Author: "Farthing, A" / Database: OAIster - Searchworks@Jio Institute Digital Library Search Results

289 results on '"Farthing, A"'

1. Engineering new approaches for pathogen separation and detection to tackle antimicrobial resistance

Author: Farthing, Andrew G. and Farthing, Andrew G.
Published: 2024

2. Multi-fidelity Hamiltonian Monte Carlo

Author: Patel, Dhruv V., Lee, Jonghyun, Farthing, Matthew W., Kitanidis, Peter K., Darve, Eric F., Patel, Dhruv V., Lee, Jonghyun, Farthing, Matthew W., Kitanidis, Peter K., and Darve, Eric F.
Abstract: Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chain Monte Carlo technique, exploiting the shape of such high-dimensional target distributions to efficiently generate samples. Despite its impressive empirical success and increasing popularity, its wide-scale adoption remains limited due to the high computational cost of gradient calculation. Moreover, applying this method is impossible when the gradient of the posterior cannot be computed (for example, with black-box simulators). To overcome these challenges, we propose a novel two-stage Hamiltonian Monte Carlo algorithm with a surrogate model. In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver. Splitting the standard HMC algorithm into these two stages allows for approximating the gradient of the posterior efficiently, while producing accurate posterior samples by using HF numerical solvers in the second stage. We demonstrate the effectiveness of this algorithm for a range of problems, including linear and nonlinear Bayesian inverse problems with in-silico data and experimental data. The proposed algorithm is shown to seamlessly integrate with various low-fidelity and HF models, priors, and datasets. Remarkably, our proposed method outperforms the traditional HMC algorithm in both computational and statistical efficiency by several orders of magnitude, all while retaining or improving the accuracy in computed posterior statistics.
Published: 2024

3. Moving Beyond the Binary: How Language and Common Research Practices Can Make Emergency Medicine Less Welcoming for Some Learners and Physicians

Author: Farthing, Alex, Farthing, Alex, Burkhardt, John, Farthing, Alex, Farthing, Alex, and Burkhardt, John
Published: 2022

4. Tracking elusive and shifting identities of the global fishing fleet

Author: Park, Jaeyoon, Van Osdel, Jennifer, Turner, Joanna, Farthing, Courtney M., Miller, Nathan A., Linder, Hannah L., Ortuño Crespo, Guillermo, Carmine, Gabrielle, Kroodsma, David A., Park, Jaeyoon, Van Osdel, Jennifer, Turner, Joanna, Farthing, Courtney M., Miller, Nathan A., Linder, Hannah L., Ortuño Crespo, Guillermo, Carmine, Gabrielle, and Kroodsma, David A.
Abstract: Illegal, unreported, and unregulated (IUU) fishing costs billions of dollars per year and is enabled by vessels obfuscating their identity. Here, we combine identities of ~35,000 vessels with a decade of GPS data to provide a global assessment of fishing compliance, reflagging patterns, and fishing by foreign-owned vessels. About 17% of high seas fishing is by potentially unauthorized or internationally unregulated vessels, with hot spots of this activity in the west Indian and the southwest Atlantic Oceans. In addition, reflagging, a tactic often used to obscure oversight, occurs in just a few ports primarily by fleets with high foreign ownership. Fishing by foreign-owned vessels is concentrated in parts of high seas and certain national waters, often flying flags of convenience. These findings can address the global scope of potential IUU fishing and enable authorities to improve oversight.
Published: 2023
Full Text: View/download PDF

5. UTS Submission in response to the House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system

Author: Buckingham Shum, S, Goh, E, Santow, E, Loble, L, Chen, F, McLean, J, Readman, K, Knight, S, Parfitt, A, Wightwick, G, Hunter, J, Farthing, S, Buckingham Shum, S, Goh, E, Santow, E, Loble, L, Chen, F, McLean, J, Readman, K, Knight, S, Parfitt, A, Wightwick, G, Hunter, J, and Farthing, S
Published: 2023

6. UTS Submission in response to the House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system

Author: Buckingham Shum, S, Goh, E, Santow, E, Loble, L, Chen, F, McLean, J, Readman, K, Knight, S, Parfitt, A, Wightwick, G, Hunter, J, Farthing, S, Buckingham Shum, S, Goh, E, Santow, E, Loble, L, Chen, F, McLean, J, Readman, K, Knight, S, Parfitt, A, Wightwick, G, Hunter, J, and Farthing, S
Published: 2023

7. Towards a responsible machine learning approach to identify forced labor in fisheries

Author: Joo, Rocío, McDonald, Gavin, Miller, Nathan, Kroodsma, David, Farthing, Courtney, Belhabib, Dyhia, Hochberg, Timothy, Joo, Rocío, McDonald, Gavin, Miller, Nathan, Kroodsma, David, Farthing, Courtney, Belhabib, Dyhia, and Hochberg, Timothy
Abstract: Many fishing vessels use forced labor, but identifying vessels that engage in this practice is challenging because few are regularly inspected. We developed a positive-unlabeled learning algorithm using vessel characteristics and movement patterns to estimate an upper bound of the number of positive cases of forced labor, with the goal of helping make accurate, responsible, and fair decisions. 89% of the reported cases of forced labor were correctly classified as positive (recall) while 98% of the vessels certified as having decent working conditions were correctly classified as negative. The recall was high for vessels from different regions using different gears, except for trawlers. We found that as much as ~28% of vessels may operate using forced labor, with the fraction much higher in squid jiggers and longlines. This model could inform risk-based port inspections as part of a broader monitoring, control, and surveillance regime to reduce forced labor. * Translated versions of the English title and abstract are available in five languages in S1 Text: Spanish, French, Simplified Chinese, Traditional Chinese, and Indonesian., Comment: 24 pages, 2 figures, 2 supp files
Published: 2023

8. Differentiable modeling to unify machine learning and physical models and advance Geosciences

Author: Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Roy, Tirthankar, Xu, Chonggang, Mohanty, Binayak, Lawson, Kathryn, Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Roy, Tirthankar, Xu, Chonggang, Mohanty, Binayak, and Lawson, Kathryn
Abstract: Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.
Published: 2023
Full Text: View/download PDF

9. ‘I can't go, I can't afford it’: Financial concern amongst health students undertaking rural and remote placements during COVID-19

Author: Jessup, B, Hoang, H, Podubinski, T, Obamiro, K, Bourke, L, Hellwege, B, Jatrana, Santosh, Heaney, S, Farthing, A, Sheepway, L, Rasiah, R, Jessup, B, Hoang, H, Podubinski, T, Obamiro, K, Bourke, L, Hellwege, B, Jatrana, Santosh, Heaney, S, Farthing, A, Sheepway, L, and Rasiah, R
Published: 2022

10. Impact of COVID-19 on rural and remote student placements in Australia: A national study

Author: Hoang, H, Jessup, B, Obamiro, K, Bourke, L, Hellwege, B, Podubinski, T, Heaney, S, Sheepway, L, Farthing, A, Rasiah, R, Fitzroy, R, Jatrana, Santosh, Argus, G, Knight, S, Hoang, H, Jessup, B, Obamiro, K, Bourke, L, Hellwege, B, Podubinski, T, Heaney, S, Sheepway, L, Farthing, A, Rasiah, R, Fitzroy, R, Jatrana, Santosh, Argus, G, and Knight, S
Published: 2022

11. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Author: Cramer, Estee Y, Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel, Cramer, Estee Y, Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel
Abstract: Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
Published: 2022

12. The Leaky Pipeline in Emergency Medicine: Understanding Factors Pushing Women Away and Informing Interventions

Author: Klekowski, Nicole, Klekowski, Nicole, Balgord, Sarah, Diaz, Rosemarie, Farthing, Alex, Escolero, Sylvia, DeCloux, Koryanne, Burkhardt, John, Abir, Mahshid, Haggins, Adrianne, Hopson, Laura, Klekowski, Nicole, Klekowski, Nicole, Balgord, Sarah, Diaz, Rosemarie, Farthing, Alex, Escolero, Sylvia, DeCloux, Koryanne, Burkhardt, John, Abir, Mahshid, Haggins, Adrianne, and Hopson, Laura
Abstract: Learning Objectives: Understand the environmental factors which influence selection of Emergency Medicine as a specialty by women medical students.Background: Women represent 28.3% of EM physicians. There is now gender parity in US medical schools, but women applicants to EM ranges 33-37%. Prior research does not explain these gender differences. There are known differences in resident experiences and assessments based on gender.Objectives: We sought to explore how clinical experiences and perceptions of the specialty influence selection of EM by women.Methods: Using purposive and convenience sampling to represent diverse learning environments, we conducted semi-structured interviews of men and women US senior medical students who considered EM as a specialty. Interviews were transcribed, de-identified, and coded using constant comparative analysis until saturation. We conducted thematic analysis using a constructivist approach and grounded theory. Reflexivity and credibility activities were performed.Results: 25 students from 11 geographically diverse schools completed interviews. 68% (17/25) were women. The majority (21/25) expressed commitment to EM. Four main themes were identified: 1. EM culture was perceived as exclusionary; 2. Beliefs about attributes of EM physicians and the specialty were influenced by gender; 3. Distressing patient encounters and physician/staff behaviors negatively affected students; and 4. Access to mentors, representation and exposure to EM affected interest. Table 1.Conclusions: The EM gender differential is affected by societal gender roles and an environment that rewards traditional masculine traits. Conflict with behavioral norms may hinder women forming their professional identity as an emergency physician. Potential interventions include recognizing the gendered perception of the field; establishing early, longitudinal mentoring and engagement with the specialty; and building a supportive culture to overcome mistreatment concerns.
Published: 2022

13. 'I can't go, I can't afford it': Financial concern amongst health students undertaking rural and remote placements during COVID-19

Author: Jessup, B, Hoang, H, Podubinski, T, Obamiro, K, Bourke, L, Hellwege, B, Jatrana, S, Heaney, S, Farthing, A, Sheepway, L, Rasiah, R, Jessup, B, Hoang, H, Podubinski, T, Obamiro, K, Bourke, L, Hellwege, B, Jatrana, S, Heaney, S, Farthing, A, Sheepway, L, and Rasiah, R
Abstract: INTRODUCTION: As the coronavirus pandemic unfolded during 2020, widespread financial uncertainty emerged amongst university students across the globe. What is not yet clear is how Australian health students were financially impacted during the initial stages of the pandemic and whether this influenced their ability to undertake planned rural or remote placements. OBJECTIVE: To examine (a) financial concern amongst health students during COVID-19, (b) the financial implications of changes to planned rural or remote placements and (c) the impact of these factors on students' ability to undertake placements during the pandemic. DESIGN: Mixed-methods design involving an online survey (n = 1210) and semi-structured interviews (n = 29). Nursing, medical and allied health students with a planned University Department of Rural Health-facilitated rural or remote placement between February and October 2020 were invited to participate. FINDINGS: 54.6% of surveyed students reported financial concern during COVID-19. Financial concern correlated with both changes in financial position and employment, with 36.6% of students reporting a reduction in income and 43.1% of students reporting a reduction in, or cessation of regular employment. Placement changes yielded a range of financial implications. Cancelled placements saved some students travel and accommodation costs, but left others out of pocket if these expenses were prepaid. Placements that went ahead often incurred increased accommodation costs due to limited availability. Financial concern and/or financial implications of placement changes ultimately prevented some students from undertaking their rural or remote placement as planned. DISCUSSION: Many nursing, allied health and medical students expressed financial concern during COVID-19, associated with a loss of regular employment and income. Placement changes also presented unforeseen financial burden for students. These factors ultimately prevented some students from un
Published: 2022

14. The Leaky Pipeline in Emergency Medicine: Understanding Factors Pushing Women Away and Informing Interventions

Author: Klekowski, Nicole, Klekowski, Nicole, Balgord, Sarah, Diaz, Rosemarie, Farthing, Alex, Escolero, Sylvia, DeCloux, Koryanne, Burkhardt, John, Abir, Mahshid, Haggins, Adrianne, Hopson, Laura, Klekowski, Nicole, Klekowski, Nicole, Balgord, Sarah, Diaz, Rosemarie, Farthing, Alex, Escolero, Sylvia, DeCloux, Koryanne, Burkhardt, John, Abir, Mahshid, Haggins, Adrianne, and Hopson, Laura
Abstract: Learning Objectives: Understand the environmental factors which influence selection of Emergency Medicine as a specialty by women medical students.Background: Women represent 28.3% of EM physicians. There is now gender parity in US medical schools, but women applicants to EM ranges 33-37%. Prior research does not explain these gender differences. There are known differences in resident experiences and assessments based on gender.Objectives: We sought to explore how clinical experiences and perceptions of the specialty influence selection of EM by women.Methods: Using purposive and convenience sampling to represent diverse learning environments, we conducted semi-structured interviews of men and women US senior medical students who considered EM as a specialty. Interviews were transcribed, de-identified, and coded using constant comparative analysis until saturation. We conducted thematic analysis using a constructivist approach and grounded theory. Reflexivity and credibility activities were performed.Results: 25 students from 11 geographically diverse schools completed interviews. 68% (17/25) were women. The majority (21/25) expressed commitment to EM. Four main themes were identified: 1. EM culture was perceived as exclusionary; 2. Beliefs about attributes of EM physicians and the specialty were influenced by gender; 3. Distressing patient encounters and physician/staff behaviors negatively affected students; and 4. Access to mentors, representation and exposure to EM affected interest. Table 1.Conclusions: The EM gender differential is affected by societal gender roles and an environment that rewards traditional masculine traits. Conflict with behavioral norms may hinder women forming their professional identity as an emergency physician. Potential interventions include recognizing the gendered perception of the field; establishing early, longitudinal mentoring and engagement with the specialty; and building a supportive culture to overcome mistreatment concerns.
Published: 2022

15. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Author: Cramer, Estee Y, Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel, Cramer, Estee Y, Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel
Abstract: Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
Published: 2022

16. Impact of COVID-19 on rural and remote student placements in Australia: A national study

Author: Hoang, H, Jessup, B, Obamiro, K, Bourke, L, Hellwege, B, Podubinski, T, Heaney, S, Sheepway, L, Farthing, A, Rasiah, R, Fitzroy, R, Jatrana, S, Argus, G, Knight, S, Hoang, H, Jessup, B, Obamiro, K, Bourke, L, Hellwege, B, Podubinski, T, Heaney, S, Sheepway, L, Farthing, A, Rasiah, R, Fitzroy, R, Jatrana, S, Argus, G, and Knight, S
Abstract: OBJECTIVE: To investigate students' perceptions of the impact of coronavirus SARS-CoV-2 on rural and remote placements facilitated by 16 University Departments of Rural Health in Australia in 2020. DESIGN: A mixed-method design comprising an online survey and semi-structured interviews. SETTING: Australia. PARTICIPANTS: Allied health, nursing and medical students with a planned University Departments of Rural Health-facilitated rural or remote placement between February and October 2020. INTERVENTION: A planned rural or remote placement in 2020 facilitated by a University Departments of Rural Health, regardless of placement outcome. MAIN OUTCOME MEASURES: Questionnaire included placement outcome (completed or not), discipline of study (nursing, allied health, medicine), and Likert measures of impact to placement (including supervision, placement tasks, location, accommodation, client contact and student learning) and placement experience (overall, support, supervision, university support). Semi-structured interviews asked about placement planning, outcome, decisions, experience and student perceptions. RESULTS: While coronavirus SARS-CoV-2 reportedly impacted on the majority of planned placements, most students (80%) were able to complete their University Departments of Rural Health-facilitated placement in some form and were satisfied with their placement experience. Common placement changes included changes to tasks, setting, supervisors and location. Allied health students were significantly more likely to indicate that their placement had been impacted and also felt more supported by supervisors and universities than nursing students. Interview participants expressed concerns regarding the potential impact of cancelled and adapted placements on graduation and future employment. CONCLUSIONS: The coronavirus SARS-CoV-2 pandemic was reported to impact the majority of University Departments of Rural Health-facilitated rural and remote placements in 2020. Fortunately
Published: 2022

17. High-Performance Data Transfer for Full Data Replication between ITER and the Remote Experimentation Centre

Author: YAMANAKA, Kenjiro, Nakanishi, Hideya, OZAKI, Takahisa, NAKAJIMA, Noriyoshi, FARTHING, Jonathan, MANDUCHI, Gabriele, ROBIN, Francois, ABE, Shunji, URUSHIDANI, Shigeo, YAMANAKA, Kenjiro, Nakanishi, Hideya, OZAKI, Takahisa, NAKAJIMA, Noriyoshi, FARTHING, Jonathan, MANDUCHI, Gabriele, ROBIN, Francois, ABE, Shunji, and URUSHIDANI, Shigeo
Abstract: A high-performance data transfer method has been developed for the Remote Experimentation Centre (REC) of ITER in Japan for the first time. The developed technology shows the technical feasibility to establish the REC with full data replication between ITER and REC for remote experiments. Test results showed that it achieved a data transfer rate of approximately 7.9 Giga-bits per second (Gbps) on a 10-Gbps network. The new double-layer storage structure can accelerate the storage read/write speed up to 2 GByte/s. Moreover, the Internet and a layer-2 virtual private network (L2VPN) comparison tests demonstrated that the latter is superior in both security and speed. This technology shows great potential for near real-time full data replication between ITER and REC, which may provide a new style of world-wide remote experimentation., source:Kenjiro Yamanaka, Hideya Nakanishi, Takahisa Ozeki, Noriyoshi Nakajima, Jonathan Farthing, Gabriele Manduchi, Francois Robin, Shunji Abe, Shigeo Urushidani, High-performance data transfer for full data replication between iter and the remote experimentation centre, Fusion Engineering and Design, Volume 138, 2019, Pages 202-209, source:https://doi.org/10.1016/j.fusengdes.2018.10.030, identifier:0000-0001-5737-7843
Published: 2021

