmHg for at least 1 minute 5, 10 and 15 minutes in advance.ResultsTwo thousand and twenty-two patients were included in the analysis, yielding 4,152,124 measurements taken at 20 second intervals. The area-under-the-curve for the index predicting hypotension analysed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947-0.964) vs 0.923 (95% CI, 0.912-0.933) 5 minutes in advance, 0.933 (95% CI, 0.924-0.942) vs 0.923 (95% CI, 0.911-0.933) 10 minutes in advance , and 0.929 (95% CI, 0.918-0.938) vs. 0.926 (95% CI, 0.914-0.937) 15 minutes in advance. No other variable had an area-under-the-curve > 0.7 except for MAP. Area-under-the-curve using forward analysis for MAP predicting hypotension 5, 10, and 15 minutes in advance was 0.932 (95% CI, 0.920-0.940), 0.929 (95% CI, 0.918-0.938), and 0.932 (95% CI, 0.921-0.940). The R 2 for the variation in the index due to MAP was 0.77.ConclusionUsing an updated methodology, we found the utility of the HPI index to predict future hypotensive events is high, with an area under the receiver-operating-characteristics curve similar to that of the original validation method.

Published
2024

12. Using moringa (Moringa oleifera) seed protein hydrolysate as a dietary protein supplement modulates hemato-biochemical indices, antioxidant response, and tissue histo-morphology of Nile tilapia (Oreochromis niloticus)

Author

Ibrahim, Rowida E., Amer, Shimaa A., Rhouma, Nasreddin R., Younis, Elsayed M., Abdelwarith, Abdelwahab A., Abdel-Ghany, Heba M., Elshobaky, Gehad E., El-Saber, Mahmoud M., Osman, Ali, Davies, Simon J., and Rahman, Afaf N. Abdel

Published
2024
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13. Efficacy of the dietary Malva parviflora leaves in modulating immune-antioxidant functions, resistance to Candida albicans and Streptococcus agalactiae infection, digestive/absorptive capacity, and growth of Nile tilapia (Oreochromis niloticus)

Author

Abdel Rahman, Afaf N., Reyad, Yasmin A., Elshopakey, Gehad E., Younis, Elsayed M., Abdelwarith, Abdelwahab A., EL-Mageed, Nehal Abd, Kishta, Afaf A., Yaseen, Asmaa, Davies, Simon J., and Ibrahim, Rowida E.

Published
2024
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14. Influence of astaxanthin-enriched Haematococcus pluvialis microalgae on the growth efficacy, immune response, antioxidant capacity, proinflammatory cytokines, and tissue histomorphology of hybrid red tilapia

Author

Eldessouki, Elsayed A. A., Elshopakey, Gehad E., Elbahnaswy, Samia, Shakweer, Medhat S., Abdelwarith, Abdelwahab A., Younis, Elsayed M., Davies, Simon J., Mili, Amira, Abd El-Aziz, Yasmin M., Abdelnour, Sameh A., and Eissa, El-Sayed Hemdan

Published
2024
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16. Antagonistic Effect of Zinc Oxide Nanoparticles Dietary Supplementation Against Chronic Copper Waterborne Exposure on Growth, Behavioral, Biochemical, and Gene Expression Alterations of African Catfish, Clarias gariepinus (Burchell, 1822)

Author

Ahmed, Shaimaa A. A., Ibrahim, Rowida E., Younis, Elsayed M., Abdelwarith, Abdelwahab A., Faroh, Khaled Yehia, El Gamal, Samar A., Badr, Shereen, Khamis, Tarek, Mansour, Abdallah Tageldein, Davies, Simon J., and ElHady, Mohamed

Published
2024
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22. Centre-level fluid management practices in the BISTRO trial and their lack of association with participant fluid status and blood pressure in non-anuric haemodialysis patients

Author

Johal, Neena, Sharma, Radha, Belcher, John, Coyle, David, Lindley, Elizabeth J., Keane, David, Caskey, Fergus J., Dasgupta, Indranil, Davenport, Andrew, Farrington, Ken, Mitra, Sandip, Ormandy, Paula, Wilkie, Martin, Macdonald, Jamie, Solis-Trapala, Ivonne, Sim, Julius, and Davies, Simon J.

