Author: "Ezanno P" - Searchworks@Jio Institute Digital Library Search Results

289 results on '"Ezanno P"'

1. Individual or collective treatments: how to target antimicrobial use to limit the spread of respiratory pathogens among beef cattle?

Author: Sorin-Dupont, Baptiste, Poyard, Antoine, Assie, Sebastien, Picault, Sebastien, and Ezanno, Pauline
Subjects: Quantitative Biology - Populations and Evolution
Abstract: The overuse of antibiotics has become a major global concern due to its role in diminishing treatment effectiveness and positively selecting antibiotic-resistant bacterial strains. This issue is particularly important in the beef cattle sector, where Bovine Respiratory Diseases (BRD) impose significant economic and welfare burdens. BRD are complex, multifactorial conditions primarily affecting young calves and feedlot cattle, caused by a combination of viral and bacterial pathogens, environmental factors, and stressors. Despite efforts to reduce antimicrobial use (AMU), the cattle production system remains heavily reliant on antibiotics to control BRD, often through the implementation of collective treatments to prevent outbreaks. This study aimed at evaluating the impact of various treatment practices on the spread of BRD, specifically focusing on criteria for implementing collective treatments. Using a mechanistic stochastic model, we simulated the spread of \textit{Mannheimia haemolytica} in a multi-pen fattening operation under sixteen different scenarios, considering pen composition, individual risk levels, and treatment strategies. Our findings suggest that an alternative criterion for collective treatments based on the speed of the disease spread, could reduce BRD incidence and AMU more effectively than conventional methods. This research highlights the importance of responsible treatment practices and the potential benefits of novel criteria for collective treatment strategies in improving animal health. Moreover, it emphasizes the need for transparency on the exposure to risk factors along the production chain., Comment: 22 pages, 10 figures
Published: 2024

2. Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya

Author: Loisel, Léa, Raquin, Vincent, Ratinier, Maxime, Ezanno, Pauline, and Beaunée, Gaël
Subjects: Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods, Statistics - Computation
Abstract: Arboviruses represent a significant threat to human, animal, and plant health worldwide. To elucidate transmission, anticipate their spread and efficiently control them, mechanistic modelling has proven its usefulness. However, most models rely on assumptions about how the extrinsic incubation period (EIP) is represented: the intra-vector viral dynamics (IVD), occurring during the EIP, is approximated by a single state. After an average duration, all exposed vectors become infectious. Behind this are hidden two strong hypotheses: (i) EIP is exponentially distributed in the vector population; (ii) viruses successfully cross the infection, dissemination, and transmission barriers in all exposed vectors. To assess these hypotheses, we developed a stochastic compartmental model which represents successive IVD stages, associated to the crossing or not of these three barriers. We calibrated the model using an ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) method with model selection. We systematically searched for literature data on experimental infections of Aedes mosquitoes infected by either dengue, chikungunya, or Zika viruses. We demonstrated the discrepancy between the exponential hypothesis and observed EIP distributions for dengue and Zika viruses and identified more relevant EIP distributions . We also quantified the fraction of infected mosquitoes eventually becoming infectious, highlighting that often only a small fraction crosses the three barriers. This work provides a generic modelling framework applicable to other arboviruses for which similar data are available. Our model can also be coupled to population-scale models to aid future arbovirus control.
Published: 2024

3. Inference using a composite-likelihood approximation for stochastic metapopulation model of disease spread

Author: Beaunée, Gaël, Ezanno, Pauline, Joly, Alain, Nicolas, Pierre, and Vergu, Elisabeta
Subjects: Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods, Statistics - Computation
Abstract: Spatio-temporal pathogen spread is often partially observed at the metapopulation scale. Available data correspond to proxies and are incomplete, censored and heterogeneous. Moreover, representing such biological systems often leads to complex stochastic models. Such complexity together with data characteristics make the analysis of these systems a challenge. Our objective was to develop a new inference procedure to estimate key parameters of stochastic metapopulation models of animal disease spread from longitudinal and spatial datasets, while accurately accounting for characteristics of census data. We applied our procedure to provide new knowledge on the regional spread of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}), which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map} spread between herds through trade movements was modeled with a stochastic mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in 12,857 dairy herds in Brittany (western France) and partial data on animal infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference was performed using a new criterion based on a Monte-Carlo approximation of a composite likelihood, coupled to a numerical optimization algorithm (Nelder-Mead Simplex-like). Our criterion showed a clear superiority to alternative ones in identifying the right parameter values, as assessed by an empirical identifiability on simulated data. Point estimates and profile likelihoods allowed us to establish the initial state of the system, identify the risk of pathogen introduction from outside the metapopulation, and confirm the assumption of the low sensitivity of the diagnostic test. Our inference procedure could easily be applied to other spatio-temporal infection dynamics, especially when ABC-like methods face challenges in defining relevant summary statistics.
Published: 2023

4. Does learning style influence performance in objective structured clinical examinations?

Author: Anne-Cécile Ezanno, Florie Pirot, Julie Lavolé, Guillaume Le Gall, Raphaelle Mestiri, Charlotte Delattre, Sébastien Le Burel, Sandra Le Mener, Marie Gominet, Cécile Ficko, and Hubert Nielly
Subjects: Objective structured clinical examinations (OSCEs), Education, Learning style, LSQ-Fa, Special aspects of education, LC8-6691, Medicine
Abstract: Abstract Background Objective Structured Clinical Examinations (OSCEs) aim to assess medical students’ clinical skills rather than just their theoretical knowledge. We propose a study between the learning style of second-cycle medical students and their performance in OSCEs. Methods During their internship at our hospital, students were invited to complete the LSQ-Fa, a questionnaire designed to identify their preferences among 4 learning styles: active, reflective, theoretical, and pragmatic. In parallel, an evaluation of their clinical skills was conducted through OSCEs. We then performed an analysis to establish a correlation between learning styles and OSCE performance, to better understand how learning preferences influence academic outcomes. Results Between April 2021 and January 2023, 55 students were evaluated by OSCEs and had completed the LSQ-Fa. The average OSCE score was 14.8/20. The theoretical learning style was predominant in 47.3% of students, followed by active (27.3%), reflective (14.6%), and pragmatic (5.4%), with 3 cases of mixed active and theoretical styles (5.4%). No significant correlation was observed between learning style and OSCE performance (p = 0.28), although students with a pragmatic style recorded a slightly higher average. Conclusion This study highlights the importance of considering diverse learning styles in the design of medical education programs.
Published: 2024
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5. Rewiring cattle movements to limit infection spread

Author: Thibaut Morel-Journel, Pauline Ezanno, and Elisabeta Vergu
Subjects: Control strategy, epidemiology, data-based, network, stochastic model, Veterinary medicine, SF600-1100
Abstract: Abstract Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.
Published: 2024
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6. Does learning style influence performance in objective structured clinical examinations?

Author: Ezanno, Anne-Cécile, Pirot, Florie, Lavolé, Julie, Gall, Guillaume Le, Mestiri, Raphaelle, Delattre, Charlotte, Le Burel, Sébastien, Le Mener, Sandra, Gominet, Marie, Ficko, Cécile, and Nielly, Hubert
Published: 2024
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7. Rewiring cattle movements to limit infection spread

Author: Morel-Journel, Thibaut, Ezanno, Pauline, and Vergu, Elisabeta
Published: 2024
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8. Building integrated plant health surveillance: a proactive research agenda for anticipating and mitigating disease and pest emergence

Author: Soubeyrand, S., Estoup, A., Cruaud, A., Malembic-Maher, S., Meynard, C., Ravigné, V., Barbier, M., Barrès, B., Berthier, K., Boitard, S., Dallot, S., Gaba, S., Grosdidier, M., Hannachi, M., Jacques, M.-A., Leclerc, M., Lucas, P., Martinetti, D., Mougel, C., Robert, C., Roques, A., Rossi, J.-P., Suffert, F., Abad, P., Auger-Rozenberg, M.-A., Ay, J.-S., Bardin, M., Bernard, H., Bohan, D. A., Candresse, T., Castagnone-Sereno, P., Danchin, E. G. J., Delmas, C. E. L., Ezanno, P., Fabre, F., Facon, B., Gabriel, E., Gaudin, J., Gauffre, B., Gautier, M., Guinat, C., Lavigne, C., Lemaire, O., Martinez, C., Michel, L., Moury, B., Nam, K., Nédellec, C., Ogliastro, M., Papaïx, J., Parisey, N., Poggi, S., Radici, A., Rasplus, J.-Y., Reboud, X., Robin, C., Roche, M., Rusch, A., Sauvion, N., Streito, J.-C., Verdin, E., Walker, A.-S., Xuéreb, A., Thébaud, G., and Morris, C. E.
Published: 2024
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9. Role of the repartition of wetland breeding sites on the spatial distribution of Anopheles and Culex, human disease vectors in Southern France

Author: Fontenille Didier, Ezanno Pauline, Balenghien Thomas, Cailly Priscilla, Toty Céline, and Tran Annelise
Subjects: Infectious and parasitic diseases, RC109-216
Abstract: Abstract Background In this study, carried out in the Camargue region (France), we combined entomological data with geomatic and modelling tools to assess whether the location of breeding sites may explain the spatial distribution of adult mosquitoes. The species studied are important and competent disease vectors in Europe: Culex modestus Ficalbi and Cx. pipiens Linnaeus (West Nile virus), Anopheles atroparvus Van Thiel, a former Plasmodium vector, and An. melanoon Hackett, competent to transmit Plasmodium. Using a logistic regression model, we first evaluated which land cover variables determined the presence of Culex and Anopheles larva. The resulting probability map of larval presence then was used to project the average probability of finding adults in a buffer area. This was compared to the actual number of adults collected, providing a quantitative assessment of adult dispersal ability for each species. Results The distribution of Cx. modestus and An. melanoon is mainly driven by the repartition of irrigated farm fields and reed beds, their specific breeding habitats. The presence of breeding sites explained the distribution of adults of both species. The buffer size, reflecting the adult dispersal ability, was 700 m for Cx. modestus and 1000 m for An. melanoon. The comparatively stronger correlation observed for Cx. modestus suggested that other factors may affect the distribution of adult An. melanoon. We did not find any association between Cx. pipiens larval presence and the biotope due to the species' ubiquist character. Conclusion By applying the same method to different species, we highlighted different strengths of association between land cover (irrigated farm fields and reed beds), larval presence and adult population distribution. This paper demonstrates the power of geomatic tools to quantify the spatial organization of mosquito populations, and allows a better understanding of links between landcover, breeding habitats, presence of immature mosquito populations and adult distributions for different species.
Published: 2011
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10. Predicting fadeout versus persistence of paratuberculosis in a dairy cattle herd for management and control purposes: a modelling study

Author: Marcé Clara, Ezanno Pauline, Seegers Henri, Pfeiffer Dirk, and Fourichon Christine
Subjects: Veterinary medicine, SF600-1100
Abstract: Abstract Epidemiological models enable to better understand the dynamics of infectious diseases and to assess ex-ante control strategies. For Mycobacterium avium subsp. paratuberculosis (Map), possible transmission routes have been described, but Map spread in a herd and the relative importance of the routes are currently insufficiently understood to prioritize control measures. We aim to predict early after Map introduction in a dairy cattle herd whether infection is likely to fade out or persist, when no control measures are implemented, using a modelling approach. Both vertical transmission and horizontal transmission via the ingestion of colostrum, milk, or faeces present in the contaminated environment were modelled. Calf-to-calf indirect transmission was possible. Six health states were represented: susceptible, transiently infectious, latently infected, subclinically infected, clinically affected, and resistant. The model was partially validated by comparing the simulated prevalence with field data. Housing facilities and contacts between animals were specifically considered for calves and heifers. After the introduction of one infected animal in a naive herd, fadeout occurred in 66% of the runs. When Map persisted, the prevalence of infected animals increased to 88% in 25 years. The two main transmission routes were via the farm's environment and in utero transmission. Calf-to-calf transmission was minor. Fadeout versus Map persistence could be differentiated with the number of clinically affected animals, which was rarely above one when fadeout occurred. Therefore, early detection of affected animals is crucial in preventing Map persistence in dairy herds.
Published: 2011
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11. Pressurized intraperitoneal aerosol chemotherapy, reasons for interrupting treatment: a systematic review of the literature

Author: Ezanno Anne-Cecile, Malgras Brice, and Pocard Marc
Subjects: colon cancer, gastric cancer, mesothelioma, peritoneal metastasis, pipac, pressurized intraperitoneal aerosol chemotherapy, safety process, Medicine, Specialties of internal medicine, RC581-951
Abstract: Pressurized intraperitoneal aerosol chemotherapy (PIPAC) gives encouraging results in the treatment of peritoneal metastasis (PM). The current recommendations require at least 3 sessions of PIPAC. However, some patients do not complete the full treatment course and stop after only 1 or 2 procedures, hence the limited benefit. A literature review was performed, with search terms including “PIPAC” and “pressurised intraperitoneal aerosol chemotherapy.”
Published: 2023
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12. Learning and strategic imitation in modelling farmers’ dynamic decisions on bovine viral diarrhoea vaccination

Author: Lina Cristancho-Fajardo, Elisabeta Vergu, Gaël Beaunée, Sandie Arnoux, and Pauline Ezanno
Subjects: Endemic disease, control, vaccination, BVD, farmers behaviour, Veterinary medicine, SF600-1100
Abstract: Abstract Considering human decision-making is essential for understanding the mechanisms underlying the propagation of real-life diseases. We present an extension of a model for pathogen spread that considers farmers’ dynamic decision-making regarding the adoption of a control measure in their own herd. Farmers can take into account the decisions and observed costs of their trade partners or of their geographic neighbours. The model and construction of such costs are adapted to the case of bovine viral diarrhoea, for which an individual-based stochastic model is considered. Simulation results suggest that obtaining information from geographic neighbours might lead to a better control of bovine viral diarrhoea than considering information from trade partners. In particular, using information from all geographic neighbours at each decision time seems to be more beneficial than considering only the information from one geographic neighbour or trade partner at each time. This study highlights the central role that social dynamics among farmers can take in the spread and control of bovine viral diarrhoea, providing insights into how public policy efforts could be targeted in order to increase voluntary vaccination uptake against this disease in endemic areas.
Published: 2022
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13. Modelling the effects of antimicrobial metaphylaxis and pen size on bovine respiratory disease in high and low risk fattening cattle

Author: Sébastien Picault, Pauline Ezanno, Kristen Smith, David Amrine, Brad White, and Sébastien Assié
Subjects: Epidemiological modelling, bovine respiratory disease, antimicrobial usage, disease control, farming practices, stochastic models, Veterinary medicine, SF600-1100
Abstract: Abstract Bovine respiratory disease (BRD) dramatically affects young calves, especially in fattening facilities, and is difficult to understand, anticipate and control due to the multiplicity of factors involved in the onset and impact of this disease. In this study we aimed to compare the impact of farming practices on BRD severity and on antimicrobial usage. We designed a stochastic individual-based mechanistic BRD model which incorporates not only the infectious process, but also clinical signs, detection methods and treatment protocols. We investigated twelve contrasted scenarios which reflect farming practices in various fattening systems, based on pen sizes, risk level, and individual treatment vs. collective treatment (metaphylaxis) before or during fattening. We calibrated model parameters from existing observation data or literature and compared scenario outputs regarding disease dynamics, severity and mortality. The comparison of the trade-off between cumulative BRD duration and number of antimicrobial doses highlighted the added value of risk reduction at pen formation even in small pens, and acknowledges the interest of collective treatments for high-risk pens, with a better efficacy of treatments triggered during fattening based on the number of detected cases.
Published: 2022
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14. The DECIDE project: from surveillance data to decision-support for farmers and veterinarians [version 1; peer review: 2 approved, 1 approved with reservations]