18. Contralateral effects of unilateral strength and skill training: Modified Delphi consensus to establish key aspects of cross-education

Author: Manca, A., Hortobágyi, T., Carroll, T. J., Enoka, R. M., Farthing, J. P., Gandevia, S. C., Kidgell, D. J., Taylor, Janet L., Deriu, F., Manca, A., Hortobágyi, T., Carroll, T. J., Enoka, R. M., Farthing, J. P., Gandevia, S. C., Kidgell, D. J., Taylor, Janet L., and Deriu, F.
Abstract: © 2020, The Author(s). Background: Cross-education refers to increased motor output (i.e., force generation, skill) of the opposite, untrained limb following a period of unilateral exercise training. Despite extensive research, several aspects of the transfer phenomenon remain controversial. Methods: A modified two-round Delphi online survey was conducted among international experts to reach consensus on terminology, methodology, mechanisms of action, and translational potential of cross-education, and to provide a framework for future research. Results: Through purposive sampling of the literature, we identified 56 noted experts in the field, of whom 32 completed the survey, and reached consensus (75% threshold) on 17 out of 27 items. Conclusion: Our consensus-based recommendations for future studies are that (1) the term ‘cross-education’ should be adopted to refer to the transfer phenomenon, also specifying if transfer of strength or skill is meant; (2) functional magnetic resonance imaging, short-interval intracortical inhibition and interhemispheric inhibition appear to be promising tools to study the mechanisms of transfer; (3) strategies which maximize cross-education, such as high-intensity training, eccentric contractions, and mirror illusion, seem worth being included in the intervention plan; (4) study protocols should be designed to include at least 13–18 sessions or 4–6 weeks to produce functionally meaningful transfer of strength, and (5) cross-education could be considered as an adjuvant treatment particularly for unilateral orthopedic conditions and sports injuries. Additionally, a clear gap in views emerged between the research field and the purely clinical field. The present consensus statement clarifies relevant aspects of cross-education including neurophysiological, neuroanatomical, and methodological characteristics of the transfer phenomenon, and provides guidance on how to improve the quality and usability of future cross-education studies.
Published: 2021

19. Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Author: Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter K., Darve, Eric F., Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.
Abstract: Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e., depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder. Estimation with uncertainty quantification (UQ) is performed on the low-dimensional latent space in a Bayesian setting. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.
Published: 2021
Full Text: View/download PDF

20. Deep learning-based fast solver of the shallow water equations

Author: Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., Darve, Eric F., Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.
Abstract: Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for this purpose. However, traditional numerical solvers of the SWEs are computationally expensive and require high-resolution riverbed profile measurement (bathymetry). In this work, we propose a two-stage process in which, first, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then use machine learning (ML) algorithms to obtain a fast solver for the SWEs. The fast solver uses realizations from the posterior bathymetry distribution and takes as input the prescribed range of BCs. The first stage allows us to predict flow velocities without direct measurement of the bathymetry. Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage. This allows the solver to incorporate future direct bathymetry measurements into the flow velocity prediction for improved accuracy, even if the bathymetry changes over time compared to its original indirect estimation. We propose and benchmark three different solvers, referred to as PCA-DNN (principal component analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder), and validate them on the Savannah river, Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities for different bathymetry and BCs with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods., Comment: arXiv admin note: substantial text overlap with arXiv:2012.02620
Published: 2021

21. Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs

Author: Dutta, Sourav, Rivera-Casillas, Peter, Cecil, Orie M., Farthing, Matthew W., Perracchione, Emma, Putti, Mario, Dutta, Sourav, Rivera-Casillas, Peter, Cecil, Orie M., Farthing, Matthew W., Perracchione, Emma, and Putti, Mario
Abstract: Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [arXiv:2104.13962], we explored the use of Neural Ordinary Differential Equations (NODE) as a non-intrusive method for propagating the latent-space dynamics in reduced order models. Here, we investigate employing deep autoencoders for discovering the reduced basis representation, the dynamics of which are then approximated by NODE. The ability of deep autoencoders to represent the latent-space is compared to the traditional proper orthogonal decomposition (POD) approach, again in conjunction with NODE for capturing the dynamics. Additionally, we compare their behavior with two classical non-intrusive methods based on POD and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as a real-world application of shallow water hydrodynamics in an estuarine system. Our findings indicate that deep autoencoders can leverage nonlinear manifold learning to achieve a highly efficient compression of spatial information and define a latent-space that appears to be more suitable for capturing the temporal dynamics through the NODE framework., Comment: 16 pages, 7 figures, To Appear in the proceedings of the IXth International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2021), 14-16 June, 2021. arXiv admin note: substantial text overlap with arXiv:2104.13962
Published: 2021

22. Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics

Author: Dutta, Sourav, Rivera-Casillas, Peter, Farthing, Matthew W., Dutta, Sourav, Rivera-Casillas, Peter, and Farthing, Matthew W.
Abstract: Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of continuous-depth, differentiable networks (Chen et al 2018), as a way to propagate latent-space dynamics in reduced order models. We compare their behavior with two classical non-intrusive methods based on proper orthogonal decomposition and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as real-world applications of shallow water hydrodynamics in riverine and estuarine systems. Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions. However, in order to facilitate their widespread adoption for large-scale systems, significant effort needs to be directed at accelerating their training times. This will enable a more comprehensive exploration of the hyperparameter space for building generalizable Neural ODE approximations over a wide range of system dynamics., Comment: 10 pages, 6 figures, To Appear in Proceedings of AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 22-24, 2021
Published: 2021