Published
2024
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23. Enhancing impact of dietary nano formulated quercetin on laying performance: egg quality, oxidative stability of stored eggs, intestinal immune and antioxidants related genes expression

Author

Al-Khalaifah, Hanan S., Ibrahim, Doaa, Kamel, Asmaa EL-Sayed, Al-Nasser, Afaf, Abdelwarith, Abdelwahab A., Roushdy, Elshimaa M., Sheraiba, Nagwa I., Shafik, Basant M., El-Badry, Sara M., Younis, Elsayed M., Mamdouh, Maha, Yassin, Engy Mohamed Mohamed, Davies, Simon J., and Kishawy, Asmaa T. Y.

Published
2024
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25. Preparing for responsive management versus preparing for renal dialysis in multimorbid older people with advanced chronic kidney disease (Prepare for Kidney Care): Study protocol for a randomised controlled trial.

Author

Worthington, Jo, Soundy, Alexandra, Frost, Jessica, Rooshenas, Leila, MacNeill, Stephanie J., Realpe Rojas, Alba, Garfield, Kirsty, Liu, Yumeng, Alloway, Karen, Ben-Shlomo, Yoav, Burns, Aine, Chilcot, Joseph, Darling, Jos, Davies, Simon, Farrington, Ken, Gibson, Andrew, Husbands, Samantha, Huxtable, Richard, McNally, Helen, Murphy, Emma, Murtagh, Fliss E. M., Rayner, Hugh, Rice, Caoimhe T., Roderick, Paul, Salisbury, Chris, Taylor, Jodi, Winton, Helen, Donovan, Jenny, Coast, Joanna, Lane, J Athene, and Caskey, Fergus J.

Published
2024
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29. Nano-selenium impacts on growth performance, digestive enzymes, antioxidant, immune resistance and histopathological scores of Nile tilapia, Oreochromis niloticus against Aspergillus flavus infection

Author

Eissa, El-Sayed Hemdan, Bazina, Walaa K., Abd El-Aziz, Yasmin M., Abd Elghany, Nadia A., Tawfik, Wesam A., Mossa, Monga I., Abd El Megeed, Ola Hasan, Abd El-Hamed, Nadia N. B., El-Saeed, Adel F., El-Haroun, Ehab, Davies, Simon J., Hasimuna, Oliver J., Eissa, Moaheda E. H., and Khalil, Hala S.

Published
2024
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31. Majority Voting Approach to Ransomware Detection

Author

Davies, Simon R, Macfarlane, Richard, and Buchanan, William J

Subjects
Computer Science - Cryptography and Security
Abstract

Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic landscape of ransomware detection. In the majority of cases, these described systems propose a method based on the result of a single test performed on either the executable code, the process under investigation, its behaviour, or its output. In a small subset of ransomware detection systems, the concept of a scorecard is employed where multiple tests are performed on various aspects of a process under investigation and their results are then analysed using machine learning. The purpose of this paper is to propose a new majority voting approach to ransomware detection by developing a method that uses a cumulative score derived from discrete tests based on calculations using algorithmic rather than heuristic techniques. The paper describes 23 candidate tests, as well as 9 Windows API tests which are validated to determine both their accuracy and viability for use within a ransomware detection system. Using a cumulative score calculation approach to ransomware detection has several benefits, such as the immunity to the occasional inaccuracy of individual tests when making its final classification. The system can also leverage multiple tests that can be both comprehensive and complimentary in an attempt to achieve a broader, deeper, and more robust analysis of the program under investigation. Additionally, the use of multiple collaborative tests also significantly hinders ransomware from masking or modifying its behaviour in an attempt to bypass detection., Comment: 17 pages

Published
2023

33. Cost of Cancer in Adolescents and Young Adults in the United States: Results of the 2021 Report by Deloitte Access Economics, Commissioned by Teen Cancer America

Author

Parsons, Susan K, Keegan, Theresa HM, Kirchhoff, Anne C, Parsons, Helen M, Yabroff, K Robin, and Davies, Simon J

Subjects
Prevention, Clinical Research, Cancer, Health Services, Pediatric, Pediatric Research Initiative, 8.2 Health and welfare economics, Health and social care services research, Good Health and Well Being, Humans, Adolescent, Young Adult, United States, Adult, Insurance, Health, Efficiency, Neoplasms, Clinical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