Author: Céline Faverjon, Miel Hostens, Anders R. Kristensen, Dan B. Jensen, Jenny Frössling, Pauline Ezanno, Britt-Bang Jensen, Fernanda Dórea, Wilma Steeneveld, Luis Pedro Carmo, William Gilbert, Jonathan Rushton, Michael Siegrist, Angela Bearth, Johannes Ripperger, Gerdien van Schaik, Jasmeet Kaler, Sjaak de Wit, Jamuna Siehler, Joaquim Segalés, Beatriz Garcia-Morante, Jade Bokma, Bart Pardon, and Mirjam Nielen
Subjects: Data-driven, decision-support, control, endemic diseases, animal health, welfare, eng, Science, Social Sciences
Abstract: Farmers, veterinarians and other animal health managers in the livestock sector are currently missing sufficient information on prevalence and burden of contagious endemic animal diseases. They need adequate tools for risk assessment and prioritization of control measures for these diseases. The DECIDE project develops data-driven decision-support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE focuses on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on reduced growth and mortality in two of the most important aquaculture species (salmon and trout). For each of these, we (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and meta-information sharing; (iv) build multivariate and multi-level models for creating early warning systems; and (v) rank interventions based on multiple criteria. Together, all of this forms decision-support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. The results of DECIDE lead to improved use of surveillance data and evidence-based decisions on disease control. Improved disease control is essential for a sustainable food chain in Europe with increased animal health and welfare and that protects human health.
Published: 2023
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15. The effect of risk-based trading and within-herd measures on Mycobacterium avium subspecies paratuberculosis spread within and between Irish dairy herds

Author: Biemans, F., Arnoux, S., More, S.J., Tratalos, J.A., Gavey, L., and Ezanno, P.
Published: 2022
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16. Modelling transmission of Mycobacterium avium subspecies paratuberculosis between Irish dairy cattle herds

Author: Floor Biemans, Jamie Tratalos, Sandie Arnoux, George Ramsbottom, Simon J. More, and Pauline Ezanno
Subjects: Johne’s disease, stochastic model, dairy cows, infectious disease, data driven, Veterinary medicine, SF600-1100
Abstract: Abstract Bovine paratuberculosis is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is mainly transmitted between herds through movement of infected but undetected animals. Our objective was to investigate the effect of observed herd characteristics on Map spread on a national scale in Ireland. Herd characteristics included herd size, number of breeding bulls introduced, number of animals purchased and sold, and number of herds the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds. A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography. Herds were connected with each other through observed animal trade movements. Data consisted of 13 353 herds, with 4 494 768 dairy female animals, and 72 991 breeding bulls. We showed that the probability of an infected animal being introduced into the herd increases both with an increasing number of animals that enter a herd via trade and number of herds from which animals are sourced. Herds that both buy and sell a lot of animals pose the highest infection risk to other herds and could therefore play an important role in Map spread between herds.
Published: 2022
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17. Preface of the African swine fever modelling challenge special issue

Author: Pauline Ezanno, Sebastien Picault, and Timothée Vergne
Subjects: Infectious and parasitic diseases, RC109-216
Published: 2023
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18. Modelling the effects of antimicrobial metaphylaxis and pen size on bovine respiratory disease in high and low risk fattening cattle

Author: Picault, Sébastien, Ezanno, Pauline, Smith, Kristen, Amrine, David, White, Brad, and Assié, Sébastien
Published: 2022
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19. Modelling transmission of Mycobacterium avium subspecies paratuberculosis between Irish dairy cattle herds

Author: Biemans, Floor, Tratalos, Jamie, Arnoux, Sandie, Ramsbottom, George, More, Simon J., and Ezanno, Pauline
Published: 2022
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20. Learning and strategic imitation in modelling farmers’ dynamic decisions on bovine viral diarrhoea vaccination

Author: Cristancho-Fajardo, Lina, Vergu, Elisabeta, Beaunée, Gaël, Arnoux, Sandie, and Ezanno, Pauline
Published: 2022
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21. Rewiring cattle trade movements helps to control bovine paratuberculosis at a regional scale

Author: Ezanno, P., Arnoux, S., Joly, A., and Vermesse, R.
Published: 2022
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22. Reasons for stopping Pressurized IntraPeritoneal Aerosol Chemotherapy (PIPAC): A retrospective study to improve future patient selection

Author: Anne-Cécile Ezanno, Brice Malgras, Pierre-Louis Conan, Adeline Aime, Jade Fawaz, Hugo Picchi, Solène Doat, and Marc Pocard
Subjects: Medicine, Science
Published: 2023

23. Mechanistic models of Rift Valley fever virus transmission: A systematic review.

Author: Hélène Cecilia, Alex Drouin, Raphaëlle Métras, Thomas Balenghien, Benoit Durand, Véronique Chevalier, and Pauline Ezanno
Subjects: Arctic medicine. Tropical medicine, RC955-962, Public aspects of medicine, RA1-1270
Abstract: Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.
Published: 2022
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24. A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