23. Analysis of ELM stability with extended MHD models in JET, JT-60U and future JT-60SA tokamak plasmas

Author: Aiba, N, Pamela, S, Honda, M, Urano, H, Giroud, C, Delabie, E, Frassinetti, L, Lupelli, I, Hayashi, N, Huijsmans, G, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Ahn, J, Aho-Mantila, L, Airila, M, Albanese, R, Aldred, V, Alegre, D, Alessi, E, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Ambrosino, R, Amicucci, L, Amosov, V, Sunden, E, Angelone, M, Anghel, M, Angioni, C, Appel, L, Appelbee, C, Arena, P, Ariola, M, Arnichand, H, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Asunta, O, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baciero, A, Baiao, D, Bailey, S, Baker, A, Balboa, I, Balden, M, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnes, M, Barnsley, R, Wiechec, A, Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, E, Ayed, N, Benterman, N, Bergsaker, H, Bernardo, J, Bernert, M, Berry, M, Bertalot, L, Besliu, C, Beurskens, M, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Bilkova, P, Binda, F, Bisoffi, A, Bizarro, J, Bjorkas, C, Blackburn, J, Blackman, K, Blackman, T, Blanchard, P, Blatchford, P, Bobkov, V, Boboc, A, Bodnar, G, Bogar, O, Bolshakova, I, Bolzonella, T, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Botrugno, A, Bottereau, C, Boulting, P, Bourdelle, C, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Bremond, S, Brennan, P, Breton, S, Brett, A, Brezinsek, S, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broslawski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buchanan, J, Buckley, M, Budny, R, Bufferand, H, Bulman, M, Bulmer, N, Bunting, P, Buratti, P, Burckhart, A, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Cahyna, P, Calabro, G, Calvo, I, Camenen, Y, Camp, P, Campling, D, Cane, J, Cannas, B, Capel, A, Card, P, Cardinali, A, Carman, P, Carr, M, Carralero, D, Carraro, L, Carvalho, B, Carvalho, I, Carvalho, P, Casson, F, Castaldo, C, Catarino, N, Caumont, J, Causa, F, Cavazzana, R, Cave-Ayland, K, Cavinato, M, Cecconello, M, Ceccuzzi, S, Cecil, E, Cenedese, A, Cesario, R, Challis, C, Chandler, M, Chandra, D, Chang, C, Chankin, A, Chapman, I, Chapman, S, Chernyshova, M, Chitarin, G, Ciraolo, G, Ciric, D, Citrin, J, Clairet, F, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Crisanti, F, Croci, G, Croft, D, Crombe, K, Crowe, R, Cruz, N, Cseh, G, Cufar, A, Cullen, A, Curuia, M, Czarnecka, A, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, C, Day, I, De Bock, M, de Castro, A, de la Cal, E, De La Luna, E, De Masi, G, de Pablos, J, De Temmerman, G, De Tommasi, G, De Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, den Harder, N, Dendy, R, Denis, J, Denner, P, Devaux, S, Devynck, P, Di Maio, F, Di Siena, A, Di Troia, C, Dinca, P, D'Inca, R, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donne, T, Dorling, S, Dormido-Canto, S, Doswon, S, Douai, D, Doyle, P, Drenik, A, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Dunai, D, Dunne, M, Duran, I, Durodie, F, Dutta, P, Duval, B, Dux, R, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Eich, T, Ekedahl, A, El-Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esposito, B, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Faitsch, M, Falie, D, Fanni, A, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Fedorczak, N, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Ferreira, J, Fessey, J, Fevrier, O, Ficker, O, Field, A, Fietz, S, Figueiredo, A, Figueiredo, J, Fil, A, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Fitzgerald, M, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna-Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Freisinger, M, Fresa, R, Frigione, D, Fuchs, V, Fuller, D, Futatani, S, Fyvie, J, Gal, K, Galassi, D, Galazka, K, Galdon-Quiroga, J, Gallagher, J, Gallart, D, Galvao, R, Gao, X, Gao, Y, Garcia, J, Garcia-Carrasco, A, Garcia-Munoz, M, Gardarein, J, Garzotti, L, Gaudio, P, Gauthier, E, Gear, D, Gee, S, Geiger, B, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Giacomelli, L, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Giovannozzi, E, Girardo, J, Giruzzi, G, Gloggler, S, Godwin, J, Goff, J, Gohil, P, Goloborod'Ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gorini, G, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Greuner, H, Grierson, B, Griph, F, Grisolia, C, Grist, D, Groth, M, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guerard, C, Guillemaut, C, Guirlet, R, Gurl, C, Utoh, H, Hackett, L, Hacquin, S, Hagar, A, Hager, R, Hakola, A, Halitovs, M, Hall, S, Cook, S, Hamlyn-Harris, C, Hammond, K, Harrington, C, Harrison, J, Harting, D, Hasenbeck, F, Hatano, Y, Hatch, D, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hellsten, T, Helou, W, Hemming, O, Hender, T, Henderson, M, Henderson, S, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hobirk, J, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horacek, J, Hornung, G, Horton, A, Horton, L, Horvath, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, A, Huber, V, Huddleston, T, Hughes, M, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Imrisek, M, Incelli, M, Innocente, P, Irishkin, M, Stanik, I, Jachmich, S, Jacobsen, A, Jacquet, P, Jansons, J, Jardin, A, Jarvinen, A, Jaulmes, F, Jednorog, S, Jenkins, I, Jeong, C, Jepu, I, Joffrin, E, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kallenbach, A, Kamiya, K, Kaniewski, J, Kantor, A, Kappatou, A, Karhunen, J, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirk, A, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Kochl, F, Kocsis, G, Kodeli, I, Kogan, L, Kogut, D, Koivuranta, S, Kominis, Y, Koppen, M, Kos, B, Koskela, T, Koslowski, H, Koubiti, M, Kovari, M, Kowalska-Strzeciwilk, E, Krasilnikov, A, Krasilnikov, V, Krawczyk, N, Kresina, M, Krieger, K, Krivska, A, Kruezi, U, Ksiazek, I, Kukushkin, A, Kundu, A, Kurki-Suonio, T, Kwak, S, Kwiatkowski, R, Kwon, O, Laguardia, L, Lahtinen, A, Laing, A, Lam, N, Lambertz, H, Lane, C, Lang, P, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Laszynska, E, Lawless, R, Lawson, A, Lawson, K, Lazaros, A, Lazzaro, E, Leddy, J, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Lehnen, M, Leichtle, D, Leichuer, P, Leipold, F, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leyland, M, Leysen, W, Li, L, Liang, Y, Likonen, J, Linke, J, Linsmeier, C, Lipschultz, B, Liu, G, Liu, Y, Lo Schiavo, V, Loarer, T, Loarte, A, Lobel, R, Lomanowski, B, Lomas, P, Lonnroth, J, Lopez, J, Lopez-Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Lyssoivan, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Maget, P, Maggi, C, Maier, H, Mailloux, J, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manas, P, Manning, A, Manso, M, Mantica, P, Mantsinen, M, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marinucci, M, Markovic, T, Marocco, D, Marot, L, Marren, C, Marshal, R, Martin, A, Martin, Y, de Aguilera, A, Martinez, F, Martin-Solis, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Mattei, M, Matthews, G, Maviglia, F, Mayer, M, Mayoral, M, May-Smith, T, Mazon, D, Mazzotta, C, Mcadams, R, Mccarthy, P, Mcclements, K, Mccormack, O, Mccullen, P, Mcdonald, D, Mcintosh, S, Mckean, R, Mckehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meisl, G, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Meyer, H, Mianowski, S, Michling, R, Middleton-Gear, D, Miettunen, J, Militello, F, Militello-Asp, E, Miloshevsky, G, Mink, F, Minucci, S, Miyoshi, Y, Mlynar, J, Molina, D, Monakhov, I, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Moulton, D, Murari, A, Muraro, A, Murphy, S, Asakura, N, Na, Y, Nabais, F, Naish, R, Nakano, T, Nardon, E, Naulin, V, Nave, M, Nedzelski, I, Nemtsev, G, Nespoli, F, Neto, A, Neu, R, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, A, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nocente, M, Nodwell, D, Nordlund, K, Nordman, H, Nouailletas, R, Nunes, I, Oberkofler, M, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, O'Mullane, M, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Paccagnella, R, Pace, N, Pacella, D, Packer, L, Page, A, Pajuste, E, Palazzo, S, Panja, S, Papp, P, Paprok, R, Parail, V, Park, M, Diaz, F, Parsons, M, Pasqualotto, R, Patel, A, Pathak, S, Paton, D, Patten, H, Pau, A, Pawelec, E, Soldan, C, Peackoc, A, Pearson, I, Pehkonen, S, Peluso, E, Penot, C, Pereira, A, Pereira, R, Puglia, P, von Thun, C, Peruzzo, S, Peschanyi, S, Peterka, M, Petersson, P, Petravich, G, Petre, A, Petrella, N, Petrzilka, V, Peysson, Y, Pfefferle, D, Philipps, V, Pillon, M, Pintsuk, G, Piovesan, P, dos Reis, A, Piron, L, Pironti, A, Pisano, F, Pitts, R, Pizzo, F, Plyusnin, V, Pomaro, N, Pompilian, O, Pool, P, Popovichev, S, Porfiri, M, Porosnicu, C, Porton, M, Possnert, G, Potzel, S, Powell, T, Pozzi, J, Prajapati, V, Prakash, R, Prestopino, G, Price, D, Price, M, Price, R, Prior, P, Proudfoot, R, Pucella, G, Puiatti, M, Pulley, D, Purahoo, K, Putterich, T, Rachlew, E, Rack, M, Ragona, R, Rainford, M, Rakha, A, Ramogida, G, Ranjan, S, Rapson, C, Rasmussen, J, Rathod, K, Ratta, G, Ratynskaia, S, Ravera, G, Rayner, C, Rebai, M, Reece, D, Reed, A, Refy, D, Regan, B, Regana, J, Reich, M, Reid, N, Reimold, F, Reinhart, M, Reinke, M, Reiser, D, Rendell, D, Reux, C, Reynolds, S, Riccardo, V, Richardson, N, Riddle, K, Rigamonti, D, Rimini, F, Risner, J, Riva, M, Roach, C, Robins, R, Robinson, S, Robinson, T, Robson, D, Roccella, R, Rodionov, R, Rodrigues, P, Rodriguez, J, Rohde, V, Romanelli, F, Romanelli, M, Romanelli, S, Romazanov, J, Rowe, S, Rubel, M, Rubinacci, G, Rubino, G, Ruchko, L, Ruiz, M, Ruset, C, Rzadkiewicz, J, Saarelma, S, Sabot, R, Safi, E, Sagar, P, Saibene, G, Saint-Laurent, F, Salewski, M, Salmi, A, Salmon, R, Salzedas, F, Samaddar, D, Samm, U, Sandiford, D, Santa, P, Santala, M, Santos, B, Santucci, A, Sartori, F, Sartori, R, Sauter, O, Scannell, R, Schlummer, T, Schmid, K, Schmidt, V, Schmuck, S, Schneider, M, Schopf, K, Schworer, D, Scott, S, Sergienko, G, Sertoli, M, Shabbir, A, Sharapov, S, Shaw, A, Shaw, R, Sheikh, H, Shepherd, A, Shevelev, A, Shumack, A, Sias, G, Sibbald, M, Sieglin, B, Silburn, S, Silva, A, Silva, C, Simmons, P, Simpson, J, Simpson-Hutchinson, J, Sinha, A, Sipila, S, Sips, A, Siren, P, Sirinelli, A, Sjostrand, H, Skiba, M, Skilton, R, Slabkowska, K, Slade, B, Smith, N, Smith, P, Smith, R, Smith, T, Smithies, M, Snoj, L, Soare, S, Solano, E, Somers, A, Sommariva, C, Sonato, P, Sopplesa, A, Sousa, J, Sozzi, C, Spagnolo, S, Spelzini, T, Spineanu, F, Stables, G, Stamatelatos, I, Stamp, M, Staniec, P, Stankunas, G, Stan-Sion, C, Stead, M, Stefanikova, E, Stepanov, I, Stephen, A, Stephen, M, Stevens, A, Stevens, B, Strachan, J, Strand, P, Strauss, H, Strom, P, Stubbs, G, Studholme, W, Subba, F, Summers, H, Svensson, J, Swiderski, L, Szabolics, T, Szawlowski, M, Szepesi, G, Suzuki, T, Tal, B, Tala, T, Talbot, A, Talebzadeh, S, Taliercio, C, Tamain, P, Tame, C, Tang, W, Tardocchi, M, Taroni, L, Taylor, D, Taylor, K, Tegnered, D, Telesca, G, Teplova, N, Terranova, D, Testa, D, Tholerus, E, Thomas, J, Thomas, P, Thompson, A, Thompson, C, Thompson, V, Thorne, L, Thornton, A, Thrysoe, A, Tigwell, P, Tipton, N, Tiseanu, I, Tojo, H, Tokitani, M, Tolias, P, Tomes, M, Tonner, P, Towndrow, M, Trimble, P, Tripsky, M, Tsalas, M, Tsavalas, P, Jun, D, Turner, I, Turner, M, Turnyanskiy, M, Tvalashvili, G, Tyrrell, S, Uccello, A, Ul-Abidin, Z, Uljanovs, J, Ulyatt, D, Uytdenhouwen, I, Vadgama, A, Valcarcel, D, Valentinuzzi, M, Valisa, M, Olivares, P, Valovic, M, Van De Mortel, M, Van Eester, D, Van Renterghem, W, van Rooij, G, Varje, J, Varoutis, S, Vartanian, S, Vasava, K, Vasilopoulou, T, Vega, J, Verdoolaege, G, Verhoeven, R, Verona, C, Rinati, G, Veshchev, E, Vianello, N, Vicente, J, Viezzer, E, Villari, S, Villone, F, Vincenzi, P, Vinyar, I, Viola, B, Vitins, A, Vizvary, Z, Vlad, M, Voitsekhovitch, I, Vondracek, P, Vora, N, Vu, T, de Sa, W, Wakeling, B, Waldon, C, Walkden, N, Walker, M, Walker, R, Walsh, M, Wang, E, Wang, N, Warder, S, Warren, R, Waterhouse, J, Watkins, N, Watts, C, Wauters, T, Weckmann, A, Weiland, J, Weisen, H, Weiszflog, M, Wellstood, C, West, A, Wheatley, M, Whetham, S, Whitehead, A, Whitehead, B, Widdowson, A, Wiesen, S, Wilkinson, J, Williams, J, Williams, M, Wilson, A, Wilson, D, Wilson, H, Wilson, J, Wischmeier, M, Withenshaw, G, Withycombe, A, Witts, D, Wood, D, Wood, R, Woodley, C, Wray, S, Wright, J, Wu, J, Wukitch, S, Wynn, A, Xu, T, Yadikin, D, Yanling, W, Yao, L, Yavorskij, V, Yoo, M, Young, C, Young, D, Young, I, Young, R, Zacks, J, Zagorski, R, Zaitsev, F, Zanino, R, Zarins, A, Zastrow, K, Zerbini, M, Zhang, W, Zhou, Y, Zilli, E, Zoita, V, Zoletnik, S, Zychor, I, Arakawa, H, Bando, T, Bierwage, A, Enoeda, M, Fukumoto, M, Hamamatsu, K, Hanada, M, Hatae, T, Hayashi, T, Higashijima, S, Hirota, M, Hiwatari, R, Ichikawa, M, Ide, S, Ikeda, Y, Imazawa, R, Inoue, S, Isayama, A, Ishida, S, Ishii, Y, Itami, K, Kamada, Y, Aiba, K, Kawano, Y, Kizu, K, Kawamura, Y, Kobayashi, T, Koide, Y, Kojima, A, Kubo, H, Kurihara, K, Kurita, G, Masaki, K, Matsukawa, M, Matsunaga, G, Matsuyama, A, Miki, K, Miya, N, Miyata, Y, Miyato, N, Mori, M, Moriyama, S, Murakami, H, Naito, O, Nakamura, M, Narita, E, Natsume, K, Oasa, K, Ohtani, Y, Ono, M, Oyama, N, Ozeki, T, Sakamoto, Y, Sakasai, A, Sakurai, S, Sano, R, Sasao, H, Shibama, Y, Shibanuma, K, Shimizu, K, Shinohara, K, Shirai, H, Shiraishi, J, Someya, Y, Sukegawa, A, Suzuki, S, Takase, H, Takechi, M, Takenaga, H, Tanigawa, H, Tobita, K, Toma, M, Tsuchiya, K, Tsuru, D, Wakatsuki, T, Yamoto, S, Yagi, M, Yoshida, K, Yoshida, M, Horiike, H, Nobuta, Y, Yamauchi, Y, Idomura, Y, Hatayama, A, Okano, K, Masamune, S, Sanpei, A, Fukuyama, A, Kado, S, Kobayashi, S, Konishi, S, Kunugi, T, Maekawa, T, Minami, T, Mizuuchi, T, Murakami, S, Nagasaki, K, Shikama, T, Watanabe, F, Yamamoto, S, Hanada, K, Idei, H, Katayama, K, Nishikawa, M, Inagaki, S, Fujita, T, Kajita, S, Maeyama, S, Ohno, N, Yamazaki, K, Watanabe, T, Akiyama, T, Isobe, M, Kanno, R, Kobayashi, M, Masuzaki, S, Miyazawa, J, Morisaki, T, Nakajima, N, Nakamura, Y, Nakata, M, Narushima, Y, Nishimura, S, Ohdachi, S, Oishi, T, Osakabe, M, Sagara, A, Sakakibara, S, Satake, S, Suzuki, Y, Takeiri, Y, Tamura, N, Tanaka, K, Todo, Y, Toi, K, Yokoyama, M, Watanabe, K, Shibata, Y, Fukuda, T, Takizuka, T, Ueda, Y, Oya, Y, Ejiri, A, Inomoto, M, Nishiura, M, Ogawa, Y, Takase, Y, Kitajima, S, Iio, S, Matsuda, S, Furukawa, M, Ichimura, M, Imai, T, Nakashima, Y, Sakamoto, M, Sumida, S, Barabaschi, P, Cardella, A, Clement-Lorenzo, S, Coletti, A, Davis, S, Di Pietro, E, Duglue, D, Farthing, J, Frello, G, Hajnal, N, Hurzlmeier, H, Jokinen, A, Kanapienyte, D, Novello, L, Peretti, E, Phillips, G, Rancsik, P, Salpietro, E, Scherber, A, Spears, B, Teuchner, B, Tomarchio, V, Verrecchia, M, Wanner, M, Zani, L, Baulaigue, O, Benoit, F, Ciazynski, D, Decool, P, Dougnac, H, Duchateau, J, Dumas, N, Fejoz, P, Geraud, A, Gharafi, S, Goncalves, R, Gonde, R, Gros, G, Jestin, F, Jiolat, G, Lacroix, B, Lamy, S, Marechal, J, Nicollet, S, Peluso, B, Santagiustina, A, Stephnie, B, Torre, A, Vagliani, A, Vallet, J, Verger, J, Bonne, F, Girard, S, Hoa, C, Lamaison, V, Michel, F, Poncet, J, Roussel, P, Abdel Maksoud, W, Ardellier, F, Disset, G, Donati, A, Genini, L, Mayri, C, Molinie, F, Nunio, F, Ponsot, P, Salanon, B, Scola, L, Vieillard, L, Alonso, J, Barrera, G, Botija, J, Cabrera Perez, S, Fernandez, P, Medrano, M, Ramos, F, Rincon, E, Soleto, A, Ferro, A, Gaio, E, Gasparini, F, Maistrello, A, Brolatti, G, Coccoluto, G, Corato, V, Costa, P, Cristofani, C, Cucchiaro, A, De Vellis, A, Di Pace, L, Frosi, P, Ginoulhiac, G, Lampasi, A, Maffia, G, Pizzuto, A, Polli, G, Rossi, P, Starace, F, Fiamozzi Zignani, C, Zito, P, Drotziger, S, Fietz, W, Heller, R, Massimi, A, Meyer, I, Radloff, D, Rita, C, Urbach, E, Collin, B, Delrez, C, Jamotton, P, Massaut, V, Sarkimaki, K, Benkadda, S, Artaud, J, Becoulet, M, Falchetto, G, Hoang, T, Lotte, P, Moreau, P, Pegourie, B, Travere, J, Bettini, P, Canton, A, Fassina, A, Giudicotti, L, Guo, S, Marchiori, G, Pigatto, L, Vallar, M, Xu, X, Cismondi, F, Barbato, E, Mastrostefano, S, de Baar, M, Bruschi, A, Farina, D, Figini, L, Granucci, G, Moro, A, Nowak, S, Perelli-Cippo, E, Platania, P, Ricci, D, Zuin, M, Conway, G, Dibon, M, Fantz, U, Happel, T, Lauber, P, Lackner, K, Pautasso, G, Schneider, P, Kuhner, G, Zocco, A, Stankiewicz, R, Stepniewski, W, Sips, G, Gleason Gonzalez, C, Luo, X, Scannapiego, M, Bonifetto, R, Decker, J, Goodman, T, Theiler, C, Ayllon-Guerola, J, Kovacsik, A, Szepesi, T, Kawashima, H, Ogawa, T, Sato, M, Seki, M, Aiba N., Pamela S., Honda M., Urano H., Giroud C., Delabie E., Frassinetti L., Lupelli I., Hayashi N., Huijsmans G. T. A., Litaudon X., Abduallev S., Abhangi M., Abreu P., Afzal M., Aggarwal K. M., Ahlgren T., Ahn J. H., Aho-Mantila L., Airila M., Albanese R., Aldred V., Alegre D., Alessi E., Aleynikov P., Alfier A., Alkseev A., Allinson M., Alper B., Alves E., Ambrosino G., Ambrosino R., Amicucci L., Amosov V., Sunden E. A., Angelone M., Anghel M., Angioni C., Appel L., Appelbee C., Arena P., Ariola M., Arnichand H., Arshad S., Ash A., Ashikawa N., Aslanyan V., Asunta O., Auriemma F., Austin Y., Avotina L., Axton M. D., Ayres C., Bacharis M., Baciero A., Baiao D., Bailey S., Baker A., Balboa I., Balden M., Balshaw N., Bament R., Banks J. W., Baranov Y. F., Barnard M. A., Barnes D., Barnes M., Barnsley R., Wiechec A. B., Orte L. B., Baruzzo M., Basiuk V., Bassan M., Bastow R., Batista A., Batistoni P., Baughan R., Bauvir B., Baylor L., Bazylev B., Beal J., Beaumont P. S., Beckers M., Beckett B., Becoulet A., Bekris N., Beldishevski M., Bell K., Belli F., Bellinger M., Belonohy E., Ayed N. B., Benterman N. A., Bergsaker H., Bernardo J., Bernert M., Berry M., Bertalot L., Besliu C., Beurskens M., Bieg B., Bielecki J., Biewer T., Bigi M., Bilkova P., Binda F., Bisoffi A., Bizarro J. P. S., Bjorkas C., Blackburn J., Blackman K., Blackman T. R., Blanchard P., Blatchford P., Bobkov V., Boboc A., Bodnar G., Bogar O., Bolshakova I., Bolzonella T., Bonanomi N., Bonelli F., Boom J., Booth J., Borba D., Borodin D., Borodkina I., Botrugno A., Bottereau C., Boulting P., Bourdelle C., Bowden M., Bower C., Bowman C., Boyce T., Boyd C., Boyer H. J., Bradshaw J. M. A., Braic V., Bravanec