PurposeThe purpose of this report, commissioned by Teen Cancer America and performed by Deloitte Access Economics in 2021, was to estimate the total costs incurred by adolescent and young adults (AYAs) after cancer diagnosis in the United States (US) over their life course.MethodsThe incidence of cancer in 2019 among AYAs age 15-39 years was estimated from the US Cancer Statistics Public Use Database, and relative survival was projected from the Surveillance, Epidemiology, and End Results Program. Cost domains included health system, productivity, and well-being costs. Components were estimated with published literature and pooled data from the Medical Expenditure Panel Survey from 2008 to 2012 and inflated to 2019 dollars.ResultsThe economic and human costs of cancer in AYAs are substantial-$23.5 billion overall, corresponding to $259,324 per person over the lifetime. The majority of costs are borne by AYA cancer survivors themselves in the form of lost productivity, loss of well-being, and loss of life.ConclusionThese findings underscore the need to address the burden of cancer in AYAs through targeted programs for AYAs, such as financial navigation and health insurance literacy interventions, as well as local and national policy initiatives to address access to and enhanced coverage for clinical trials participation, fertility services, and survivorship care.

Published
2023

35. Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification

Author

Davies, Simon R, Macfarlane, Richard, and Buchanan, William J.

Subjects
Computer Science - Cryptography and Security
Abstract

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file's entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square, Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. he hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware.

Published
2022
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40. Nano-Bacillus amyloliquefaciens as a dietary intervention in nile tilapia (Oreochromis niloticus): Effects on resistance to Aeromonas hydrophila challenge, immune-antioxidant responses, digestive/absorptive capacity, and growth

Author

Hassanin, Mohammed E., El-Murr, Abdelhakeem, EL-Khattib, Amr R., Abdelwarith, Abdelwahab A., Younis, Elsayed M., Metwally, Mohamed M.M., Ismail, Sameh H., Davies, Simon J., Abdel Rahman, Afaf N., and Ibrahim, Rowida E.

Published
2024
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45. NapierOne: A modern mixed file data set alternative to Govdocs1

Author

Davies, Simon R, Macfarlane, Richard, and Buchanan, William J

Subjects
Computer Science - Cryptography and Security
Abstract

It was found when reviewing the ransomware detection research literature that almost no proposal provided enough detail on how the test data set was created, or sufficient description of its actual content, to allow it to be recreated by other researchers interested in reconstructing their environment and validating the research results. A modern cybersecurity mixed file data set called NapierOne is presented, primarily aimed at, but not limited to, ransomware detection and forensic analysis research. NapierOne was designed to address this deficiency in reproducibility and improve consistency by facilitating research replication and repeatability. The methodology used in the creation of this data set is also described in detail. The data set was inspired by the Govdocs1 data set and it is intended that NapierOne be used as a complement to this original data set. An investigation was performed with the goal of determining the common files types currently in use. No specific research was found that explicitly provided this information, so an alternative consensus approach was employed. This involved combining the findings from multiple sources of file type usage into an overall ranked list. After which 5000 real-world example files were gathered, and a specific data subset created, for each of the common file types identified. In some circumstances, multiple data subsets were created for a specific file type, each subset representing a specific characteristic for that file type. For example, there are multiple data subsets for the ZIP file type with each subset containing examples of a specific compression method. Ransomware execution tends to produce files that have high entropy, so examples of file types that naturally have this attribute are also present.

Published
2022
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46. Sex dependence of postoperative pulmonary complications – A post hoc unmatched and matched analysis of LAS VEGAS