Author: Bravo, Laura, Simões, Joana FF, Cardoso, Victor R, Adisa, Adewale, Aguilera, Maria L, Arnaud, Alexis, Biccard, Bruce, Calvache, Jose, Chernbumroong, Saisakul, Elhadi, Muhammed, Ghosh, Dhruv, Gujjuri, Rohan, Harrison, Ewen, Ho, Michael WS, Kasivisvanathan, Veerappan, Kouli, Omar, Lederhuber, Hans, Li, Elizabeth, Löffler, Markus W, Isik, Arda, Marcus, Hani, Martin, Janet, McLean, Kenneth A, Minaya-Bravo, Ana, Modolo, María Marta, Nepogodiev, Dmitri, Pellino, Gianluca, Picciochi, Maria, Pockney, Peter, van Ramshorst, Gabriëlle, Riad, Aya, Sayyed, Raza, Sund, Malin, Gkoutos, Georgios, Bhangu, Aneel A, Glasbey, James C, Bravo, L, Cardoso, V, Glasbey, J, Chernbumroong, S, Nepogodiev, D, Simoes, JFF, Mclean, KA, Bhangu, AA, Gkoutos, G, Kadir, B, Omar, O, Revell, E, Bahrami-Hessari, M, Ahmed, Waheed-Ul-Rahman, Argus, Leah, Ball, Alasdair, Bhangu, Aneel, Bywater, Edward P, Blanco-Colino, Ruth, Brar, Amanpreet, Chaudhry, Daoud, Dawson, Brett E, Duran, Irani, Elhadi, Muhammed, Glasbey, James C, Gujjuri, Rohan R, Jones, Conor S, Harrison, Ewen M, Kamarajah, Sivesh K, Keatley, James M, Lawday, Samuel, Li, Elizabeth, Mann, Harvinder, Marson, Ella J, Mclean, Kenneth A, Nepogodiev, Dmitri, Norman, Lisa, Ots, Riinu, Outani, Oumaima, Picciochi, Maria, Santos, Irène, Shaw, Catherine, Taylor, Elliott H, Trout, Isobel M, Varghese, Chris, Venn, Mary L, Xu, William, Dajti, Irida, Gjata, Arben, Kacimi, Salah Eddine Oussama, Boccalatte, Luis, Modolo, Maria Marta, Cox, Daniel, Pockney, Peter, Aigner, Felix, Kronberger, Irmgard Elisabeth, Samadov, Elgun, Alderazi, Amer, Padmore, Greg, van Ramshorst, Gabrielle, Lawani, Ismaïl, Cerovac, Anis, Delibegovic, Samir, Baiocchi, Glauco, Gomes, Gustavo Mendonça Ataíde, Lima Buarque, Igor, Gohar, Muhammad, Slavchev, Mihail, Nwegbu, Chukwuemeka, Agarwal, Arnav, Brar, Amanpreet, Martin, Janet, Ng-Kamstra, Joshua, Olivos, Maricarmen, Lou, Wenhui, Ren, Dong-Lin, Calvache, Jose Andres, Perez Rivera, Carlos J-, Danic Hadzibegovic, Ana, Kopjar, Tomislav, Mihanovic, Jakov, Avilés Jiménez, Pablo Mijahil, Gouvas, Nikolaos, Klat, Jaroslav, Novysedlák, René, Amisi, Nicolas, Christensen, Peter, El-Hussuna, Alaa, Batista, Sylvia, Lincango-Naranjo, Eddy, Emile, Sameh, Arévalo Sandoval, Danilo Alfonso, Dhufera, Hailu, Hailu, Samuel, Mengesha, Mengistu G, Kauppila, Joonas H, Arnaud, Alexis P, Demetrashvili, Zaza, Albertsmeier, Markus, Lederhuber, Hans, Löffler, Markus W., Kwesi Acquah, Daniel, Ofori, Bernard, Tabiri, Stephen, Metallidis, Symeon, Tsoulfas, Georgios, Aguilera-Arevalo, Maria-Lorena, Recinos, Gustavo, Mersich, Tamás, Wettstein, Dániel, Ghosh, Dhruva, Kembuan, Gabriele, Brouki Milan, Peiman, Khosravi, Mohammad Hossein, Mozafari, Masoud, Hilmi, Ahmed, Mohan, Helen, Zmora, Oded, Gallo, Gaetano, Pata, Francesco, Pellino, Gianluca, Fujimoto, Yuki, Kuroda, Naoto, Satoi, Sohei, Abou Chaar, Mohamad K., Ayasra, Faris, Fakhradiyev, Ildar, Hamdun, Intisar Hisham Said, Jin-Young, Jang, Jamal, Mohammad, Karout, Lina, Elhadi, Muhammed, Gulla, Aiste, Rasoaherinomenjanahary, Fanjandrainy, Samison, Luc Hervé, Roslani, April Camilla, Durán Sánchez, Iran Irani, Gonzalez, Diana Samantha, Martinez, Laura, Martínez, María José, Nayen, Alejandra, Ramos-De la Medina, Antonio, Nunez, Jade, Outani, Oumaima, Nashidengo, Pueya Rashid, Shrestha, Ashish Lal, Jonker, Pascal, Kruijff, Schelto, Noltes, Milou, Steinkamp, Pieter, Varghese, Chris, Wright, Deborah, Abdur-Rahman, Lukman, Ademuyiwa, Adesoji, Adisa, Adewale, Osinaike, Babatunde, Seyi-Olajide, Justina, Williams, Omolara, Williams, Emmanuel, Pejkova, Sofija, Al Balushi, Zainab, Qureshi, Ahmad Uzair, Sayyed, Raza, Abo Mohsen, Mustafa, Abukhalaf, Sadi A., Cukier, Moises, Gomez-Fernandez, Hugo, Shu Yip, Sebastian, Vasquez Ojeda, Ximena Paola, Sacdalan, Marie Dione, Major, Piotr, Azevedo, José, Cunha, Miguel F., Zarour, Ahmad, Bonci, Eduard-Alexandru, Negoi, Ionut, Efetov, Sergey, Kochetkov, Viktor, Litvin, Andrey, Allen Ingabire, Jc, Bucyibaruta, Georges, Faustin, Ntirenganya, 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Maa Albared, Sara, Naem, Antoine, Alnahr, Hareth, Jisry, Ghassan, Hammed, Ali, Isik, Arda, Francis Xaviour, Okedi, Turinawe, Gaston, Mubezi, Isaac, Sikakulya, Franck K., Kakeeto, Andrew, Kabweru, Dr. Wilberforce M., Lekuya, Hervé Monka, Kiweewa, Ronald, Lule, Herman, Matovu, Paul, Isaac, Otolia, Mashhour, Mohamed, El Helw, Amin, Alshryda, Sattar, Mohamed, Awadelkarim, Abbas, Ferial Mohamed Ali, Mohammed, Diary, Aldlyami, Ehab, Kundra, Rakesh, Michael, Antony Louis Rex, Alsaadi, Hayder, Khalil, Kareem S., Dbeis, Rachel, Shaikh, Shafaque, Ferry, Jenny, Jamal, Aiman, Siddique, Haleema, Das, Rishi, Ponugoti, Nikhil, Kamarajah, Sivesh K, Rattanasirivilai, Pornjittra, Sultana, Asma, Mosley, Frances, Chan, Matthew, Bateman, Antony, Davies-Jones, Gareth, Georgiades, Fanourios, Stewart, Grant D., Ahmadi, Navid, Coonar, Aman, Baig, Mariam, Khatri, Chetan, Surendran, Arthika, Sonksen, Julian, Sinnerton, Robert, Brennan, Caitlin, Gemma, Greenhalgh, Michael, Emerson, Hannah, Singisetti, Kiran, Totty, Joshua, Wilson, Michael, Lo, Terence, Corbett, Harriet, Okonkwo, Ijeoma, Arbane, Gill, El-Boghdadly, Kariem, Kerawala, Cyrus, Parmar, Chetan, Abbott, Tom, Bath, Michael, Odejinmi, Funlayo, Sagar, Jayesh, Talwar, Rishi, Newman, Samuel, Hammond, John, Moir, John, Duric, Natalie, Szakmany, Tamas, Iftikhar Talib, Ahmar, Youssef, Mina, Lewis-Lloyd, Christopher, Lami, Mariam, Ayub, Khurram, Dean, Benjamin, Balasubramanya, Supriya, Lakpriya, Sathya, Rogers, Luke, Turner, Paul, Maher, Mark, Sigamoney, Kohila, Edwards, John, El Kafsi, Jihène, Hardie, John, Johnson, David, Henein, Christin, Hollyman, Marianne, Agarwal, Ketan, Powell, Simon, Chauhan, Govind Singh, Patel, Rakesh, Gagnier, Joel, Carmichael, Heather, Olson, Kristofor A., Etchill, Eric, Incorvia, Joseph, Hirji, Sameer, Naunheim, Matthew, Drake, Frederick, Kaafarani, Haytham, Reinke, Caroline, Alecci, Anna, Vaysburg, Dennis, Rodriquez, Jennifer, Shih, Emily, Ban, Vin Shen, Coleman, Julia, Rice, Henry E., Kaups, Krista, 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Maximiliano, Zezular, Damaris Idara Anabel, Abuawad, Carla, Albani Forneris, Agustin, Brandariz, Rodrigo, Burchakchi, Arturo, Buttaro, Martin, Cano Busnelli, Virginia, Carminatti, Tomas, Cereghini, Julian, Crespi Amor, María Sol, Figari, Marcelo, Gonzalez Salazar, Esteban, Higuera, Felipe, Llano, Lionel, Lobos, Pablo, Mc Loughlin, Santiago, Mendoza, Efrain, Mercado, Pedro, Moyano, Pablo, Noll, Florencia, Odetto, Diego, Ramos, María Lourdes, Saadi, Jose, Scherñuk, Jordán, Slullitel, Pablo, Taboada, María Victoria, Vagni, Roberto, Montal, Silvina, Alvarez, Fernando Andres, Bruera, Nicolás, Parodi, Matias, Picon molina, Héctor, Belisle, Diego, Esteban, Agustin, García, José Sebastian, Granero, Lucas, Yaryura Montero, José Gabriel, D’Addino, José Luis, Calderón Arancibia, José Alfredo, John, Brooke-Smith, Mark, Hii, Amanda, Kundu, Nikhil, Marshall-Webb, Matthew, McVeay, Christina, Ong, Bee Shan, Parker, Dominic, Barnett, Dylan Richard, Buddingh, Karel, Dobbins, Christopher, Dudi-Venkata, Nagendra, Haque, Izhar-Ul, Herath, Matheesha, Kroon, Hidde M, Lim, Alicia, Moller, Cea-Cea, Neo, Eu Nice, Neo, Eu Ling, Russell, Christine, Tourani, Saam, Tran, Steven, Yin, Yijie, Watson, Matthew, Barker, John, Rugendyke, Anya, Watson, Eleanor, Sercombe, Ashleigh, Tee, Chin Li, Batstone, Martin, Field, Michael, Scott, Benjamin, Stevens, Sean, Charlton, Gabriella, Chen, Si, Gauri, Neha, Hayhurst, Ross, Jang, Seyoung, Zhang, Helen, Latif, Haider, Nagra, Sonal, Rudock, Gareth, Tan, Hannah, Underwood, Kirk, Youssef, Daniel, Bengeri, Savitha, Cheah, Hock Ping, Chen, Paul, Clark, Sara, Dawson, Amanda Caroline, Devan, Eliya, Fitt, Sophia, Gaul, John, Jolly, Danielle, Kane, James Dimitri, Kwok, Kelvin, Laura, Sharon, Lee, Vivian, Liang, Ina, Luo, Haili, Miles, Stephanie, Noor, Anthony, Simpson, Renwick, Cox, Aram, Thani, Nova, Egoroff, Natasha, Farik, Shebani, Moss, Jana-Lee, Wright, Deborah, Salama, Paul, Phan, Du, Townend, David, Chuan, Alwin, Barnett, Stephen, Hasan Kheslat, Hajar, Jamieson-Grigg, Chloe, Larner, Brett, Proud, David, Riddiough, Georgina, Seevanayagam, Sivendran, Zhao, Chris, An, Vinna, Drake, Anna, Hunt, Benjamin, Jain, Anshini, Lim, Christopher Seng Hong, Luck, Tara, Maroske, Georgia, Roubos, Nicholas, Sengupta, Shomik, Tsigaras, Zac, Ward, Salena, Zhou, Jessie, Coates, Amy, Nataraja, Ramesh, Pacilli, Maurizio, Gillard, Andrew, Petrakis, Nikki, Qureshi, Maryum, Seow, Sean Ezekiel, Vivian-Taylor, Josephine, Wheatley, Jennifer, O’Connor, Brendan, Teague, Warwick, Hii, Michael Wei, Isaacs, Anna, Johnson, Mary Ann, Price, Veronique, Rowcroft, Alistair, Shaw, Kalai, Stark, Claire, Lauritz, Brianne, Ong, Chui Foong, Tang, Howard, Davis, Amelia, Peerally, Bibi Nabeeha, Aubin, Anne-Marie, Gourlay, Ralph, Lim, Zhi Ying, Alsoudani, Ali, Amico, Francesco, Anning, Rebecca, Dale, Tyson, Duggal, Anchal, Ennis, Brendan, Fu, Yi Xin Joanna, Gani, Jonathan, Gelzinnis, Scott, Hadlow, Claudia, Hardstaff, Ruth, Lau, Wayne Yan, Liang, Hsin-Ping, Lim, Daniel, Mcilwain, Elysse, Ming, Yan Joyce, Ocsan, Ryan James, Organ, Nicole, Paterson, Amanda, Perez Cerdeira, Marisol, Ting, Caleb, Willoughby, Ellisha, Bailey, Ashley, Narayanan, Viswanathan, Wilkes, Anna, Zhao, Jie, D’Souza, Carl, Forbes, Clara, Haddleton, Monique, Mohammed Abdul, Naseer, Archer, Leigh, Dilevska, Tina, Hans, Tasvinder, Kumar, Mohit, Pham, Terence, Picazo Pineda, Fernando, Suk-Udom, Supisara, Jeyamanoharan, Nivedan, Sala, Michael, Dravid, Madhulika, McCartney, Conor, Sam, Apostolou, Christos, Berney, Christophe, Lam, Andrew, Ma, Vanessa, Cope, Jennifer, Llorando, Alyssa, Mahin, Humaira Haider, Putnis, Soni, Syed, Faisal, Ireland, Patrick, Zhu, Bonnie, Zitt, Matthias, Königsrainer, Ingmar, Allmer, Caterina, Mitteregger, Martin, Seitinger, Gerald, Duller, David, Mathew, Erwin, Skias, Christoph, Brinskelle, Petra, Holzmeister, Clemens, Kahn, Judith, Kresic, Josip, Lindenmann, Joerg, Lumenta, David, Puchwein, Paul, Singer, Georg, Wolf, Axel, Arco, Daniel, Kilic, Nura, Widschwendter, 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Supha, Melka, Abebe, Nebyou, Chanie, Abera, Gebre, Hiwot, Laeke, Tsegazeab, Mammo, Tihitena Negussie, Sahlu, Abat, Tiruneh, Abraham Genetu, Zewdneh, Betelhem, Fentaw, Jibril, Tabore, Ketema, Addisalem, Abreha, Tassew, Abdissa, Desalegn, Tesfaw, Mequannet, Asmamaw, Dawit, Bedane, Dereje, Bekele, Ephrem, Getie, Aderaw, Girma, Adissu, Shimelash, Dagim, Ayalew, Tewabe, Kassahun, Berhanu, Mulugeta, Esubalew, Sisay, Silamlak, Ibrahim, Adem, Mekete, Alem, Mohammed, Adnan Abdulkadir, Abdullahi, Yasir Younis, Adem, Ephrem, Siyoum, Eneyew Getachew, Zeleke, Ewunetu, Degefe, Derje Worku, Derilo, Habtamu, Eliyas, Nebiyu, Deressa, Yadani, Melese, Lemi, Muleta, Lemesa, Teshome, Abraham, Tesso, Birhanu, Gebrekirstos, Dr.Gebreagziabher, Pitkänen, Joel, Qian, Cheng, Sinikumpu, Jaakko, Dakpé, Stéphanie, Devauchelle, Bernard, Lavagen, Nolwenn, Bastard, François, Bin, Kim, Moukoko, Didier, Alshawared, Fadi, Beyrne, Carlos Daniel, Lugans, Laurene, Doussot, Alexandre, Onorati, Ilaria, Radu, Dana, André, 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Del, Mathilde, Vancon, Antoine, Machemedze, Solomon, Muhemi, Roger, Ndizeye, Olivier, O’Connor, Jennifer, Tchoba, Simplice, Modabber, Ali, Schäfer, Benedikt, Wallqvist, Julia, Ziemann, Sebastian, Anthuber, Matthias, Broecheler, Tobias, Goßlau, Yvonne, Maksymiw, Florian, Sommer, Florian, Sommer, Björn, Loch, Florian N, Aghalarov, Ilgar, Braumann, Chris, Höhn, Philipp, Horn, Julian, Egger, Eva, Enderes, Jana, Strieth, Sebastian, Treede, Hendrik, Wittmann, Maria, Marche, Benedikt, Schulz, Sissy-Amelie, Fuchs, Hans, Bork, Ulrich, Meisel, Cornelia, Petzold, Andrea, Fluegen, Georg, Knoefel, Wolfram Trudo, Solodarenko, Oleksandra, Grützmann, Robert, Hackner, Danilo, Faqar-Uz-Zaman, Sara Fatima, Schnitzbauer, Andreas, Fung, Christian, Leiber, Christian, Neidert, Nicolas, Sandkamp, Richard, Schlager, Daniel, Strähle, Jakob, Spelsberg, Fritz, Becker, Johannes, Gonschor, Bernhard, Herbolzheimer, Marit, Keppler, Lena, Hecker, Matthias, Reichert, Martin, Sander, Michael, Schmidt, Götz, Schneck, Emmanuel, Kleeff, Jorg, Lorenz, Kerstin, Ronellenfitsch, Ulrich, Thomssen, Christoph, Dumpies, Christian W., Fischer, Isabel, Gessner, Michael, Klauke, Friederike, Schmidt, Birte, Vinz, Franziska, Sander, Johannes, Betz, Christian Stephan, Bewarder, Julian, Burg, Simon, Klatte, Till Orla, König, Daniela, Köpke, Leon-Gordian, Stangenberg, Martin, Roux, Frederic, Hakami, Ibrahim Abdullah, Mall, Julian W, Graeb, Christian, Huber-Strößner, Kristin, Pempe, Christina, Kelly, Kathrin, Siebert, Julia, Agrawal, Rachit, Gousias, Konstantinos, Qureischie, Homeira, Meyer, Frank, Rissmann, Anke, Turial, Salmai, Al-Nawas, Bilal, Battista, Marco Johannes, Heider, Julia, Mueller, Lena Katharina, Jentschura, Sina- Louisa Patrizia, Kowalewski, Karl-Friedrich, Rassweiler-Seyfried, Marie-Claire, Reissfelder, Christoph, Rotter, Nicole, Scherl, Claudia, Seyfried, Steffen, Fichter, Andreas, Gempt, Jens, Schäffer, Christoph, Wegmann, Helmut, Aghamaliyev, Ughur, Böcker, Wolfgang, von Ehrlich-Treuenstätt, Viktor H., Ladurner, Roland, Schlager, Justin Gabriel, Smolka, Wenko, Glowalla, Claudio, Schneidmueller, Dorien, Henkel, Karl Wilhelm, Stadler, Josef, Hölz, Katharina, Brunner, Stefan M., Prantl, Lukas, Herzberg, Jonas, Kröger, Marie, Niemeier, Andreas, Sras, Yara, Reinhard, Tobias, Löffler, Markus W., Steidle, Christoph, Dayan, Davut, Greve, Jens, Löbig, Niklas, Schochter, Fabienne, Wezel, Felix, Pusch von, Silke, Schmidbauer, Stefan, Boenicke, Lars, Degener, Stephan, Seiberth, Rose, Smit, Marieke, von Rundstedt, Friedrich-Carl, Germer, Christoph-Thomas, Lock, Johan, Lotz, Christopher, Schlesinger, Tobias, Schmid, Benedikt, Treutlein, Agnes, Agyeman-Duah, Nana Kwaku, Appiah, Enoch, Gakpetor, Delali, Minlah Allah, Stephen, Adu-Aryee, Nii Armah, Agboadoh, Nelson, Bediako Bowan, Antoinette, Brown, George Darko, Clegg-Lamptey, Joe-Nat, Fenu, Benjamin Sena, Kumassah, Philemon, Hussey, Romeo, Agbeno, Evans Kofi, Amoako-Boateng, Mabel, Debrah, Samuel, Doku, Dr Kingsley, Mensah, Philip, Nortey, Michael, Opandoh, Isabella Naa Morkor, Yigah, Makafui, Jiagge, Nuna, Acquaye, Jane, Agyemang-Prempeh, Akosua, Amo-Antwi, Kwabena, Amoah, Michael, Amoah, George, Aning, Daniel Gyawu, Annor Mintah, Dominic, Arthur, Joshua, Darko- Asante, Regina, Gaveh, Valerie, Gyamfi, Frank Enoch, Jonathan, Boakye - Yiadom, Konney, Thomas Okpoti, Kyei, Ishmael, Nimako, Boateng, Ofosu-Barko, Ben Blay, Osabutey, Anita, Sagoe, Robert, Yifieyeh, Abiboye, Ansong, George, Abdul-Mumin, Alhassan, Amadu, Munira, Azahares Leal, German, Buunaaim, Alexis, Cheyuo, Ernest, Daboo Salifu, Latif, Issaka, Adamu, Yakubu, Musah, Grypiotis, Ioannis, Papadopoulou, Triada, Korkolis, Dimitrios, Thomakos, Nikolaos, Dellaportas, Dionysios, Dimitra, Papalouka, Hadjizacharias, Theodoros, Konstadoulakis, Manousos, Massaras, Dimitrios, Vezakis, Antonios, Avgerinos, Konstantinos, Kechagias, Aristotelis, Ioannidis, Argyrios, Patelis, Nikolaos, Deskou, Eirini, Stamos, Nikolaos, Tasis, Nikolaos, Danias, Nikolaos, Selmani, Jonida, Kalamatianos, Theodosis, Stranjalis, George, Kondilis, Panagiotis, Kanellopoulou, Vasiliki, Larentzakis, Andreas, Papanikolaou, Vasileios, Potamianos, Spyridon, Triantafyllou, Tania, Panagiotou, Peter, Avramidis, Emmanouil, Bessias, Nikolaos, Tsiantoula, Paraskevi, Charalabopoulos, Alexandros, Kykalos, Stylianos, Kyros, Eleandros, Machairas, Nikolaos, Mastoraki, Aikaterini, Papalampros, Alexandros, Terras, Alexis, Tsourouflis, Gerasimos, Zografos, Constantinos, Antzaka, Christina, Falara, Areti, Perreas, Konstantinos, Rellia, Panagiota, Tasouli, Androniki, Skolarikos, Andreas, Tzelves, Lazaros, Theodorakis, Emmanuel, Karona, Paraskevi, Tzouganakis, Angelos, Chrysos, Emmanuel, Nikolouzakis, Taxiarchis, Tourountzi, Paraskevi, Al, Aggeliki, Kalfountzos, Christos, Arnaoutoglou, Eleni, Christodoulidis, Gregory, Gkolias, Nick, Hajiioannou, Jiannis, Krestinidis, Giorgos, Ntalouka, Maria, Saini, Fani, Skoulakis, Charalampos, Terzoudis, Christos Dimitrios, Zacharoulis, Dimitris, Δακής, Κωνσταντίνος, Filo, Eva, Kontopoulou, Konstantina, Patoulias, Ioannis, Astreidis, Ioannis, Christidis, Panagiotis, Loutzidou, Lydia, Mantevas, Antonis, Tatsis, Dimitris, Tsakiridis, Ioannis, Iordanidou, Eirini, Brolo, Estuardo, Gutiérrez Ruiz, Alitza, De León Lima, María Alejandra, Flores, Mario-Andrés, Morales, Steffanía, Rivera, Pablo, Rodas, Lesly, Santos, Victor, Sosa, Dianne, Barrios Duarte, Amalia, Talé-Rosales, Luis-Fernando, Villeda, Sergio Alejandro, Anicetti, Joshua, Lowey, Megan, Ardebol, Javier, Barillas, Kathia, Barillas, Sabrina, Recinos, Salvador, Hon, Sophie, Liu, Alex Qinyang, Liu, Shirley, Ip, Ho Wai, Mak, Brian, Mok, Chung Ying, Wong, Kiu Fung, Au-Yeung, Kit Ying, Chan, David Yuen Chung, Chiu, Wang Kei, Ho, Jacky Yan Kit, Kung, Janet Wui Cheung, Ling, Samuel Ka Kin, Lok, Hon Ting, Teoh, Jeremy Yuen-Chun, Wong, Randolph, Antal, Zsuzsanna, Bahrehmand, Kiarash, Sztipits, Tamás, Hidi, László, Piros, Laszlo, Rózsa, Balazs, Herczeg, György, Saftics, György, Kulcsicka-Gut, Judit, Sipos, Zsófia, Majmudar, Hardil, Madabhavi, Irappa, G S, Anitha, Kesarla, Venkatesh, Kumar Venkatappa, Sunil, P B, Hareesh, Shivashankar Chikkanayakanahalli, Santhosh, Sunil, Tanvi, Rajagopal, Niranjana, P L, Thirumanikandan, Mishra, Nitu, Wadhwani, Dr Rekha, Dhurwe, Ritika, Pushpalatha, Kameshwarachari, Raj, Sumit, Singh, Pooja, Kumar, Pankaj, Mittal, Yash, Panigrahi, Sibasish, Parija, Sucheta, Patra, Saroj, Sahu, Rabi, Sultania, Mahesh, Tripathy, Sujit, Kansay, Rajeev, Gupta, Sunil Kumar, Kaman, Lileswar, Salunke, Pravin, Singh, Kavindra, Rela, Mohamed, Goudar, Swati, B, Barani kumar P, Murugesan, Anandan, Subbian, Anbukkani, Kumar, Arvind, Chauhan, Tapan singh, Govil, Akhil, Gupta, Shubhra, Sharma, Ashish, Tiwari, Saurabh, Vishwani, Vartika, Evelyn. R, Nissi, Deb, Mainak, Jayaram, Harish, Nagpal, Pooja, Pathak, Prachi, Choudhary, Gautam Ram, Chugh, Ankita, Elhence, Abhay, Madduri, Vijay, Pandey, Himanshu, Saxena, Rahul, Sharma, Naveen, Shekhar, Shashank, Vishnoi, Jeewan Ram, Bhende, Vishal, Chatterjee, Debarshi, Ghosh, Indranil, Naskar, Upasana, Jain, Prateek, Jain, Deepak, Kewlani, Vishal, Priya, Noopur, Parijat, Anand, Anand, Akshay, Bajaj, Ankur, Chaurasia, Akhilanand, Enny, Loreno