R., Breizman B., Bremond S., Brennan P. D., Breton S., Brett A., Brezinsek S., Bright M. D. J., Brix M., Broeckx W., Brombin M., Broslawski A., Brown D. P. D., Brown M., Bruno E., Bucalossi J., Buch J., Buchanan J., Buckley M. A., Budny R., Bufferand H., Bulman M., Bulmer N., Bunting P., Buratti P., Burckhart A., Buscarino A., Busse A., Butler N. K., Bykov I., Byrne J., Cahyna P., Calabro G., Calvo I., Camenen Y., Camp P., Campling D. C., Cane J., Cannas B., Capel A. J., Card P. J., Cardinali A., Carman P., Carr M., Carralero D., Carraro L., Carvalho B. B., Carvalho I., Carvalho P., Casson F. J., Castaldo C., Catarino N., Caumont J., Causa F., Cavazzana R., Cave-Ayland K., Cavinato M., Cecconello M., Ceccuzzi S., Cecil E., Cenedese A., Cesario R., Challis C. D., Chandler M., Chandra D., Chang C. S., Chankin A., Chapman I. T., Chapman S. C., Chernyshova M., Chitarin G., Ciraolo G., Ciric D., Citrin J., Clairet F., Clark E., Clark M., Clarkson R., Clatworthy D., Clements C., Cleverly M., Coad J. P., Coates P. A., Cobalt A., Coccorese V., Cocilovo V., Coda S., Coelho R., Coenen J. W., Coffey I., Colas L., Collins S., Conka D., Conroy S., Conway N., Coombs D., Cooper D., Cooper S. R., Corradino C., Corre Y., Corrigan G., Cortes S., Coster D., Couchman A. S., Cox M. P., Craciunescu T., Cramp S., Craven R., Crisanti F., Croci G., Croft D., Crombe K., Crowe R., Cruz N., Cseh G., Cufar A., Cullen A., Curuia M., Czarnecka A., Dabirikhah H., Dalgliesh P., Dalley S., Dankowski J., Darrow D., Davies O., Davis W., Day C., Day I. E., De Bock M., de Castro A., de la Cal E., De La Luna E., De Masi G., de Pablos J. L., De Temmerman G., De Tommasi G., De Vries P., Deakin K., Deane J., Degli Agostini F., Dejarnac R., den Harder N., Dendy R. O., Denis J., Denner P., Devaux S., Devynck P., Di Maio F., Di Siena A., Di Troia C., Dinca P., D'Inca R., Ding B., Dittmar T., Doerk H., Doerner R. P., Donne T., Dorling S. E., Dormido-Canto S., Doswon S., Douai D., Doyle P. T., Drenik A., Drewelow P., Drews P., Duckworth Ph., Dumont R., Dumortier P., Dunai D., Dunne M., Duran I., Durodie F., Dutta P., Duval B. P., Dux R., Dylst K., Dzysiuk N., Edappala P. V., Edmond J., Edwards A. M., Edwards J., Eich Th., Ekedahl A., El-Jorf R., Elsmore C. G., Enachescu M., Ericsson G., Eriksson F., Eriksson J., Eriksson L. G., Esposito B., Esquembri S., Esser H. G., Esteve D., Evans B., Evans G. E., Evison G., Ewart G. D., Fagan D., Faitsch M., Falie D., Fanni A., Fasoli A., Faustin J. M., Fawlk N., Fazendeiro L., Fedorczak N., Felton R. C., Fenton K., Fernades A., Fernandes H., Ferreira J., Fessey J. A., Fevrier O., Ficker O., Field A., Fietz S., Figueiredo A., Figueiredo J., Fil A., Finburg P., Firdaouss M., Fischer U., Fittill L., Fitzgerald M., Flammini D., Flanagan J., Fleming C., Flinders K., Fonnesu N., Fontdecaba J. M., Formisano A., Forsythe L., Fortuna L., Fortuna-Zalesna E., Fortune M., Foster S., Franke T., Franklin T., Frasca M., Freisinger M., Fresa R., Frigione D., Fuchs V., Fuller D., Futatani S., Fyvie J., Gal K., Galassi D., Galazka K., Galdon-Quiroga J., Gallagher J., Gallart D., Galvao R., Gao X., Gao Y., Garcia J., Garcia-Carrasco A., Garcia-Munoz M., Gardarein J. -L., Garzotti L., Gaudio P., Gauthier E., Gear D. F., Gee S. J., Geiger B., Gelfusa M., Gerasimov S., Gervasini G., Gethins M., Ghani Z., Ghate M., Gherendi M., Giacalone J. C., Giacomelli L., Gibson C. S., Giegerich T., Gil C., Gil L., Gilligan S., Gin D., Giovannozzi E., Girardo J. B., Giruzzi G., Gloggler S., Godwin J., Goff J., Gohil P., Goloborod'Ko V., Gomes R., Goncalves B., Goniche M., Goodliffe M., Goodyear A., Gorini G., Gosk M., Goulding R., Goussarov A., Gowland R., Graham B., Graham M. E., Graves J. P., Grazier N., Grazier P., Green N. R., Greuner H., Grierson B., Griph F. S., Grisolia C., Grist D., Groth M., Grove R., Grundy C. N., Grzonka J., Guard D., Guerard C., Guillemaut C., Guirlet R., Gurl C., Utoh H. H., Hackett L. J., Hacquin S., Hagar A., Hager R., Hakola A., Halitovs M., Hall S. J., Cook S. P. H., Hamlyn-Harris C., Hammond K., Harrington C., Harrison J., Harting D., Hasenbeck F., Hatano Y., Hatch D. R., Haupt T. D. V., Hawes J., Hawkes N. C., Hawkins J., Hawkins P., Haydon P. W., Hayter N., Hazel S., Heesterman P. J. L., Heinola K., Hellesen C., Hellsten T., Helou W., Hemming O. N., Hender T. C., Henderson M., Henderson S. S., Henriques R., Hepple D., Hermon G., Hertout P., Hidalgo C., Highcock E. G., Hill M., Hillairet J., Hillesheim J., Hillis D., Hizanidis K., Hjalmarsson A., Hobirk J., Hodille E., Hogben C. H. A., Hogeweij G. M. D., Hollingsworth A., Hollis S., Homfray D. A., Horacek J., Hornung G., Horton A. R., Horton L. D., Horvath L., Hotchin S. P., Hough M. R., Howarth P. J., Hubbard A., Huber A., Huber V., Huddleston T. M., Hughes M., Hunter C. L., Huynh P., Hynes A. M., Iglesias D., Imazawa N., Imbeaux F., Imrisek M., Incelli M., Innocente P., Irishkin M., Stanik I. I., Jachmich S., Jacobsen A. S., Jacquet P., Jansons J., Jardin A., Jarvinen A., Jaulmes F., Jednorog S., Jenkins I., Jeong C., Jepu I., Joffrin E., Johnson R., Johnson T., Johnston J., Joita L., Jones G., Jones T. T. C., Hoshino K. K., Kallenbach A., Kamiya K., Kaniewski J., Kantor A., Kappatou A., Karhunen J., Karkinsky D., Karnowska I., Kaufman M., Kaveney G., Kazakov Y., Kazantzidis V., Keeling D. L., Keenan T., Keep J., Kempenaars M., Kennedy C., Kenny D., Kent J., Kent O. N., Khilkevich E., Kim H. T., Kim H. S., Kinch A., King C., King D., King R. F., Kinna D. J., Kiptily V., Kirk A., Kirov K., Kirschner A., Kizane G., Klepper C., Klix A., Knight P., Knipe S. J., Knott S., Kobuchi T., Kochl F., Kocsis G., Kodeli I., Kogan L., Kogut D., Koivuranta S., Kominis Y., Koppen M., Kos B., Koskela T., Koslowski H. R., Koubiti M., Kovari M., Kowalska-Strzeciwilk E., Krasilnikov A., Krasilnikov V., Krawczyk N., Kresina M., Krieger K., Krivska A., Kruezi U., Ksiazek I., Kukushkin A., Kundu A., Kurki-Suonio T., Kwak S., Kwiatkowski R., Kwon O. J., Laguardia L., Lahtinen A., Laing A., Lam N., Lambertz H. T., Lane C., Lang P. T., Lanthaler S., Lapins J., Lasa A., Last J. R., Laszynska E., Lawless R., Lawson A., Lawson K. D., Lazaros A., Lazzaro E., Leddy J., Lee S., Lefebvre X., Leggate H. J., Lehmann J., Lehnen M., Leichtle D., Leichuer P., Leipold F., Lengar I., Lennholm M., Lerche E., Lescinskis A., Lesnoj S., Letellier E., Leyland M., Leysen W., Li L., Liang Y., Likonen J., Linke J., Linsmeier Ch., Lipschultz B., Liu G., Liu Y., Lo Schiavo V. P., Loarer T., Loarte A., Lobel R. C., Lomanowski B., Lomas P. J., Lonnroth J., Lopez J. M., Lopez-Razola J., Lorenzini R., Losada U., Lovell J. J., Loving A. B., Lowry C., Luce T., Lucock R. M. A., Lukin A., Luna C., Lungaroni M., Lungu C. P., Lungu M., Lunniss A., Lyssoivan A., Macdonald N., Macheta P., Maczewa K., Magesh B., Maget P., Maggi C., Maier H., Mailloux J., Makkonen T., Makwana R., Malaquias A., Malizia A., Manas P., Manning A., Manso M. E., Mantica P., Mantsinen M., Manzanares A., Maquet Ph., Marandet Y., Marcenko N., Marchetto C., Marchuk O., Marinelli M., Marinucci M., Markovic T., Marocco D., Marot L., Marren C. A., Marshal R., Martin A., Martin Y., de Aguilera A. M., Martinez F. J., Martin-Solis J. R., Martynova Y., Maruyama S., Masiello A., Maslov M., Matejcik S., Mattei M., Matthews G. F., Maviglia F., Mayer M., Mayoral M. L., May-Smith T., Mazon D., Mazzotta C., McAdams R., McCarthy P. J., McClements K. G., McCormack O., McCullen P. A., McDonald D., McIntosh S., McKean R., McKehon J., Meadows R. C., Meakins A., Medina F., Medland M., Medley S., Meigh S., Meigs A. G., Meisl G., Meitner S., Meneses L., Menmuir S., Mergia K., Merrigan I. R., Mertens Ph., Meshchaninov S., Messiaen A., Meyer H., Mianowski S., Michling R., Middleton-Gear D., Miettunen J., Militello F., Militello-Asp E., Miloshevsky G., Mink F., Minucci S., Miyoshi Y., Mlynar J., Molina D., Monakhov I., Moneti M., Mooney R., Moradi S., Mordijck S., Moreira L., Moreno R., Moro F., Morris A. W., Morris J., Moser L., Mosher S., Moulton D., Murari A., Muraro A., Murphy S., Asakura N. N., Na Y. S., Nabais F., Naish R., Nakano T., Nardon E., Naulin V., Nave M. F. F., Nedzelski I., Nemtsev G., Nespoli F., Neto A., Neu R., Neverov V. S., Newman M., Nicholls K. J., Nicolas T., Nielsen A. H., Nielsen P., Nilsson E., Nishijima D., Noble C., Nocente M., Nodwell D., Nordlund K., Nordman H., Nouailletas R., Nunes I., Oberkofler M., Odupitan T., Ogawa M. T., O'Gorman T., Okabayashi M., Olney R., Omolayo O., O'Mullane M., Ongena J., Orsitto F., Orszagh J., Oswuigwe B. I., Otin R., Owen A., Paccagnella R., Pace N., Pacella D., Packer L. W., Page A., Pajuste E., Palazzo S., Panja S., Papp P., Paprok R., Parail V., Park M., Diaz F. P., Parsons M., Pasqualotto R., Patel A., Pathak S., Paton D., Patten H., Pau A., Pawelec E., Soldan C. P., Peackoc A., Pearson I. J., Pehkonen S. P., Peluso E., Penot C., Pereira A., Pereira R., Puglia P. P. P., von Thun C. P., Peruzzo S., Peschanyi S., Peterka M., Petersson P., Petravich G., Petre A., Petrella N., Petrzilka V., Peysson Y., Pfefferle D., Philipps V., Pillon M., Pintsuk G., Piovesan P., dos Reis A. P., Piron L., Pironti A., Pisano F., Pitts R., Pizzo F., Plyusnin V., Pomaro N., Pompilian O. G., Pool P. J., Popovichev S., Porfiri M. T., Porosnicu C., Porton M., Possnert G., Potzel S., Powell T., Pozzi J., Prajapati V., Prakash R., Prestopino G., Price D., Price M., Price R., Prior P., Proudfoot R., Pucella G., Puglia P., Puiatti M. E., Pulley D., Purahoo K., Putterich Th., Rachlew E., Rack M., Ragona R., Rainford M. S. J., Rakha A., Ramogida G., Ranjan S., Rapson C. J., Rasmussen J. J., Rathod K., Ratta G., Ratynskaia S., Ravera G., Rayner C., Rebai M., Reece D., Reed A., Refy D., Regan B., Regana J., Reich M., Reid N., Reimold F., Reinhart M., Reinke M., Reiser D., Rendell D., Reux C., Cortes S. D. A. R., Reynolds S., Riccardo V., Richardson N., Riddle K., Rigamonti D., Rimini F. G., Risner J., Riva M., Roach C., Robins R. J., Robinson S. A., Robinson T., Robson D. W., Roccella R., Rodionov R., Rodrigues P., Rodriguez J., Rohde V., Romanelli F., Romanelli M., Romanelli S., Romazanov J., Rowe S., Rubel M., Rubinacci G., Rubino G., Ruchko L., Ruiz M., Ruset C., Rzadkiewicz J., Saarelma S., Sabot R., Safi E., Sagar P., Saibene G., Saint-Laurent F., Salewski M., Salmi A., Salmon R., Salzedas F., Samaddar D., Samm U., Sandiford D., Santa P., Santala M. I. K., Santos B., Santucci A., Sartori F., Sartori R., Sauter O., Scannell R., Schlummer T., Schmid K., Schmidt V., Schmuck S., Schneider M., Schopf K., Schworer D., Scott S. D., Sergienko G., Sertoli M., Shabbir A., Sharapov S. E., Shaw A., Shaw R., Sheikh H., Shepherd A., Shevelev A., Shumack A., Sias G., Sibbald M., Sieglin B., Silburn S., Silva A., Silva C., Simmons P. A., Simpson J., Simpson-Hutchinson J., Sinha A., Sipila S. K., Sips A. C. C., Siren P., Sirinelli A., Sjostrand H., Skiba M., Skilton R., Slabkowska K., Slade B., Smith N., Smith P. G., Smith R., Smith T. J., Smithies M., Snoj L., Soare S., Solano E. R., Somers A., Sommariva C., Sonato P., Sopplesa A., Sousa J., Sozzi C., Spagnolo S., Spelzini T., Spineanu F., Stables G., Stamatelatos I., Stamp M. F., Staniec P., Stankunas G., Stan-Sion C., Stead M. J., Stefanikova E., Stepanov I., Stephen A. V., Stephen M., Stevens A., Stevens B. D., Strachan J., Strand P., Strauss H. R., Strom P., Stubbs G., Studholme W., Subba F., Summers H. P., Svensson J., Swiderski L., Szabolics T., Szawlowski M., Szepesi G., Suzuki T. T., Tal B., Tala T., Talbot A. R., Talebzadeh S., Taliercio C., Tamain P., Tame C., Tang W., Tardocchi M., Taroni L., Taylor D., Taylor K. A., Tegnered D., Telesca G., Teplova N., Terranova D., Testa D., Tholerus E., Thomas J., Thomas J. D., Thomas P., Thompson A., Thompson C. -A., Thompson V. K., Thorne L., Thornton A., Thrysoe A. S., Tigwell P. A., Tipton N., Tiseanu I., Tojo H., Tokitani M., Tolias P., Tomes M., Tonner P., Towndrow M., Trimble P., Tripsky M., Tsalas M., Tsavalas P., Jun D. T., Turner I., Turner M. M., Turnyanskiy M., Tvalashvili G., Tyrrell S. G. J., Uccello A., Ul-Abidin Z., Uljanovs J., Ulyatt D., Uytdenhouwen I., Vadgama A. P., Valcarcel D., Valentinuzzi M., Valisa M., Olivares P. V., Valovic M., Van De Mortel M., Van Eester D., Van Renterghem W., van Rooij G. J., Varje J., Varoutis S., Vartanian S., Vasava K., Vasilopoulou T., Vega J., Verdoolaege G., Verhoeven R., Verona C., Rinati G. V., Veshchev E., Vianello N., Vicente J., Viezzer E., Villari S., Villone F., Vincenzi P., Vinyar I., Viola B., Vitins A., Vizvary Z., Vlad M., Voitsekhovitch I., Vondracek P., Vora N., Vu T., de Sa W. W. P., Wakeling B., Waldon C. W. F., Walkden N., Walker M., Walker R., Walsh M., Wang E., Wang N., Warder S., Warren R. J., Waterhouse J., Watkins N. W., Watts C., Wauters T., Weckmann A., Weiland J., Weisen H., Weiszflog M., Wellstood C., West A. T., Wheatley M. R., Whetham S., Whitehead A. M., Whitehead B. D., Widdowson A. M., Wiesen S., Wilkinson J., Williams J., Williams M., Wilson A. R., Wilson D. J., Wilson H. R., Wilson J., Wischmeier M., Withenshaw G., Withycombe A., Witts D. M., Wood D., Wood R., Woodley C., Wray S., Wright J., Wright J. C., Wu J., Wukitch S., Wynn A., Xu T., Yadikin D., Yanling W., Yao L., Yavorskij V., Yoo M. G., Young C., Young D., Young I. D., Young R., Zacks J., Zagorski R., Zaitsev F. S., Zanino R., Zarins A., Zastrow K. D., Zerbini M., Zhang W., Zhou Y., Zilli E., Zoita V., Zoletnik S., Zychor I., Arakawa H., Bando T., Bierwage A., Enoeda M., Fukumoto M., Hamamatsu K., Hanada M., Hatae T., Hayashi T., Higashijima S., Hirota M., Hiwatari R., Ichikawa M., Ide S., Ikeda Y., Imazawa R., Inoue S., Isayama A., Ishida S., Ishii Y., Itami K., Kamada Y., Aiba K. N., Kawano Y., Kizu K., Kawamura Y., Kobayashi T., Koide Y., Kojima A., Kubo H., Kurihara K., Kurita G., Masaki K., Matsukawa M., Matsunaga G., Matsuyama A., Miki K., Miya N., Miyata Y., Miyato N., Mori M., Moriyama S., Murakami H., Naito O., Nakamura M., Narita E., Natsume K., Oasa K., Ohtani Y., Ono M., Oyama N., Ozeki T., Sakamoto Y., Sakasai A., Sakurai S., Sano R., Sasao H., Shibama Y. K., Shibanuma K., Shimizu K., Shinohara K., Shirai H., Shiraishi J., Someya Y., Sukegawa A., Suzuki S., Takase H., Takechi M., Takenaga H., Tanigawa H., Tobita K., Toma M., Tsuchiya K., Tsuru D., Wakatsuki T., Yamoto S., Yagi M., Yoshida K., Yoshida M., Horiike H., Nobuta Y., Yamauchi Y., Idomura Y., Hatayama A., Hoshino K., Okano K., Masamune S., Sanpei A., Fukuyama A., Kado S., Kobayashi S., Konishi S., Kunugi T., Maekawa T., Minami T., Mizuuchi T., Murakami S., Nagasaki K., Shikama T., Watanabe F., Yamamoto S., Hanada K., Idei H., Katayama K., Nishikawa M., Inagaki S., Fujita T., Kajita S., Maeyama S., Ohno N., Yamazaki K., Watanabe T., Akiyama T., Isobe M., Kanno R., Kobayashi M., Masuzaki S., Miyazawa J., Morisaki T., Nakajima N., Nakamura Y., Nakata M., Narushima Y., Nishimura S., Ohdachi S., Oishi T., Osakabe M., Sagara A., Sakakibara S., Satake S., Suzuki Y., Takeiri Y., Tamura N., Tanaka K., Todo Y., Toi K., Yokoyama M., Watanabe K., Shibata Y., Fukuda T., Takizuka T., Ueda Y., Oya Y., Ejiri A., Inomoto M., Nishiura M., Ogawa Y., Takase Y., Kitajima S., Iio S., Matsuda S., Furukawa M., Ichimura M., Imai T., Nakashima Y., Sakamoto M., Sumida S., Barabaschi P., Cardella A., Clement-Lorenzo S., Coletti A., Davis S., Di Pietro E., Duglue D., Farthing J., Frello G., Hajnal N., Hurzlmeier H., Jokinen A., Kanapienyte D., Novello L., Peretti E., Phillips G., Rancsik P., Salpietro E., Scherber A., Spears B., Teuchner B., Tomarchio V., Verrecchia M., Wanner M., Zani L., Baulaigue O., Benoit F., Ciazynski D., Decool P., Dougnac H., Duchateau J. -L., Dumas N., Fejoz P., Geraud A., Gharafi S., Goncalves R., Gonde R., Gros G., Jestin F., Jiolat G., Lacroix B., Lamy S., Marechal J. -L., Nicollet S., Peluso B., Santagiustina A., Stephnie B., Torre A., Vagliani A., Vallet J. -C., Verger J. -M., Bonne F., Girard S., Hoa C., Lamaison V., Michel F., Poncet J. -M., Roussel P., Abdel Maksoud W., Ardellier F., Disset G., Donati A., Genini L., Mayri C., Molinie F., Nunio F., Ponsot P., Salanon B., Scola L., Vieillard L., Alonso J., Barrera G., Botija J., Cabrera Perez S., Fernandez P., Medrano M., Ramos F. J., Rincon E., Soleto A., Ferro A., Gaio E., Gasparini F., Maistrello A., Brolatti G., Coccoluto G., Corato V., Costa P., Cristofani C., Cucchiaro A., De Vellis A., Di Pace L., Frosi P., Ginoulhiac G., Lampasi A., Maffia G., Pizzuto A., Polli G. M., Rossi P., Starace F., Fiamozzi Zignani C., Zito P., Drotziger S., Fietz W., Heller R., Massimi A., Meyer I., Radloff D., Rita C., Urbach E., Collin B., Delrez C., Jamotton P., Massaut V., Sarkimaki K., Benkadda S., Artaud J. -F., Becoulet M., Falchetto G., Hoang T., Lotte P., Moreau P., Pegourie B., Travere J. -M., Bettini P., Canton A., Fassina A., Giudicotti L., Guo S. C., Marchiori G., Pigatto L., Vallar M., Xu X., Cismondi F., Barbato E., Mastrostefano S., de Baar M., Bruschi A., Farina D., Figini L., Granucci G., Moro A., Nowak S., Perelli-Cippo E., Platania P., Ricci D., Zuin M., Conway G., Dibon M., Fantz U., Happel T., Lauber P., Lackner K., Pautasso G., Schneider P., Kuhner G., Zocco A., Stankiewicz R., Stepniewski W., Sips G., Gleason Gonzalez C., Luo X., Scannapiego M., Bonifetto R., Decker J., Goodman T., Theiler C., Ayllon-Guerola J., Kovacsik A., Szepesi T., Kawashima H., Ogawa T., Sato M., Seki