Author

Kroell, Wolfgang, Metzler, Helfried, Struber, Gerd, Wegscheider, Thomas, Gombotz, Hans, Hiesmayr, Michael, Schmid, Werner, Urbanek, Bernhard, Kahn, David, Momeni, Mona, Pospiech, Audrey, Lois, Fernande, Forget, Patrice, Grosu, Irina, Poelaert, Jan, van Mossevelde, Veerle, van Malderen, Marie-Claire, Dylst, Dimitri, van Melkebeek, Jeroen, Beran, Maud, de Hert, Stefan, De Baerdemaeker, Luc, Heyse, Bjorn, Van Limmen, Jurgen, Wyffels, Piet, Jacobs, Tom, Roels, Nathalie, De Bruyne, Ann, van de Velde, Stijn, Leva, Brigitte, Damster, Sandrine, Plichon, Benoit, Juros-Zovko, Marina, Djonoviċ-Omanoviċ, Dejana, Pernar, Selma, Zunic, Josip, Miskovic, Petar, Zilic, Antonio, Kvolik, Slavica, Ivic, Dubravka, Azenic-Venzera, Darija, Skiljic, Sonja, Vinkovic, Hrvoje, Oputric, Ivana, Juricic, Kazimir, Frkovic, Vedran, Kopic, Jasminka, Mirkovic, Ivan, Karanovic, Nenad, Carev, Mladen, Dropulic, Natasa, Saric, Jadranka Pavicic, Erceg, Gorjana, Dvorscak, Matea Bogdanovic, Mazul-Sunko, Branka, Pavicic, Anna Marija, Goranovic, Tanja, Maldini, Branka, Radocaj, Tomislav, Gavranovic, Zeljka, Mladic-Batinica, Inga, Sehovic, Mirna, Stourac, Petr, Harazim, Hana, Smekalova, Olga, Kosinova, Martina, Kolacek, Tomas, Hudacek, Kamil, Drab, Michal, Brujevic, Jan, Vitkova, Katerina, Jirmanova, Katerina, Volfova, Ivana, Dzurnakova, Paula, Liskova, Katarina, Dudas, Radovan, Filipsky, Radek, el Kafrawy, Samir, Abdelwahab, Hisham Hosny, Metwally, Tarek, Abdel-Razek, Ahmed, El-Shaarawy, Ahmed Mostafa, Hasan, Wael Fathy, Ahmed, Ahmed Gouda, Yassin, Hany, Magdy, Mohamed, Abdelhady, Mahdy, Mahran, Mohamed, Herodes, Eiko, Kivik, Peeter, Oganjan, Juri, Aun, Annika, Sormus, Alar, Sarapuu, Kaili, Mall, Merilin, Karjagin, Juri, Futier, Emmanuel, Petit, Antoine, Gerard, Adeline, Marret, Emmanuel, Solier, Marc, Jaber, Samir, Prades, Albert, Krassler, Jens, Merzky, Simone, de Abreu, Marcel Gama, Uhlig, Christopher, Kiss, Thomas, Bundy, Anette, Bluth, Thomas, Gueldner, Andreas, Spieth, Peter, Scharffenberg, Martin, Thiem, Denny Tran, Koch, Thea, Treschan, Tanja, Schaefer, Maximilian, Bastin, Bea, Geib, Johann, Weiss, Martin, Kienbaum, Peter, Pannen, Benedikt, Gottschalk, Andre, Konrad, Mirja, Westerheide, Diana, Schwerdtfeger, Ben, Wrigge, Hermann, Simon, Philipp, Reske, Andreas, Nestler, Christian, Valsamidis, Dimitrios, Stroumpoulis, Konstantinos, Antholopoulos, Georgios, Andreou, Antonis, Karapanos, Dimitris, Theodoraki, Kassiani, Gkiokas, Georgios, Tasoulis, Marios-Konstantinos, Sidiropoulou, Tatiana, Zafeiropoulou, Foteini, Florou, Panagiota, Pandazi, Aggeliki, Tsaousi, Georgia, Nouris, Christos, Pourzitaki, Chryssa, Bystritski, Dmitri, Pizov, Reuven, Eden, Arieh, Pesce, Caterina Valeria, Campanile, Annamaria, Marrella, Antonella, Grasso, Salvatore, De Michele, Michele, Bona, Francesco, Giacoletto, Gianmarco, Sardo, Elena, Sottosanti, Luigi Giancarlo Vicari, Solca, Maurizio, Volta, Carlo Alberto, Spadaro, Savino, Verri, Marco, Ragazzi, Riccardo, Zoppellari, Roberto, Cinnella, Gilda, Raimondo, Pasquale, La