E., Kumar, Ambrish, Mishra, Brijesh, Pandey, Amita, Rajan, Shiv, Roy, Ashutosh, Sachan, Rekha, Sankhwar, Pushp, Singh, Uma, Singh, Urmila, Srivastava, Chhitij, Verma, Manju Lata, Yadav, Awdhesh, Agarwal, Gaurav, Chanthar, K.m.m.vishvak, Hoysal, Dileep, Mishra, Anjali, Batra, Nitin, Bhatti, Arun, Dhar, Tapasya, Dutta, Rohini, Haque, Parvez David, Jain, Ritu, Luther, Anil, Mahajan, Amit, Pargal, Dr Pinki, Samuel, Abhishek, Singh, Inderjot, Singh Grewal, Sarvpreet, Singhania, Anusha, Alexander, Philip, Thomas, Josy, Krishna, Sunil, Singh, Narinder, Agarwal, Sunny, Balasubramaniam, Srikant, Dharap, Satish, Mhamane, Rameshwar, Nirgude, Anand, Parab, Sandesh, Peswani, Amit, Sahu, Anjana, Samel, Sarika, Shah, Fagun, Abraham, Bejoy, Asari, Ashishkumar, Badhe, Vivek, Badhwar, Sanjiv, Bothara, Vipul, Dalal, Anuj, Date, Avinash, Deshpande, Mandar, Doshi, Anirudha, Gade, Maya, Gajjar, Shreyash, Ghugare, Dr bhavisha, Jain, Divakar, Jenasamant, Saumya Sekhar, Joshi, Vinay, Kalwadia, Neha, Kayal, Akshat, Kulkarni, Yogesh, Mistry, Rajesh, Mulchandani, Manoj, Parab, Dr. Mrunal, Pednekar, Abhinav, Rahmani, Faizan, Rajput, Sunil, Sandlas, Gursev, Sarkar, Hrishikesh, Shetty, Sanket sadanand, Shukla kulkarni, Anshumala, Thete, Raghavendraswami, Wasnik, Shruti, Dhumane, Parag, Sharma, Rahul Deo, Deshmukh, Anuja, Gupta, Stuti, Nayak, Prakash, Thiagarajan, Shivakumar, Tiwari, Virendra Kumar, Voppuru, Saiesh Reddy, Soni, Rajesh, Bajpai, Minu, Farooque, Kamran, Parshad, Rajinder, Sharma, Vijay, Bansal, Kuldeep, Bains, Lovenish, Bhardwaj, Rohit, Mittal, Abhishek, Ponnusamy, Sabarirajan, Ghodke, Rahul, Himani, Kumar, Ashwani, Gadekar, Anup, Panjwani, Taufiq, Maji, Suvendu, Basu, Somprakas, Kandwal, Pankaj, Kumar, Shashank, Poonia, Dharma Ram, Sadhasivam, Saravanan, Mahakalkar, Chandrashekhar, Choudhury, Yousuf, Das, Devishmita, Goala, Subhadra, Singha, S Thoibisana, Singha, Damayanti, Mehraj, Asif, Parray, Fazl, Shah, Raahil, Wani, Rauf, Hegde, Siddhi, Ankadavar, Dr.sushil, Bafna, Darshan, Lad, Parag, Chisthi, Meer, Jothi, Harihara, .C. Mathew, Godwin David, Abel, Livingston, Barla, Ravi Kishore, Benjamin, Santosh, Bliss, Jeremy, Ete, Geley, James, Deeptiman, Jesudason, Mark Ranjan, Karnan, Gomathi, Mark Samuel, Vasanth, Mittal, Rohin, Nathan, Senthil K, Selvaraj, Daniel, Surendran, Suraj, T D, Hariharan, Thangaraj, Santhosh Kumar, Thomas, Varghese, Dilmy, Mohammad Adya Firmansha, Pratama, Dedy, Sugiarto, Adhrie, Surya, Ilham Utama, Tango, Tamara, Hasyim, Andi, Susanti, Ade, Saputra, Teddy, Syarifuddin, Erwin, Islam, Andi Asadul, Anam, Khoirul, Sugianto, Julius Albert, Lestari, Mayang Indah, Amba, Erick Gamaliel, Apriawan, Tedy, Wathoni, Roidah Taqiyya Zahra, Widyastuti, Yunita, Yun Jufan, Akhmad, Etezadpour, Mohammad, Mehrad-Majd, Hassan, Rajaei, Parisa, Mehdizadeh, Mohammad, Soltani, Mina, Pour, Ali Moazami, Azarnia, Gholamreza, Alizadeh, Narges, Mokarami, Hossein, Tehrani, Shahrzad, Alborzi, Majid, Bagheri, Hamed, Eshraghi, Mohsen, Fatemi manesh, Hassan, Hejazi, Seyed fakhreddin, Pezeshki Modarres, Mahdi, Shafiee, Ali, Vafaeimanesh, Jamshid, Hosseini, Elahe, Nikoupour, Hamed, Bastaninejad, Shahin, Firouzifar, Mohammadreza, Motasaddi zarandy, Masoud, Yazdani, Nasrin, Yousefzadeh Kandevani, Naser, Mortazavi, SM Javad, Ahmadi, Sayedali, Khosravi, Mohammad Hossein, Vosoughi, Fardis, Golbakhsh, Mohammadreza, Al-Isawi, Ali, Al-Masood, Mohammed, Gahtan, Yarub, Ahmed, Abdullah, Hilmi, Ahmed, Muhssein, Haidar, McNestry, Catherine, Ekwere, Bosom, Essajee, Murtaza, Chan Chin, Bruno, Edwards Murphy, Amy, Fleming, Christina, Foley, Niamh, Gardiner, Padraig, Hechtl, Daniel, Kayyal, Mohd yasser, Lyons, Maria, O’Brien, Stephen, Ng, Mei Yee, Oduola, Oladayo, Aljohmani, Lylas, Nugent, Timothy, Harris, Johnathon, Kearney, David, Keenan, Robert Anthony, Nolan, Deirdre, Salloum, Croghan, Stefanie M, Donohoe, Emma, Larkin, John, Lynch, Thomas Hugh, Manecksha, Rustom, Nagassima, Katharina, Barry, Mary, Cullinane, Carolyn, Dowdall, Joseph, Hurson, Conor, Kelly, Orlaith, Kennelly, Rory, Kiernan, Aoife, Murphy, Ben, Njeze, Nwabundo, Reynolds, Ian Sean, Winter, Des, Ravi, Akshaya, Ridgway, Paul, Fowler, Amy, McAnena, Peter, Bahadoor, Muhammad Usaama, Kabir, Syed Mohammad Umar, Khalid, Mohamed Hamed, Mujtaba, Syed Nadeem, Sarwar, Muhammad assam, Sugrue, Michael, Varzgalis, Manvydas, Jones-Whiting, Larne, Lim, Seantee, Ryan, Éanna, Sara, Al-Mukhaizeem, Youssef, Crowley, Clare, Flanagan, Michael, Fogarty, Amy, Glynn, Orna, Hill, Rhodri, Khalid, Muhammad Abdullah, Majeed, Zubair, McCullough, Peter, Neary, Peter, Noone, Anthony, O’Kelly, John, Abu abed, Arsan, Haim, Nadav, Hershkovitz, Yehuda, Majadla, Omar, Rabin, Igor, Zmora, Osnat, Demma, Yonatan Avraham, Fishman, Yuri, Farkas, Amicur, Gozal, Yaacov, Greenman, Dmitry, Ostrovsky, Israel Alexander, Pardes, Rivka, Shahar, Tal, Teren, David, Yahud, Reuven, Yellinek, Shlomo, Cianci, Pasquale, Minafra, Marina, Restini, Enrico, Bussolin, Edoardo, De Prizio, Marco, Sulce, Rezart, Poiasina, Elia, Antonacci, Filippo, Arena, Alessandro, Belvedere, Angela, Bernagozzi, Fabio, Boussedra, Safia, Cescon, Matteo, Cipolli, Alessandro, Daddi, Niccolo’, De Crescenzo, Eugenia, De Iaco, Pierandrea, Del Gaudio, Massimo, Della Gatta, Anna Nunzia, Droghetti, Matteo, Giorgini, Federico A., Lanci Lanci, Antonio, Masetti, Michele, Minni, Francesco, Morezzi, Daniele, Perrone, Anna Myriam, Pignatti, Marco, Puglisi, Silvana Bernadetta, Rottoli, Matteo, Schiavina, Riccardo, Serenari, Matteo, Serra, Margherita, Tesei, Marco, Violante, Tommaso, Aspide, Raffaele, Carretta, Alessandro, Conti, Alfredo, De Vita, Carla, Friso, Filippo, La Corte, Emanuele, Rosetti, Vittoria, Sturiale, Carmelo, Donati, Davide Maria, Palmerini, Emanuela, Berselli, Bruno, Bolognesi, Silvia, Farnia, Francesco, Maremonti, Pietro, Razzaboni, Alessandra, La Mendola, Roberta, Arrigoni, Giulia, Baiocchi, Gian Luca, Molfino, Sarah, Panciani, Pier Paolo, Sartori, Enrico, Zanin, Luca, Perrone, Fabrizio, Cappellani, Alessandro, Giaquinta, Alessia, Gioco, Rossella, Veroux, Pierfrancesco, Cianci, Antonio, Lo Giudice, Arturo, Russo, Giorgio Ivan, Sarpietro, Giuseppe, Scandura, Carmen Emanuela, Barca, Ida, Carnevali, Adriano, De Paola, Gilda, Giannaccare, Giuseppe, Sammarco, Giuseppe, D’Andrea, Marcello, Tosatto, Luigino, Mucilli, Felice, Muraglia, Angelo, Giuliani, Domenico Benvenuto, Segalini, Edoardo, Daniele, Alberto, Gelarda, Enrico, Olearo, Elena, Puppo, Andrea, Testa, Valentina, Porta, Andrea, Birindelli, Arianna, Bernabei, Massimiliano, Feo, Carlo V., Virgilio, Edoardo, Bartolini, Ilenia, Bottari, Andrea, Checcucci, Carlotta, De Vincenti, Rosita, Fambrini, Massimiliano, Fortuna, Laura, Gallo, Oreste, Maltinti, Gherardo, Taddei, Antonio, Michelagnoli, Stefano, Maccagnano, Giuseppe, Pesce, Vito, Ercolani, Giorgio, Fappiano, Francesca, Barberis, Andrea, Filauro, Marco, Santoliquido, Matteo, Aprile, Alessandra, Barra, Fabio, De Rosa, Raffaele, Ferrero, Simone, Sparavigna, Marco, Vagge, Aldo, Gambardella, Denise, Tedesco, Manfredo, Spampinato, Marcello Giuseppe, Chiarelli, Marco, Grandi, Samuele, Lenzi, Riccardo, Muscatello, Luca, Germano’, Antonino Francesco, Giani, Alessandro, Bissacco, Daniele, Cassinotti, Elisa, Pignataro, Lorenzo, Torretta, Sara, Armellin, Daniele, Basato, Silvia, Carrano, Francesco Maria, Colombo, Giovanni, De Lucia, Francesca, De Virgilio, Armando, Ferreli, Fabio, La Raja, Carlotta, Milana, Flavio, Rossi, Vanessa, Russo, Elena, Tamburello, Sara, Zerbi, Alessandro, Bona, Davide, Adamoli, Laura, Ansarin, Mohssen, Chu, Francesco, De Berardinis, Rita, Pietrobon, Giacomo, Sedda, Giulia, Caputo, Maria, Cellerino, Paola, Crespi, Michele Achille, Danelli, Piergiorgio, Ferrara, Francesco, Baccellieri, Domenico, Candiani, Massimo, Casiraghi, Arianna, Cipriani, Federica, De Nardi, Paola, Mortini, Pietro, Nocera, Gianluca, Pozzoni, Mirko, Ruffolo, Alessandro Ferdinando, Spina, Alfio, Barberio, Cristina, Beretta, Luigi, Cavenago, Francesca, Fresilli, Stefano, Landoni, Giovanni, Lombardi, Gaetano, Bonomi, Stefano, Guaglio, Marcello, Sala, Laura, Segattini, Silvia, Piccoli, Micaela, Migliore, Marco, Sasia, Diego, Cereda, Marco, Dell’Oro, Cristina, Fogliati, Alessandro, Tamini, Nicolò, Castaldi, Antonio, Bracale, Umberto, Lionetti, Ruggero, Pagano, Gianluca, Peltrini, Roberto, Pirozzi, Nello, D’amico, Maria, Izzo, Francesco, Marra, Ester, Marte, Gianpaolo, Bianco, Francesco, Cappiello, Antonio, Iovino, Claudio, Menna, Maria Paola, Romano, Francesco maria, Rossi, Settimio, Sciaudone, Guido, Ascari, Francesca, Bellora, Paolo, Cerri, Cristina, D’Aloisio, Giordana, Ferrari, Maurizio, Chioffi, Franco, Ciccarino, Pietro, Nezi, Giulia, Bissolotti, Guido, Di Donna, Mariano Catello, Licari, Leo, Salamone, Giuseppe, Toia, Francesca, Annicchiarico, Alfredo, Berretta, Roberto, Capozzi, Vito Andrea, Freyrie, Antonio, Frusca, Tiziana, Arici, Vittorio, Bozzani, Antonio, Filardo, Matteo, de Manzoni Garberini, Andrea, Cauteruccio, Michele, Caristo, Giuseppe, Calabrò, Marcello, Andreani, Lorenzo, Antonio, D’arienzo, Berrettini, Stefano, Colangeli, Simone, Cremonini, Camilla, Dallan, Iacopo, Di Franco, Gregorio, Korasidis, Stylianos, Musetti, Serena, Neri, Carlo Maria, Palmeri, Matteo, Picariello, Miriana, Statuti, Erica, Tartaglia, Dario, Pinotti, Enrico, Basso, Stefano Maria Massimiliano, Ubiali, Paolo, Coiro, Saverio, Mele, Simone, Corbellini, Carlo, Masciandaro, Antonio, Carannante, Filippo, Denaro, Vincenzo, Mazzotta, Erica, Zampogna, Biagio, Cinquepalmi, Matteo, Del Basso, Celeste, Guglielmo, Nicola, Meniconi, Roberto Luca, Sinibaldi, Giovanni, Agnes, Annamaria, Belia, Francesco, Bianchi, Valentina, Cozza, Valerio, Fico, Valeria, Fransvea, Pietro, La Greca, Antonio, Litta, Francesco, Pascale, Marco Maria, Silvia, Tedesco, Benevolo, Maria, Campo, Flaminia, Dona’, Maria Gabriella, Marchesi, Paolo, Mastroianni, Riccardo, Mazzola, Francesco, Moretto, Silvia, Petruzzi, Gerardo, Pichi, Barbara, Tuderti, Gabriele, Bellato, Vittoria, Grande, Michele, Petagna, Lorenzo, Sensi, Bruno, Bruzzaniti, Placido, Ciccarone, Flavia, Cicerchia, Pierfranco Maria, Cirillo, Bruno, D’ambrosio, Giancarlo, De Toma, Giorgio, Familiari, Pietro, Fonsi, Giovanni Battista, Pata, Francesco, Picchetto, Andrea, Salvati, Maurizio, Valentini, Valentino, Zambon, Martina, Zancana, Giuseppa, Zuppi, Emma, Ibrahim, Mohsen, Menna, Cecilia, Rendina, Erino Angelo, Accarino, Giulio, Giancarlo, Accarino, Froiio, Caterina, Copelli, Chiara, Di Maio, Pasquale, Giudice, Marco, Altana, Cristian, Ciccarello, Sandro, Cossu, Maria Laura, Dessole, Francesco, Dettori, Salvatora, Madonia, Massimo, Petrillo, Marco, Piredda, Franco, Rizzo, Davide, Scognamillo, Fabrizio, Soma, Damiano, Tanca, Anna Rita, Marano, Luigi, Pesce, Anna Lisa, Piccioni, Stefania Angela, Resca, Luca, Roviello, Franco, Abrate, Alberto, Clarizia, Guglielmo, Franzini, Marco, Fratto, Antonio, Grechi, Alessandro, Scarnecchia, Elisa, Maiuri, Vincenzo, Dalprà, Francesca, Battistella, Enrico, Romano, Maurizio, Aizza, Giada, de Manzini, Nicolò, Drigo, Davide, Mastronardi, Manuela, Palmisano, Silvia, Bardelli, Laura, Borroni, Giacomo, Livraghi, Lorenzo, Palumbo, Mara, Zullo, Alessandra, Vignotto, Chiara, Caravati, Andrea, De Cristofaro, Carlotta, Giuliani, Tommaso, Guglielmi, Alfredo, Pedrazzani, Corrado, Butturini, Giovanni, Moretto, Gianluigi, Mastriale, Francesco, Olmi, Stefano, Kushikata, Tetsuya, Oyama, Tasuku, Kamiyama, Mizue, Tarao, Kentaroh, Yamada, Takayuki, Shiga, Toshiya, Deguchi, Yoshihiko, Hoshino, Tatsuki, Inoue, Hiroyuki, Ito, Shingo, Takano, Emi, Yamamoto, Masae, Matsuki, Yuka, Matsumoto, Takashi, Sawada, Atsushi, Sato, Kozo, Makino, Jun, Mizota, Toshiyuki, Yonekura, Hiroshi, Ishikawa, Haruka, Kaiho, Yu, Ogawa, Munehiro, Endo, Shunji, Fujiwara, Yoshinori, Kubota, Hisako, Okada, Toshimasa, Hisamatsu, Yoji, Michiura, Taku, Satoshi, Okazaki, Fujita, Tomoyuki, Fukushima, Satsuki, Otsuka, Yuji, Sawada, Ikumi, Miyamoto, Morikazu, Isada, Tetsuro, Bani Amer, Qaed, Fujimoto, Yuki, Kuratani, Norifumi, Takada, Misa, Noro, Shusaku, Kuroda, Naoto, Okamura, Yukiyasu, Uesaka, Katshuhiko, Kuroda, Kento, Abe, Nobutsugu, Ando, Tadao, Hirano, Kouichi, Kobayashi, Yoichi, Motoyasu, Akira, Noguchi, Hikari, Tajima, Atsushi, Toyama, Satoshi, Uchida, Tokujiro, Furukawa, Yuri, Hasumi, Yoko, Iida, Katsuyuki, Ikeda, Tatsuhiko, Kuzuhara, Shigeki, Yamamoto, Naoya, Yamamoto, Mari, Yokota, Toshiya, Yoshioka, Yuki, Kawai, Akira, Nakata, Yoshinori, Sawamura, Shigehito, Kobayashi, Takayuki, Alqudah, Majdi Ali, Alrabadi, Mera, Owais, Qais, Smadi, Aseel, Abunawas, Huthifa, Alhawatmeh, Mohammad, Alwardat, Abdel Rahman Mohannad Ahmad, Dhoon, Slsabela, Jaradat, Enas, Mubaydeen, Teeba, Alzraikat, Sayel H., Omari, Rand Y., Suradi, Haya H., Al Abdallah, Murad, Al Bdour, Zakaria, Al-thaher, Mohammad, Alnajjar, Tareq, AlZaatreh, Mohammad, Ayasra, Faris, Ghayada, Ibrahim, Hasanein, Khaled Moh’d Ahmed, Hassouneh, Anas, Hijazeen, Raid, Khasawneh, Barihan, Semrin, Qusai, Theab, Mohammad, Abazeed, Alaa, Alkurdieh, Moh’d Mujahed, Hammouri, Mohammad, Khader, Sereen, Abu Obead, Hamza, Abudari, Hadeel, Al-azzeh, Nimer, Al-Darabah, Ali, Al-Qannas, Mohammad, Ashour, Subhi, Chabaan, Reem, Fawzi, Ahmad, Haij, Manal, Hassouneh, Esraa, Ibrahim, Zainab, Mohamed, Ishak, Qandeel, Mahmoud, Qassem, Faisal, Shorman, Marwan, Nofal, Sandra, Salameh, Mohammed, Sanjuq, Ghaidaa, Sharabi, Alaa, Sweiti, Alaa, Baninasr, Eman, Abuleil, Amro, Abou Chaar, Mohamad K., Al-latayfeh, Motasem, Al-Shudifat, Abdel-Ellah, Shaikh Ahmad, Abd Almonem, Abu Abed, Laith, Allouzi, Mohammad, Almi’ani, Sari, Alsaiad, Abdulhakim, Daoud, Maher, Dawod, Omar, Fadli, Saba, Jimaale, Elmi Ahmed Mohamed, Qulaghassi, Zaki, Al-howthi, Mohammed, Ababneh, Laila, Ababneh, Roba, Al Khassawneh, Ahmad, Al Sharie, Ahmed, Al-Dabaa, Tawfik, Al-Jarrah, Salsabeel, Alshannaq, Qutaiba, Alsulaiman, Rima, Audat, Ziad, Bdour, Saja, Khatatbeh, Ro’a, Marji, Fares, Abu Ismail, Dima.Y, Abu-Ismail, Luai, Alzoubi, Mai, Alzoubi, Malak, Hussein, Hasan, Rawashdeh, Shireen, Ababneh, Adnan, Abu-Eisheh, Basil, Al Shurman, Hana’, Al-atiyah, Hussam, Aljaiuossi, Anas, Hamdoni, Sara, Kulimbet, Mukhtar, Sakhov, Orazbek, Fakhradiyev, Ildar, Tanabayeva, Shynar, Zhussupov, Baurzhan, Amrayev, Sultan, Kukubassov, Yerlan, Parker, Robert, Mwaria, Claude, Kibunyi, Muthoni, Mailu, Claire, Osea, Lydia, Hirsi, Omar, Dashti, Mustafa, Aldalal, Shuaib, Alozairi, Ous, Al-Shaiji, Tariq, Alhunaidi, Omar, Aljewaied, Ali, Farag, Ahmed, Termos, Salah, Sivins, Armands, Apse, Ingus Arnolds, Kaminskis, Aleksejs, Pāvulāns, Jānis, Itani, Rania, Owiedat, Mustafa, Antonios, Ingrid, Milan Moussa, Rita, Abdel Sater, Ali H., Souleiman, Bassem, Ghiya, Peter, Fakih, Ghinwa, Tamoos, Khalil, Abdulrahman, Taha, Omar Abunaaja, Hayat, Al Gasi, Abd El Jawad, Al maadany, Faraj, Alsaeiti, Sara, Elkhafeefi, Fatimah, Alhouni, Tarek, Alkeelani, Hana, Alkuwafi, Rauoof, Elbargathe, Osama, Elfadli, Mustafa, Elmahgoub, Asma, Alansari, Arowa hassan abdulrahman, Alhaje, Awatif, Haron, Aaya, Alsuwiyah, Suhir, Khel, Samer, Yousef, Rowaida, Abuhlaiga, Ma’aly, Abushahma, Ahmed omar, Aldelensi Alzubi, Alhosen Saleh M, Alnehum, Mohamed alnaser, Alzedam, Ahmad, Matoug, Faisel, Mohammed, Burooj, Sawalem, Mohamed, Kamoka, Elhusain, Salamah, Abdulrauf, Abdeewi, Saedah, Eshnaf, Mabroka, Yahmad, Mohammad, Aljuroushi, Omar, Attia, Hajer, Emran, Ayyah, Samer, Ashraf, Abdullah, Duha milad, Ben esmael, Esraa, Benmasoud, Fatma, Debri, Laila, Younis, Balkees, Elmabrouk, Amna, Hasan, Ameerah Mahdi Abraheem, Suliaman, Taha, Alawami, Mohammed, Alshareea, Entisar, Aribi, Tomather, Lamari, Amani hamid, Mufth, Mohammed, Aburima, Sarah, Al shukri, Asmaa, Alarabi, Rehab, Alelwany, Aisha, Bashir, Rasha, Elhadi, Ahmed, Elkouba, Esraa, Ellojli, Ibrahim, Elmsherghi, Nabiha, Haidar, Arwa, Helal, Hala, Kriem, Eslam, Turshani, Laila, Alayan, Mohammed, Ben Hasan, Hayat, Shaban Ben Hasan, Najat, Shaban Ben Hasan, Rabab, Dambrauskas, Zilvinas, Dekeryte, Inga, Koženiauskaitė, Akvilė, Maleckas, Almantas, Venclauskas, Linas, Zilinskas, Justas, Syminas, Vilius, Aliosin, Oleg, Cizauskaite, Agne, Jurgaitis, Jonas, Mikutaitis, Vytenis, Samalavicius, Narimantas Evaldas, Slepavicius, Algirdas, Vasiliauskas, Bernardas, Dulskas, Audrius, Cekauskas, Albertas, Gulla, Aiste, Zelvys, Arunas, Noël, Audrey, Rajaonarivony, Maheriandrianina Fanambinana Voahary, Andriantsoa, Herimampionona E, Samison, Luc Hervé, Tsiambanizafy, Guillaume Odilon, Solo, Corinne Eulalie, Safiry Andofenohasina, Fanonjomahasoa, Sablon Herinirina, Angelin, Das, Andre, Osman, Mohd Firdauss, Soh, Jien Yen, Zakaria, Zaidi, Chan, Kheng Hooi, Johan, Syamim, Mah, Jin Jiun, Subramaniam, Sentilnathan, Ibrahim, Mohd Razali, Nazimi, Abd Jabar, Bal, Pavin, Fadzli, Ahmad nazran, Jamaris, Suniza, Lee, Yeong Sing, Lim, Jeffery Zk, Moh Pauzi, Siti Farhan, Nair, Ashvin Krishna, Ng, Doris Sin Wen, Ng We Yong, Yuki Julius, Roslani, April Camilla, Saaid, Rahmah, Sethi, Neha, Syed Jafer Hussain Zaidi, Syeda Nureena, Teoh, Li Ying, Munjih, Islah, Sainal, Mohd Yusof, Sarif, Mat Salleh, Chua, Richelle, Ahmad, Abdul Rauf bin, Lau, Yii Ling, Liew, Ka Yee, Palayan, Kandasami, Abdul Rahman, Noorneza, Firman, Fatin, Hazleigh, Nik Mohd, Mohd Miswan, Mohd Fairudz, Mohd Zahid, Akmal Zulayla, Yahaya, Mohd Yusoff bin Yahaya, Yusoff, Ahmad Ramzi, Darryl, Brincat, Svetlana, Caruana, Clifford, Chircop, Daniel Mario, Czok, Viktoria, Muscat, Esther, Roshan, Mohsin Hassan Khan, Schembri, Nicholas, Jonathan Rubén, Cedillo Rodrìguez, Rojas Aguilar, Antonio, Sosa Duran, Erik Efrain, Arroyo, Diego, Martinez-Said, Hector, Medina-Franco, Heriberto, Pérez de León Vázquez, Martha Patricia, Soto Hernández, Fidel, Stenner, Juan Ignacio, Zuloaga Fernandez del Valle, Carlos Jose, Colunga Tinajero, Carlos Andres, Yanowsky-Reyes, Guillermo, Camacho Gomez, Aldo, Cueva-Martinez, Esteban, Durán Anguiano, Oscar, García Castillo, Miguel, Gonzalez Ojeda, Alejandro, González-García de Rojas, Erick Alonso, Guzman Barba, Jose Aldo, Ibarrola Peña, Juan Carlos, Íñiguez Martínez, César, Llamas- Macias, Francisco Javier, Ambriz Gonzalez, Gabriela, Morales valencia, Eduardo, Santos, Blanca, Torres Cisneros, Juan Roberto, Bozada-Gutiérrez, Katya, Alvarez, Maria Regina, de la Rosa Abaroa, Marco Antonio, Hernandez, Roberto, Bernardo, Anaya, Ernesto, Arceo-Olaiz, Ricardo, Lopez Flores, Sonia, Esparza Arias, Nereida, Romero Bañuelos, Esmeralda, Salcedo-Hernández, Rosa, Buerba, Gabriela Alejandra, Chan, Carlos, Garay