M., Aiba, N, Pamela, S, Honda, M, Urano, H, Giroud, C, Delabie, E, Frassinetti, L, Lupelli, I, Hayashi, N, Huijsmans, G, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Ahn, J, Aho-Mantila, L, Airila, M, Albanese, R, Aldred, V, Alegre, D, Alessi, E, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Ambrosino, R, Amicucci, L, Amosov, V, Sunden, E, Angelone, M, Anghel, M, Angioni, C, Appel, L, Appelbee, C, Arena, P, Ariola, M, Arnichand, H, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Asunta, O, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baciero, A, Baiao, D, Bailey, S, Baker, A, Balboa, I, Balden, M, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnes, M, Barnsley, R, Wiechec, A, Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, E, Ayed, N, Benterman, N, Bergsaker, H, Bernardo, J, Bernert, M, Berry, M, Bertalot, L, Besliu, C, Beurskens, M, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Bilkova, P, Binda, F, Bisoffi, A, Bizarro, J, Bjorkas, C, Blackburn, J, Blackman, K, Blackman, T, Blanchard, P, Blatchford, P, Bobkov, V, Boboc, A, Bodnar, G, Bogar, O, Bolshakova, I, Bolzonella, T, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Botrugno, A, Bottereau, C, Boulting, P, Bourdelle, C, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Bremond, S, Brennan, P, Breton, S, Brett, A, Brezinsek, S, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broslawski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buchanan, J, Buckley, M, Budny, R, Bufferand, H, Bulman, M, Bulmer, N, Bunting, P, Buratti, P, Burckhart, A, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Cahyna, P, Calabro, G, Calvo, I, Camenen, Y, Camp, P, Campling, D, Cane, J, Cannas, B, Capel, A, Card, P, Cardinali, A, Carman, P, Carr, M, Carralero, D, Carraro, L, Carvalho, B, Carvalho, I, Carvalho, P, Casson, F, Castaldo, C, Catarino, N, Caumont, J, Causa, F, Cavazzana, R, Cave-Ayland, K, Cavinato, M, Cecconello, M, Ceccuzzi, S, Cecil, E, Cenedese, A, Cesario, R, Challis, C, Chandler, M, Chandra, D, Chang, C, Chankin, A, Chapman, I, Chapman, S, Chernyshova, M, Chitarin, G, Ciraolo, G, Ciric, D, Citrin, J, Clairet, F, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Crisanti, F, Croci, G, Croft, D, Crombe, K, Crowe, R, Cruz, N, Cseh, G, Cufar, A, Cullen, A, Curuia, M, Czarnecka, A, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, C, Day, I, De Bock, M, de Castro, A, de la Cal, E, De La Luna, E, De Masi, G, de Pablos, J, De Temmerman, G, De Tommasi, G, De Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, den Harder, N, Dendy, R, Denis, J, Denner, P, Devaux, S, Devynck, P, Di Maio, F, Di Siena, A, Di Troia, C, Dinca, P, D'Inca, R, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donne, T, Dorling, S, Dormido-Canto, S, Doswon, S, Douai, D, Doyle, P, Drenik, A, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Dunai, D, Dunne, M, Duran, I, Durodie, F, Dutta, P, Duval, B, Dux, R, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Eich, T, Ekedahl, A, El-Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esposito, B, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Faitsch, M, Falie, D, Fanni, A, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Fedorczak, N, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Ferreira, J, Fessey, J, Fevrier, O, Ficker, O, Field, A, Fietz, S, Figueiredo, A, Figueiredo, J, Fil, A, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Fitzgerald, M, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna-Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Freisinger, M, Fresa, R, Frigione, D, Fuchs, V, Fuller, D, Futatani, S, Fyvie, J, Gal, K, Galassi, D, Galazka, K, Galdon-Quiroga, J, Gallagher, J, Gallart, D, Galvao, R, Gao, X, Gao, Y, Garcia, J, Garcia-Carrasco, A, Garcia-Munoz, M, Gardarein, J, Garzotti, L, Gaudio, P, Gauthier, E, Gear, D, Gee, S, Geiger, B, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Giacomelli, L, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Giovannozzi, E, Girardo, J, Giruzzi, G, Gloggler, S, Godwin, J, Goff, J, Gohil, P, Goloborod'Ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gorini, G, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Greuner, H, Grierson, B, Griph, F, Grisolia, C, Grist, D, Groth, M, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guerard, C, Guillemaut, C, Guirlet, R, Gurl, C, Utoh, H, Hackett, L, Hacquin, S, Hagar, A, Hager, R, Hakola, A, Halitovs, M, Hall, S, Cook, S, Hamlyn-Harris, C, Hammond, K, Harrington, C, Harrison, J, Harting, D, Hasenbeck, F, Hatano, Y, Hatch, D, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hellsten, T, Helou, W, Hemming, O, Hender, T, Henderson, M, Henderson, S, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hobirk, J, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horacek, J, Hornung, G, Horton, A, Horton, L, Horvath, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, A, Huber, V, Huddleston, T, Hughes, M, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Imrisek, M, Incelli, M, Innocente, P, Irishkin, M, Stanik, I, Jachmich, S, Jacobsen, A, Jacquet, P, Jansons, J, Jardin, A, Jarvinen, A, Jaulmes, F, Jednorog, S, Jenkins, I, Jeong, C, Jepu, I, Joffrin, E, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kallenbach, A, Kamiya, K, Kaniewski, J, Kantor, A, Kappatou, A, Karhunen, J, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirk, A, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Kochl, F, Kocsis, G, Kodeli, I, Kogan, L, Kogut, D, Koivuranta, S, Kominis, Y, Koppen, M, Kos, B, Koskela, T, Koslowski, H, Koubiti, M, Kovari, M, Kowalska-Strzeciwilk, E, Krasilnikov, A, Krasilnikov, V, Krawczyk, N, Kresina, M, Krieger, K, Krivska, A, Kruezi, U, Ksiazek, I, Kukushkin, A, Kundu, A, Kurki-Suonio, T, Kwak, S, Kwiatkowski, R, Kwon, O, Laguardia, L, Lahtinen, A, Laing, A, Lam, N, Lambertz, H, Lane, C, Lang, P, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Laszynska, E, Lawless, R, Lawson, A, Lawson, K, Lazaros, A, Lazzaro, E, Leddy, J, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Lehnen, M, Leichtle, D, Leichuer, P, Leipold, F, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leyland, M, Leysen, W, Li, L, Liang, Y, Likonen, J, Linke, J, Linsmeier, C, Lipschultz, B, Liu, G, Liu, Y, Lo Schiavo, V, Loarer, T, Loarte, A, Lobel, R, Lomanowski, B, Lomas, P, Lonnroth, J, Lopez, J, Lopez-Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Lyssoivan, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Maget, P, Maggi, C, Maier, H, Mailloux, J, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manas, P, Manning, A, Manso, M, Mantica, P, Mantsinen, M, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marinucci, M, Markovic, T, Marocco, D, Marot, L, Marren, C, Marshal, R, Martin, A, Martin, Y, de Aguilera, A, Martinez, F, Martin-Solis, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Mattei, M, Matthews, G, Maviglia, F, Mayer, M, Mayoral, M, May-Smith, T, Mazon, D, Mazzotta, C, Mcadams, R, Mccarthy, P, Mcclements, K, Mccormack, O, Mccullen, P, Mcdonald, D, Mcintosh, S, Mckean, R, Mckehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meisl, G, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Meyer, H, Mianowski, S, Michling, R, Middleton-Gear, D, Miettunen, J, Militello, F, Militello-Asp, E, Miloshevsky, G, Mink, F, Minucci, S, Miyoshi, Y, Mlynar, J, Molina, D, Monakhov, I, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Moulton, D, Murari, A, Muraro, A, Murphy, S, Asakura, N, Na, Y, Nabais, F, Naish, R, Nakano, T, Nardon, E, Naulin, V, Nave, M, Nedzelski, I, Nemtsev, G, Nespoli, F, Neto, A, Neu, R, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, A, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nocente, M, Nodwell, D, Nordlund, K, Nordman, H, Nouailletas, R, Nunes, I, Oberkofler, M, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, O'Mullane, M, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Paccagnella, R, Pace, N, Pacella, D, Packer, L, Page, A, Pajuste, E, Palazzo, S, Panja, S, Papp, P, Paprok, R, Parail, V, Park, M, Diaz, F, Parsons, M, Pasqualotto, R, Patel, A, Pathak, S, Paton, D, Patten, H, Pau, A, Pawelec, E, Soldan, C, Peackoc, A, Pearson, I, Pehkonen, S, Peluso, E, Penot, C, Pereira, A, Pereira, R, Puglia, P, von Thun, C, Peruzzo, S, Peschanyi, S, Peterka, M, Petersson, P, Petravich, G, Petre, A, Petrella, N, Petrzilka, V, Peysson, Y, Pfefferle, D, Philipps, V, Pillon, M, Pintsuk, G, Piovesan, P, dos Reis, A, Piron, L, Pironti, A, Pisano, F, Pitts, R, Pizzo, F, Plyusnin, V, Pomaro, N, Pompilian, O, Pool, P, Popovichev, S, Porfiri, M, Porosnicu, C, Porton, M, Possnert, G, Potzel, S, Powell, T, Pozzi, J, Prajapati, V, Prakash, R, Prestopino, G, Price, D, Price, M, Price, R, Prior, P, Proudfoot, R, Pucella, G, Puiatti, M, Pulley, D, Purahoo, K, Putterich, T, Rachlew, E, Rack, M, Ragona, R, Rainford, M, Rakha, A, Ramogida, G, Ranjan, S, Rapson, C, Rasmussen, J, Rathod, K, Ratta, G, Ratynskaia, S, Ravera, G, Rayner, C, Rebai, M, Reece, D, Reed, A, Refy, D, Regan, B, Regana, J, Reich, M, Reid, N, Reimold, F, Reinhart, M, Reinke, M, Reiser, D, Rendell, D, Reux, C, Reynolds, S, Riccardo, V, Richardson, N, Riddle, K, Rigamonti, D, Rimini, F, Risner, J, Riva, M, Roach, C, Robins, R, Robinson, S, Robinson, T, Robson, D, Roccella, R, Rodionov, R, Rodrigues, P, Rodriguez, J, Rohde, V, Romanelli, F, Romanelli, M, Romanelli, S, Romazanov, J, Rowe, S, Rubel, M, Rubinacci, G, Rubino, G, Ruchko, L, Ruiz, M, Ruset, C, Rzadkiewicz, J, Saarelma, S, Sabot, R, Safi, E, Sagar, P, Saibene, G, Saint-Laurent, F, Salewski, M, Salmi, A, Salmon, R, Salzedas, F, Samaddar, D, Samm, U, Sandiford, D, Santa, P, Santala, M, Santos, B, Santucci, A, Sartori, F, Sartori, R, Sauter, O, Scannell, R, Schlummer, T, Schmid, K, Schmidt, V, Schmuck, S, Schneider, M, Schopf, K, Schworer, D, Scott, S, Sergienko, G, Sertoli, M, Shabbir, A, Sharapov, S, Shaw, A, Shaw, R, Sheikh, H, Shepherd, A, Shevelev, A, Shumack, A, Sias, G, Sibbald, M, Sieglin, B, Silburn, S, Silva, A, Silva, C, Simmons, P, Simpson, J, Simpson-Hutchinson, J, Sinha, A, Sipila, S, Sips, A, Siren, P, Sirinelli, A, Sjostrand, H, Skiba, M, Skilton, R, Slabkowska, K, Slade, B, Smith, N, Smith, P, Smith, R, Smith, T, Smithies, M, Snoj, L, Soare, S, Solano, E, Somers, A, Sommariva, C, Sonato, P, Sopplesa, A, Sousa, J, Sozzi, C, Spagnolo, S, Spelzini, T, Spineanu, F, Stables, G, Stamatelatos, I, Stamp, M, Staniec, P, Stankunas, G, Stan-Sion, C, Stead, M, Stefanikova, E, Stepanov, I, Stephen, A, Stephen, M, Stevens, A, Stevens, B, Strachan, J, Strand, P, Strauss, H, Strom, P, Stubbs, G, Studholme, W, Subba, F, Summers, H, Svensson, J, Swiderski, L, Szabolics, T, Szawlowski, M, Szepesi, G, Suzuki, T, Tal, B, Tala, T, Talbot, A, Talebzadeh, S, Taliercio, C, Tamain, P, Tame, C, Tang, W, Tardocchi, M, Taroni, L, Taylor, D, Taylor, K, Tegnered, D, Telesca, G, Teplova, N, Terranova, D, Testa, D, Tholerus, E, Thomas, J, Thomas, P, Thompson, A, Thompson, C, Thompson, V, Thorne, L, Thornton, A, Thrysoe, A, Tigwell, P, Tipton, N, Tiseanu, I, Tojo, H, Tokitani, M, Tolias, P, Tomes, M, Tonner, P, Towndrow, M, Trimble, P, Tripsky, M, Tsalas, M, Tsavalas, P, Jun, D, Turner, I, Turner, M, Turnyanskiy, M, Tvalashvili, G, Tyrrell, S, Uccello, A, Ul-Abidin, Z, Uljanovs, J, Ulyatt, D, Uytdenhouwen, I, Vadgama, A, Valcarcel, D, Valentinuzzi, M, Valisa, M, Olivares, P, Valovic, M, Van De Mortel, M, Van Eester, D, Van Renterghem, W, van Rooij, G, Varje, J, Varoutis, S, Vartanian, S, Vasava, K, Vasilopoulou, T, Vega, J, Verdoolaege, G, Verhoeven, R, Verona, C, Rinati, G, Veshchev, E, Vianello, N, Vicente, J, Viezzer, E, Villari, S, Villone, F, Vincenzi, P, Vinyar, I, Viola, B, Vitins, A, Vizvary, Z, Vlad, M, Voitsekhovitch, I, Vondracek, P, Vora, N, Vu, T, de Sa, W, Wakeling, B, Waldon, C, Walkden, N, Walker, M, Walker, R, Walsh, M, Wang, E, Wang, N, Warder, S, Warren, R, Waterhouse, J, Watkins, N, Watts, C, Wauters, T, Weckmann, A, Weiland, J, Weisen, H, Weiszflog, M, Wellstood, C, West, A, Wheatley, M, Whetham, S, Whitehead, A, Whitehead, B, Widdowson, A, Wiesen, S, Wilkinson, J, Williams, J, Williams, M, Wilson, A, Wilson, D, Wilson, H, Wilson, J, Wischmeier, M, Withenshaw, G, Withycombe, A, Witts, D, Wood, D, Wood, R, Woodley, C, Wray, S, Wright, J, Wu, J, Wukitch, S, Wynn, A, Xu, T, Yadikin, D, Yanling, W, Yao, L, Yavorskij, V, Yoo, M, Young, C, Young, D, Young, I, Young, R, Zacks, J, Zagorski, R, Zaitsev, F, Zanino, R, Zarins, A, Zastrow, K, Zerbini, M, Zhang, W, Zhou, Y, Zilli, E, Zoita, V, Zoletnik, S, Zychor, I, Arakawa, H, Bando, T, Bierwage, A, Enoeda, M, Fukumoto, M, Hamamatsu, K, Hanada, M, Hatae, T, Hayashi, T, Higashijima, S, Hirota, M, Hiwatari, R, Ichikawa, M, Ide, S, Ikeda, Y, Imazawa, R, Inoue, S, Isayama, A, Ishida, S, Ishii, Y, Itami, K, Kamada, Y, Aiba, K, Kawano, Y, Kizu, K, Kawamura, Y, Kobayashi, T, Koide, Y, Kojima, A, Kubo, H, Kurihara, K, Kurita, G, Masaki, K, Matsukawa, M, Matsunaga, G, Matsuyama, A, Miki, K, Miya, N, Miyata, Y, Miyato, N, Mori, M, Moriyama, S, Murakami, H, Naito, O, Nakamura, M, Narita, E, Natsume, K, Oasa, K, Ohtani, Y, Ono, M, Oyama, N, Ozeki, T, Sakamoto, Y, Sakasai, A, Sakurai, S, Sano, R, Sasao, H, Shibama, Y, Shibanuma, K, Shimizu, K, Shinohara, K, Shirai, H, Shiraishi, J, Someya, Y, Sukegawa, A, Suzuki, S, Takase, H, Takechi, M, Takenaga, H, Tanigawa, H, Tobita, K, Toma, M, Tsuchiya, K, Tsuru, D, Wakatsuki, T, Yamoto, S, Yagi, M, Yoshida, K, Yoshida, M, Horiike, H, Nobuta, Y, Yamauchi, Y, Idomura, Y, Hatayama, A, Okano, K, Masamune, S, Sanpei, A, Fukuyama, A, Kado, S, Kobayashi, S, Konishi, S, Kunugi, T, Maekawa, T, Minami, T, Mizuuchi, T, Murakami, S, Nagasaki, K, Shikama, T, Watanabe, F, Yamamoto, S, Hanada, K, Idei, H, Katayama, K, Nishikawa, M, Inagaki, S, Fujita, T, Kajita, S, Maeyama, S, Ohno, N, Yamazaki, K, Watanabe, T, Akiyama, T, Isobe, M, Kanno, R, Kobayashi, M, Masuzaki, S, Miyazawa, J, Morisaki, T, Nakajima, N, Nakamura, Y, Nakata, M, Narushima, Y, Nishimura, S, Ohdachi, S, Oishi, T, Osakabe, M, Sagara, A, Sakakibara, S, Satake, S, Suzuki, Y, Takeiri, Y, Tamura, N, Tanaka, K, Todo, Y, Toi, K, Yokoyama, M, Watanabe, K, Shibata, Y, Fukuda, T, Takizuka, T, Ueda, Y, Oya, Y, Ejiri, A, Inomoto, M, Nishiura, M, Ogawa, Y, Takase, Y, Kitajima, S, Iio, S, Matsuda, S, Furukawa, M, Ichimura, M, Imai, T, Nakashima, Y, Sakamoto, M, Sumida, S, Barabaschi, P, Cardella, A, Clement-Lorenzo, S, Coletti, A, Davis, S, Di Pietro, E, Duglue, D, Farthing, J, Frello, G, Hajnal, N, Hurzlmeier, H, Jokinen, A, Kanapienyte, D, Novello, L, Peretti, E, Phillips, G, Rancsik, P, Salpietro, E, Scherber, A, Spears, B, Teuchner, B, Tomarchio, V, Verrecchia, M, Wanner, M, Zani, L, Baulaigue, O, Benoit, F, Ciazynski, D, Decool, P, Dougnac, H, Duchateau, J, Dumas, N, Fejoz, P, Geraud, A, Gharafi, S, Goncalves, R, Gonde, R, Gros, G, Jestin, F, Jiolat, G, Lacroix, B, Lamy, S, Marechal, J, Nicollet, S, Peluso, B, Santagiustina, A, Stephnie, B, Torre, A, Vagliani, A, Vallet, J, Verger, J, Bonne, F, Girard, S, Hoa, C, Lamaison, V, Michel, F, Poncet, J, Roussel, P, Abdel Maksoud, W, Ardellier, F, Disset, G, Donati, A, Genini, L, Mayri, C, Molinie, F, Nunio, F, Ponsot, P, Salanon, B, Scola, L, Vieillard, L, Alonso, J, Barrera, G, Botija, J, Cabrera Perez, S, Fernandez, P, Medrano, M, Ramos, F, Rincon, E, Soleto, A, Ferro, A, Gaio, E, Gasparini, F, Maistrello, A, Brolatti, G, Coccoluto, G, Corato, V, Costa, P, Cristofani, C, Cucchiaro, A, De Vellis, A, Di Pace, L, Frosi, P, Ginoulhiac, G, Lampasi, A, Maffia, G, Pizzuto, A, Polli, G, Rossi, P, Starace, F, Fiamozzi Zignani, C, Zito, P, Drotziger, S, Fietz, W, Heller, R, Massimi, A, Meyer, I, Radloff, D, Rita, C, Urbach, E, Collin, B, Delrez, C, Jamotton, P, Massaut, V, Sarkimaki, K, Benkadda, S, Artaud, J, Becoulet, M, Falchetto, G, Hoang, T, Lotte, P, Moreau, P, Pegourie, B, Travere, J, Bettini, P, Canton, A, Fassina, A, Giudicotti, L, Guo, S, Marchiori, G, Pigatto, L, Vallar, M, Xu, X, Cismondi, F, Barbato, E, Mastrostefano, S, de Baar, M, Bruschi, A, Farina, D, Figini, L, Granucci, G, Moro, A, Nowak, S, Perelli-Cippo, E, Platania, P, Ricci, D, Zuin, M, Conway, G, Dibon, M, Fantz, U, Happel, T, Lauber, P, Lackner, K, Pautasso, G, Schneider, P, Kuhner, G, Zocco, A, Stankiewicz, R, Stepniewski, W, Sips, G, Gleason Gonzalez, C, Luo, X, Scannapiego, M, Bonifetto, R, Decker, J, Goodman, T, Theiler, C, Ayllon-Guerola, J, Kovacsik, A, Szepesi, T, Kawashima, H, Ogawa, T, Sato, M, Seki, M, Aiba N., Pamela S., Honda M., Urano H., Giroud C., Delabie E., Frassinetti L., Lupelli I., Hayashi N., Huijsmans G. T. A., Litaudon X., Abduallev S., Abhangi M., Abreu P., Afzal M., Aggarwal K. M., Ahlgren T., Ahn J. H., Aho-Mantila L., Airila M., Albanese R., Aldred V., Alegre D., Alessi E., Aleynikov P., Alfier A., Alkseev A., Allinson M., Alper B., Alves E., Ambrosino G., Ambrosino R., Amicucci L., Amosov V., Sunden E. A., Angelone M., Anghel M., Angioni C., Appel L., Appelbee C., Arena P., Ariola M., Arnichand H., Arshad S., Ash A., Ashikawa N., Aslanyan V., Asunta O., Auriemma F., Austin Y., Avotina L., Axton M. D., Ayres C., Bacharis M., Baciero A., Baiao D., Bailey S., Baker A., Balboa I., Balden M., Balshaw N., Bament R., Banks J. W., Baranov Y. F., Barnard M. A., Barnes D., Barnes M., Barnsley R., Wiechec A. B., Orte L. B., Baruzzo M., Basiuk V., Bassan M., Bastow R., Batista A., Batistoni P., Baughan R., Bauvir B., Baylor L., Bazylev B., Beal J., Beaumont P. S., Beckers M., Beckett B., Becoulet A., Bekris N., Beldishevski M., Bell K., Belli F., Bellinger M., Belonohy E., Ayed N. B., Benterman N. A., Bergsaker H., Bernardo J., Bernert M., Berry M., Bertalot L., Besliu C., Beurskens M., Bieg B., Bielecki J., Biewer T., Bigi M., Bilkova P., Binda F., Bisoffi A., Bizarro J. P. S., Bjorkas C., Blackburn J., Blackman K., Blackman T. R., Blanchard P., Blatchford P., Bobkov V., Boboc A., Bodnar G., Bogar O., Bolshakova I., Bolzonella T., Bonanomi N., Bonelli F., Boom J., Booth J., Borba D., Borodin D., Borodkina I., Botrugno A., Bottereau C., Boulting P., Bourdelle C., Bowden M., Bower C., Bowman C., Boyce T., Boyd C., Boyer H. J., Bradshaw J. M. A., Braic V., Bravanec R., Breizman B., Bremond S., Brennan P. D., Breton S., Brett A., Brezinsek S., Bright M. D. J., Brix M., Broeckx W., Brombin M., Broslawski A., Brown D. P. D., Brown M., Bruno E., Bucalossi J., Buch J., Buchanan J., Buckley M. A., Budny R., Bufferand H., Bulman M., Bulmer N., Bunting P., Buratti P., Burckhart A., Buscarino A., Busse A., Butler N. K., Bykov I., Byrne J., Cahyna P., Calabro G., Calvo I., Camenen Y., Camp P., Campling D. C., Cane J., Cannas B., Capel A. J., Card P. J., Cardinali A., Carman P., Carr M., Carralero D., Carraro L., Carvalho B. B., Carvalho I., Carvalho P., Casson F. J., Castaldo C., Catarino N., Caumont J., Causa F., Cavazzana R., Cave-Ayland K., Cavinato M., Cecconello M., Ceccuzzi S., Cecil E., Cenedese A., Cesario R., Challis C. D., Chandler M., Chandra D., Chang C. S., Chankin A., Chapman I. T., Chapman S. C., Chernyshova M., Chitarin G., Ciraolo G., Ciric D., Citrin J., Clairet F., Clark E., Clark M., Clarkson