Bella, Daniela, Mirabella, Lucia, D'antini, Davide, Pelosi, Paolo, Molin, Alexandre, Brunetti, Iole, Gratarola, Angelo, Pellerano, Giulia, Sileo, Rosanna, Pezzatto, Stefano, Montagnani, Luca, Pasin, Laura, Landoni, Giovanni, Zangrillo, Alberto, Beretta, Luigi, Di Parma, Ambra Licia, Tarzia, Valentina, Dossi, Roberto, Sassone, Marta Eugenia, Sances, Daniele, Tredici, Stefano, Spano, Gianluca, Castellani, Gianluca, Delunas, Luigi, Peradze, Sopio, Venturino, Marco, Arpino, Ines, Sher, Sara, Tommasino, Concezione, Rapido, Francesca, Morelli, Paola, Vargas, Maria, Servillo, Giuseppe, Cortegiani, Andrea, Raineri, Santi Maurizio, Montalto, Francesca, Russotto, Vincenzo, Giarratano, Antonino, Baciarello, Marco, Generali, Michela, Cerati, Giorgia, Leykin, Yigal, Bressan, Filippo, Bartolini, Vittoria, Zamidei, Lucia, Brazzi, Luca, Liperi, Corrado, Sales, Gabriele, Pistidda, Laura, Severgnini, Paolo, Brugnoni, Elisa, Musella, Giuseppe, Bacuzzi, Alessandro, Muhardri, Dalip, Gecaj-Gashi, Agreta, Sada, Fatos, Bytyqi, Adem, Karbonskiene, Aurika, Aukstakalniene, Ruta, Teberaite, Zivile, Salciute, Erika, Tikuisis, Renatas, Miliauskas, Povilas, Jurate, Sipylaite, Kontrimaviciute, Egle, Tomkute, Gabija, Xuereb, John, Bezzina, Maureen, Borg, Francis Joseph, Hemmes, Sabrine, Schultz, Marcus, Hollmann, Markus, Wiersma, Irene, Binnekade, Jan, Bos, Lieuwe, Nijbroek, Sunny, Boer, Christa, Duvekot, Anne, ’t Veld, Bas in, Werger, Alice, Dennesen, Paul, Severijns, Charlotte, De Jong, Jasper, Hering, Jens, van Beek, Rienk, Ivars, Stefan, Jammer, Ib, Breidablik, Alena, Hodt, Katharina Skirstad, Fjellanger, Frode, Avalos, Manuel Vico, Mellin-Olsen, Jannicke, Andersson, Elisabeth, Shafi-Kabiri, Amir, Molina, Ruby, Wutai, Stanley, Morais, Erick, Tareco, Glória, Ferreira, Daniel, Amaral, Joana, de Lurdes Goncalves Castro, Maria, Cadilha, Susana, Appleton, Sofia, Parente, Suzana, Correia, Mariana, Martins, Diogo, Monteirosa, Angela, Ricardo, Ana, Rodrigues, Sara, Horhota, Lucian, Grintescu, Ioana Marina, Mirea, Liliana, Grintescu, Ioana Cristina, Corneci, Dan, Negoita, Silvius, Dutu, Madalina, Garotescu, Ioana Popescu, Filipescu, Daniela, Prodan, Alexandru Bogdan, Droc, Gabriela, Fota, Ruxandra, Popescu, Mihai, Tomescu, Dana, Petcu, Ana Maria, Tudoroiu, Marian Irinel, Moise, Alida, Guran, Catalin-Traian, Gherghina, Iorel, Costea, Dan, Cindea, Iulia, Copotoiu, Sanda-Maria, Copotoiu, Ruxandra, Barsan, Victoria, Tolcser, Zsolt, Riciu, Magda, Moldovan, Septimiu Gheorghe, Veres, Mihaly, Gritsan, Alexey, Kapkan, Tatyana, Gritsan, Galina, Korolkov, Oleg, Kulikov, Alexander, Lubnin, Andrey, Ovezov, Alexey, Prokoshev, Pavel, Lugovoy, Alexander, Anipchenko, Natalia, Babayants, Andrey, Komissarova, Irina, Zalina, Karginova, Likhvantsev, Valery, Fedorov, Sergei, Lazukic, Aleksandra, Pejakovic, Jasmina, Mihajlovic, Dunja, Kusnierikova, Zuzana, Zelinkova, Maria, Bruncakova, Katarina, Polakovicova, Lenka, Sobona, Villiam, Novak-Supe, Barbka, Pekle-Golez, Ana, Jovanov, Miroljub, Strazisar, Branka, Markovic-Bozic, Jasmina, Novak-Jankovic, Vesna, Voje, Minca, Grynyuk, Andriy, Kostadinov, Ivan, Spindler-Vesel, Alenka, Moral, Victoria, Unzueta, Mari Carmen, Puigbo, Carlos, Fava, Josep, Canet, Jaume, Moret, Enrique, Nunez, Mónica Rodriguez, Sendra, Mar, Brunelli, Andrea, Rodenas, Frederic, Monedero, Pablo, Martinezv, Francisco Hidalgo, Temino, Maria Jose Yepes, Simon, Antonio Martínez, de Abajo Larriba, Ana, Lisi, Alberto, Perez, Gisela, Martinez, Raquel, Granell, Manuel, Vivo, Jose Tatay, Ruiz, Cristina Saiz, de Andrés Ibañez, Jose Antonio, Pastor, Ernesto, Soro, Marina, Ferrando, Carlos, Defez, Mario, Alvares-Santullano, Cesar Aldecoa, Perez, Rocio, Rico, Jesus, Jawad, Monir, Saeed, Yousif, Gillberg, Lars, Hedenstierma, Göran, Bengisun, Zuleyha Kazak, Kazbek, Baturay Kansu, Coskunfirat, Nesil, Boztug, Neval, Sanli, Suat, Yilmaz, Murat, Hadimioglu, Necmiye, Senturk, Nuzhet Mert, Camci, Emre, Kucukgoncu, Semra, Sungur, Zerrin, Sivrikoz, Nukhet, Ozgen, Serpil Ustalar, Toraman, Fevzi, Selvi, Onur, Senturk, Ozgur, Yildiz, Mine, Kuvaki, Bahar, Gunenc, Ferim, Kucukguclu, Semih, Ozbilgin, Şule, Maral, Jale, Canli, Seyda, Arun, Oguzhan, Saltali, Ali, Aydogan, Eyup, Akgun, Fatma Nur, Sanlikarip, Ceren, Karaman, Fatma Mine, Mazur, Andriy, Vorotyntsev, Sergiy, Rousseau, Guy, Barrett, Colin, Stancombe, Lucia, Shelley, Ben, Scholes, Helen, Limb, James, Rafi, Amir, Wayman, Lisa, Deane, Jill, Rogerson, David, Williams, John, Yates, Susan, Rogers, Elaine, Pulletz, Mark, Moreton, Sarah, Jones, Stephanie, Venkatesh, Suresh, Burton, Maudrian, Brown, Lucy, Goodall, Cait, Rucklidge, Matthew, Fuller, Debbie, Nadolski, Maria, Kusre, Sandeep, Lundberg, Michael, Everett, Lynn, Nutt, Helen, Zuleika, Maka, Carvalho, Peter, Clements, Deborah, Creagh-Brown, Ben, Watt, Philip, Raymode, Parizade, Pearse, Rupert, Mohr, Otto, Raj, Ashok, Creary, Thais, Chishti, Ahmed, Bell, Andrea, Higham, Charley, Cain, Alistair, Gibb, Sarah, Mowat, Stephen, Franklin, Danielle, West, Claire, Minto, Gary, Boyd, Nicholas, Mills, Gary, Calton, Emily, Walker, Rachel, Mackenzie, Felicity, Ellison, Branwen, Roberts, Helen, Chikungwa, Moses, Jackson, Clare, Donovan, Andrew, Foot, Jayne, Homan, Elizabeth, Montgomery, Jane, Portch, David, Mercer, Pauline, Palmer, Janet, Paddle, Jonathan, Fouracres, Anna, Datson, Amanda, Andrew, Alyson, Welch, Leanne, Rose, Alastair, Varma, Sandeep, Simeson, Karen, Rambhatla, Mrutyunjaya, Susarla, Jaysimha, Marri, Sudhakar, Kodaganallur, Krishnan, Das, Ashok, Algarsamy, Shivarajan, Colley, Julie, Davies, Simon, Szewczyk, Margaret, Smith, Thomas, Fernandez-Bustamante, Ana, Luzier, Elizabeth, Almagro, Angela, Melo, Marcos Vidal, Fernando, Luiz, Sulemanji, Demet, Sprung, Juraj, Weingarten, Toby, Kor, Daryl, Scavonetto, Federica, Tze, Yeo, Vermeulen, Tom D., Hol, Liselotte, Swart, Pien, Mills, Gary H., Putensen, Christian, Serpa Neto, Ary, Vidal Melo, Marcos F., Hollmann, Markus W., Gama de Abreu, Marcelo, Schultz, Marcus J., Hemmes, Sabrine N., and van Meenen, David M.

Published
2024
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