Lechuga, Daniel, Hinojosa, Carlos, Mercado, Miguel Ángel, Peña Gómez Portugal, Emmanuel, Posadas-Trujillo, Oscar Emmanuel, Hernandez Skewes, Karla Yareli, Licona-Meníndez, Roque Delfino, Romero Lechuga, Fernanda, Roque, Oscar, Núñez-González, Roberto Ángel, Alvarez Manilla Orendain, Alfonso, Garcia, Felipe, González León, Juan Antonio, Guzman, Marlin, Herappe, Dorihela, Lerma, Ricardo, Mendoza Pedraza, Fabián Hilario, Noguez Castillo, Monica, Resendiz, Jose Anatolio, Fernandez Rios, Laura Elena, Frigerio, Pamela, Garcia, Iset, Mendoza Frías, Gloria Isela, Anzures Mendoza, Alejandra, Hdez Miguelena, Luis, Zarate Saenz, David, Zavala gonzalez, Miguel angel, Andrade González, Francisco, Cortes, Ludivina, Gurghiș, Radu, Sanjaa, Burmaa, Ait benhamou, Rita, Rghioui, Mounir, Saaf, Sarra, Letrache, Ahmed, Zekhnini, Meryem, Abichou, Wijden, Baroudi, Salma, Hassoun, Chiraz, Zaim, Ouijdane, Arkha, Yasser, Bechri, Hajar, Benchekroun Belabbes, Meryem, Benslimane, Zaineb, Boualila, Lina, El Ouahabi, Abdessamad, Essakalli Hossyni, Leila, Tagmouti, Adam, Belkhadir, Zakaria Houssaïn, Benkabbou, Amine, Boutayeb, Saber, El Ahmadi, Brahim, Ghannam, Abdelilah, Kabbaj, Taha, Majbar, Anass Mohammed, Ndayishyigikiye, Marcel Didier, Ndatewapo, Fillipus, Njuki, Peter Ngungi, Abrahams, Jenevive, Bhusal, Subarna, Dwa, Yam, Bloemers, Frank, Besselink, Marc, van Amstel, Paul, Smeele, Ludi, Harlaar, Niels, van der Burg, Sjirk, Noltes, Milou, Steinkamp, Pieter, Franken, Josephine, Oosterling, Steven, Nolte, Peter, Alwayn, Ian, Braat, Andries, Goudsmit, Ben, van de Leemkolk, Fenna E.M., Albers, Kim, Van Goor, Harry, van Ginhoven, Tessa M., Schweitzer, Donald, Leclercq, Wouter, Vancoillie, Peter-Jan, Konsten, Joop, den Boer, Frank, Djamali, Kaveh, Ghamri, Naji, Joseph, Reece, Lie, Samuel, Olliff, Sue, Wood, Daniel, Furkert, Chris, Teague, Rebecca, Lim, Eric, Mclauchlan, Jared, Ailabouni, Ramez, Alqassab, Mohammed, Hawkins, Robyn, Baker, Joseph, Ou, Cindy Xin Yi, Singh, Vidit, Frunt, Brytt, Lakshman, Prashant, Henderson, Nigel, Ha, Jeong, Haimona, Maira, Jury, Steffanie, Weeratunga, George, Witcomb Cahill, Henry, Abdussalam, Hameedat Opeyemi, Ahmed, Aminat Olayinka, Adeyeye, Ademola, Kayode-Nissi, Victor, Kadiri, Innih, Oduah, Stella, Olaogun, Julius, Abdull, Mohammed, Abdullahi, Mohammed Kabir, Ja’afar, Abubakar Bappah, Ja’afaru, Umma Hani, Yakubu Mohammed, Husseini, Edetanlen, Ekaniyere, Iribhogbe, Oseihie, Irowa, Omorodion, Nwashilli, Nnamdi, Obahiagbon, Osarobo, Onyeagwara, Ngozi, Oseghale, Eustace, Osemwegie, Osarenkhoe, Osho, Philip, Akanni, Bolaji, Ezomike, Uchechukwu, Okoro, Kelechukwu, Okwara, Blasius, Abdulsalam, Khalifa, Agida, Eyaofun, Ajibola, Hafees, Benard, Adeka, Buba, Amina, Eniola, Sefiu, Isah, Aliyu, Olute, Anne, Osagie, Olabisi, Pius Ogolekwu, Idoko, Ademola, Samuel, Afuwape, Oludolapo, Daodu, Israel, Egbuchulem, Ifeanyichukwu Kelvin, Lawal, Taiwo Akeem, Nwaorgu, Onyekwere, Obadan, Izegaegbe, Odeyemi, Olubunmi, Ogundoyin, Omowonuola, Okere, Oghenekevwe, Oladiran, Ajibola, Olagunju, Naomi, Olawoye, Olayinka, Olusanya, Adeola, Oyelakin, Oyeleye, Ugwu, Ebere, Ademuyiwa, Adesoji, Atoyebi, Oluwole, Bode, Chris, Chibuike George, Ihediwa, Ezenwankwo, Francis, Fatuga, Adedeji, Ladipo-Ajayi, Oluwaseun, Ojewola, Rufus Wale, Okunowo, Adeyemi, Seyi-Olajide, Justina, Ugwu, Aloy Okechukwu, Adebara, Idowu, Adeyemo, Olabisi, Banjo, Oluseyi, Obateru, John, Ojo, Owolabi, Okunlola, Abiodun, Oluwafemi, Fatudimu, Adelaja, Aderinsola, Atobatele, Kazeem, Ayodele, Olabamidele, Bello, Funmi, Omisanjo, Olufunmilade, Aderounmu, Adewale, Adisa, Adewale, Ajekwu, Temitope, Ajekwu, Samuel, Akeem Aderogba, Adeleke, Balogun, Simon, Ojo, Olugbenga, Olasehinde, Olalekan, Olugbami, Adebayo, Wuraola, Funmilola, Mohammed, Tajudeen, Afolabi, Abdulrahman O., Aremu, Isiaka, Ibraheem, Gbadebo, Odeyemi, Peter Olalekan, Olabinjo, Afusat, Raji, Hadijat Olaide, Salawu, Hafeez, Uche-Okonkwo, Kenechukwu, Anipole, Olalekan, Adesina, Oluwaseyi, Enwerem, Kenneth, Isichei, Mercy, Aghadi, Ifeanyi, Kache, Stephen, Lawal, Jamila, Taingson, Matthew, Yusuf, Nuhu, Sale, Danjuma, Takai, Idris Usman, Aji, Sani Ali, Anyanwu, Lofty-John, Bello-Muhammad, Nafisatu, Garzali, Ibrahim Umar, Hasheem, Muhammad Ghazali, Hassan, Sadiq, Ibrahim Usman, Mustapha, Magashi, Mahmoud Kawu, Mukhtar, Ibrahim Aliyu, Nwachukwu, Callistus, Suleiman, Ibrahim, A, Maryam, Mahmood, Farrukh, Salihu, Mohammed, Okereke, Chukwuma, Alegbeleye, Justina Omoikhefe, Awopeju, Abimbola, Fyneface-Ogan, Sotonye, Kasso, Terhemen, Onwuagha, Ijeoma, Salami, Babatunde, Ajagha, Okeoghene, Awaisu, Mudi, Nwabuoku, Stanley Emeka, Oyelowo, Nasir, Tolani, Musliu Adetola, Filipce, Venko, Rendevski, Vladimir, Shuntov, Blagoj, Todorovic, Lazar, Jovanovska, Katerina, Nikolovska, Bisera, Peev, Igor, Asha, Al Amri, Ayman, Al Badaai, Ghalib, Al Balushi, Zainab, Al Farsi, Maather, Al HOSNI, Mohammed, Al Kalbani, Moza, Al Riyami, Nihal, Al shaibi, Maha, Al-Mujaini, Abdullah, AlBusaidi, Hanadi, Alwashahi, Mohammed, Alzadjali, Adil, Dalmar, Khadija, Dawud, Bashar, Gargouri, Mohamed Mourad, Laghari, Farman Ali, Manjunatha, attibele Palaksha, Mathew, Joseph Kunju, Muzaffar, Rahil, Naiya, Hafsa Ismail Ibrahim, Pillai, Silja, Rahim, Ferdous, Stephen, Edwin, Younas, Shahzad, Fatima, Mishal, Javed, Ayesha, Shakir, Fatima Tuz Zahra, Siddiqui, Riaz Hussain, Sultan, Rizwan, Syed, Danish, Ur Rehman, Ishtiaq, Warraich, Shahrukh, Amir, Mohammed, Asghar, Saleha yurf, Haider Bangash, Ali, Ahmed, Arooj, Ansar, Mehwish, Arslan, Muhammad, Ashraf, Sumaira, Irshad, Abeer, Khan, Farheen, Shami, Dr Ali, Ul Haq, M Burhan, Waqar, Shahzad Hussain, Ahmed, Shahnoor, Ahsan, Kunwar Armash, Akhund, Ramsha, Chishti, Uzma, Haroon, Waqqas, Kassam, Nausheen, Kazmi, Abbas, Khan, Muhammad Osama, Kumari, Sameeta, Malik, Muhammad Jehangir, Mansoor, Maheen, Mubarik, Areeba, Naz, Falak, Qamar, Javeria Bilal, Sadia, Haleema, Safri, Mohammad, Sattar, abida K., Siddiqui, Muhammad Ahsan Iqbal, Waqar, Usama, Kerawala, Asad Ali, Ghufran, Muhammad Ali, Memon, Amjad Siraj, Rai, Lajpat, Samo, Khursheed Ahmed, Aswani, Manisha, Manzoor, Shahneela, Muhammad, Shoaib, Moin, Inshrah, Najjad, Muhammad Kazim Rahim, Zia, Kashif, Ali Shaikh, Shafqat, Ayub, Bushra, Hussaini, Dr, Kamal, Wajahat, Mughal, Zeeshan Uddin, Nadeem, Areeba, Nayyer, Fatima, Nisa, Kanwal, Rauf, Yaseen, Saleem, Shah Masabat, Sayyed, Raza, Shah, Aaqil, Siddiqui, Safia, Tariq, Nida, Zamir, Mehroz, Arif, Hafiz Muhammad, Ahmed, Ehsan, Ahmed, Jawad, Akbar, Bilal, Ali, Abrar Ashraf, Iqbal, Zafar, Jameel, Muhammad Khurram, Janjua, Muhammad Haris, Janjua, Azwa, Naqi, Syed Asghar, Naumeri, Fatima, Nazir, Umer, Rafi, Yaseen, Saleem, Irfan, Shaukat, Ayesha, Sikandar, Hafiz Saqib, Tu zahara, Fatima, Ali, Ahsan, Jawad, Muhammad, Noman, Hamza, Taha, Dr Hassan, Raana, Dr. Gul e, Qadir, Muhammad Ghulam, Ali, Imran, Asjad, Muhammad, Ayyaz, Mahmood, Bajwa, Luqman Ali, Bhatti, Samiullah, Butt, Usman, Chishti, Muhammad Haris, Hassan, Muhammad, Hassan, Usman, Hayat, Khizar, Khan, Rohma, Latif, Usman, Majeed, Faizan, Malik, Kiren, Nabi, Muhammad shoaib, Sadiq, Muhammad Hamza, Saeed, Komal, Sarfraz, Kiran, Sohail, Rubina, Syed muhammad, Bilal, Umair, Usama, Wasim, Shafqat, Wasim, Tayyiba, Imran Anwar, Muhammad, Ali, Ammar, Aslam, Muhammad, Sher e Murtaza, Muhammad, Farooq, Umer, Ahmad, Siddique, Faizi, Fouzia, Khan, Hamza, Mabood, Waleed, Akhtar, Muhammad Shaheer, Amin, Hasbi Allah, Ayub, Bakhtawar, Basir, Muhammad, Gul, Hina, Khan, Muhammad faisal, Moeen, Aiman, Mohmand, Obaid, Rahim, Rehana, Shafqat, Tanveer, Ur Rehman, Shams, Daud, Mashal, Abid, Rubaba, Farkhanda, Tallat, Malik, Sobia, Salim, Ruqyyah, Shaheen, Aneela, Ahmed, Faryal, Amjid, Zarafshan, Ishtiaq, Daniyal, Javed, Saad, Nagra, Ramisha Naseer, Saleem, Maleeha, Saleem, Bushra, Shabbir, Raja Khalid, Yaqoob, Eesha, Shaheen, Azka, Awad, Hadeel, Wafi, Dalia, Al-Slaibi, Ibrahim, I. 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Charles, Rossano, Ayane, Trust, Marc, Bigelow, Benjamin, Jain, Amit, Yesantharao, Lekha, Zhang, Bo, Abdou, Hossam, Mazzeffi, Michael, Rock, Peter, Afshar, Salim, Carlson, Laura, Incorvia, Joseph, Kurtz, Michael, Meara, John G., Nathan, Meena, Taghinia, Amir, Williams, Vivian, Cooper, Zara, Mahvi, David, Okafor, Barbara, Roxo, Vanessa, Burks, Ciersten, Jabbour, Nicolette, Sabra, Joseph, Wason, Shaun, Dorken Gallastegi, Ander, Gebran, Anthony, McTague, Michael, Reinke, Caroline, Ross, Samuel, Alvarado, Rosalinda, Bhama, Anuradha, Govekar, Henry, Kokosis, George, Millikan, Keith, Myers, Jonathan, Pappas, Sam, Siparsky, Nicole, Tsambarlis, Peter, Quillin, Ralph, Ekrami, Elyad, Kai, Li, Decker, Cassie, Aoun, Salah, Kwon, David, Barbas, Andrew, Dotters-Katz, Sarah K., Ellett, Tressa, Flamant, Etienne, Lazarides, Alexander, Refaat, Motasem, Soderlund, Torre, Velasquez, Camilo, Arrangoiz, Rodrigo, Mijares Olivo, Maria Fernanda, Vasudevan, Vanitha, Katz, Matthew, Nguyen, Huan, Pieretti, Alberto, Roland, Christina L, Baral, Perel, Beahm, D. David, Carver, Shea, Guidry, Christopher A., Hedlund, Megan, Kumer, Sean, Larson, Kelsey E, Urich, Alissa, Rosenkranz, Kari, Bhavaraju, Avi, Kalkwarf, Kyle, Hua-Feng, Lien, Juillard, Catherine, Tillou, Areti, Mouw, Tyler, Dunn, Julie, Wyatt, Thomas, Aiken, Taylor, Foley, Eugene, Zafar, Syed Nabeel, Lewit, Ruth A., Cioci, Alessia, Goel, Neha, Kronenfeld, Joshua, Rattan, Rishi, LaRocca, Christopher J., Ramaswamy, Archana, Mattson, Jordan, Vogel, Rachel, Bethurum, Alva, Bhalla, Rohan, Bichell, David, Bonfield, Christopher, Dewan, Michael, Ghani, Muhammad Owais Abdul, Hawkins, Alexander, Khan, Aimal, Kynes, J. Matthew, Ruzgar, Nensi Melissa, Alqunaibit, Dalia, Liveris, Anna, McNelis, John, Goldstein, Carma, Nguyen, Anh, Rattigan, Deviney, Solar, Sara, Tsui, Grace, Gaitanidis, Apostolos, Fitzmaurice, Laura, Jutric, Zeljka, Tay Lasso, Erika Linmey, Yamamoto, Maki, Meola, Antonio, Holena, Daniel, Imran, Jonathan, Lorenzi, Cara, Huckaby, Lauren, Hamandi, Mohanad, Lanfear, Allison T., Squiers, John, Behm, Kevin, Boughey, Judy, Reisenauer, Janani, Shawki, Sherief, Wigle, Dennis, Elshabrawy, Ahmed, Mansour, Ahmed, Todd, Grace Ihsiu, Yan, Qi, Curry, Terry, Abel, Mary Kathryn, Andaya, Veronica, Boeck, Marissa, Chen, Lee-may, Compadre, Amanda, Ha, Patrick, Kratz, Johannes R, Lane, Rondall, Lee, Rex H, Ozgediz, Doruk, Varma, Madhulika, Zimel, Melissa, Escobar, Pedro, Azam, Mohammad, Hammond, Eric, Marx, William, Angel, Carol, Seegert, Sara, Stanek, Stephen, Sumra, Hibba, Fischer, Anne, Henry, Jaymie, Lottenberg, Lawrence, Kelly, John, Scott, Erin, Bonilla Cal, Fernando, Haro, Camila, Álvarez, Sofia, Carbajal, Ana Carmen, PereiraNuñez, Diego, Tarigo, Josefina, Al-saban, Rafat, Al-Eryani, Fatima, Reem, Al sanany, Ghadeer, Al-jarmozi, Buthina, Al-ameri, Saba, Al-Shehari, Mohammed, Al-surimi, Dhyia Alhuq, Alakhdury, Amira, ALhumaida, Adnan, Aljabri, Maniee, ALodine, Marwa, Alqudaimi, Basma, Alsabri, Mohammed, Alsayadi, Musaed, Atiah, Nora, Sayad, Shaima, Shukri, Amal, Zeid, Shahd, Abdullah, Nabila, Al-Garadi, Abdulrahman Faisal, Ali Al-Radhi, Ahmed M., Naji, Norhan, Al-madhrahi, Ahmed, Al-maswari, Sakhr Abdulhakeem, Al-Raimi, Ibrahim, Alshargabi, Haneen, Mkandawire, Catherine, Nkole, Theresa, Chivanga, Shelton, Dube, Handsome, Mangena, Moses, Moyo, Nkosikhona, Tshuma, Oltah, Gwini, Grace, Moyana Muguto, Vimbai, Mushawarima, Trust, Shoko, Tinashe, Siamuchembu, Maphios, Jamela, Rorisang, Machiri, Marvellous, Mushiwokufa, Willard, Chimhina, Maxwell, Chinyowa, Simbarashe, Glasbey, James, Ademuyiwa, Adesoji, Adisa, Adewale, AlAmeer, Ehab, Arnaud, Alexis P, Ayasra, Faris, Azevedo, José, 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Kaur, T, Kawesha, A, Kisiel, A, Woodmass, M, Adelowo, T, Ahari, D, Alhwaishel, K, Atherton, R, Clayton, B, Cockroft, A, Curtis Lopez, C, Hilton, M, Ismail, N, Kouadria, M, Lee, L, MacConnachie, A, Monks, F, Mungroo, S, Nikoletopoulou, C, Pearce, L, Sara, X, Shahid, A, Suresh, G, Wilcha, R, Atiyah, A, Davies, E, Dermanis, A, Gibbons, H, Hyde, A, Lawson, A, Lee, C, Leung-Tack, M, Li Saw Hee, J, Mostafa, O, Nair, D, Pattani, N, Plumbley-Jones, J, Pufal, K, Ramesh, P, Sanghera, J, Saram, S, Scadding, S, See, S, Stringer, H, Torrance, A, Vardon, H, Wyn-Griffiths, F, Brew, A, Kaur, G, Soni, D, Tickle, A, Akbar, Z, Appleyard, T, Figg, K, Jayawardena, P, Johnson, A, Kamran Siddiqui, Z, Lacy-Colson, J, Oatham, R, Rowlands, B, Sludden, E, Turnbull, C, Allin, D, Ansar, Z, Azeez, Z, Dale, VH, Garg, J, Horner, A, Jones, S, Knight, S, McGregor, C, McKenna, J, McLelland, T, Packham-Smith, A, Rowsell, K, Spector-Hill, I, Adeniken, E, Baker, J, Bartlett, M, Chikomba, L, Connell, B, Deekonda, P, Dhar, M, Elmansouri, A, Gamage, K, Goodhew, R, Hanna, P, Knight, J, Luca, A, Maasoumi, N, Mahamoud, F, Manji, S, Marwaha, PK, Mason, F, Oluboyede, A, Pigott, L, Razaq, AM, Richardson, M, Saddaoui, I, Wijeyendram, P, Yau, S, Atkins, W, Liang, K, Miles, N, Praveen, B, Ashai, S, Braganza, J, Common, J, Cundy, A, Davies, R, Guthrie, J, Handa, I, Iqbal, M, Ismail, R, Jones, C, Jones, I, Lee, KS, Levene, A, Okocha, M, Olivier, J, Smith, A, Subramaniam, E, Tandle, S, Wang, A, Watson, A, Wilson, C, Chan, XHF, Khoo, E, Montgomery, C, Norris, M, Pugalenthi, PP, Common, T, Cook, E, Mistry, H, Shinmar, HS, Agarwal, G, Bandyopadhyay, S, Brazier, B, Carroll, L, Goede, A, Harbourne, A, Lakhani, A, Lami, M, Larwood, J, Martin, J, Merchant, J, Pattenden, S, Pradhan, A, Raafat, N, Rothwell, E, Shammoon, Y, Sudarshan, R, Vickers, E, Wingfield, L, Ashworth, I, Azizi, S, Bhate, R, Chowdhury, T, Christou, A, Davies, L, Dwaraknath, M, Farah, Y, Garner, J, Gureviciute, E, Hart, E, Jain, A, Javid, S, Kankam, HK, Kaur Toor, P, Kaz, R, Kermali, M, Khan, I, Mattson, A, McManus, A, Murphy, M, Nair, K, Ngemoh, D, Norton, E, Olabiran, A, Parry, L, Payne, T, Pillai, K, Price, S, Punjabi, K, Raghunathan, A, Ramwell, A, Raza, M, Ritehnia, J, Simpson, G, Smith, W, Sodeinde, S, Studd, L, Subramaniam, M, Thomas, J, Towey, S, Tsang, E, Tuteja, D, Vasani, J, Vio, M, Badran, A, Adams, J, Anthony Wilkinson, J, Asvandi, S, Austin, T, Bald, A, Bix, E, Carrick, M, Chander, B, Chowdhury, S, Cooper Drake, B, Crosbie, S, D Portela, S, Francis, D, Gallagher, C, Gillespie, R, Gravett, H, Gupta, P, Ilyas, C, James, G, Johny, J, Jones, A, Kinder, F, MacLeod, C, Macrow, C, Maqsood-Shah, A, Mather, J, McCann, L, McMahon, R, Mitham, E, Mohamed, M, Munton, E, Nightingale, K, O‚ÄôNeill, K, Onyemuchara, I, Senior, R, Shanahan, A, Sherlock, J, Spyridoulias, A, Stavrou, C, Stokes, D, Tamang, R, Taylor, E, Trafford, C, Uden, C, Waddington, C, Yassin, D, Zaman, M, Bangi, S, Cheng, T, Chew, D, Hussain, N, Imani-Masouleh, S, Mahasivam, G, McKnight, G, Ng, HL, Ota, HC, Pasha, T, Ravindran, W, Shah, K, Vishnu K, S, Zaman, S, Carr, W, Cope, S, Eagles, EJ, Howarth-Maddison, M, Li, CY, Reed, J, Ridge, A, Stubbs, T, Teasdaled, D, Umar, R, Worthington, J, Dhebri, A, Kalenderov, R, Alattas, A, Arain, Z, Bhudia, R, Chia, D, Daniel, S, Dar, T, Garland, H, Girish, M, Hampson, A, Kyriacou, H, Lehovsky, K, Mullins, W, Omorphos, N, Vasdev, N, Venkatesh, A, Waldock, W, Bhandari, A, Brown, G, Choa, G, Eichenauer, CE, Ezennia, K, Kidwai, Z, Lloyd-Thomas, A, Macaskill Stewart, A, Massardi, C, Sinclair, E, Skajaa, N, Smith, M, Tan, I, Afsheen, N, Anuar, A, Azam, Z, Bhatia, P, Davies-kelly, N, Dickinson, S, Elkawafi, M, Ganapathy, M, Gupta, S, Khoury, EG, Licudi, D, Mehta, V, Neequaye, S, Nita, G, Tay, VL, Zhao, S, Botsa, E, Cuthbert, H, Elliott, J, Furlepa, M, Lehmann, J, Mangtani, A, Narayan, A, Nazarian, S, Parmar, C, Shah, D, Shaw, C, Zhao, Z, Beck, C, Caldwell, S, Clements, JM, French, B, Kenny, R, Kirk, S, Lindsay, J, McClung, A, McLaughlin, N, Watson, S, Whiteside, E, Alyacoubi, S, Arumugam, V, Beg, R, Dawas, K, Garg, S, Lloyd, ER, Mahfouz, Y, Manobharath, N, Moonesinghe, R, Morka, N, Patel, K, Prashar, J, Yip, S, Adeeko, ES, Ajekigbe, F, Bhat, A, Evans, C, Farrugia, A, Gurung, C, Long, T, Malik, B, Manirajan, S, Newport, D, Rayer, J, Ridha, A, Ross, E, Saran, T, Sinker, A, Waruingi, D, Allen, R, Al Sadek, Y, Alves do Canto Brum, H, Asharaf, H, Ashman, M, Balakumar, V, Barrington, J, Baskaran, R, Berry, A, Bhachoo, H, Bilal, A, Boaden, L, Chia, WL, Covell, G, Crook, D, Dadnam, F, Davis, L, De Berker, H, Doyle, C, Fox, C, Gruffydd-Davies, M, Hafouda, Y, Hill, A, Hubbard, E, Hunter, A, Inpadhas, V, Jamshaid, M, Jandu, G, Jeyanthi, M, Jones, T, Kantor, C, Kwak, SY, Malik, N, Matt, R, McNulty, P, Miles, C, Mohomed, A, Myat, P, Niharika, J, Nixon, A, O‚ÄôReilly, D, Parmar, K, Pengelly, S, Price, L, Ramsden, M, Turnor, R, Wales, E, Waring, H, Wu, M, Yang, T, Ye, TTS, Zander, A, Zeicu, C, Bellam, S, Francombe, J, Kawamoto, N, Rahman, MR, Sathyanarayana, A, Tang, HT, Cheung, J, Hollingshead, J, Page, V, Sugarman, J, Wong, E, Chiong, J, Fung, E, Kan, SY, Kiang, J, Kok, J, Krahelski, O, Liew, MY, Lyell, B, Sharif, Z, Speake, D, Alim, L, Amakye, NY, Chandrasekaran, J, Chandratreya, N, Drake, J, Owoso, T, Thu, YM, Abou El Ela Bourquin, B, Alberts, J, Chapman, D, Rehnnuma, N, Ainsworth, K, Carpenter, H, Emmanuel, T, Fisher, T, Gabrel, M, Guan, Z, Hollows, S, Hotouras, A, Ip Fung Chun, N, Jaffer, S, Kallikas, G, Kennedy, N, Lewinsohn, B, Liu, FY, Mohammed, S, Rutherfurd, A, Situ, T, Stammer, A, Taylor, F, Thin, N, Urgesi, E, Zhang, N, Ahmad, MA, Bishop, A, Bowes, A, Dixit, A, Glasson, R, Hatta, S, Hatt, K, Larcombe, S, Preece, J, Riordan, E, Fegredo, D, Haq, MZ, Li, C, McCann, G, Stewart, D, Baraza, W, Bhullar, D, Burt, G, Coyle, J, Deans, J, Devine, A, Hird, R, Ikotun, O, Manchip, G, Ross, C, Storey, L, Tan, WWL, Tse, C, Warner, C, Whitehead, M, Wu, F, Court, EL, Crisp, E, Huttman, M, Mayes, F, Robertson, H, Rosen, H, Sandberg, C, Smith, H, Al Bakry, M, Ashwell, W, Bajaj, S, Bandyopadhyay, D, Browlee, O, Burway, S, Chand, CP, Elsayeh, K, Elsharkawi, A, Evans, E, Ferrin, S, Fort-Schaale, A, Iacob, M, I, K, Impelliziere Licastro, G, Mankoo, AS, Olaniyan, T, Otun, J, Pereira, R, Reddy, R, Saeed, D, Simmonds, O, Singhal, G, Tron, K, Wickstone, C, Williams, R, Bradshaw, E, De Kock Jewell, V, Houlden, C, Knight, C, Metezai, H, Mirza-Davies, A, Seymour, Z, Spink, D, and Wischhusen, S
Abstract: Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery.
Published: 2024
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25. Spread of Coxiella burnetii between dairy cattle herds in an enzootic region: modelling contributions of airborne transmission and trade

Author: Pandit, Pranav, Hoch, Thierry, Ezanno, Pauline, Beaudeau, François, and Vergu, Elisabeta
Subjects: Veterinary Sciences, Agricultural, Veterinary and Food Sciences, Infectious Diseases, Vaccine Related, Prevention, Emerging Infectious Diseases, Biodefense, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Animals, Cattle, Cattle Diseases, Coxiella burnetii, Dairying, France, Models, Theoretical, Prevalence, Q Fever, Stochastic Processes, Microbiology, Veterinary sciences
Abstract: Q fever, a worldwide zoonotic disease caused by Coxiella burnetii, is a looming concern for livestock and public health. Epidemiological features of inter-herd transmission of C. burnetii in cattle herds by wind and trade of cows are poorly understood. We present a novel dynamic spatial model describing the inter-herd regional spread of C. burnetii in dairy cattle herds, quantifying the ability of airborne transmission and animal trade in C. burnetii propagation in an enzootic region. Among all the new herd infections, 92% were attributed to airborne transmission and the rest to cattle trade. Infections acquired following airborne transmission were shown to cause relatively small and ephemeral intra-herd outbreaks. On the contrary, disease-free herds purchasing an infectious cow experienced significantly higher intra-herd prevalence. The results also indicated that, for short duration, both transmission routes were independent from each other without any synergistic effect. The model outputs applied to the Finistère department in western France showed satisfactory sensitivity (0.71) and specificity (0.80) in predicting herd infection statuses at the end of one year in a neighbourhood of 3 km around expected incident herds, when compared with data. The model developed here thus provides important insights into the spread of C. burnetii between dairy cattle herds and paves the way for implementation and assessment of control strategies.
Published: 2016

26. Modelling transmission and control of Mycobacterium avium subspecies paratuberculosis within Irish dairy herds with compact spring calving

Author: Biemans, F., Ben Romdhane, R., Gontier, P., Fourichon, C., Ramsbottom, G., More, S.J., and Ezanno, P.
Published: 2021
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27. European Partnership on Animal Health and Welfare: Initiatives for Rapid Risk Assessment

Author: Conrady, Beate, Prada, J.M., Vergne, T, Ezanno, P, de Vos, C.J., Tomassone, L., Condoleo, R., Antoniou, S.E., Hultén, C., Simons, R., Marques, Ana Rita Pinheiro, Apenteng, O.O, Rasmussen, P., Jokelainen, P., Simons, X., Conrady, Beate, Prada, J.M., Vergne, T, Ezanno, P, de Vos, C.J., Tomassone, L., Condoleo, R., Antoniou, S.E., Hultén, C., Simons, R., Marques, Ana Rita Pinheiro, Apenteng, O.O, Rasmussen, P., Jokelainen, P., and Simons, X.
Published: 2024

28. Heterogeneity of Rift Valley fever virus transmission potential across livestock hosts, quantified through a model-based analysis of host viral load and vector infection.

Author: Hélène Cecilia, Roosmarie Vriens, Paul J Wichgers Schreur, Mariken M de Wit, Raphaëlle Métras, Pauline Ezanno, and Quirine A Ten Bosch
Subjects: Biology (General), QH301-705.5
Abstract: Quantifying the variation of pathogens' life history traits in multiple host systems is crucial to understand their transmission dynamics. It is particularly important for arthropod-borne viruses (arboviruses), which are prone to infecting several species of vertebrate hosts. Here, we focus on how host-pathogen interactions determine the ability of host species to transmit a virus to susceptible vectors upon a potentially infectious contact. Rift Valley fever (RVF) is a viral, vector-borne, zoonotic disease, chosen as a case study. The relative contributions of livestock species to RVFV transmission has not been previously quantified. To estimate their potential to transmit the virus over the course of their infection, we 1) fitted a within-host model to viral RNA and infectious virus measures, obtained daily from infected lambs, calves, and young goats, 2) estimated the relationship between vertebrate host infectious titers and probability to infect mosquitoes, and 3) estimated the net infectiousness of each host species over the duration of their infectious periods, taking into account different survival outcomes for lambs. Our results indicate that the efficiency of viral replication, along with the lifespan of infectious particles, could be sources of heterogeneity between hosts. Given available data on RVFV competent vectors, we found that, for similar infectious titers, infection rates in the Aedes genus were on average higher than in the Culex genus. Consequently, for Aedes-mediated infections, we estimated the net infectiousness of lambs to be 2.93 (median) and 3.65 times higher than that of calves and goats, respectively. In lambs, we estimated the overall infectiousness to be 1.93 times higher in individuals which eventually died from the infection than in those recovering. Beyond infectiousness, the relative contributions of host species to transmission depend on local ecological factors, including relative abundances and vector host-feeding preferences. Quantifying these contributions will ultimately help design efficient, targeted, surveillance and vaccination strategies.
Published: 2022
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29. Research perspectives on animal health in the era of artificial intelligence

Author: Pauline Ezanno, Sébastien Picault, Gaël Beaunée, Xavier Bailly, Facundo Muñoz, Raphaël Duboz, Hervé Monod, and Jean-François Guégan
Subjects: Animal disease, Data, Livestock, Modelling, Artificial intelligence, Decision support tool, Veterinary medicine, SF600-1100
Abstract: Abstract Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.
Published: 2021
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30. Minimizing the number of origins in batches of weaned calves to reduce their risks of developing bovine respiratory diseases

Author: Thibaut Morel-Journel, Sébastien Assié, Elisabeta Vergu, Jean-Baptiste Mercier, Florence Bonnet-Beaugrand, and Pauline Ezanno
Subjects: Veterinary medicine, SF600-1100
Abstract: Abstract Bovine respiratory diseases (BRD) are a major concern for the beef cattle industry, as beef calves overwhelmingly develop BRD symptoms during the first weeks after their arrival at fattening units. These cases occur after weaned calves from various cow-calf producers are grouped into batches to be sold to fatteners. Cross-contaminations between calves from different origins (potentially carrying different pathogens), together with increased stress because of the process of batch creation, can increase their risks of developing BRD symptoms. This study investigated whether reducing the number of different origins per batch is a strategy to reduce the risk of BRD cases. We developed an algorithm aimed at creating batches with as few origins as possible, while respecting constraints on the number and breed of the calves. We tested this algorithm on a dataset of 137,726 weaned calves grouped into 9701 batches by a French organization. We also computed an index assessing the risks of developing BRD because of the batch composition by considering four pathogens involved in the BRD system. While increasing the heterogeneity of batches in calf bodyweight, which is not expected to strongly impact the performance, our algorithm successfully decreased the average number of origins in the same batch and their risk index. Both this algorithm and the risk index can be used as part of decision tool to assess and possibly minimize BRD risk at batch creation, but they are generic enough to assess health risk for other production animals, and optimize the homogeneity of selected characteristics.
Published: 2021
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31. Spatial epidemiological modelling of infection by Vibrio aestuarianus shows that connectivity and temperature control oyster mortality

Author: C Lupo, BL Dutta, S Petton, P Ezanno, D Tourbiez, MA Travers, F Pernet, and C Bacher
Subjects: Aquaculture. Fisheries. Angling, SH1-691, Ecology, QH540-549.5
Abstract: Vibrio aestuarianus infection in oyster populations causes massive mortality, resulting in losses for oyster farmers. Such dynamics result from host-pathogen interactions and contagion through water-borne transmission. To assess the spatiotemporal spread of V. aestuarianus infection and associated oyster mortality at a bay scale, we built a mathematical model informed by experimental infection data at 2 temperatures and spatially dependent marine connectivity of oyster farms. We applied the model to a real system and tested the importance of each factor using a number of modelling scenarios. Results suggest that introducing V. aestuarianus in a fully susceptible adult oyster population in the bay would lead to the mortality of all farmed oysters over 6 to 12 mo, depending on the location in which infection was initiated. The effect of temperature was captured by the basic reproduction number (R0), which was >1 at high seawater temperatures, as opposed to values
Published: 2020
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32. How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Author: Ezanno, P., Andraud, M., Beaunée, G., Hoch, T., Krebs, S., Rault, A., Touzeau, S., Vergu, E., and Widgren, S.
Published: 2020
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33. The Map3k12 (Dlk)/JNK3 signaling pathway is required for pancreatic beta-cell proliferation during postnatal development

Author: Tenenbaum, Mathie, Plaisance, Valérie, Boutry, Raphael, Pawlowski, Valérie, Jacovetti, Cécile, Sanchez-Parra, Clara, Ezanno, Hélène, Bourry, Julien, Beeler, Nicole, Pasquetti, Gianni, Gmyr, Valery, Dalle, Stéphane, Kerr-Conte, Julie, Pattou, François, Hirai, Syu-ichi, Regazzi, Romano, Bonnefond, Amélie, Froguel, Philippe, and Abderrahmani, Amar
Published: 2021
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34. Subcritical Contact Process Seen from the Edge: Convergence to Quasi-Equilibrium

Author: Andjel, Enrique, Ezanno, François, Groisman, Pablo, and Rolla, Leonardo T.
Subjects: Mathematics - Probability
Abstract: The subcritical contact process seen from the rightmost infected site has no invariant measures. We prove that nevertheless it converges in distribution to a quasi-stationary measure supported on finite configurations.
Published: 2014
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35. Publisher Correction: Accounting for farmers’ control decisions in a model of pathogen spread through animal trade

Author: Cristancho Fajardo, Lina, Ezanno, Pauline, and Vergu, Elisabeta
Published: 2021
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36. Selecting sorting centres to avoid long distance transport of weaned beef calves

Author: Morel-Journel, T., Vergu, E., Mercier, J.-B., Bareille, N., and Ezanno, P.
Published: 2021
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37. Research perspectives on animal health in the era of artificial intelligence

Author: Ezanno, Pauline, Picault, Sébastien, Beaunée, Gaël, Bailly, Xavier, Muñoz, Facundo, Duboz, Raphaël, Monod, Hervé, and Guégan, Jean-François
Published: 2021
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38. Accounting for farmers’ control decisions in a model of pathogen spread through animal trade

Author: Cristancho Fajardo, Lina, Ezanno, Pauline, and Vergu, Elisabeta
Published: 2021
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39. Minimizing the number of origins in batches of weaned calves to reduce their risks of developing bovine respiratory diseases

Author: Morel-Journel, Thibaut, Assié, Sébastien, Vergu, Elisabeta, Mercier, Jean-Baptiste, Bonnet-Beaugrand, Florence, and Ezanno, Pauline
Published: 2021
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40. Some results about ergodicity in shape for a crystal growth model

Author: Ezanno, François
Subjects: Mathematics - Probability, 60J27, 60G55, 60K25, 60J10
Abstract: We study a crystal growth Markov model proposed by Gates and Westcott (\cite{Kinetics1}, \cite{Kinetics2}). This is an aggregation process where particles are packed in a square lattice accordingly to prescribed deposition rates. This model is parametrized by three values $(\beta_i, i=0,1,2)$ corresponding to depositions on three different types of sites. The main problem is to determine, for the shape of the crystal, when recurrence and when ergodicity do occur. In \cite{AMS} and \cite{MarkovModels} sufficient conditions are given both for ergodicity and transience. We establish some improved conditions and give a precise description of the asymptotic behavior in a special case., Comment: 21 pages, 2 figures
Published: 2012

41. A coupling construction for spin systems with infinite range interactions

Author: Ezanno, François
Subjects: Mathematics - Probability, 60K35
Abstract: Given a countable set of sites and a collection of flip rates at each site, we give a sufficient condition on the long-range dependancies of the flip rates ensuring the well-definedness of the corresponding spin system. This hypothesis has already been widely used since the interacting particle systems were introduced, but our construction brings a new insight to understand why it is natural. The process is first constructed as a limit of finite spin systems. Then we identify its generator and give a simple criterion for a measure to be invariant with respect to it., Comment: 12 pages
Published: 2012

42. Diagnostic et prise en charge de la maladie de Verneuil inguino-scrotale : ce que l'urologue doit savoir.

Author: Peyrottes, A., Tokarski, E., Chiron, P., Fougerousse, A.-C., and Ezanno, A.-C.
Abstract: L'hidradénite suppurée (HS), également connue sous le nom de maladie de Verneuil, est une affection cutanée inflammatoire chronique dont la pathogénie est multifactorielle et partiellement connue. Bien que rare, elle peut avoir des répercussions majeures pour le patient et impacter sévèrement sa qualité de vie. Sa prise en charge doit être multidisciplinaire et impliquer dermatologues et chirurgiens. L'urologue peut être confronté à cette pathologie dans sa présentation inguino-scrotale, notamment chez le sujet de sexe masculin. En aigu, il doit savoir reconnaître les signes devant faire évoquer une hidradénite suppurée et connaître les grands principes de sa prise en charge médicale et chirurgicale. En cas de situation complexe ou d'évolution chronique, le patient pourra être référé dans un centre expert. Cet article propose de résumer les notions clés que l'urologue doit maîtriser concernant la maladie de Verneuil périnéale. Hidradenitis suppurativa (HS), also known as Verneuil's disease, is a chronic inflammatory skin condition. Although uncommon, it can have significant repercussions for the patients and severely impact their quality of life. Its management should be multidisciplinary, involving dermatologists and surgeons. Urologists may encounter this condition in its perineal presentation, especially in male patients. In acute cases, they should be able to recognize signs in favour of hidradenitis suppurativa and be familiar with the key principles of its medical and surgical management. In complex situations or long-term evolutions, patients may be referred to expert institutions. This article aims to summarize the key points useful for urologists regarding perineal Verneuil's disease. [ABSTRACT FROM AUTHOR]
Published: 2024
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43. It’s risky to wander in September: Modelling the epidemic potential of Rift Valley fever in a Sahelian setting

Author: Hélène Cecilia, Raphaëlle Métras, Assane Gueye Fall, Modou Moustapha Lo, Renaud Lancelot, and Pauline Ezanno
Subjects: Rift Valley fever virus, Basic reproduction number, Mathematical modelling, Vector-borne disease, Risk map, Infectious and parasitic diseases, RC109-216
Abstract: Estimating the epidemic potential of vector-borne diseases, along with the relative contribution of underlying mechanisms, is crucial for animal and human health worldwide. In West African Sahel, several outbreaks of Rift Valley fever (RVF) have occurred over the last decades, but uncertainty remains about the conditions necessary to trigger these outbreaks. We use the basic reproduction number (R0) as a measure of RVF epidemic potential in northern Senegal, and map its value in two distinct ecosystems, namely the Ferlo and the Senegal River delta and valley. We consider three consecutive rainy seasons (July-November 2014, 2015 and 2016) and account for several vector and animal species. We parametrize our model with estimates of Aedes vexans arabiensis, Culex poicilipes, Culex tritaeniorhynchus, cattle, sheep and goat abundances. The impact of RVF virus introduction is assessed every week over northern Senegal. We highlight September as the period of highest epidemic potential in northern Senegal, resulting from distinct dynamics in the two study areas. Spatially, in the seasonal environment of the Ferlo, we observe that high-risk locations vary between years. We show that decreased vector densities do not greatly reduce R0 and that cattle immunity has a greater impact on reducing transmission than small ruminant immunity. The host preferences of vectors and the temperature-dependent time interval between their blood meals are crucial parameters needing further biological investigations.
Published: 2020
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44. Intelligence artificielle et santé animale

Author: Pauline EZANNO, Sébastien PICAULT, Nathalie WINTER, Gaël BEAUNÉE, Hervé MONOD, and Jean-François GUÉGAN
Subjects: Animal culture, SF1-1100, Aquaculture. Fisheries. Angling, SH1-691
Abstract: Mobiliser les approches issues de l’intelligence artificielle (IA) en santé animale (SA) permet d’aborder des problèmes de forte complexité logique ou algorithmique tels que rencontrés en épidémiologie quantitative et prédictive, en médecine de précision, ou dans l’étude des relations hôtes × pathogènes. L’IA peut dans certaines situations faciliter le diagnostic et la détection de cas, fiabiliser les prédictions et réduire les erreurs, permettre des représentations plus réalistes et lisibles par des non informaticiens de systèmes biologiques complexes, accélérer les décisions, améliorer la précision des analyses de risque et permettre de mieux cibler les interventions et d’en anticiper les effets. De plus, les fronts de science en SA engendrent de nouveaux challenges pour l’IA, du fait de la spécificité des systèmes, des données, des contraintes, et des objectifs d’analyse. Sur la base d’une revue de la littérature à l’interface entre IA et SA couvrant la période 2009-2019, et d’entretiens conduits avec des chercheurs français positionnés à cette interface, cette synthèse explicite les grands domaines de recherche en SA dans lesquels l’IA est actuellement mobilisée, comment elle contribue à revisiter les questions de recherche en SA et lever des verrous méthodologiques, et comment des questions de SA stimulent de nouveaux travaux en IA. Après avoir présenté les freins et leviers possibles, nous proposons des recommandations pour se saisir au mieux de l’enjeu que représente cette interface SA/IA.
Published: 2020
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45. What should a surgeon know about hidradenitis suppurativa?

Author: Ezanno, Anne Cecile, Guillem, Philippe, Gorin, Cécile, Gabison, Germaine, Malgras, Brice, and Fougerousse, Anne-Claire
Subjects: INFLAMMATORY bowel diseases, THERAPEUTICS, MEDICAL research, SUICIDE risk factors, SURGEONS, CHRONIC wounds & injuries, HIDRADENITIS suppurativa
Abstract: • Hidradenitis suppurativa affects 1% of the population, particularly women (3:1 ratio). • Its pathophysiology has been imperfectly elucidated; it may be associated with chronic inflammatory bowel disease, spondyloarthropathy, and trisomy 21. • The diagnosis is based on the association of three clinical characteristics:. ∘ characteristic lesion with nodule, abscess and/or draining fistula; ∘ typical localization (cutaneous folds); ∘ chronic and/or recurrent nature. • Its evolutivity is appraised by several scores including the Hurley severity score for routine clinical practice, and the Hidradenitis Suppurativa Clinical Response (HiSCR) score regarding disease evolution and clinical research. • Exacerbated by obesity and smoking, this progressive disease has a major impact on quality of life and entails a risk of suicide. • According to stage, medical treatment includes antibiotic therapy (in case of flare-up), long-course antibiotic therapy or biologics, but surgery remains the one and only curative option. Otherwise known as Verneuil's disease, hidradenitis suppurative (HS) is a severe dermatosis of heterogeneous appearance affecting 1% of the population. Its pathophysiology is multifactorial, involving genetic predisposition, inflammatory disorder and environmental elements. Its diagnosis is based on the association of three clinical characteristics: characteristic lesions, typical localizations, and the chronic and recurrent nature of the lesions. Given its diversified aspects, diagnosis can be difficult to achieve. As its manifestations often include abscesses, it is important for the surgeon to know how to detect this pathology. The development of biologics has improved treatment of this disease, treatment that necessitates a multidisciplinary medical and surgical approach involving dermatologists and proctologists as well as surgeons. The objective of this report is to synthesize what a surgeon will need to know so as effectively treat HS patients. [ABSTRACT FROM AUTHOR]
Published: 2023
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46. EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology.

Author: Sébastien Picault, Yu-Lin Huang, Vianney Sicard, Sandie Arnoux, Gaël Beaunée, and Pauline Ezanno
Subjects: Biology (General), QH301-705.5
Abstract: Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/www/emulsion-public.
Published: 2019
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47. Que doit connaître le chirurgien sur l’hidradénite suppurée ?

Author: Ezanno, A.C., Guillem, P., Gorin, C., Gabison, G., Malgras, B., and Fougerousse, A.C.
Abstract: L’hidradénite suppurée (HS) ou maladie de Verneuil, est une dermatose d’aspect hétérogène touchant 1 % de la population. Sa physiopathologie est multifactorielle avec une part de prédisposition génétique, un dérèglement inflammatoire et des facteurs environnementaux. Le diagnostic repose sur l’association de 3 caractéristiques cliniques : lésions caractéristiques, localisations typiques et caractère chronique et récidivant des lésions. Du fait de son aspect hétérogène, le diagnostic peut être difficile à poser. Le mode d’entrée dans la maladie est souvent l’abcès d’où l’importance pour tout chirurgien de savoir évoquer cette pathologie. Le développement des biothérapies a amélioré la prise en charge de cette maladie qui nécessite une approche multidisciplinaire médicochirurgicale impliquant dermatologues, proctologues, chirurgiens… L’objectif de cette mise au point est de synthétiser les connaissances nécessaires à chaque chirurgien pour mieux prendre en charge les patients atteints d’HS.
Published: 2023
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48. Neighbourhood contacts and trade movements drive the regional spread of bovine viral diarrhoea virus (BVDV)

Author: Qi, Luyuan, Beaunée, Gaël, Arnoux, Sandie, Dutta, Bhagat Lal, Joly, Alain, Vergu, Elisabeta, and Ezanno, Pauline
Published: 2019
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49. Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management

Author: Lambert, Sébastien, Ezanno, Pauline, Garel, Mathieu, and Gilot-Fromont, Emmanuelle
Published: 2018
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50. Evaluation of testing strategies to identify infected animals at a single round of testing within dairy herds known to be infected with Mycobacterium avium ssp. paratuberculosis

Author: More, S.J., Cameron, A.R., Strain, S., Cashman, W., Ezanno, P., Kenny, K., Fourichon, C., and Graham, D.
Published: 2015
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