R., Clatworthy D., Clements C., Cleverly M., Coad J. P., Coates P. A., Cobalt A., Coccorese V., Cocilovo V., Coda S., Coelho R., Coenen J. W., Coffey I., Colas L., Collins S., Conka D., Conroy S., Conway N., Coombs D., Cooper D., Cooper S. R., Corradino C., Corre Y., Corrigan G., Cortes S., Coster D., Couchman A. S., Cox M. P., Craciunescu T., Cramp S., Craven R., Crisanti F., Croci G., Croft D., Crombe K., Crowe R., Cruz N., Cseh G., Cufar A., Cullen A., Curuia M., Czarnecka A., Dabirikhah H., Dalgliesh P., Dalley S., Dankowski J., Darrow D., Davies O., Davis W., Day C., Day I. E., De Bock M., de Castro A., de la Cal E., De La Luna E., De Masi G., de Pablos J. L., De Temmerman G., De Tommasi G., De Vries P., Deakin K., Deane J., Degli Agostini F., Dejarnac R., den Harder N., Dendy R. O., Denis J., Denner P., Devaux S., Devynck P., Di Maio F., Di Siena A., Di Troia C., Dinca P., D'Inca R., Ding B., Dittmar T., Doerk H., Doerner R. P., Donne T., Dorling S. E., Dormido-Canto S., Doswon S., Douai D., Doyle P. T., Drenik A., Drewelow P., Drews P., Duckworth Ph., Dumont R., Dumortier P., Dunai D., Dunne M., Duran I., Durodie F., Dutta P., Duval B. P., Dux R., Dylst K., Dzysiuk N., Edappala P. V., Edmond J., Edwards A. M., Edwards J., Eich Th., Ekedahl A., El-Jorf R., Elsmore C. G., Enachescu M., Ericsson G., Eriksson F., Eriksson J., Eriksson L. G., Esposito B., Esquembri S., Esser H. G., Esteve D., Evans B., Evans G. E., Evison G., Ewart G. D., Fagan D., Faitsch M., Falie D., Fanni A., Fasoli A., Faustin J. M., Fawlk N., Fazendeiro L., Fedorczak N., Felton R. C., Fenton K., Fernades A., Fernandes H., Ferreira J., Fessey J. A., Fevrier O., Ficker O., Field A., Fietz S., Figueiredo A., Figueiredo J., Fil A., Finburg P., Firdaouss M., Fischer U., Fittill L., Fitzgerald M., Flammini D., Flanagan J., Fleming C., Flinders K., Fonnesu N., Fontdecaba J. M., Formisano A., Forsythe L., Fortuna L., Fortuna-Zalesna E., Fortune M., Foster S., Franke T., Franklin T., Frasca M., Freisinger M., Fresa R., Frigione D., Fuchs V., Fuller D., Futatani S., Fyvie J., Gal K., Galassi D., Galazka K., Galdon-Quiroga J., Gallagher J., Gallart D., Galvao R., Gao X., Gao Y., Garcia J., Garcia-Carrasco A., Garcia-Munoz M., Gardarein J. -L., Garzotti L., Gaudio P., Gauthier E., Gear D. F., Gee S. J., Geiger B., Gelfusa M., Gerasimov S., Gervasini G., Gethins M., Ghani Z., Ghate M., Gherendi M., Giacalone J. C., Giacomelli L., Gibson C. S., Giegerich T., Gil C., Gil L., Gilligan S., Gin D., Giovannozzi E., Girardo J. B., Giruzzi G., Gloggler S., Godwin J., Goff J., Gohil P., Goloborod'Ko V., Gomes R., Goncalves B., Goniche M., Goodliffe M., Goodyear A., Gorini G., Gosk M., Goulding R., Goussarov A., Gowland R., Graham B., Graham M. E., Graves J. P., Grazier N., Grazier P., Green N. R., Greuner H., Grierson B., Griph F. S., Grisolia C., Grist D., Groth M., Grove R., Grundy C. N., Grzonka J., Guard D., Guerard C., Guillemaut C., Guirlet R., Gurl C., Utoh H. H., Hackett L. J., Hacquin S., Hagar A., Hager R., Hakola A., Halitovs M., Hall S. J., Cook S. P. H., Hamlyn-Harris C., Hammond K., Harrington C., Harrison J., Harting D., Hasenbeck F., Hatano Y., Hatch D. R., Haupt T. D. V., Hawes J., Hawkes N. C., Hawkins J., Hawkins P., Haydon P. W., Hayter N., Hazel S., Heesterman P. J. L., Heinola K., Hellesen C., Hellsten T., Helou W., Hemming O. N., Hender T. C., Henderson M., Henderson S. S., Henriques R., Hepple D., Hermon G., Hertout P., Hidalgo C., Highcock E. G., Hill M., Hillairet J., Hillesheim J., Hillis D., Hizanidis K., Hjalmarsson A., Hobirk J., Hodille E., Hogben C. H. A., Hogeweij G. M. D., Hollingsworth A., Hollis S., Homfray D. A., Horacek J., Hornung G., Horton A. R., Horton L. D., Horvath L., Hotchin S. P., Hough M. R., Howarth P. J., Hubbard A., Huber A., Huber V., Huddleston T. M., Hughes M., Hunter C. L., Huynh P., Hynes A. M., Iglesias D., Imazawa N., Imbeaux F., Imrisek M., Incelli M., Innocente P., Irishkin M., Stanik I. I., Jachmich S., Jacobsen A. S., Jacquet P., Jansons J., Jardin A., Jarvinen A., Jaulmes F., Jednorog S., Jenkins I., Jeong C., Jepu I., Joffrin E., Johnson R., Johnson T., Johnston J., Joita L., Jones G., Jones T. T. C., Hoshino K. K., Kallenbach A., Kamiya K., Kaniewski J., Kantor A., Kappatou A., Karhunen J., Karkinsky D., Karnowska I., Kaufman M., Kaveney G., Kazakov Y., Kazantzidis V., Keeling D. L., Keenan T., Keep J., Kempenaars M., Kennedy C., Kenny D., Kent J., Kent O. N., Khilkevich E., Kim H. T., Kim H. S., Kinch A., King C., King D., King R. F., Kinna D. J., Kiptily V., Kirk A., Kirov K., Kirschner A., Kizane G., Klepper C., Klix A., Knight P., Knipe S. J., Knott S., Kobuchi T., Kochl F., Kocsis G., Kodeli I., Kogan L., Kogut D., Koivuranta S., Kominis Y., Koppen M., Kos B., Koskela T., Koslowski H. R., Koubiti M., Kovari M., Kowalska-Strzeciwilk E., Krasilnikov A., Krasilnikov V., Krawczyk N., Kresina M., Krieger K., Krivska A., Kruezi U., Ksiazek I., Kukushkin A., Kundu A., Kurki-Suonio T., Kwak S., Kwiatkowski R., Kwon O. J., Laguardia L., Lahtinen A., Laing A., Lam N., Lambertz H. T., Lane C., Lang P. T., Lanthaler S., Lapins J., Lasa A., Last J. R., Laszynska E., Lawless R., Lawson A., Lawson K. D., Lazaros A., Lazzaro E., Leddy J., Lee S., Lefebvre X., Leggate H. J., Lehmann J., Lehnen M., Leichtle D., Leichuer P., Leipold F., Lengar I., Lennholm M., Lerche E., Lescinskis A., Lesnoj S., Letellier E., Leyland M., Leysen W., Li L., Liang Y., Likonen J., Linke J., Linsmeier Ch., Lipschultz B., Liu G., Liu Y., Lo Schiavo V. P., Loarer T., Loarte A., Lobel R. C., Lomanowski B., Lomas P. J., Lonnroth J., Lopez J. M., Lopez-Razola J., Lorenzini R., Losada U., Lovell J. J., Loving A. B., Lowry C., Luce T., Lucock R. M. A., Lukin A., Luna C., Lungaroni M., Lungu C. P., Lungu M., Lunniss A., Lyssoivan A., Macdonald N., Macheta P., Maczewa K., Magesh B., Maget P., Maggi C., Maier H., Mailloux J., Makkonen T., Makwana R., Malaquias A., Malizia A., Manas P., Manning A., Manso M. E., Mantica P., Mantsinen M., Manzanares A., Maquet Ph., Marandet Y., Marcenko N., Marchetto C., Marchuk O., Marinelli M., Marinucci M., Markovic T., Marocco D., Marot L., Marren C. A., Marshal R., Martin A., Martin Y., de Aguilera A. M., Martinez F. J., Martin-Solis J. R., Martynova Y., Maruyama S., Masiello A., Maslov M., Matejcik S., Mattei M., Matthews G. F., Maviglia F., Mayer M., Mayoral M. L., May-Smith T., Mazon D., Mazzotta C., McAdams R., McCarthy P. J., McClements K. G., McCormack O., McCullen P. A., McDonald D., McIntosh S., McKean R., McKehon J., Meadows R. C., Meakins A., Medina F., Medland M., Medley S., Meigh S., Meigs A. G., Meisl G., Meitner S., Meneses L., Menmuir S., Mergia K., Merrigan I. R., Mertens Ph., Meshchaninov S., Messiaen A., Meyer H., Mianowski S., Michling R., Middleton-Gear D., Miettunen J., Militello F., Militello-Asp E., Miloshevsky G., Mink F., Minucci S., Miyoshi Y., Mlynar J., Molina D., Monakhov I., Moneti M., Mooney R., Moradi S., Mordijck S., Moreira L., Moreno R., Moro F., Morris A. W., Morris J., Moser L., Mosher S., Moulton D., Murari A., Muraro A., Murphy S., Asakura N. N., Na Y. S., Nabais F., Naish R., Nakano T., Nardon E., Naulin V., Nave M. F. F., Nedzelski I., Nemtsev G., Nespoli F., Neto A., Neu R., Neverov V. S., Newman M., Nicholls K. J., Nicolas T., Nielsen A. H., Nielsen P., Nilsson E., Nishijima D., Noble C., Nocente M., Nodwell D., Nordlund K., Nordman H., Nouailletas R., Nunes I., Oberkofler M., Odupitan T., Ogawa M. T., O'Gorman T., Okabayashi M., Olney R., Omolayo O., O'Mullane M., Ongena J., Orsitto F., Orszagh J., Oswuigwe B. I., Otin R., Owen A., Paccagnella R., Pace N., Pacella D., Packer L. W., Page A., Pajuste E., Palazzo S., Panja S., Papp P., Paprok R., Parail V., Park M., Diaz F. P., Parsons M., Pasqualotto R., Patel A., Pathak S., Paton D., Patten H., Pau A., Pawelec E., Soldan C. P., Peackoc A., Pearson I. J., Pehkonen S. P., Peluso E., Penot C., Pereira A., Pereira R., Puglia P. P. P., von Thun C. P., Peruzzo S., Peschanyi S., Peterka M., Petersson P., Petravich G., Petre A., Petrella N., Petrzilka V., Peysson Y., Pfefferle D., Philipps V., Pillon M., Pintsuk G., Piovesan P., dos Reis A. P., Piron L., Pironti A., Pisano F., Pitts R., Pizzo F., Plyusnin V., Pomaro N., Pompilian O. G., Pool P. J., Popovichev S., Porfiri M. T., Porosnicu C., Porton M., Possnert G., Potzel S., Powell T., Pozzi J., Prajapati V., Prakash R., Prestopino G., Price D., Price M., Price R., Prior P., Proudfoot R., Pucella G., Puglia P., Puiatti M. E., Pulley D., Purahoo K., Putterich Th., Rachlew E., Rack M., Ragona R., Rainford M. S. J., Rakha A., Ramogida G., Ranjan S., Rapson C. J., Rasmussen J. J., Rathod K., Ratta G., Ratynskaia S., Ravera G., Rayner C., Rebai M., Reece D., Reed A., Refy D., Regan B., Regana J., Reich M., Reid N., Reimold F., Reinhart M., Reinke M., Reiser D., Rendell D., Reux C., Cortes S. D. A. R., Reynolds S., Riccardo V., Richardson N., Riddle K., Rigamonti D., Rimini F. G., Risner J., Riva M., Roach C., Robins R. J., Robinson S. A., Robinson T., Robson D. W., Roccella R., Rodionov R., Rodrigues P., Rodriguez J., Rohde V., Romanelli F., Romanelli M., Romanelli S., Romazanov J., Rowe S., Rubel M., Rubinacci G., Rubino G., Ruchko L., Ruiz M., Ruset C., Rzadkiewicz J., Saarelma S., Sabot R., Safi E., Sagar P., Saibene G., Saint-Laurent F., Salewski M., Salmi A., Salmon R., Salzedas F., Samaddar D., Samm U., Sandiford D., Santa P., Santala M. I. K., Santos B., Santucci A., Sartori F., Sartori R., Sauter O., Scannell R., Schlummer T., Schmid K., Schmidt V., Schmuck S., Schneider M., Schopf K., Schworer D., Scott S. D., Sergienko G., Sertoli M., Shabbir A., Sharapov S. E., Shaw A., Shaw R., Sheikh H., Shepherd A., Shevelev A., Shumack A., Sias G., Sibbald M., Sieglin B., Silburn S., Silva A., Silva C., Simmons P. A., Simpson J., Simpson-Hutchinson J., Sinha A., Sipila S. K., Sips A. C. C., Siren P., Sirinelli A., Sjostrand H., Skiba M., Skilton R., Slabkowska K., Slade B., Smith N., Smith P. G., Smith R., Smith T. J., Smithies M., Snoj L., Soare S., Solano E. R., Somers A., Sommariva C., Sonato P., Sopplesa A., Sousa J., Sozzi C., Spagnolo S., Spelzini T., Spineanu F., Stables G., Stamatelatos I., Stamp M. F., Staniec P., Stankunas G., Stan-Sion C., Stead M. J., Stefanikova E., Stepanov I., Stephen A. V., Stephen M., Stevens A., Stevens B. D., Strachan J., Strand P., Strauss H. R., Strom P., Stubbs G., Studholme W., Subba F., Summers H. P., Svensson J., Swiderski L., Szabolics T., Szawlowski M., Szepesi G., Suzuki T. T., Tal B., Tala T., Talbot A. R., Talebzadeh S., Taliercio C., Tamain P., Tame C., Tang W., Tardocchi M., Taroni L., Taylor D., Taylor K. A., Tegnered D., Telesca G., Teplova N., Terranova D., Testa D., Tholerus E., Thomas J., Thomas J. D., Thomas P., Thompson A., Thompson C. -A., Thompson V. K., Thorne L., Thornton A., Thrysoe A. S., Tigwell P. A., Tipton N., Tiseanu I., Tojo H., Tokitani M., Tolias P., Tomes M., Tonner P., Towndrow M., Trimble P., Tripsky M., Tsalas M., Tsavalas P., Jun D. T., Turner I., Turner M. M., Turnyanskiy M., Tvalashvili G., Tyrrell S. G. J., Uccello A., Ul-Abidin Z., Uljanovs J., Ulyatt D., Uytdenhouwen I., Vadgama A. P., Valcarcel D., Valentinuzzi M., Valisa M., Olivares P. V., Valovic M., Van De Mortel M., Van Eester D., Van Renterghem W., van Rooij G. J., Varje J., Varoutis S., Vartanian S., Vasava K., Vasilopoulou T., Vega J., Verdoolaege G., Verhoeven R., Verona C., Rinati G. V., Veshchev E., Vianello N., Vicente J., Viezzer E., Villari S., Villone F., Vincenzi P., Vinyar I., Viola B., Vitins A., Vizvary Z., Vlad M., Voitsekhovitch I., Vondracek P., Vora N., Vu T., de Sa W. W. P., Wakeling B., Waldon C. W. F., Walkden N., Walker M., Walker R., Walsh M., Wang E., Wang N., Warder S., Warren R. J., Waterhouse J., Watkins N. W., Watts C., Wauters T., Weckmann A., Weiland J., Weisen H., Weiszflog M., Wellstood C., West A. T., Wheatley M. R., Whetham S., Whitehead A. M., Whitehead B. D., Widdowson A. M., Wiesen S., Wilkinson J., Williams J., Williams M., Wilson A. R., Wilson D. J., Wilson H. R., Wilson J., Wischmeier M., Withenshaw G., Withycombe A., Witts D. M., Wood D., Wood R., Woodley C., Wray S., Wright J., Wright J. C., Wu J., Wukitch S., Wynn A., Xu T., Yadikin D., Yanling W., Yao L., Yavorskij V., Yoo M. G., Young C., Young D., Young I. D., Young R., Zacks J., Zagorski R., Zaitsev F. S., Zanino R., Zarins A., Zastrow K. D., Zerbini M., Zhang W., Zhou Y., Zilli E., Zoita V., Zoletnik S., Zychor I., Arakawa H., Bando T., Bierwage A., Enoeda M., Fukumoto M., Hamamatsu K., Hanada M., Hatae T., Hayashi T., Higashijima S., Hirota M., Hiwatari R., Ichikawa M., Ide S., Ikeda Y., Imazawa R., Inoue S., Isayama A., Ishida S., Ishii Y., Itami K., Kamada Y., Aiba K. N., Kawano Y., Kizu K., Kawamura Y., Kobayashi T., Koide Y., Kojima A., Kubo H., Kurihara K., Kurita G., Masaki K., Matsukawa M., Matsunaga G., Matsuyama A., Miki K., Miya N., Miyata Y., Miyato N., Mori M., Moriyama S., Murakami H., Naito O., Nakamura M., Narita E., Natsume K., Oasa K., Ohtani Y., Ono M., Oyama N., Ozeki T., Sakamoto Y., Sakasai A., Sakurai S., Sano R., Sasao H., Shibama Y. K., Shibanuma K., Shimizu K., Shinohara K., Shirai H., Shiraishi J., Someya Y., Sukegawa A., Suzuki S., Takase H., Takechi M., Takenaga H., Tanigawa H., Tobita K., Toma M., Tsuchiya K., Tsuru D., Wakatsuki T., Yamoto S., Yagi M., Yoshida K., Yoshida M., Horiike H., Nobuta Y., Yamauchi Y., Idomura Y., Hatayama A., Hoshino K., Okano K., Masamune S., Sanpei A., Fukuyama A., Kado S., Kobayashi S., Konishi S., Kunugi T., Maekawa T., Minami T., Mizuuchi T., Murakami S., Nagasaki K., Shikama T., Watanabe F., Yamamoto S., Hanada K., Idei H., Katayama K., Nishikawa M., Inagaki S., Fujita T., Kajita S., Maeyama S., Ohno N., Yamazaki K., Watanabe T., Akiyama T., Isobe M., Kanno R., Kobayashi M., Masuzaki S., Miyazawa J., Morisaki T., Nakajima N., Nakamura Y., Nakata M., Narushima Y., Nishimura S., Ohdachi S., Oishi T., Osakabe M., Sagara A., Sakakibara S., Satake S., Suzuki Y., Takeiri Y., Tamura N., Tanaka K., Todo Y., Toi K., Yokoyama M., Watanabe K., Shibata Y., Fukuda T., Takizuka T., Ueda Y., Oya Y., Ejiri A., Inomoto M., Nishiura M., Ogawa Y., Takase Y., Kitajima S., Iio S., Matsuda S., Furukawa M., Ichimura M., Imai T., Nakashima Y., Sakamoto M., Sumida S., Barabaschi P., Cardella A., Clement-Lorenzo S., Coletti A., Davis S., Di Pietro E., Duglue D., Farthing J., Frello G., Hajnal N., Hurzlmeier H., Jokinen A., Kanapienyte D., Novello L., Peretti E., Phillips G., Rancsik P., Salpietro E., Scherber A., Spears B., Teuchner B., Tomarchio V., Verrecchia M., Wanner M., Zani L., Baulaigue O., Benoit F., Ciazynski D., Decool P., Dougnac H., Duchateau J. -L., Dumas N., Fejoz P., Geraud A., Gharafi S., Goncalves R., Gonde R., Gros G., Jestin F., Jiolat G., Lacroix B., Lamy S., Marechal J. -L., Nicollet S., Peluso B., Santagiustina A., Stephnie B., Torre A., Vagliani A., Vallet J. -C., Verger J. -M., Bonne F., Girard S., Hoa C., Lamaison V., Michel F., Poncet J. -M., Roussel P., Abdel Maksoud W., Ardellier F., Disset G., Donati A., Genini L., Mayri C., Molinie F., Nunio F., Ponsot P., Salanon B., Scola L., Vieillard L., Alonso J., Barrera G., Botija J., Cabrera Perez S., Fernandez P., Medrano M., Ramos F. J., Rincon E., Soleto A., Ferro A., Gaio E., Gasparini F., Maistrello A., Brolatti G., Coccoluto G., Corato V., Costa P., Cristofani C., Cucchiaro A., De Vellis A., Di Pace L., Frosi P., Ginoulhiac G., Lampasi A., Maffia G., Pizzuto A., Polli G. M., Rossi P., Starace F., Fiamozzi Zignani C., Zito P., Drotziger S., Fietz W., Heller R., Massimi A., Meyer I., Radloff D., Rita C., Urbach E., Collin B., Delrez C., Jamotton P., Massaut V., Sarkimaki K., Benkadda S., Artaud J. -F., Becoulet M., Falchetto G., Hoang T., Lotte P., Moreau P., Pegourie B., Travere J. -M., Bettini P., Canton A., Fassina A., Giudicotti L., Guo S. C., Marchiori G., Pigatto L., Vallar M., Xu X., Cismondi F., Barbato E., Mastrostefano S., de Baar M., Bruschi A., Farina D., Figini L., Granucci G., Moro A., Nowak S., Perelli-Cippo E., Platania P., Ricci D., Zuin M., Conway G., Dibon M., Fantz U., Happel T., Lauber P., Lackner K., Pautasso G., Schneider P., Kuhner G., Zocco A., Stankiewicz R., Stepniewski W., Sips G., Gleason Gonzalez C., Luo X., Scannapiego M., Bonifetto R., Decker J., Goodman T., Theiler C., Ayllon-Guerola J., Kovacsik A., Szepesi T., Kawashima H., Ogawa T., Sato M., and Seki M.
Abstract: The stability with respect to a peelingballooning mode (PBM) was investigated numerically with extended MHD simulation codes in JET, JT-60U and future JT-60SA plasmas. The MINERVA-DI code was used to analyze the linear stability, including the effects of rotation and ion diamagnetic drift (w∗i), in JET-ILW and JT-60SA plasmas, and the JOREK code was used to simulate nonlinear dynamics with rotation, viscosity and resistivity in JT-60U plasmas. It was validated quantitatively that the ELM trigger condition in JET-ILW plasmas can be reasonably explained by taking into account both the rotation and w∗i effects in the numerical analysis. When deuterium poloidal rotation is evaluated based on neoclassical theory, an increase in the effective charge of plasma destabilizes the PBM because of an acceleration of rotation and a decrease in w∗i. The difference in the amount of ELM energy loss in JT-60U plasmas rotating in opposite directions was reproduced qualitatively with JOREK. By comparing the ELM affected areas with linear eigenfunctions, it was confirmed that the difference in the linear stability property, due not to the rotation direction but to the plasma density profile, is thought to be responsible for changing the ELM energy loss just after the ELM crash. A predictive study to determine the pedestal profiles in JT-60SA was performed by updating the EPED1 model to include the rotation and w∗i effects in the PBM stability analysis. It was shown that the plasma rotation predicted with the neoclassical toroidal viscosity degrades the pedestal performance by about 10% by destabilizing the PBM, but the pressure pedestal height will be high enough to achieve the target parameters required for the ITER-like shape inductive scenario in JT-60SA.
Published: 2018

24. Contralateral effects of unilateral strength and skill training: Modified Delphi consensus to establish key aspects of cross-education

Author: Manca, A., Hortobágyi, T., Carroll, T. J., Enoka, R. M., Farthing, J. P., Gandevia, S. C., Kidgell, D. J., Taylor, Janet L., Deriu, F., Manca, A., Hortobágyi, T., Carroll, T. J., Enoka, R. M., Farthing, J. P., Gandevia, S. C., Kidgell, D. J., Taylor, Janet L., and Deriu, F.
Abstract: © 2020, The Author(s). Background: Cross-education refers to increased motor output (i.e., force generation, skill) of the opposite, untrained limb following a period of unilateral exercise training. Despite extensive research, several aspects of the transfer phenomenon remain controversial. Methods: A modified two-round Delphi online survey was conducted among international experts to reach consensus on terminology, methodology, mechanisms of action, and translational potential of cross-education, and to provide a framework for future research. Results: Through purposive sampling of the literature, we identified 56 noted experts in the field, of whom 32 completed the survey, and reached consensus (75% threshold) on 17 out of 27 items. Conclusion: Our consensus-based recommendations for future studies are that (1) the term ‘cross-education’ should be adopted to refer to the transfer phenomenon, also specifying if transfer of strength or skill is meant; (2) functional magnetic resonance imaging, short-interval intracortical inhibition and interhemispheric inhibition appear to be promising tools to study the mechanisms of transfer; (3) strategies which maximize cross-education, such as high-intensity training, eccentric contractions, and mirror illusion, seem worth being included in the intervention plan; (4) study protocols should be designed to include at least 13–18 sessions or 4–6 weeks to produce functionally meaningful transfer of strength, and (5) cross-education could be considered as an adjuvant treatment particularly for unilateral orthopedic conditions and sports injuries. Additionally, a clear gap in views emerged between the research field and the purely clinical field. The present consensus statement clarifies relevant aspects of cross-education including neurophysiological, neuroanatomical, and methodological characteristics of the transfer phenomenon, and provides guidance on how to improve the quality and usability of future cross-education studies.
Published: 2020

25. Application of deep learning to large scale riverine flow velocity estimation

Author: Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., Darve, Eric F., Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.
Abstract: Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for prediction of the flow velocities. However, accurate and fast prediction with standard SWE solvers is challenging in many cases. Traditional approaches are computationally expensive and require high-resolution riverbed profile measurement ( bathymetry) for accurate predictions. As a result, they are a poor fit in situations where they need to be evaluated repetitively due, for example, to varying boundary condition (BC), or when the bathymetry is not known with certainty. In this work, we propose a two-stage process that tackles these issues. First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs. The first step allows us to predict flow velocities without direct measurement of the bathymetry. Furthermore, the augmentation of the distribution in the second stage allows incorporation of the additional bathymetry information into the flow velocity prediction for improved accuracy and generalization, even if the bathymetry changes over time. Here, we use three solvers, referred to as PCA-DNN (principal component analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder), and validate them on a reach of the Savannah river near Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods.
Published: 2020

26. Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry

Author: Qian, Yizhou, Forghani, Mojtaba, Lee, Jonghyun Harry, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter, Darve, Eric, Qian, Yizhou, Forghani, Mojtaba, Lee, Jonghyun Harry, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter, and Darve, Eric
Abstract: Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. Hence, it is increasingly important for a wide variety of applications, including shipping operations, coastal management, and risk assessment. However, direct high resolution surveys of nearshore bathymetry are rarely performed due to budget constraints and logistical restrictions. Another option when only sparse observations are available is to use Gaussian Process regression (GPR), also called Kriging. But GPR has difficulties recognizing patterns with sharp gradients, like those found around sand bars and submerged objects, especially when observations are sparse. In this work, we present several deep learning-based techniques to estimate nearshore bathymetry with sparse, multi-scale measurements. We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution. We train our neural networks based on synthetic data generated from nearshore surveys provided by the U.S.\ Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We compare our methods with Kriging on real surveys as well as surveys with artificially added sharp gradients. Results show that direct estimation by DNN gives better predictions than Kriging in this application. We use bootstrapping with DNN for uncertainty quantification. We also propose a method, named DNN-Kriging, that combines deep learning with Kriging and shows further improvement of the posterior estimates.
Published: 2020

27. A performance evaluation framework for building fault detection and diagnosis algorithms

Author: Frank, S, Frank, S, Lin, G, Jin, X, Singla, R, Farthing, A, Granderson, J, Frank, S, Frank, S, Lin, G, Jin, X, Singla, R, Farthing, A, and Granderson, J
Abstract: Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products—a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.
Published: 2019

28. A performance evaluation framework for building fault detection and diagnosis algorithms

Author: Frank, S, Frank, S, Lin, G, Jin, X, Singla, R, Farthing, A, Granderson, J, Frank, S, Frank, S, Lin, G, Jin, X, Singla, R, Farthing, A, and Granderson, J
Abstract: Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products—a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.
Published: 2019

29. Faith and Art: Anne Bradstreet’s Puritan Creativity

Author: Farthing, Sophia and Farthing, Sophia
Abstract: As one of Puritanism’s best-known Puritan writers, Anne Bradstreet is a popular topic for scholars exploring gender issues in a Puritan context. Bradstreet’s poetry has drawn attention to the possibility of Puritan theology as inspiration for art. However, misunderstanding of Puritan cultural complexity and cursory readings of Bradstreet’s texts have resulted in misrepresentations of Bradstreet’s interaction with Puritan culture and ideas. This thesis examines Bradstreet’s life and work, including the variety of supportive literary influences she experienced as a child. The historical value of Bradstreet’s texts is made clear by her poetic insight on political issues, history, and gender conflict, as well as her meditations on God’s presence in personal suffering. Throughout, this essay compares Bradstreet to contemporary Puritan women writers who explored similar themes through poetry, meditations, theological treatises, and a variety of other genres. Against this backdrop of female Puritan voices, Bradstreet’s Puritanism becomes the inspiration for her creativity.
Published: 2018

30. Faith and Art: Anne Bradstreet’s Puritan Creativity

Author: Farthing, Sophia and Farthing, Sophia
Abstract: As one of Puritanism’s best-known Puritan writers, Anne Bradstreet is a popular topic for scholars exploring gender issues in a Puritan context. Bradstreet’s poetry has drawn attention to the possibility of Puritan theology as inspiration for art. However, misunderstanding of Puritan cultural complexity and cursory readings of Bradstreet’s texts have resulted in misrepresentations of Bradstreet’s interaction with Puritan culture and ideas. This thesis examines Bradstreet’s life and work, including the variety of supportive literary influences she experienced as a child. The historical value of Bradstreet’s texts is made clear by her poetic insight on political issues, history, and gender conflict, as well as her meditations on God’s presence in personal suffering. Throughout, this essay compares Bradstreet to contemporary Puritan women writers who explored similar themes through poetry, meditations, theological treatises, and a variety of other genres. Against this backdrop of female Puritan voices, Bradstreet’s Puritanism becomes the inspiration for her creativity.
Published: 2018

31. Ruralization of students’ horizons: insights into Australian health professional students’ rural and remote placements

Author: Smith,Tony, Cross,Merylin, Waller,Susan, Chambers,Helen, Farthing,Annie, Barraclough,Frances, Pit,Sabrina W, Sutton,Keith, Muyambi,Kuda, King,Stephanie, Anderson,Jessie, Smith,Tony, Cross,Merylin, Waller,Susan, Chambers,Helen, Farthing,Annie, Barraclough,Frances, Pit,Sabrina W, Sutton,Keith, Muyambi,Kuda, King,Stephanie, and Anderson,Jessie
Abstract: Tony Smith,1 Merylin Cross,2 Susan Waller,3 Helen Chambers,3 Annie Farthing,4 Frances Barraclough,5 Sabrina W Pit,6 Keith Sutton,3 Kuda Muyambi,7 Stephanie King,8 Jessie Anderson4 1Department of Rural Health, University of Newcastle, Taree, NSW, 2Centre for Rural Health, University of Tasmania, Launceston, TAS, 3Department of Rural Health, Monash University, Moe, VIC, 4Centre for Remote Health, Flinders University, Alice Springs, NT, 5University Centre for Rural Health, University of Sydney, Lismore, NSW, 6University Centre for Rural Health, University of Western Sydney, Lismore, NSW, NSW, 7Department of Rural Health, University of South Australia, Whyalla, SA, 8Centre for Rural and Remote Health, James Cook University, Mt Isa, QLD, Australia Introduction: Health workforce shortages have driven the Australian and other Western governments to invest in engaging more health professional students in rural and remote placements. The aim of this qualitative study was to provide an understanding of the lived experiences of students undertaking placements in various nonmetropolitan locations across Australia. In addition to providing their suggestions to improve rural placements, the study provides insight into factors contributing to positive and negative experiences that influence students’ future rural practice intentions. Methods: Responses to open-ended survey questions from 3,204 students from multiple health professions and universities were analyzed using two independent methods applied concurrently: manual thematic analysis and computerized content analysis using Leximancer software. Results: The core concept identified from the thematic analysis was “ruralization of students’ horizons,” a construct representing the importance of preparing health professional students for practice in nonmetropolitan locations. Ruralization embodies three interrelated themes, “preparation and support,” “rural or
Published: 2018

32. Exploring the roles of membrane proteins in Manganese homeostasis of higher plants

Author: Farthing, Emily and Farthing, Emily
Abstract: Manganese (Mn) is an essential heavy metal micronutrient in plant growth and development, but becomes toxic when present in excess, with potential agricultural yield losses under both nutritional extremes. Membrane transporters play a key role in enabling plant growth under Mn deficiency and toxicity. This study directly compares the roles of members from different ubiquitous transporter families in Mn homeostasis: Group 8/9 members of the Metal Tolerance Proteins (MTPs), At MTP8–MTP11; At ECA3, a P2A-type ATPase; and Natural Resistance Associated Macrophage Proteins, At NRAMP1 and At NRAMP2. MTP8 plays a significant role in Mn detoxification at the tonoplast, conferring Mn hypertolerance when overexpressed; it is also the only Group 8/9-MTP involved in alleviating Mn/iron antagonism. A role in Mn detoxification is also assigned to MTP10, identified through comparison of novel double and triple mtp knockout mutants. Expression in yeast provides evidence that these proteins can transport Mn, with greater Mn tolerance conferred by MTP8 and MTP10 than MTP11. ECA3 is also shown to play a minor role in conferring tolerance to Mn toxicity in planta, but this is only apparent when MTP11 is non-functional; its major role appears to lie in alleviating Mn deficiency. This thesis also clarifies the disputed subcellular localisations of ECA3 and MTP11, demonstrating targeting to the trans- and cis-Golgi, respectively. Additionally, NRAMP2 is shown to alleviate Mn deficiency at the cis-Golgi. Characterisation of double and triple mutants has begun to clarify the relative contribution of NRAMP1, NRAMP2 and ECA3 in Mn deficiency. The use of mutants in this study has also highlighted the antagonistic effects of calcium in Mn homeostasis. Overall, this study provides a more comprehensive understanding of how key transporters function together in Mn homeostasis, identifying targets that may be useful for crop improvement.
Published: 2017

33. Role of art centres for Aboriginal Australians living with dementia in remote communities

Author: Lindeman, M, Mackell, P, Lin, X, Farthing, A, Jensen, H, Meredith, M, Haralambous, B, Lindeman, M, Mackell, P, Lin, X, Farthing, A, Jensen, H, Meredith, M, and Haralambous, B
Abstract: OBJECTIVE: To explore the role art centres in remote communities play for Aboriginal and Torres Strait Islander Australians living with dementia. METHODS: A comprehensive literature search was undertaken, with no restrictions on articles regarding year of publication. RESULTS: Art programmes have been found to be of benefit to both people living with dementia and their carers, particularly when programmes are administered in environments that are culturally revered. Findings indicate remote art centres play a key role in maintaining traditions, culture and practices unique to Aboriginal and Torres Strait Islanders, but there is a gap in knowledge regarding how they cater for the needs of people with dementia. CONCLUSION: Addressing this gap will be helpful in remote areas where prevalence of dementia is up to five times that of non-Aboriginal people, and there are limited health and support services. Further research is required to explore strengths and gaps of current practices.
Published: 2017

34. Interview with Stephen Farthing about Portraiture

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This interview is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2017

35. On the Role of the Electrical Field in Spark Plasma Sintering of UO2+x

Author: Tyrpekl, V., Najii, M., Holzhäuser, M., Freis, D., Prieur, D., Martin, P., Cremer, B., Murray-Farthing, M., Cologna, M., Tyrpekl, V., Najii, M., Holzhäuser, M., Freis, D., Prieur, D., Martin, P., Cremer, B., Murray-Farthing, M., and Cologna, M.
Abstract: The electric field has a large effect on the stoichiometry and grain growth of UO2+x during Spark Plasma Sintering. UO2+x is gradually reduced to UO2.00 as a function of sintering temperature and time. A gradient in the oxidation state within the pellets is observed in intermediate conditions. The shape of the gradient depends unequivocally on the direction of the electrical field. The positive surface of the pellet shows a higher oxidation state compared to the negative one. An area with larger grain size is found close to the positive electrode, but not in contact with it. We interpret these findings with the redistribution of defects under an electric field, which affect the stoichiometry of UO2+x and thus the cation diffusivity. The results bear implications for understanding the electric field assisted sintering of UO2 and non-stoichiometric oxides in general.
Published: 2017

36. Interview with Stephen Farthing about Portraiture

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This interview is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2017

37. Interview with Stephen Farthing about Portraiture

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This interview is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2017

38. Interview with Stephen Farthing about Portraiture

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This interview is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2017

39. Interview with Stephen Farthing about Portraiture

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This interview is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2017

40. Exploring the roles of membrane proteins in Manganese homeostasis of higher plants

Author: Farthing, Emily. and Farthing, Emily.
Abstract: Manganese (Mn) is an essential heavy metal micronutrient in plant growth and development, but becomes toxic when present in excess, with potential agricultural yield losses under both nutritional extremes. Membrane transporters play a key role in enabling plant growth under Mn deficiency and toxicity. This study directly compares the roles of members from different ubiquitous transporter families in Mn homeostasis: Group 8/9 members of the Metal Tolerance Proteins (MTPs), At MTP8–MTP11; At ECA3, a P2A-type ATPase; and Natural Resistance Associated Macrophage Proteins, At NRAMP1 and At NRAMP2. MTP8 plays a significant role in Mn detoxification at the tonoplast, conferring Mn hypertolerance when overexpressed; it is also the only Group 8/9-MTP involved in alleviating Mn/iron antagonism. A role in Mn detoxification is also assigned to MTP10, identified through comparison of novel double and triple mtp knockout mutants. Expression in yeast provides evidence that these proteins can transport Mn, with greater Mn tolerance conferred by MTP8 and MTP10 than MTP11. ECA3 is also shown to play a minor role in conferring tolerance to Mn toxicity in planta, but this is only apparent when MTP11 is non-functional; its major role appears to lie in alleviating Mn deficiency. This thesis also clarifies the disputed subcellular localisations of ECA3 and MTP11, demonstrating targeting to the trans- and cis-Golgi, respectively. Additionally, NRAMP2 is shown to alleviate Mn deficiency at the cis-Golgi. Characterisation of double and triple mutants has begun to clarify the relative contribution of NRAMP1, NRAMP2 and ECA3 in Mn deficiency. The use of mutants in this study has also highlighted the antagonistic effects of calcium in Mn homeostasis. Overall, this study provides a more comprehensive understanding of how key transporters function together in Mn homeostasis, identifying targets that may be useful for crop improvement.
Published: 2017

41. A multidimensional assessment of reformed teaching practice in geoscience classrooms

Author: Bartley, Julie K., Teasdale, Rachel; Viskupic, Karen; McConnell, David; Manduca, Cathryn; Bruckner, Monica; Farthing, Dori; Iverson, Ellen, Bartley, Julie K., and Teasdale, Rachel; Viskupic, Karen; McConnell, David; Manduca, Cathryn; Bruckner, Monica; Farthing, Dori; Iverson, Ellen
Abstract: This study analyzed quantitative and qualitative data from classroom observations combined with instructor survey results to characterize the application of reformed teaching practices in undergraduate geoscience classes in the United States.
Published: 2017

42. Biomechanische Evaluation der Kyphoplastie mit dem injizierbaren gebrauchsfertigen Calciumphosphatzement Velox®

Author: Grünewald, H, Farthing, C, Schmal, H, Südkamp, NP, Konstantinidis, L, Grünewald, H, Farthing, C, Schmal, H, Südkamp, NP, and Konstantinidis, L
Published: 2016

43. Biomechanische Evaluation der Kyphoplastie mit dem injizierbaren gebrauchsfertigen Calciumphosphatzement Velox®

Author: Grünewald, H, Farthing, C, Schmal, H, Südkamp, NP, Konstantinidis, L, Grünewald, H, Farthing, C, Schmal, H, Südkamp, NP, and Konstantinidis, L
Published: 2016

44. Capability Brown: Stairway To Heaven

Author: Farthing, Stephen and Farthing, Stephen
Abstract: A Commemorative Painting, 2 x 3 meters, that in topographical terms describes the landscape as it was shaped by Capability Brown in the area around Milton Abbey, Dorset and as a history focuses on two key figures in the life of that landscapes: Athelstan, King of the Anglo-Saxons from 924 to 927 and King of the English from 927 to 939, and Capability Brown "England's Greatest Gardener" 1716 – 1783. About the exhibition: 2016 marks the 300th anniversary of the birth of Lancelot ‘Capability’ Brown, a designer who changed the national landscape and created a style which has shaped people’s picture of the quintessential English countryside. He transformed the face of eighteenth century England, designing country estates and mansions, moving hills and constructing flowing lakes and serpentine rivers, a magical world of green. He was responsible for more than 200 gardens and parks, and over 150 still survive today. This major exhibition, ’A Capability Brown Experience’ includes a new film featuring John Phibbs, the leading expert on Brown, as narrator; artwork by Royal Academician Stephen Farthing; artist and author Tim Scott- Bolton; eleven artists of Dorset Visual Arts; embroidery from the National Association; interactive digital imagery; photography from a range of leading photographers, and games for children.
Published: 2016

45. A Story of Drawing

Author: Farthing, Stephen and Farthing, Stephen
Abstract: A pedagogical film designed to explain the bigger 'cross disciplinary' picture of drawing. The film centres its case around the drawing of a jug in charcoal, and a performative drawing of the world, that was made by 30 postgraduates in the autumn of 2015 in Granary Square, Kings Cross. The film is narrated and written as an aid to teaching drawing at all levels by Professor Stephen Farthing, Rootstein Hopkins Professor of Drawing at University of the Arts London.
Published: 2016

46. Whose Face Is It?

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This article is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2016

47. Whose Face Is It?

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This article is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2016

48. Capability Brown: Stairway To Heaven

Author: Farthing, Stephen and Farthing, Stephen
Abstract: A Commemorative Painting, 2 x 3 meters, that in topographical terms describes the landscape as it was shaped by Capability Brown in the area around Milton Abbey, Dorset and as a history focuses on two key figures in the life of that landscapes: Athelstan, King of the Anglo-Saxons from 924 to 927 and King of the English from 927 to 939, and Capability Brown "England's Greatest Gardener" 1716 – 1783. About the exhibition: 2016 marks the 300th anniversary of the birth of Lancelot ‘Capability’ Brown, a designer who changed the national landscape and created a style which has shaped people’s picture of the quintessential English countryside. He transformed the face of eighteenth century England, designing country estates and mansions, moving hills and constructing flowing lakes and serpentine rivers, a magical world of green. He was responsible for more than 200 gardens and parks, and over 150 still survive today. This major exhibition, ’A Capability Brown Experience’ includes a new film featuring John Phibbs, the leading expert on Brown, as narrator; artwork by Royal Academician Stephen Farthing; artist and author Tim Scott- Bolton; eleven artists of Dorset Visual Arts; embroidery from the National Association; interactive digital imagery; photography from a range of leading photographers, and games for children.
Published: 2016

49. A Story of Drawing

Author: Farthing, Stephen and Farthing, Stephen
Abstract: A pedagogical film designed to explain the bigger 'cross disciplinary' picture of drawing. The film centres its case around the drawing of a jug in charcoal, and a performative drawing of the world, that was made by 30 postgraduates in the autumn of 2015 in Granary Square, Kings Cross. The film is narrated and written as an aid to teaching drawing at all levels by Professor Stephen Farthing, Rootstein Hopkins Professor of Drawing at University of the Arts London.
Published: 2016

50. Whose Face Is It?

Author: Farthing, Stephen and Farthing, Stephen
Abstract: This article is a contribution to the project About Face. About Face is a research group at the University of the Arts London which emerged from the UAL's commitment to encouraging communities of practice to work collaboratively and in cross disciplinary contexts. It brings together artists, theorists, curators and writers to examine what happens when different disciplines and perspectives are brought to bear on the edges of portraiture. The website https://aboutface.arts.ac.uk/ has been archived by the UK Web Archive (UKWA) and therefore can be found via http://www.webarchive.org.uk
Published: 2016

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Publication Year Range

Publication Type

Journal

Database

Publisher

289 results on '"Farthing, A"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources