Author: "Liu, Yuejiang" - Searchworks@Jio Institute Digital Library Search Results

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46 results on '"Liu, Yuejiang"'

1. TAROT: Targeted Data Selection via Optimal Transport

Author: Feng, Lan, Nie, Fan, Liu, Yuejiang, and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract: We propose TAROT, a targeted data selection framework grounded in optimal transport theory. Previous targeted data selection methods primarily rely on influence-based greedy heuristics to enhance domain-specific performance. While effective on limited, unimodal data (i.e., data following a single pattern), these methods struggle as target data complexity increases. Specifically, in multimodal distributions, these heuristics fail to account for multiple inherent patterns, leading to suboptimal data selection. This work identifies two primary factors contributing to this limitation: (i) the disproportionate impact of dominant feature components in high-dimensional influence estimation, and (ii) the restrictive linear additive assumptions inherent in greedy selection strategies. To address these challenges, TAROT incorporates whitened feature distance to mitigate dominant feature bias, providing a more reliable measure of data influence. Building on this, TAROT uses whitened feature distance to quantify and minimize the optimal transport distance between the selected data and target domains. Notably, this minimization also facilitates the estimation of optimal selection ratios. We evaluate TAROT across multiple tasks, including semantic segmentation, motion prediction, and instruction tuning. Results consistently show that TAROT outperforms state-of-the-art methods, highlighting its versatility across various deep learning tasks. Code is available at https://github.com/vita-epfl/TAROT.
Published: 2024

2. Paraneoplastic renal dysfunction in fly cancer models driven by inflammatory activation of stem cells

Author: Kwok, Sze Hang, Liu, Yuejiang, Bilder, David, and Kim, Jung
Subjects: Biomedical and Clinical Sciences, Immunology, Kidney Disease, Cancer, 2.1 Biological and endogenous factors, Renal and urogenital, Animals, Stem Cells, Disease Models, Animal, Inflammation, Humans, Kidney Tubules, Drosophila melanogaster, Kidney Diseases, Kidney, Paraneoplastic Syndromes, Animals, Genetically Modified, Interleukin-6, Drosophila, cancer model, kidney, paraneoplasia, tumor–host
Abstract: Tumors can induce systemic disturbances in distant organs, leading to physiological changes that enhance host morbidity. In Drosophila cancer models, tumors have been known for decades to cause hypervolemic "bloating" of the abdominal cavity. Here we use allograft and transgenic tumors to show that hosts display fluid retention associated with autonomously defective secretory capacity of fly renal tubules, which function analogous to those of the human kidney. Excretion from these organs is blocked by abnormal cells that originate from inappropriate activation of normally quiescent renal stem cells (RSCs). Blockage is initiated by IL-6-like oncokines that perturb renal water-transporting cells and trigger a damage response in RSCs that proceeds pathologically. Thus, a chronic inflammatory state produced by the tumor causes paraneoplastic fluid dysregulation by altering cellular homeostasis of host renal units.
Published: 2024

3. Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling

Author: Liu, Yuejiang, Hamid, Jubayer Ibn, Xie, Annie, Lee, Yoonho, Du, Maximilian, and Finn, Chelsea
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Predicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. Yet, its reported effects on the learned policy are inconsistent: some studies find it crucial for achieving strong results, while others observe decreased performance. In this paper, we first dissect how action chunking impacts the divergence between a learner and a demonstrator. We find that action chunking allows the learner to better capture the temporal dependencies in demonstrations but at the cost of reduced reactivity in stochastic environments. To address this tradeoff, we propose Bidirectional Decoding (BID), a test-time inference algorithm that bridges action chunking with closed-loop operations. BID samples multiple predictions at each time step and searches for the optimal one based on two criteria: (i) backward coherence, which favors samples that align with previous decisions; (ii) forward contrast, which seeks samples of high likelihood for future plans. By coupling decisions within and across action chunks, BID promotes consistency over time while maintaining reactivity to unexpected changes. Experimental results show that BID boosts the performance of two state-of-the-art generative policies across seven simulation benchmarks and two real-world tasks. Code and videos are available at https://bid-robot.github.io., Comment: Project website: https://bid-robot.github.io/
Published: 2024

4. Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models

Author: Wang, Jifeng, Messaoud, Kaouther, Liu, Yuejiang, Gall, Juergen, and Alahi, Alexandre
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract: Recent progress in motion forecasting has been substantially driven by self-supervised pre-training. However, adapting pre-trained models for specific downstream tasks, especially motion prediction, through extensive fine-tuning is often inefficient. This inefficiency arises because motion prediction closely aligns with the masked pre-training tasks, and traditional full fine-tuning methods fail to fully leverage this alignment. To address this, we introduce Forecast-PEFT, a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. This approach not only preserves the pre-learned representations but also significantly reduces the number of parameters that need retraining, thereby enhancing efficiency. This tailored strategy, supplemented by our method's capability to efficiently adapt to different datasets, enhances model efficiency and ensures robust performance across datasets without the need for extensive retraining. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks, achieving higher accuracy with only 17% of the trainable parameters typically required. Moreover, our comprehensive adaptation, Forecast-FT, further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods. Code will be available at https://github.com/csjfwang/Forecast-PEFT., Comment: This work has been submitted to the IEEE for possible publication
Published: 2024

5. Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts

Author: Liu, Yuejiang and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Steering the behavior of a strong model pre-trained on internet-scale data can be difficult due to the scarcity of competent supervisors. Recent studies reveal that, despite supervisory noises, a strong student model may surpass its weak teacher when fine-tuned on specific objectives. Yet, the effectiveness of such weak-to-strong generalization remains limited, especially in the presence of large capability gaps. In this paper, we propose to address this challenge by harnessing a diverse set of specialized teachers, instead of a single generalist one, that collectively supervises the strong student. Our approach resembles the classical hierarchical mixture of experts, with two components tailored for co-supervision: (i) we progressively alternate student training and teacher assignment, leveraging the growth of the strong student to identify plausible supervisions; (ii) we conservatively enforce teacher-student and local-global consistency, leveraging their dependencies to reject potential annotation noises. We validate the proposed method through visual recognition tasks on the OpenAI weak-to-strong benchmark and additional multi-domain datasets. Our code is available at \url{https://github.com/yuejiangliu/csl}., Comment: Preprint
Published: 2024

6. Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Author: Liu, Yuejiang, Rahimi, Ahmad, Luan, Po-Chien, Rajič, Frano, and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multiagent Systems, Computer Science - Robotics
Abstract: Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the causal relationships behind agent interactions. In this work, we take an in-depth look at the causal awareness of these representations, from computational formalism to real-world practice. First, we cast doubt on the notion of non-causal robustness studied in the recent CausalAgents benchmark. We show that recent representations are already partially resilient to perturbations of non-causal agents, and yet modeling indirect causal effects involving mediator agents remains challenging. To address this challenge, we introduce a metric learning approach that regularizes latent representations with causal annotations. Our controlled experiments show that this approach not only leads to higher degrees of causal awareness but also yields stronger out-of-distribution robustness. To further operationalize it in practice, we propose a sim-to-real causal transfer method via cross-domain multi-task learning. Experiments on pedestrian datasets show that our method can substantially boost generalization, even in the absence of real-world causal annotations. We hope our work provides a new perspective on the challenges and potential pathways towards causally-aware representations of multi-agent interactions. Our code is available at https://github.com/socialcausality., Comment: Preprint
Published: 2023

7. On Pitfalls of Test-Time Adaptation

Author: Zhao, Hao, Liu, Yuejiang, Alahi, Alexandre, and Lin, Tao
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts. However, the lack of consistent settings and systematic studies in prior literature hinders thorough assessments of existing methods. To address this issue, we present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols. Through extensive experiments, our benchmark reveals three common pitfalls in prior efforts. First, selecting appropriate hyper-parameters, especially for model selection, is exceedingly difficult due to online batch dependency. Second, the effectiveness of TTA varies greatly depending on the quality and properties of the model being adapted. Third, even under optimal algorithmic conditions, none of the existing methods are capable of addressing all common types of distribution shifts. Our findings underscore the need for future research in the field to conduct rigorous evaluations on a broader set of models and shifts, and to re-examine the assumptions behind the empirical success of TTA. Our code is available at \url{https://github.com/lins-lab/ttab}., Comment: Accepted at ICML 2023
Published: 2023

8. Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

Author: Liu, Yuejiang, Alahi, Alexandre, Russell, Chris, Horn, Max, Zietlow, Dominik, Schölkopf, Bernhard, and Locatello, Francesco
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work. Our code and datasets will be available at https://sites.google.com/view/causaltriplet., Comment: Conference on Causal Learning and Reasoning (CLeaR) 2023
Published: 2023

9. An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Author: Han, Guangchun, Sinjab, Ansam, Rahal, Zahraa, Lynch, Anne M., Treekitkarnmongkol, Warapen, Liu, Yuejiang, Serrano, Alejandra G., Feng, Jiping, Liang, Ke, Khan, Khaja, Lu, Wei, Hernandez, Sharia D., Liu, Yunhe, Cao, Xuanye, Dai, Enyu, Pei, Guangsheng, Hu, Jian, Abaya, Camille, Gomez-Bolanos, Lorena I., Peng, Fuduan, Chen, Minyue, Parra, Edwin R., Cascone, Tina, Sepesi, Boris, Moghaddam, Seyed Javad, Scheet, Paul, Negrao, Marcelo V., Heymach, John V., Li, Mingyao, Dubinett, Steven M., Stevenson, Christopher S., Spira, Avrum E., Fujimoto, Junya, Solis, Luisa M., Wistuba, Ignacio I., Chen, Jichao, Wang, Linghua, and Kadara, Humam
Published: 2024
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10. Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Author: Kothari, Parth, Li, Danya, Liu, Yuejiang, and Alahi, Alexandre
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts. Unlike the conventional fine-tuning approach that updates the whole encoder, our main idea is to reduce the amount of tunable parameters that can precisely account for the target domain-specific motion style. To this end, we introduce two components that exploit our prior knowledge of motion style shifts: (i) a low-rank motion style adapter that projects and adjusts the style features at a low-dimensional bottleneck; and (ii) a modular adapter strategy that disentangles the features of scene context and motion history to facilitate a fine-grained choice of adaptation layers. Through extensive experimentation, we show that our proposed adapter design, coined MoSA, outperforms prior methods on several forecasting benchmarks., Comment: CoRL 2022
Published: 2022

11. Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective

Author: Liu, Yuejiang, Cadei, Riccardo, Schweizer, Jonas, Bahmani, Sherwin, and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under distribution shifts and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant variables, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice mixing datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations; (ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a sparse causal graph; (iii) we introduce a style contrastive loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiments on synthetic and real datasets show that our proposed method improves the robustness and reusability of learned motion representations, significantly outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer., Comment: CVPR 2022. Code is available at https://github.com/vita-epfl/causalmotion. v4: fixed typo
Published: 2021

12. Social NCE: Contrastive Learning of Socially-aware Motion Representations

Author: Liu, Yuejiang, Yan, Qi, and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks still struggle to generalize in closed-loop predictions (e.g., output colliding trajectories). This issue largely arises from the non-i.i.d. nature of sequential prediction in conjunction with ill-distributed training data. Intuitively, if the training data only comes from human behaviors in safe spaces, i.e., from "positive" examples, it is difficult for learning algorithms to capture the notion of "negative" examples like collisions. In this work, we aim to address this issue by explicitly modeling negative examples through self-supervision: (i) we introduce a social contrastive loss that regularizes the extracted motion representation by discerning the ground-truth positive events from synthetic negative ones; (ii) we construct informative negative samples based on our prior knowledge of rare but dangerous circumstances. Our method substantially reduces the collision rates of recent trajectory forecasting, behavioral cloning and reinforcement learning algorithms, outperforming state-of-the-art methods on several benchmarks. Our code is available at https://github.com/vita-epfl/social-nce., Comment: ICCV 2021
Published: 2020

13. Author Correction: An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Author: Han, Guangchun, Sinjab, Ansam, Rahal, Zahraa, Lynch, Anne M., Treekitkarnmongkol, Warapen, Liu, Yuejiang, Serrano, Alejandra G., Feng, Jiping, Liang, Ke, Khan, Khaja, Lu, Wei, Hernandez, Sharia D., Liu, Yunhe, Cao, Xuanye, Dai, Enyu, Pei, Guangsheng, Hu, Jian, Abaya, Camille, Gomez-Bolanos, Lorena I., Peng, Fuduan, Chen, Minyue, Parra, Edwin R., Cascone, Tina, Sepesi, Boris, Moghaddam, Seyed Javad, Scheet, Paul, Negrao, Marcelo V., Heymach, John V., Li, Mingyao, Dubinett, Steven M., Stevenson, Christopher S., Spira, Avrum E., Fujimoto, Junya, Solis, Luisa M., Wistuba, Ignacio I., Chen, Jichao, Wang, Linghua, and Kadara, Humam
Published: 2024
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14. Two Brain Pathways Initiate Distinct Forward Walking Programs in Drosophila

Author: Bidaye, Salil S, Laturney, Meghan, Chang, Amy K, Liu, Yuejiang, Bockemühl, Till, Büschges, Ansgar, and Scott, Kristin
Subjects: Neurosciences, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Brain, Drosophila melanogaster, Female, Male, Neurons, Walking, Drosophila, behavior, courtship, descending neurons, locomotion, neural circuits, walking, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract: An animal at rest or engaged in stationary behaviors can instantaneously initiate goal-directed walking. How descending brain inputs trigger rapid transitions from a non-walking state to an appropriate walking state is unclear. Here, we identify two neuronal types, P9 and BPN, in the Drosophila brain that, upon activation, initiate and maintain two distinct coordinated walking patterns. P9 drives forward walking with ipsilateral turning, receives inputs from central courtship-promoting neurons and visual projection neurons, and is necessary for a male to pursue a female during courtship. In contrast, BPN drives straight, forward walking and is not required during courtship. BPN is instead recruited during and required for fast, straight, forward walking bouts. Thus, this study reveals separate brain pathways for object-directed walking and fast, straight, forward walking, providing insight into how the brain initiates context-appropriate walking programs.
Published: 2020

15. Collaborative Sampling in Generative Adversarial Networks

Author: Liu, Yuejiang, Kothari, Parth, and Alahi, Alexandre
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract: The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a collaborative sampling scheme between the generator and the discriminator for improved data generation. Guided by the discriminator, our approach refines the generated samples through gradient-based updates at a particular layer of the generator, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can smoothen the loss landscape provided by the discriminator for effective sample refinement. Through extensive experiments on synthetic and image datasets, we demonstrate that our proposed method can improve generated samples both quantitatively and qualitatively, offering a new degree of freedom in GAN sampling., Comment: Accepted to AAAI 2020
Published: 2019

16. Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Author: Chen, Changan, Liu, Yuejiang, Kreiss, Sven, and Alahi, Alexandre
Subjects: Computer Science - Robotics, Computer Science - Machine Learning
Abstract: Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods., Comment: Accepted at ICRA2019
Published: 2018

17. Paraneoplastic renal dysfunction in fly cancer models driven by inflammatory activation of stem cells

Author: Kwok, Sze Hang, primary, Liu, Yuejiang, additional, Bilder, David, additional, and Kim, Jung, additional
Published: 2024
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18. The surgical safety checklist and patient outcomes after surgery: a prospective observational cohort study, systematic review and meta-analysis

Author: Pearse, Rupert M., Beattie, Scott, Clavien, Pierre-Alain, Demartines, Nicolas, Fleisher, Lee A., Grocott, Mike, Haddow, James, Hoeft, Andreas, Holt, Peter, Moreno, Rui, Pritchard, Naomi, Rhodes, Andrew, Wijeysundera, Duminda, Wilson, Matt, Ahmed, Tahania, Everingham, Kirsty, Hewson, Russell, Januszewska, Marta, Phull, Mandeep-Kaur, Halliwell, Richard, Shulman, Mark, Myles, Paul, Schmid, Werner, Hiesmayr, Michael, Wouters, Patrick, de Hert, Stefan, Lobo, Suzana, Fang, Xiangming, Rasmussen, Lars, Futier, Emmanuel, Biais, Matthieu, Venara, Aurélien, Slim, Karem, Sander, Michael, Koulenti, Despoina, Arvaniti, Kostoula, Chan, Mathew, Kulkarni, Atul, Chandra, Susilo, Tantri, Aida, Geddoa, Emad, Abbas, Muntadhar, Della Rocca, Giorgio, Sivasakthi, Datin, Mansor, Marzida, Luna, Pastor, Bouwman, Arthur, Buhre, Wolfgang, Beavis, Vanessa, Campbell, Douglas, Short, Tim, Osinaike, Tunde, Matos, Ricardo, Grigoras, Ioana, Kirov, Mikhail, Protsenko, Denis, Biccard, Bruce, Aldecoa, Cesar, Chew, Michelle, Hofer, Christoph, Hubner, Martin, Ditai, James, Szakmany, Tamas, Fleisher, Lee, Ferguson, Marissa, MacMahon, Michael, Cherian, Ritchie, Currow, Helen, Kanathiban, Kathirgamanathan, Gillespie, David, Pathmanathan, Edward, Phillips, Katherine, Reynolds, Jenifer, Rowley, Joanne, Douglas, Jeanene, Kerridge, Ross, Garg, Sameer, Bennett, Michael, Jain, Megha, Alcock, David, Terblanche, Nico, Cotter, Rochelle, Leslie, Kate, Stewart, Marcelle, Zingerle, Nicolette, Clyde, Antony, Hambidge, Oliver, Rehak, Adam, Cotterell, Sharon, Huynh, Wilson Binh Quan, McCulloch, Timothy, Ben-Menachem, Erez, Egan, Thomas, Cope, Jennifer, Fellinger, Paul, Haisjackl, Markus, Haselberger, Simone, Holaubek, Caroline, Lichtenegger, Paul, Scherz, Florian, Hoffer, Franz, Cakova, Veronika, Eichwalder, Andreas, Fischbach, Norbert, Klug, Reinhold, Schneider, Elisabeth, Vesely, Martin, Wickenhauser, Reinhart, Grubmueller, Karl Gernot, Leitgeb, Marion, Lang, Friedrich, Toro, Nancy, Bauer, Marlene, Laengle, Friedrich, Haberl, Claudia, Mayrhofer, Thomas, Trybus, Christoph, Buerkle, Christian, Forstner, Karin, Germann, Reinhard, Rinoesl, Harald, Schindler, Elke, Trampitsch, Ernst, Bogner, Gerhard, Dankl, Daniel, Duenser, Martin, Fritsch, Gerhard, Gradwohl-Matis, Ilse, Hartmann, Andreas, Hoelzenbein, Thomas, Jaeger, Tarkan, Landauer, Franz, Lindl, Gregor, Lux, Michael, Steindl, Johannes, Stundner, Ottokar, Szabo, Christian, Bidgoli, Jawad, Verdoodt, Hans, Forget, Patrice, Kahn, David, Lois, Fernande, Momeni, Mona, Prégardien, Caroline, Pospiech, Audrey, Steyaert, Arnaud, Veevaete, Laurent, De Kegel, Dirk, De Jongh, Karen, Foubert, Luc, Smitz, Carine, Vercauteren, Marcel, Poelaert, Jan, Van Mossevelde, Veerle, Abeloos, Jacques, Bouchez, Stefaan, Coppens, Marc, De Baerdemaeker, Luc, Deblaere, Isabel, De Bruyne, Ann, Fonck, Kristine, Heyse, Bjorn, Jacobs, Tom, Lapage, Koen, Moerman, Anneliese, Neckebroek, Martine, Parashchanka, Aliaksandra, Roels, Nathalie, Van Den Eynde, Nancy, Vandenheuvel, Michael, Limmen, JurgenVan, Vanluchene, Ann, Vanpeteghem, Caroline, Wyffels, Piet, Huygens, Christel, Vandenbempt, Punitha, Van de Velde, Marc, Dylst, Dimitri, Janssen, Bruno, Schreurs, Evelien, Aleixo, Fábia Berganton, Candido, Keulle, Batista, Hugo Dias, Guimarães, Mario, Guizeline, Jaqueline, Hoffmann, João, Lobo, Francisco Ricardo Marques, Nascimento, Vinícius, Nishiyama, Katia, Pazetto, Lucas, Souza, Daniela, Rodrigues, Rodrigo Souza, Vilela dos Santos, Ana Maria, Jardim, Jaquelline, Sá Malbouisson, Luiz Marcelo, Silva, Joao, Nascimento Junior, Paulo do, Baio, Thalissa Hermínia, Pereira de Castro, Gabriel Isaac, Watanabe Oliveira, Henri Roger, Amendola, Cristina Prata, Cardoso, Gutemberg, Ortega, Daniela, Brotto, Ana Flavia, De Oliveira, Mirella Cristine, Réa-Neto, Álvaro, Dias, Fernando, Travi, Maria Eduarda, Zerman, Luiza, Azambuja, Pedro, Knibel, Marcos Freitas, Martins, Antonio, Almeida, William, Neto, Calim Neder, Tardelli, Maria Angela, Caser, Eliana, Machado, Marcio, Aguzzoli, Crisitiano, Baldisserotto, Sérgio, Tabajara, Fernanda Beck, Bettega, Fernanda, Rodrigues Júnior, La Hore Correa, de Gasperi, Julia, Faina, Lais, Nolasco, Marcos Farias, Ferreira da Costa Fischer, Bruna, Fosch de Campos Ferreira, Mariana, Hartmann, Cristina, Kliemann, Marta, Hubert Ribeiro, Gustavo Luis, Fraga, Julia Merladete, Netto, Thiago Motta, Pozza, Laura Valduga, Wendling, Paulo Rafael, Azevedo, Caroline, Garcia, Juliana, Lopes, Marcel, Maia, Bernardo, Maselli, Paula, Melo, Ralph, Mendes, Weslley, Neves, Matheus, Ney, Jacqueline, Piras, Claudio, Applewhaite, Christopher, Carr, Adrienne, Chow, Lorraine, Duttchen, Kaylene, Foglia, Julena, Greene, Michael, Hinther, Ashley, Houston, Kendra, McCormick, Thomas Jared, Mikhayel, Jennifer, Montasser, Sam, Ragan, Alex, Suen, Andrew, Woolsey, Adrianna, Yu, Hai Chuan, Funk, Duane, Kowalski, Stephen, Legaspi, Regina, McDonald, Heather, Siddiqui, Faisal, Pridham, Jeremy, Rowe, Bernadette, Sampson, Sonia, Thiessen, Barton, Zbitnew, Geoff, Bernard, Andre, George, Ronald, Jones, Philip, Moor, Rita, Siddiqui, Naveed, Wolfer, Alexandra, Tran, Diem, Winch, Denyse, Dobson, Gary, McCormick, Thomas, Montasser, Osama, Hall, Richard, Baghirzada, Leyla, Curley, Gerard, Dai, Si Yuan, Hare, Gregory, Lee, Esther, Shastri, Uma, Tsui, Albert, Yagnik, Anmol, Alvares, Danielle, Choi, Stephen, Dwyer, Heather, Flores, Kathrina, McCartney, Colin, Somascanthan, Priya, Carroll, Jo, Pazmino-Canizares, Janneth, Ami, Noam, Chan, Vincent, Perlas, Anahi, Argue, Ruth, Huang, Yang, Lavis, Katie, Mayson, Kelly, Cao, Ying, Gao, Hong, Hu, Tingju, Lv, Jie, Yang, Jian, Yang, Yang, Zhong, Yi, Zhou, Jing, Zou, Xiaohua, He, Miao, Li, Xiaoying, Luo, Dihuan, Wang, Haiying, Yu, Tian, Chen, Liyong, Wang, Lijun, Cai, Yunfei, Cao, Zhongming, Li, Yanling, Lian, Jiaxin, Sun, Haiyun, Wang, Sheng, Wang, Zhipeng, Wang, Kenru, Zhu, Yi, Du, Xindan, Fan, Hao, Fu, Yunbin, Huang, Lixia, Huang, Yanming, Hwan, Haifang, Luo, Hong, Qu, Pi-Sheng, Tao, Fan, Wang, Zhen, Wang, Guoxiang, Wang, Shun, Zhang, Yan, Zhang, Xiaolin, Chen, Chao, Wang, Weixing, Liu, Zhengyuan, Fan, Lihua, Tang, Jing, Chen, Yijun, Chen, Yongjie, Han, Yangyang, Huang, Changshun, Liang, Guojin, Shen, Jing, Wang, Jun, Yang, Qiuhong, Zhen, Jungang, Zhou, Haidong, Chen, Junping, Chen, Zhang, Li, Xiaoyu, Meng, Bo, Ye, Haiwang, Zhang, Xiaoyan, Bi, Yanbing, Cao, Jianqiao, Guo, Fengying, Lin, Hong, Liu, Yang, Lv, Meng, Shi, Pengcai, Song, Xiumei, Sun, Chuanyu, Sun, Yongtao, Wang, Yuelan, Wang, Shenhui, Zhang, Min, Chen, Rong, Hou, Jiabao, Leng, Yan, Meng, Qing-tao, Qian, Li, Shen, Zi-ying, Xia, Zhong-yuan, Xue, Rui, Zhang, Yuan, Zhao, Bo, Zhou, Xian-jin, Chen, Qiang, Guo, Huinan, Guo, Yongqing, Qi, Yuehong, Wang, Zhi, Wei, Jianfeng, Zhang, Weiwei, Zheng, Lina, Bao, Qi, Chen, Yaqiu, Chen, Yijiao, Fei, Yue, Hu, Nianqiang, Hu, Xuming, Lei, Min, Li, Xiaoqin, Lv, Xiaocui, Miao, Fangfang, Ouyang, Lingling, Qian, Lu, Shen, Conyu, Sun, Yu, Wang, Yuting, Wang, Dong, Wu, Chao, Xu, Liyuan, Yuan, 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Cristina, Tosa, Flaviu, Tranca, Sebastian, Copotoiu, Sanda Maria, Ungureanu, Liviu Lucian, Harsan, Cristian Remus, Papurica, Marius, Cernea, Daniela Denisa, Dragoescu, Nicoleta Alice, CarmenVaida, Laura Aflori, Ciobotaru, Oana Roxana, Aignatoaie, Mariana, Carp, Cristina Paula, Cobzaru, Isabelle, Mardare, Oana, Purcarin, Bianca, Tutunaru, Valentin, Ionita, Victor, Arustei, Mirela, Codita, Anisoara, Busuioc, Mihai, Chilinciuc, Ion, Ciobanu, Cristina, Belciu, Ioana, Tincu, Eugen, Blaj, Mihaela, Grosu, Ramona - Mihaela, Sandu, Gigel, Bruma, Dana, Corneci, Dan, Dutu, Madalina, Krepil, Adriana, Copaciu, Elena, Dumitrascu, Clementina Oana, Jemna, Ramona, Mihaescu, Florentina, Petre, Raluca, Tudor, Cristina, Ursache, Elena, Kulikov, Alexander, Lubnin, Andrey, Grigoryev, Evgeny, Pugachev, Stanislav, Tolmasov, Alexander, Hussain, Ayyaz, Ilyina, Yana, Roshchina, Anna, Iurin, Aleksandr, Chazova, Elena, Dunay, Artem, Karelov, Alexey, Khvedelidze, Irina, Voldaeva, Olga, Belskiy, Vladislav, Dzhamullaev, Parvin, Grishkowez, Elena, Kretov, Vladimir, Levin, Valeriy, Molkov, Aleksandr, Puzanov, Sergey, Samoilenko, Aleksandr, Tchekulaev, Aleksandr, Tulupova, Valentina, Utkin, Ivan, Allorto, Nikki Leigh, Bishop, David Gray, Builu, Pierre Monji, Cairns, Carel, Dasrath, Ashish, de Wet, Jacques, Hoedt, Marielle den, Grey, Ben, Hayes, Morgan Philip, Küsel, Belinda Senta, Shangase, Nomcebo, Wise, Robert, Cacala, Sharon, Farina, Zane, Govindasamy, Vishendran, Kruse, Carl-Heinz, Lee, Carolyn, Marais, Leonard, Naidoo, Thinagrin Dhasarthun, Rajah, Chantal, Rodseth, Reitze Nils, Ryan, Lisa, von Rhaden, Richard, Adam, Suwayba, Alphonsus, Christella, Ameer, Yusuf, Anderson, Frank, Basanth, Sujith, Bechan, Sudha, Bhula, Chettan, Biccard, Bruce M., Biyase, Thuli, Buccimazza, Ines, Cardosa, Jorge, Chen, James, Daya, Bhavika, Drummond, Leanne, Elabib, Ali, Abdel Goad, Ehab Helmy, Goga, Ismail E., Goga, Riaz, Harrichandparsad, R., Hodgson, Richard E., Jordaan, J., Kalafatis, Nicky, Kampik, Christian, Landers, A.T., Loots, Emil, Madansein, Rajhmum, Madaree, Anil, Madiba, Thandinkosi E., Manzini, Vukani T., Mbuyisa, Mbali, Moodley, Rajan, Msomi, Mduduzi, Mukama, Innocent, Naidoo, Desigan, Naidoo, Rubeshan, Naidu, Tesuven K., Ntloko, Sindiswa, Padayachee, Eneshia, Padayachee, Lucelle, Phaff, Martijn, Pillay, Bala, Pillay, Desigan, Pillay, Lutchmee, Ramnarain, Anupa, Ramphal, Suren R., Ryan, Paul, Saloojee, Ahmed, Sebitloane, Motshedisi, Sigcu, Noluyolo, Taylor, Jenna L., Torborg, Alexandra, Visser, Linda, Anderson, Philip, Conradie, Alae, de Swardt, Mathew, de Villiers, Martin, Eikman, Johan, Liebenberg, Riaan, Mouton, Johan, Paton, Abbey, van der Merwe, Louwrence, Wilscott-Davids, Candice, Barrett, Wendy Joan, Bester, Marlet, de Beer, Johan, Geldenhuys, Jacques, Gouws, Hanni, Potgieter, Jan-Hendrik, Strydom, Magdel, WilberforceTurton, Edwin, Chetty, Rubendraj R., Chirkut, Subash, Cronje, Larissa, de Vasconcellos, Kim, Dube, Nokukhanya Z., Gama, N. Sibusiso, Green, Garyth M., Green-Thompson, Randolph, Kinoo, Suman Mewa, Kistnasami, Prenolin, Maharaj, Kapil, Moodley, Manogaran S., Mothae, Sibongile J., Naidoo, Ruvashni, Aslam F Noorbhai, M., Rughubar, Vivesh, Reddy, Jenendhiran, Singh, Avesh, Skinner, David L., Smith, Murray J., Singh, Bhagwan, Misra, Ravi, Naidoo, Maheshwar, Ramdharee, Pireshin, Selibea, Yvonne, Sewpersad, Selina, Sham, Shailendra, Wessels, Joseph D., Africander, Cucu, Bejia, Tarek, Blakemore, Stephen P., Botes, Marisa, Bunwarie, Bimalshakth, Hernandez, Carlos B., Jeeraz, Mohammud A., Legutko, Dagmara A., Lopez, Acela G., De Meyer, Jenine N., Muzenda, Tanaka, Naidoo, Noel, Patel, Maryam, Pentela, Rao, Junge, Marina, Mansoor, Naj, Rademan, Lana, Scislowski, Pawel, Seedat, Ismail, van den Berg, Bianca, van der Merwe, Doreen, van Wyk, Steyn, Govender, Komalan, Naicker, Darshan, Ramjee, Rajesh, Saley, Mueen, Kuhn, Warren Paul, Matos-Puig, Roel, Alberto Lisi, Zaheer Moolla, Perez, Gisela, Beltran, Anna Valle, Lozano, Angels, Navarro, Carlos Delgado, Duca, Alejandro, Ernesto, Ernesto Pastor Martinez, Ferrando, Carlos, Fuentes, Isabel, García-Pérez, Maria Luisa, Gracia, Estefania, Palomares, Ana Izquierdo, Katime, Antonio, Miñana, Amanda, Incertis, Raul Raul, Romero, Esther, Romero Garcia, Carolina Soledad, Rubio, Concepcion, Artiles, Tania Socorro, Soro, Marina, Valls, Paola, Laguarda, Gisela Alaman, Benavent, Pau, Cuenca, Vicente Chisbert, Cueva, Andreu, Lafuente, Matilde, Parra, Asuncion Marques, Rodrigo, Alejandra Romero, Sanchez-Morcillo, Silvia, Tormo, Sergi, Redondo, Francisco Javier, De Andrés Ibanez, José Antonio, Diago, Lorena Gómez, José Hernández Cádiz, Maria, Manuel, Granell Gil, Peris, Raquel, Saiz, Cristina, Vivo, Jose Tatay, Soto, Maria Teresa Tebar, Brunete, Tamara, Cancho, David, Delgado García, David R., Zamudio, Diana, Del Valle, Santiago Garcia, Serrano, M. Luz, Alonso, Eduardo, Anillo, Victor, Maseda, Emilio, Salgado, Patricia, Suarez, Luis, Suarez-de-la-Rica, Alejandro, Villagrán, María José, Alonso, José Ignacio, Cabezuelo, Estefania, Garcia-Saiz, Irene, Lopez del Moral, Olga, Martín, Silvia, Gonzalez, Alba Perez, Doncel, Ma Sherezade Tovar, Vera, Martin Agüero, José Ávila Sánchez, Francisco, Castaño, Beatriz, Moreira, Beatriz Castaño, Risco, Sahely Flores, Martín, Daniel Paz, Martín, Fernando Pérez, Poza, Paloma, Ruiz, Adela, Serna Martínez, Wilson Fabio, Vicente, Bárbara Vázquez, Dominguez, Saul Velaz, Fernández, Salvador, Munoz-López, Alfonso, Bernat, Maria Jose, Mas, Arantxa, Planas, Kenneth, Jawad, Monir, Saeed, Yousif, Hedin, Annika, Levander, Helena, Holmström, Sandra, Lönn, David, Zoerner, Frank, Åkring, Irene, Widmark, Carl, Zettergren, Jan, Liljequist, Victor Aspelund, Nystrom, Lena, Odeberg-Wernerman, Suzanne, Oldner, Anders, Fagerlund, Malin Jonsson, Reje, Patrik, Lyckner, Sara, Sperber, Jesper, Adolfsson, Anne, Klarin, Bengt, Ögren, Katrin, Barras, Jean-Pierre, Bührer, Thomas, Despotidis, Vasileios, Helmy, Naeder, Holliger, Stephan, Raptis, Dimitri Aristotle, Schmid, Roger, Meyer, Antoine, Jaquet, Yves, Kessler, Ulf, Muradbegovic, Mirza, Nahum, Solange R., Rotunno, Teresa, Schiltz, Boris, Voruz, François, Worreth, Marc, Christoforidis, Dimitri, Popeskou, Sotirios Georgios, Furrer, Markus, Prevost, Gian Andrea, Stocker, Andrea, Lang, Klaus, Breitenstein, Stefan, Ganter, Michael T., Geisen, Martin, Soll, Christopher, Korkmaz, Michelle, Lubach, Iris, Schmitz, Michael, Meyer zu Schwabedissen, Moritz, Moritz, Meyer zu Schwabedissen, Zingg, Urs, Hillermann, Thomas, Wildi, Stefan, Pinto, Bernardo Bollen, Walder, Bernhard, Mariotti, Giustina, Slankamenac, Ksenija, Namuyuga, Mirioce, Kyomugisha, Edward, Kituuka, Olivia, Shikanda, Anne Wesonga, Kakembo, Nasser, Tom, Charles Otim, Antonina, Webombesa, Bua, Emmanuel, Ssettabi, Eden Michael, Epodoi, Joseph, Kabagenyi, Fiona, Kirya, Fred, Dempsey, Ged, Seasman, Colette, Nawaz Khan, Raja Basit, Kurasz, Claire, Macgregor, Mark, Shawki, Burhan, Francis, Daren, Hariharan, Vimal, Chau, Simon, Ellis, Kate, Butt, Georgina, Chicken, Dennis-Wayne, Christmas, Natasha, Allen, Samantha, Daniel, Gayatri Daniel, Dempster, Angie, Kemp, Juliette, Matthews, Lewis, Mcglone, Philip, Tambellini, Joanne, Trodd, Dawn, Freitas, Katie, Garg, Atul, Gupta, Janesh Kumar, Karpate, Shilpaja, Kulkarni, Aditi, O'Hara, Chloe, Troko, Jtroko, Angus, Kirsty, Bradley, Jacqueline, Brennan, Emma, Brooks, Carolyn, Brown, Janette, Brown, Gemma, Finch, Amanda, Gratrix, Karen, Hesketh, Sue, Hill, Gillian, Jeffs, Carol, Morgan, Maureen, Pemberton, Chris, Slawson, Nicola, Spickett, Helen, Swarbrick, Gemma, Thomas, Megan, Van Duyvenvoorde, Greta, Brennan, Andrew, Briscoe, Richard, Cooper, Sarah, Lawton, Tom, Northey, Martin, Senaratne, Rashmi, Stanworth, Helen, Burrows, Lorna, Cain, Helen, Craven, Rachael, Davies, Keith, Jonas, Attila, Pachucki, Marcin, Walkden, Graham, Davies, Helen, Gudaca, Mariethel, Hobrok, Maria, Arawwawala, Dilshan, Fergey, Lauren, Gardiner, Matthew, Gunn, Jacqueline, Johnson, Lyndsay, Lofting, Amanda, Lyle, Amanda, Neela, Fiona Mc, Smolen, Susan, Topliffe, Joanne, Williams, Sarah, Bland, Martin, Balaji, Packianathaswamy, Kaura, Vikas, Lanka, Prasad, Smith, Neil, Ahmed, Ahmed, Myatt, John, Shenoy, Ravikiran, Soon, Wai Cheong, Tan, Jessica, Karadia, Sunny, Self, James, Durant, Emma, Tripathi, Shiva, Bullock, Clare, Campbell, Debbie, Ghosh, Alison, Hughes, Thomas, Zsisku, Lajos, Bengeri, Sheshagiri, Cowton, Amanda, Khalid, Mohammed Shazad, Limb, James, McAdam, Colin, Porritt, Mandy, Rafi, M. Amir, Shekar, Priya, Adams, David, Harden, Catherine, Hollands, Heidi, King, Angela, March, Linda, Minto, Gary, Patrick, Abigail, Squire, Rosalyn, Waugh, Darren, Kumara, Paramesh, Simeson, Karen, Yarwood, Jamie, Browning, Julie, Hatton, Jonathan, Julian, Howes, Mitra, Atideb, Newton, Maria, Pernu, Pawan Kootelu, Wilson, Alison, Commey, Thelma, Foot, Helen, Glover, Lyn, Gupta, Ajay, Lancaster, Nicola, Levin, Jill, Mackenzie, Felicity, Mestanza, Claire, Nofal, Emma, Pout, Lauren, Varden, Rosanna, Wild, Jonathan, Jones, Stephanie, Moreton, Sarah, Pulletz, Mark, Davies, Charlotte, Martin, Matthew, Thomas, Sian, Burns, Karen, McArthur, Carol, Patel, Panna, Lau, Gary, Rich, Natalie, Davis, Fiona, Lyons, Rachel, Port, Beth, Prout, Rachel, Smith, Christopher, Adelaja, Yemi, Bennett, Victoria, Bidd, Heena, Dumitrescu, Alexandra, Murphy, Jacqui Fox, Keen, Abigail, Mguni, Nhlanhla, Ong, Cheng, Adams, George, Boshier, Piers, Brown, Richard, Butryn, Izabella, Chatterjee, Jayanta, Freethy, Alexander, Lockwood, Geoffrey, Tsakok, Maria, Tsiligiannis, Sophia, Peat, William, Stephenson, Lorraine, Bradburn, Mike, Pick, Sara, Cunha, Pedro, Olagbaiye, Olufemi, Tayeh, Salim, Packianathaswamy, Balaji, Abernethy, Caroline, Balasubramaniam, Madhu, Bennett, Rachael, Bolton, David, Martinson, Victoria, Naylor, Charde, Bell, Stephanie, Heather, Blaylock, Kushakovsky, Vlad, Alcock, Liam, Alexander, Hazel, Anderson, Colette, Baker, Paul, Brookes, Morag, Cawthorn, Louise, Cirstea, Emanuel, Clarkson, Rachel, Colling, Kerry, Coulter, Ian, Das, Suparna, Haigh, Kathryn, Hamdan, Alhafidz, Hugill, Keith, Kottam, Lucksy, Lisseter, Emily, Mawdsley, Matthew, McGivern, Julie, Padala, Krishnaveni, Phelps, Victoria, Ramesh kumar, Vineshykaa, Stewart, Kirsten, Towse, Kayley, Tregonning, Julie, Vahedi, Ali, Walker, Alycon, Baines, Duncan, Bilolikar, Anjali, Chande, Shiv, Copley, Edward, Dunk, Nigel, Kulkarni, Raghavendra, Kumar, Pawan, Metodiev, Yavor, Ncomanzi, Dumisani, Raithatha, Bhavesh, Raymode, Parizade, Szafranski, Jan, Twohey, Linda, Watt, Philip, Weatherall, Lucie, Weatherill, J., Whitman, Zoe, Wighton, Elinor, Abayasinghe, Chamika, Chan, Alexander, Darwish, Sharif, Gill, James, Glasgow, Emma, Hadfield, Daniel, Harris, Clair, Hopkins, Phil, Kochhar, Arun, Kunst, Gudrun, Mellis, Clare, Pool, Andrew, Riozzi, Paul, Selman, Andrew, Smith, Emma-Jane, Vele, Liana, Gercek, Yuksel, Guy, Kramer, Holden, Douglas, Watson, Nicholas, Whysall, Karen, Andreou, Prematie, Hales, Dawn, Thompson, Jonathan, Bowrey, Sarah, McDonald, Shara, Gilmore, Jemma, Hills, Vicky, Kelly, Chan, Kelly, Sinead, Lloyd, Geraint, Abbott, Tom, Gall, Lewis, Torrance, Hew, Vivian, Mark, Berntsen, Emer, Nolan, Tracey, Turner, Angus, Vohra, Akbar, Brown, Andrew, Clark, Richard, Coughlan, Elaine, Daniel, Conway, Patvardhan, Chinmay, Pearson, Rachel, Predeep, Sheba, Saad, Hesham, Shanmugam, Mohanakrishnan, Varley, Simon, Wylie, Katharine, Cooper, Lucy, Makowski, Arystarch, Misztal, Beata, Moldovan, Eliza, Pegg, Claire, Donovan, Andrew, Foot, Jayne, Large, Simon, Claxton, Andrew, Netke, Bhagyashree, Armstrong, Richard, Calderwood, Claire, Kwok, Andy, Mohr, Otto, Oyeniyi, Peter, Patnaik, Lisa, Post, Benjamin, Ali, Sarah, Arshad, Homa, Baker, Gerard, Brenner, Laura, Brincat, Maximilian, Brunswicker, Annemarie, Cox, Hannah, Cozar, Octavian Ionut, Cheong, Edward, Durst, Alexander, Fengas, Lior, Flatt, Jim, Glister, Georgina, Narwani, Vishal, Photi, Evangelos, Rankin, Adeline, Rosbergen, Melissa, Tan, Mark, Beaton, Ceri, Horn, Rachel, Hunt, Jane, Rousseau, Guy, Stancombe, Lucia, Absar, Mohammed, Allsop, Joanne, Drinkwater, Zoe, Hodgkiss, Tracey, Smith, Kirsty, Brown, Jamie, Alexander-Sefre, Farhad, Campey, Lorraine, Dudgeon, Lucy, Hall, Kathryn, Hitchcock, Rachael, James, Lynne, Smith, Kate, Winstone, Ulrika, Ahmad, Norfaizan, Bauchmuller, Kris, Harrison, Jonathan, Jeffery, Holly, Miller, Duncan, Pinder, Angela, Pothuneedi, Sailaja, Rosser, Jonathan, Sanghera, Sumayer, Swift, Diane, Walker, Rachel, Bester, Delia, Cavanagh, Sarah, Cripps, Heather, Daniel, Harvey, Lynch, Julie, Paton, Alison, Pyke, Shirley, Scholefield, John, Whitworth, Helen, Bottrill, Fiona, Ramalingam, Ganesh, Webb, Stephen, Akerman, Nik, Antill, Philip, Bourner, Lynsey, Buckley, Sarah, Castle, Gail, Charles, Rob, Eggleston, Christopher, Foster, Rebecca, Gill, Satwant, Lindley, Kate, Lklouk, Mohamed, Lowery, Tracey, Martin, Oliver, Milne, David, O'Connor, Patrick, Ratcliffe, Andrew, Rose, Alastair, Smith, Annie, Varma, Sandeep, Ward, Jackie, Barcraft-Barnes, Helena, Camsooksai, Julie, Colvin, Carolyn, Reschreiter, Henrik, Tbaily, Lee, Venner, Nicola, Hamilton, Caroline, Kelly, Lewis, Toth-Tarsoly, Piroska, Dodsworth, Kerry, Foord, Denise, Gordon, Paul, Hawes, Elizabeth, Lamb, Nikki, Mouland, Johanna, Nightingale, Jeremy, Rose, Steve, Schrieber, Joe, Al'Amri, Khalid, Aladin, Hafiz, Arshad, Mohammed Asif, Barraclough, James, Bentley, Conor, Bergin, Colin, Carrera, Ronald, Clarkson, Aisling, Collins, Michelle, Cooper, Lauren, Denham, Samuel, Griffiths, Ewen, Ip, Peter, Jeyanthan, Somasundaram, Joory, Kavita, Kaur, Satwant, Marriott, Paul, Mitchell, Natalie, Nagaiah, Sukumar, Nilsson, Annette, Parekh, Nilesh, Pope, Martin, Seager, Joseph, Serag, Hosam, Tameem, Alifia, Thomas, Anna, Thunder, Joanne, Torrance, Andrew, Vohra, Ravinder, Arlo whitehouse, Wong, Tony, Blunt, Mark, Wong, Kate, Giles, Julian, Reed, Isabelle, Weller, Debbie, Bell, Gillian, Birch, Julie, Damant, Rose, Maiden, Jane, Mewies, Clare, Prince, Claire, Radford, Jane, Reynolds, Tim, Balain, Birender, Banerjee, Robin, Barnett, Andrew, Burston, Ben, Davies, Kirsty, Edwards, Jayne, Evans, Chris, Ford, David, Gallacher, Pete, Hill, Simon, Jaffray, David, Karlakki, Sudheer, Kelly, Cormac, Kennedy, Julia, Kiely, Nigel, Lewthwaite, Simon, Marquis, Chris, Ockendon, Matthew, Phillips, Stephen, Pickard, Simon, Richardson, James, Roach, Richard, Smith, Tony, Spencer-Jones, Richard, Steele, Niall, Steen, Julie, Van Liefland, Marck, White, Steve, Faulds, Matthew, Harris, Meredyth, Kelly, Carrie, Nicol, Scott, Pearson, Sally Anne, Chukkambotla, Srikanth, Andrew, Alyson, Attrill, Elizabeth, Campbell, Graham, Datson, Amanda, Fouracres, Anna, Graterol, Juan, Graves, Lynne, Hong, Bosun, Ishimaru, Alexander, Karthikeyan, Arvind, King, Helen, Lawson, Tom, Lee, Gregory, Lyons, Saoirse, Hall, Andrew Macalister, Mathoulin, Sophie, Mcintyre, Eilidh, Mclaughlin, Danny, Mulcahy, Kathleen, Paddle, Jonathan, Ratcliffe, Anna, Robbins, James, Sung, Weilin, Tayo, Adeoluwa, Trembath, Lisa, Venugopal, Suneetha, Walker, Robert, Wigmore, Geoffrey, Boereboom, Catherine, Downes, Charlotte, Humphries, Ryan, Melbourne, Susan, Smith, Coral, Tou, Samson, Ullah, Shafa, Batchelor, Nick, Boxall, Leigh, Broomby, Rupert, Deen, Tariq, Hellewell, Alistair, Helliwell, Laurence, Hutchings, Melanie, Hutchins, David, Keenan, Samantha, Mackie, Donna, Potter, Alison, Smith, Frances, Stone, Lucy, Thorpe, Kevin, Wassall, Richard, Woodgate, Andrew, Baillie, Shelley, Campbell, Tara, James, Sarah, King, Chris, Marques de Araujo, Daniela, Martin, Daniel, Morkane, Clare, Neely, Julia, Rajendram, Rajkumar, Burton, Megan, James, Kathryn, Keevil, Edward, Minik, Orsolya, Morgan, Jenna, Musgrave, Anna, Rajanna, Harish, Roberts, Tracey, Adamson, Michael, Jumbe, Sandra, Kendall, Jennie, Muthuswamy, Mohan Babu, Anderson, Charlotte, Cruikshanks, Andrew, Wrench, Ian, Zeidan, Lisa, Ardern, Diane, Harris, Benjamin, Hellstrom, Johanna, Martin, Jane, Thomas, Richard, Varsani, Nimu, Brown, Caroline Wrey, Docherty, Philip, Gillies, Michael, McGregor, Euan, Usher, Helen, Craig, Jayne, Smith, Andrew, Ahmad, Tahania, Bodger, Phoebe, Creary, Thais, Fowler, Alexander, Hewson, Russ, Ijuo, Eke, Jones, Timothy, Kantsedikas, Ilya, Lahiri, Sumitra, McLean, Aaron Lawson, Niebrzegowska, Edyta, Phull, Mandeep, Wang, Difei, Wickboldt, Nadine, Baldwin, Jacqueline, Doyle, Donna, Mcmullan, Sean, Oladapo, Michelle, Owen, Thomas, Williams, Alexandra, Daniel, Hull, Gregory, Peter, Husain, Tauqeer, Kirk-Bayley, Justin, Mathers, Edward, Montague, Laura, Harper, Mark, White, Stuart, Jack, James, Ridley, Carrie, Avis, Joanne, Cook, Tim, Dali-Kemmery, Lola, Kerslake, Ian, Lambourne, Victoria, Pearson, Annabel, Boyd, Christine, Callaghan, Mark, Lawson, Cathy, McCrossan, Roopa, Nesbitt, Vanessa, O'connor, Laura, Scott, Julia, Sinclair, Rhona, Farid, Nahla, Morgese, Ciro, Bhatia, Kailash, Karmarkar, Swati, Ahmed, Jamil, Branagan, Graham, Hutton, Monica, Swain, Andrew, Brookes, Jamie, Cornell, Jonathan, Dolan, Rachael, Hulme, Jonathan, Jansen van Vuuren, Amanda, Jowitt, Tom, Kalashetty, Gunasheela, Lloyd, Fran, Patel, Kiran, Sherwood, Nicholas, Brown, Lynne, Chandler, Ben, Deighton, Kerry, Emma, Temlett, Haunch, Kirsty, Cheeseman, Michelle, Dent, Kathy, Garg, Sanjeev, Gray, Carol, Hood, Marion, Jones, Dawn, Juj, Joanne, Rao, Roshan, Walker, Tara, Al Anizi, Mashel, Cheah, Clarissa, Cheing, Yushio, Coutinho, Francisco, Gondo, Prisca, Hadebe, Bernard, Hove, Mazvangu Onie, Ahamed khader, Krishnachetty, Bobby, Rhodes, Karen, Sokhi, Jagdish, Baker, Katie-Anne, Bertram, Wendy, Looseley, Alex, Mouton, Ronelle, Hanna, George, Arnold, Glenn, Arya, Shobhit, Balfoussia, Danai, Baxter, Linden, Harris, James, Jones, Craig, Knaggs, Alison, Markar, Sheraz, Perera, Anisha, Scott, Alasdair, Shida, Asako, Sirha, Ravneet, Wright, Sally, Frost, Victoria, Gray, Catherine, Andrews, Emma, Arrandale, Lindsay, Barrett, Stephen, Cifra, Elna, Cooper, Mariese, Dragnea, Dragos, Elna, Cifra, Maclean, Jennifer, Meier, Sonja, Milliken, Donald, Munns, Christopher, Ratanshi, Nadir, Ramessur, Suneil, Salvana, Abegail, Watson, Anthony, Ali, Hani, Campbell, Gill, Critchley, Rebecca, Endersby, Simon, Hicks, Catherine, Liddle, Alison, Pass, Marc, Ritchie, Charlotte, Thomas, Charlotte, Too, Lingxi, Welsh, Sarah, Gill, Talvinder, Johnson, Joanne, Reed, Joanne, Davis, Edward, Papadopoullos, Sam, Attwood, Clare, Biffen, Andrew, Boulton, Kerenza, Gray, Sophie, Hay, David, Mills, Sarah, Montgomery, Jane, Riddell, Rory, Simpson, James, Bhardwaj, Neeraj, Paul, Elaine, Uwubamwen, Nosakhare, Alexander, Maini, Arrich, James, Arumugam, Swarna, Blackwood, Douglas, Boggiano, Victoria, Brown, Robyn, Chan, Yik Lam, Chatterjee, Devnandan, Chhabra, Ashok, Christian, Rachel, Costelloe, Hannah, Matthewman, Madeline Coxwell, Dalton, Emma, Darko, Julia, Davari, Maria, Dave, Tejal, Deacon, Matthew, Deepak, Shantal, Edmond, Holly, Ellis, Jessica, El-Sayed, Ahmed, Eneje, Philip, English, Rose, Ewe, Renee, Foers, William, Franklin, John, Gallego, Laura, Garrett, Emily, Goldberg, Olivia, Goss, Harry, Greaves, Rosanna, Harris, Rudy, Hennings, Charles, Jones, Eleanor, Kamali, Nelson, Kokkinos, Naomi, Lewis, Carys, Lignos, Leda, Malgapo, Evaleen Victoria, Malik, Rizwana, Milne, Andrew, Mulligan, John-Patrick, Nicklin, Philippa, Palipane, Natasha, Parsons, Thomas, Piper, Rebecca, Prakash, Rohan, Ramesh, Byron, Rasip, Sarah, Reading, Jacob, Rela, Mariam, Reyes, Anna, Stephens, Robert, Rooms, Martin, Shah, Karishma, Simons, Henry, Solanki, Shalil, Spowart, Emma, Stevens, Amy, Thomas, Christopher, Waggett, Helena, Yassaee, Arrash, Kennedy, Anthony, Scott, Sara, Somanath, Sameer, Berg, Andrew, Hernandez, Miguel, Nanda, Rajesh, Tank, Ghanshyambhai, Wilson, Natalie, Wilson, Debbie, Al-Soudaine, Yassr, Baldwin, Matthew, Cornish, Julie, Davies, Zoe, Davies, Leigh, Edwards, Marc, Frewer, Natasha, Gallard, Sian, Glasbey, James, Harries, Rhiannon, Hopkins, Luke, Kim, Taeyang, Koompirochana, Vilavan, Lawson, Simon, Lewis, Megan, Makzal, Zaid, Scourfield, Sarah, Ahmad, Yousra, Bates, Sarah, Blackwell, Clare, Bryant, Helen, Collins, Hannah, Coulter, Suzanne, Cruickshank, Ross, Daniel, Sonya, Daubeny, Thomas, Edwards, Mark, Golder, Kim, Hawkins, Lesley, Helen, Bryant, Hinxman, Honor, Levett, Denny, Salmon, Karen, Seaward, Leanne, Skinner, Ben, Tyrell, Bryony, Wadams, Beverley, Walsgrove, Joseph, Dickson, Jane, Constantin, Kathryn, Karen, Markwell, O'Brien, Peter, O'Donohoe, Lynn, Payne, Hannah, Sundayi, Saul, Walker, Elaine, Brooke, Jenny, Cardy, Jon, Humphreys, Sally, Kessack, Laura, Kubitzek, Christiane, Kumar, Suhas, Cotterill, Donna, Hodzovic, Emil, Hosdurga, Gurunath, Miles, Edward, Saunders, Glenn, Campbell, Marta, Chan, Peter, Jemmett, Kim, Raj, Ashok, Naik, Aditi, Oshowo, Ayo, Ramamoorthy, Rajarajan, Shah, Nimesh, Sylvan, Axel, Blyth, Katharine, Burtenshaw, Andrew, Freeman, David, Johnson, Emily, Lo, Philip, Martin, Terry, Plunkett, Emma, Wollaston, Julie, Allison, Joanna, Carroll, Christine, Craw, Nicholas, Craw, Sarah, Pitt-Kerby, Tressy, Rowland-Axe, Rebecca, Spurdle, Katie, McDonald, Andrew, Simon, Davies, Sinha, Vivek, Smith, Thomas, Banner-Goodspeed, Valerie, Boone, Myles, Campbell, Kathleen, Lu, Fengxin, Scannell, Joseph, Sobol, Julia, Balajonda, Naraida, Clemmons, Karen, Conde, Carlos, Elgasim, Magdi, Funk, Bonita, Hall, Roger, Hopkins, Thomas, Olaleye, Omowunmi, Omer, Omer, Pender, Michelle, Porto, Angelo, Stevens, Alice, Waweru, Peter, Yeh, Erlinda, Bodansky, Daniella, Evans, Adam, Kleopoulos, Steven, Maril, Robert, Mathney, Edward, Sanchez, Angela, Tinuoye, Elizabeth, Bateman, Brian, Eng, Kristen, Jiang, Ning, Ladha, Karim, Needleman, Joseph, Chen, Lee-lynn, Lane, Rondall, Robinowitz, David, Ghushe, Neil, Irshad, Mariam, O'Connor, John, Patel, Samir, Takemoto, Steven, Wallace, Art, Mazzeffi, Michael, Rock, Peter, Wallace, Karin, Zhu, Xiaomao, Chua, Pandora, Mattera, Matthew, Sharar, Rebecca, Thilen, Stephan, Treggiari, Miriam, Morgan, Angela, Sofjan, Iwan, Subramaniam, Kathirvel, Avidan, Michael, Maybrier, Hannah, Muench, Maxwell, Wildes, Troy, Abbott, T.E.F., Ahmad, T., Phull, M.K., Fowler, A.J., Hewson, R., Biccard, B.M., Chew, M.S., Gillies, M., and Pearse, R.M.
Published: 2018
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19. 1330 Gut microbiome dysbiosis promotes immune suppression and lung cancer development

Author: Rahal, Zahraa, primary, Liu, Yuejiang, additional, Peng, Fuduan, additional, Ross, Matthew, additional, Sinjab, Ansam, additional, Liang, Ke, additional, Feng, Jiping, additional, Chukwuocha, Chidera, additional, Sharma, Manvi, additional, Tang, Elizabeth, additional, Abaya, Camille, additional, Petrosino, Joseph, additional, Fujimoto, Junya, additional, Moghaddam, Seyed Javad, additional, Wang, Linghua, additional, Hoffman, Kristi, additional, and Kadara, Humam, additional
Published: 2023
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20. Abstract 113: An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Author: Han, Guangchun, primary, Sinjab, Ansam, additional, Treekitkarnmongkol, Warapen, additional, Rahal, Zahraa, additional, Liu, Yuejiang, additional, Serrano, Alejandra G., additional, Feng, Jiping, additional, Liang, Ke, additional, Khan, Khaja, additional, Lu, Wei, additional, Hernandez, Sharia, additional, Cao, Xuanye, additional, Dai, Enyu, additional, Liu, Yunhe, additional, Pei, Guangsheng, additional, Hu, Jian, additional, Bolanos, Lorena I. Gomez, additional, Parra, Edwin R., additional, Cascone, Tina, additional, Sepesi, Boris, additional, Moghaddam, Seyed Javad, additional, Scheet, Paul, additional, Negrao, Marcelo V., additional, Heymach, John V., additional, Li, Mingyao, additional, Chen, Jichao, additional, Dubinett, Steven M., additional, Fujimoto, Junya, additional, Solis, Luisa M., additional, Wistuba, Ignacio I., additional, Stevenson, Christopher S., additional, Spira, Avrum, additional, Wang, Linghua, additional, and Kadara, Humam, additional
Published: 2023
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21. Abstract 2883: Gut microbiome dysbiosis promotes immune suppression and lung cancer development

Author: Rahal, Zahraa, primary, Peng, Fuduan, additional, Liu, Yuejiang, additional, Ross, Matthew C., additional, Sinjab, Ansam, additional, Liang, Ke, additional, Feng, Jiping, additional, Chukwuocha, Chidera O., additional, Sharma, Manvi, additional, Tang, Elizabeth, additional, Abaya, Camille, additional, Petrosino, Joseph, additional, Fujimoto, Junya, additional, Moghaddam, Seyed Javad, additional, Wang, Linghua, additional, Hoffman, Kristi L., additional, and Kadara, Humam, additional
Published: 2023
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22. Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries

Author: Kahan, Brennan C., Koulenti, Desponia, Arvaniti, Kostoula, Beavis, Vanessa, Campbell, Douglas, Chan, Matthew, Moreno, Rui, Pearse, Rupert M., Pearse, Rupert M., Beattie, Scott, Clavien, Pierre-Alain, Demartines, Nicolas, Fleisher, Lee A., Grocott, Mike, Haddow, James, Hoeft, Andreas, Holt, Peter, Moreno, Rui, Pritchard, Naomi, Rhodes, Andrew, Wijeysundera, Duminda, Wilson, Matt, Ahmed, Tahania, Everingham, Kirsty, Hewson, Russell, Januszewska, Marta, Pearse, Rupert M., Phull, Mandeep-Kaur, Halliwell, Richard, Shulman, Mark, Myles, Paul, Schmid, Werner, Hiesmayr, Michael, Wouters, Patrick, de Hert, Stefan, Lobo, Suzana, Beattie, Scott, Wijeysundera, Duminda, Fang, Xiangming, Rasmussen, Lars, Futier, Emmanuel, Biais, Matthieu, Venara, Aurélien, Slim, Karem, Sander, Michael, Koulenti, Despoina, Arvaniti, Kostoula, Chan, Mathew, Kulkarni, Atul, Chandra, Susilo, Tantri, Aida, Geddoa, Emad, Abbas, Muntadhar, Della Rocca, Giorgio, Sivasakthi, Datin, Mansor, Marzida, Luna, Pastor, Bouwman, Arthur, Buhre, Wolfgang, Beavis, Vanessa, Campbell, Douglas, Short, Tim, Osinaike, Tunde, Matos, Ricardo, Grigoras, Ioana, Kirov, Mikhail, Protsenko, Denis, Biccard, Bruce, Aldecoa, Cesar, Chew, Michelle, Hofer, Christoph, Hubner, Martin, Ditai, James, Szakmany, Tamas, Fleisher, Lee, Ferguson, Marissa, MacMahon, Michael, Shulman, Mark, Cherian, Ritchie, Currow, Helen, Kanathiban, Kathirgamanathan, Gillespie, David, Pathmanathan, Edward, Phillips, Katherine, Reynolds, Jenifer, Rowley, Joanne, Douglas, Jeanene, Kerridge, Ross, Currow, Helen, Garg, Sameer, Bennett, Michael, Jain, Megha, Alcock, David, Terblanche, Nico, Cotter, Rochelle, Leslie, Kate, Stewart, Marcelle, Zingerle, Nicolette, Clyde, Antony, Hambidge, Oliver, Rehak, Adam, Cotterell, Sharon, Binh QuanHuynh, Wilson, McCulloch, Timothy, Ben-Menachem, Erez, Egan, Thomas, Cope, Jennifer, Halliwell, Richard, Fellinger, Paul, Haselberger, Simone, Holaubek, Caroline, Lichtenegger, Paul, Scherz, Florian, Schmid, Werner, Hoffer, Franz, Cakova, Veronika, Eichwalder, Andreas, Fischbach, Norbert, Klug, Reinhold, Schneider, Elisabeth, Vesely, Martin, Wickenhauser, Reinhart, Grubmueller, Karl Gernot, Leitgeb, Marion, Lang, Friedrich, Toro, Nancy, Bauer, Marlene, Laengle, Friedrich, Mayrhofer, Thomas, Buerkle, Christian, Forstner, Karin, Germann, Reinhard, Rinoesl, Harald, Schindler, Elke, Trampitsch, Ernst, Fritsch, Gerhard, Szabo, Christian, Bidgoli, Jawad, Verdoodt, Hans, Forget, Patrice, Kahn, David, Lois, Fernande, Momeni, Mona, Prégardien, Caroline, Pospiech, Audrey, Steyaert, Arnaud, Veevaete, Laurent, De Kegel, Dirk, De Jongh, Karen, Foubert, Luc, Smitz, Carine, Vercauteren, Marcel, Poelaert, Jan, Van Mossevelde, Veerle, Abeloos, Jacques, Bouchez, Stefaan, Coppens, Marc, De Baerdemaeker, Luc, Deblaere, Isabel, De Bruyne, Ann, De Hert, Stefan, Fonck, Kristine, Heyse, Bjorn, Jacobs, Tom, Lapage, Koen, Moerman, Anneliese, Neckebroek, Martine, Parashchanka, Aliaksandra, Roels, Nathalie, Van Den Eynde, Nancy, Vandenheuvel, Michael, Van Limmen, Jurgen, Vanluchene, Ann, Vanpeteghem, Caroline, Wouters, Patrick, Wyffels, Piet, Huygens, Christel, Vandenbempt, Punitha, Van de Velde, Marc, Dylst, Dimitri, Janssen, Bruno, Schreurs, Evelien, Aleixo, Fábia Berganton, Candido, Keulle, Dias Batista, Hugo, Guimarães, Mario, Guizeline, Jaqueline, Hoffmann, João, Lobo, Suzana M., Lobo, Francisco Ricardo, Nascimento, Vinícius, Nishiyama, Katia, Pazetto, Lucas, Souza, Daniela, Souza Rodrigues, Rodrigo, Vilela dos Santos, Ana Maria, Jardim, Jaquelline, Silva, Joao, do NascimentoJunior, Paulo, Baio, Thalissa Hermínia, Pereira de Castro, Gabriel Isaac, Watanabe Oliveira, Henri Roger, Amendola, Cristina Prata, Cardoso, Gutemberg, Ortega, Daniela, Brotto, Ana Flavia, De Oliveira, Mirella Cristine, Réa-Neto, Álvaro, Dias, Fernando, Azambuja, Pedro, Knibel, Marcos Freitas, Martins, Antonio, Almeida, William, Neto, Calim Neder, Tardelli, Maria Angela, Caser, Eliana, Machado, Marcio, Aguzzoli, Crisitiano, Baldisserotto, Sérgio, Beck Tabajara, Fernanda, Bettega, Fernanda, Rodrigues Júnior, La Hore Correa, de Gasperi, Julia, Faina, Lais, Nolasco, Marcos Farias, da Costa Fischer, Bruna Ferreira, de Campos Ferreira, Mariana Fosch, Hartmann, Cristina, Kliemann, Marta, Ribeiro, Gustavo LuisHubert, Fraga, Julia Merladete, Netto, Thiago Motta, Pozza, Laura Valduga, Wendling, Paulo Rafael, Azevedo, Caroline, Garcia, Juliana, Lopes, Marcel, Maia, Bernardo, Maselli, Paula, Melo, Ralph, Mendes, Weslley, Neves, Matheus, Ney, Jacqueline, Piras, Claudio, Applewhaite, Christopher, Carr, Adrienne, Chow, Lorraine, Duttchen, Kaylene, Foglia, Julena, Greene, Michael, Hinther, Ashley, Houston, Kendra, McCormick, Thomas Jared, Mikhayel, Jennifer, Montasser, Sam, Ragan, Alex, Suen, Andrew, Woolsey, Adrianna, Yu, Hai Chuan, Funk, Duane, Kowalski, Stephen, Legaspi, Regina, McDonald, Heather, Siddiqui, Faisal, Pridham, Jeremy, Rowe, Bernadette, Sampson, Sonia, Thiessen, Barton, Zbitnew, Geoff, Bernard, Andre, George, Ronald, Jones, Philip, Moor, Rita, Siddiqui, Naveed, Wolfer, Alexandra, Tran, Diem, Winch, Denyse, Dobson, Gary, Hinther, Ashley, McCormick, Thomas, Montasser, Osama, Suen, Andrew, Woolsey, Adrianna, Bernard, Andre, George, Ronald, Hall, Richard, Bernard, Andre, George, Ronald, Hall, Richard, Applewhaite, Christopher, Baghirzada, Leyla, McCormick, Thomas Jared, Suen, Andrew, Dai, Si Yuan, Hare, Gregory, Lee, Esther, Shastri, Uma, Tsui, Albert, Yagnik, Anmol, Alvares, Danielle, Choi, Stephen, Dwyer, Heather, Flores, Kathrina, McCartney, Colin, Somascanthan, Priya, Beattie, Scott, Carroll, Jo, Pazmino-Canizares, Janneth, Wijeysundera, Duminda, Wolfer, Alexandra, Ami, Noam, Chan, Vincent, Perlas, Anahi, Argue, Ruth, Lavis, Katie, Mayson, Kelly, Cao, Ying, Gao, Hong, hu, tingju, Lv, Jie, Yang, Jian, Yang, Yang, Zhong, Yi, Zhou, Jing, Zou, Xiaohua, He, Miao, Li, Xiaoying, Luo, Dihuan, Wang, Haiying, Yu, Tian, Chen, Liyong, Wang, Lijun, Cai, Yunfei, Cao, Zhongming, Li, Yanling, Lian, Jiaxin, Sun, Haiyun, Wang, Sheng, Wang, Zhipeng, Wang, Kenru, Zhu, Yi, Du, Xindan, Fan, Hao, Fu, Yunbin, Huang, Lixia, Huang, Yanming, Hwan, Haifang, Luo, Hong, Qu, Pi-Sheng, Tao, Fan, Wang, Zhen, Wang, Guoxiang, Wang, Shun, Zhang, Yan, Zhang, Xiaolin, Chen, Chao, Wang, Weixing, Liu, Zhengyuan, Fan, Lihua, Tang, Jing, Chen, Yijun, Chen, Yongjie, Han, Yangyang, Huang, Changshun, Liang, Guojin, Shen, Jing, Wang, Jun, Yang, Qiuhong, Zhen, Jungang, Zhou, Haidong, Chen, Junping, Chen, Zhang, Li, Xiaoyu, Meng, Bo, Ye, Haiwang, Zhang, Xiaoyan, Bi, Yanbing, Cao, Jianqiao, Guo, Fengying, Lin, Hong, Liu, Yang, Lv, Meng, Shi, Pengcai, Song, Xiumei, Sun, Chuanyu, Sun, Yongtao, Wang, Yuelan, Wang, Shenhui, Zhang, Min, Chen, Rong, Hou, Jiabao, Leng, Yan, Meng, Qing-tao, Qian, Li, Shen, Zi-ying, Xia, Zhong-yuan, Xue, Rui, Zhang, Yuan, Zhao, Bo, Zhou, Xian-jin, Chen, Qiang, Guo, Huinan, Guo, Yongqing, Qi, Yuehong, Wang, Zhi, Wei, Jianfeng, Zhang, Weiwei, Zheng, Lina, Bao, Qi, Chen, Yaqiu, Chen, Yijiao, Fei, Yue, Hu, Nianqiang, Hu, Xuming, Lei, Min, Li, Xiaoqin, Lv, Xiaocui, Lv, Jie, Miao, Fangfang, Ouyang, Lingling, Qian, Lu, Shen, Conyu, Sun, Yu, Wang, Yuting, Wang, Dong, Wu, Chao, Xu, Liyuan, Yuan, Jiaqi, Zhang, Lina, Zhang, Huan, Zhang, Yapping, Zhao, Jinning, Zhao, Chong, Zhao, Lei, Zheng, Tianzhao, Zhou, Dachun, Zhou, Haiyan, Zhou, Ce, Lu, Kaizhi, Zhao, Ting, He, Changlin, Chen, Hong, Chen, Shasha, Cheng, Baoli, He, Jie, Jin, Lin, Li, Caixia, Li, Hui, Pan, Yuanming, Shi, Yugang, Wen, Xiao Hong, Wu, Shuijing, Xie, Guohao, Zhang, Kai, Zhao, Bing, Lu, Xianfu, Chen, Feifei, Liang, Qisheng, Lin, Xuewu, Ling, Yunzhi, Liu, Gang, Tao, Jing, Yang, Lu, Zhou, Jialong, Chen, Fumei, Feng, Yunlin, Hou, Benchao, Lin, Jiamei, Liu, Mei, Luo, Foquan, Shi, Xiaoyun, Xiong, Yingfen, Xu, Lin, Yang, Shuangjia, Zhang, Qin, Zhang, Huaigen, Zhao, Weihong, Zhao, Weilu, Bai, Yun, Chen, Linbi, Chen, Sijia, Dai, Qinxue, Geng, Wujun, Han, Kunyuan, He, Xin, Huang, Luping, Ji, Binbin, Jia, Danyun, Jin, Shenhui, Li, Qianjun, Liang, Dongdong, Luo, Shan, Lwang, Lulu, Mo, Yunchang, Pan, Yuanyuan, Qi, Xinyu, Qian, Meizi, Qin, Jinling, Ren, Yelong, Shi, Yiyi, Wang, Junlu, Wang, Junkai, Wang, Leilei, Xie, Junjie, Yan, Yixiu, Yao, Yurui, Zhang, Mingxiao, Zhao, Jiashi, Zhuang, Xiuxiu, Ai, Yanqiu, Du, Fang, He, Long, Huang, Ledan, Li, Zhisong, Li, Huijuan, Li, Yetong, Li, Liwei, Meng, Su, Yuan, Yazhuo, Zhang, Enman, Zhang, Jie, Zhao, Shuna, Ji, Zhenrong, Pei, Ling, Wang, Li, Chen, Chen, Dong, Beibei, Li, Jing, Miao, Ziqiang, Mu, Hongying, Qin, Chao, Su, Lin, Wen, Zhiting, Xie, Keliang, Yu, Yonghao, Yuan, Fang, Hu, Xianwen, Zhang, Ye, Xiao, Wangpin, Zhu, Zhipeng, Dai, Qingqing, Fu, Kaiwen, Hu, Rong, Hu, Xiaolan, Huang, Song, Li, Yaqi, Liang, Yingping, Yu, Shuchun, Guo, Zheng, Jing, Yan, Tang, Na, Wu, Jie, Yuan, Dajiang, Zhang, Ruilin, Zhao, Xiaoying, Li, Yuhong, Bai, Hui-Ping, Liu, Chun-Xiao, Liu, Fei-Fei, Ren, Wei, Wang, Xiu-Li, Xu, Guan-Jie, Hu, Na, Li, Bo, Ou, Yangwen, Tang, Yongzhong, Yao, Shanglong, Zhang, Shihai, Kong, Cui-Cui, Liu, Bei, Wang, Tianlong, Xiao, Wei, Lu, Bo, Xia, Yanfei, Zhou, Jiali, Cai, Fang, Chen, Pushan, Hu, Shuangfei, Wang, Hongfa, Wu, Jie, Xu, Qiong, Hu, Liu, Jing, Liang, Li, Jing, Li, Bin, Liu, Qiang, Liu, Yuejiang, Lu, Xinjian, Peng, Zhen Dan, Qiu, Xiaodong, Ren, Quan, Tong, Youliang, Wang, Zhen, Wang, Jin, Wen, Yazhou, Wu, Qiong, Xia, Jiangyan, Xie, Jue, Xiong, Xiapei, Xu, Shixia, Yang, Tianqin, Ye, Hui, Yin, Ning, Yuan, Jing, Zeng, Qiuting, Zhang, Baoling, Zheng, Kang, Cang, Jing, Chen, Shiyu, Du, Fang, Fan, Yu, Fu, Shuying, Ge, Xiaodong, Guo, Baolei, Huang, Wenhui, Jiang, Linghui, Jiang, Xinmei, Jin, Lin, Liu, Yi, Pan, Yan, Ren, Yun, Shan, Qi, Wang, Jiaxing, Wang, Fei, Wu, Chi, Zhang, Xiaoguang, Christiansen, Ida Cecilie, Granum, Simon Nørgaard, Rasmussen, Bodil Steen, Daugaard, Morten, Gambhir, Rajiv, Steingrímsdóttir, Guðný Erla, Jensen-Gadegaard, Peter, Olsen, Karsten Skovgaard, Siegel, Hanna, Zwicky Eskildsen, Katrine, Gätke, Mona Ring, Wibrandt, Ida, Heintzelmann, Simon Bisgaard, Lange, Kai Henrik Wiborg, Lundsgaard, Rune Sarauw, Amstrup-Hansen, Louise, Hovendal, Claus, Larsen, Michael, Lenstrup, Mette, Kobborg, Tina, Larsen, Jens Rolighed, Pedersen, Anette Barbre, Larsen, Jens Rolighed, Smith, Søren Hübertz, Oestervig, Rebecca Monett, Rasmussen, Lars, Afshari, Arash, Andersen, Cheme, Ekelund, Kim, Secher, Erik Lilja, Brandsborg, Birgitte, Beloeil, Helene, Lasocki, Sigismond, Venara, Aurélien, Biais, Matthieu, Ouattara, Alexandre, Sineus, Marlene, Molliex, Serge, Legouge, Marie Lim, Wallet, Florent, Tesniere, Antoine, Gaudin, Christophe, Lehur, Paul, Forsans, Emma, de Rudnicki, Stéphane, Serra Maudet, Valerie, Mutter, Didier, Sojod, Ghassan, Ouaissi, Mehdi, Regimbeau, Jean-Marc, Futier, Emmanuel, Desbordes, Jacques, Comptaer, Nicolas, elManser, Diae, Ethgen, Sabine, Lebuffe, Gilles, Auer, Patrick, Härtl, Christine, Deja, Maria, Legashov, Kirill, Sonnemann, Susanne, Wiegand-Loehnert, Carola, Falk, Elke, Habicher, Marit, Angermair, Stefan, Laetsch, Beatrix, Schmidt, Katrin, Sonnemann, Susanne, Von Heymann, Christian, Ramminger, Axel, Jelschen, Florian, Pabel, Svenja, Weyland, Andreas, Czeslick, Elke, Gille, Jochen, Malcharek, Michael, Sablotzki, Armin, Lueke, Katharina, Wetzel, Peter, Weimann, Joerg, Lenhart, Franz-Peter, Reichle, Florian, Schirmer, Frederike, Hüppe, Michael, Klotz, Karl, Nau, Carla, Schön, Julika, Mencke, Thomas, Wasmund, Christina, Bankewitz, Carla, Baumgarten, Georg, Fleischer, Andreas, Guttenthaler, Vera, Hack, Yvonne, Hoeft, Andreas, Kirchgaessner, Katharina, Männer, Olja, Schurig-Urbaniak, Marlen, Struck, Rafael, vanZyl, Rebekka, Wittmann, Maria, Goebel, Ulrich, Harris, Sarah, Veit, Siegfried, Andreadaki, Evangelia, Souri, Flora, Katsiadramis, Ioannis, Skoufi, Anthi, Vasileiou, Maria, Aimoniotou-Georgiou, Eleni, Katsourakis, Anastasios, Veroniki, Fotini, Vlachogianni, Glyceria, Petra, Konstantina, Chlorou, Dimitra, Oloktsidou, Eirini, Ourailoglou, Vasileios, Papapostolou, Konstantinos, Tsaousi, Georgia, Daikou, Panagoula, Dedemadi, Georgia, Kalaitzopoulos, Ioannis, Loumpias, Christos, Bristogiannis, Sotirios, Dafnios, Nikolaos, Gkiokas, Georgios, Kontis, Elissaios, Kozompoli, Dimitra, Papailia, Aspasia, Theodosopoulos, Theodosios, Bizios, Christol, Koutsikou, Anastasia, Moustaka, Aleaxandra, Plaitakis, Ioannis, Armaganidis, Apostolos, Christodoulopoulou, Theodora, Lignos, Mihail, Theodorakopoulou, Maria, Asimakos, Andreas, Ischaki, Eleni, Tsagkaraki, Angeliki, Zakynthinos, Spyros, Antoniadou, Eleni, Koutelidakis, Ioannis, Lathyris, Dimitrios, Pozidou, Irene, Voloudakis, Nikolaos, Dalamagka, Maria, Gkonezou, Elena, Chronis, Christos, Manolakaki, Dimitra, Mosxogiannidis, Dimitris, Slepova, Tatiana, Tsakiridou, Isaia–sissy, Lampiri, Claire Lampiri, Vachlioti, Anastasia Vachlioti, Panagiotakis, Christos Panagiotakis, Sfyras, Dimitrios Sfyras, Tsimpoukas, FotiosTsimpoukas, Tsirogianni, Athanasia, Axioti, Elena, Filippopoulos, Andreas, Kalliafa, Elli, Kassavetis, George, Katralis, Petros, Komnos, Ioannis, Pilichos, Georgios, Ravani, Ifigenia, Totis, Antonis, Apagaki, Eymorfia, Efthymiadi, Andromachi, Kampagiannis, Nikolaos, Paraforou, Theoniki, Tsioka, Agoritsa, Georgiou, Georgios, Vakalos, Aristeidis, Bairaktari, Aggeliki, Charitos, Efthimios, Markou, George, Niforopoulou, Panagiota, Papakonstantinou, Nikolaos, Tsigou, Evdoxia, Xifara, Archontoula, Zoulamoglou, Menelaos, Gkioni, Panagiota, Karatzas, Stylianos, Kyparissi, Aikaterini, Mainas, Efstratios, Papapanagiotou, Ioannis, Papavasilopoulou, Theonymfi, Fragandreas, George, Georgopoulou, Eleni, Katsika, Eleni, Psarras, Kyriakos, Synekidou, Eirini, Verroiotou, Maria, Vetsiou, Evangelia, Zaimi, Donika, Anagnou, Athina, Apostolou, Konstantinos, Melissopoulou, Theodora, Rozenberg, Theophilos, Tsigris, Christos, Boutsikos, Georgios, Kalles, Vasileios, Kotsalas, Nikolaos, Lavdaiou, Christina, Paikou, Fotini, Panagou, Georgia-Laura, Spring, Anna, Arvaniti, Kostoula, Botis, Ioannis, Drimala, Maria, Georgakakis, Georgios, Kiourtzieva, Ellada, Ntouma, Panagiota, Prionas, Apostolos, Xouplidis, Kyriakos, Dalampini, Eleftheria, Giannaki, Chrysavgi, Iasonidou, Christina, Ioannidis, Orestis, Lavrentieva, Athina, Lavrentieva, Athena, Papageorgiou, George, Kokkinoy, Maria, Stafylaraki, Maria, Gaitanakis, Stylianos, Karydakis, Periclis, Paltoglou, Josef, Ponireas, Panagiotis, Chaloulis, Panagiotis, Provatidis, Athanasios, Sousana, Anisoglou, Gardikou, Varvara Vanessa, Konstantivelli, Maria, Lataniotou, Olga, Lisari, Elisavet, Margaroni, Maria, Stamatiou, Konstantinos, Nikolaidis, Edouardos, Pnevmatikos, Ioannis, Sertaridou, Eleni, Andreou, Alexandros, Arkalaki, Eleni, Athanasakis, Elias, Chaniotaki, Fotini, Chatzimichali, Aikaterini, Christofaki, Maria, Dermitzaki, Despina, Fiorentza, Klara, Frantzeskos, Georgios, Geromarkaki, Elisavet, Kafkalaki, Kalliopi, Kalogridaki, Marina, Karydi, Konstyllia, Kokkini, Sofia, Kougentakis, Georgios, Lefaki, Tatiana, Lilitsis, Emmanouhl, Makatounaki, Aikaterini, Malliotakis, Polychronis, Michelakis, Dimosthenis, Neonaki, Maria, Nyktari, Vasileia, Palikyra, Iliana, Papadakis, Eleftherios, Papaioannou, Alexandra, Sfakianakis, Konstantinos, Sgouraki, Maria, Souvatzis, Xenia, Spartinou, Anastasia, Stefanidou, Nefeli, Syrogianni, Paulina, Tsagkaraki, Georgia, Arnaoutoglou, Elena, Arnaoutoglou, Christina, Bali, Christina, Bouris, Vasilios, Doumos, Rodamanthos, Gkini, Konstantia-Paraskevi, Kapaktsi, Clio, Koulouras, Vasilios, Lena, Arian, Lepida, Dimitra, Michos, Evangelos, Papadopoulos, Dimitrios, Paschopoulos, Minas, Rompou, Vaia Aliki, Siouti, Ioanna, Tsampalas, Stavros, Ververidou, Ourania, Zilis, Georgios, Charlalampidoy, Alexandra, Christodoulidis, Gregory, Flossos, Andreas, Stamoulis, Konstantinos, Chan, Matthew, Tsang, Man Shing Caleb, Tsang, Man Shing, Lai, Man Ling, Yip, Chi Pang, Chan, Hey Man Heymans, Law, Bassanio, Li, Wing Sze, Chu, Hiu Man, Koo, Emily Gar Yee, Lam, Chi Cheong Joe, Cheng, Ka Ho, Chan, Matthew, 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Ramona–Mihaela, Sandu, Gigel, Bruma, Dana, Corneci, Dan, Dutu, Madalina, Krepil, Adriana, Copaciu, Elena, Dumitrascu, Clementina Oana, Jemna, Ramona, Mihaescu, Florentina, Petre, Raluca, Tudor, Cristina, Ursache, Elena, Kulikov, Alexander, Lubnin, Andrey, Grigoryev, Evgeny, Pugachev, Stanislav, Protsenko, Denis, Tolmasov, Alexander, Hussain, Ayyaz, Ilyina, Yana, Kirov, Mikhail, Roshchina, Anna, Iurin, Aleksandr, Chazova, Elena, Dunay, Artem, Karelov, Alexey, Khvedelidze, Irina, Voldaeva, Olga, Belskiy, Vladislav, Dzhamullaev, Parvin, Grishkowez, Elena, Kretov, Vladimir, Levin, Valeriy, Molkov, Aleksandr, Puzanov, Sergey, Samoilenko, Aleksandr, Tchekulaev, Aleksandr, Tulupova, Valentina, Utkin, Ivan, Allorto, Nikki Leigh, Bishop, David Gray, Builu, Pierre Monji, Cairns, Carel, Dasrath, Ashish, de Wet, Jacques, denHoedt, Marielle, Grey, Ben, Hayes, Morgan Philip, Küsel, Belinda Senta, Shangase, Nomcebo, Wise, Robert, Cacala, Sharon, Farina, Zane, Govindasamy, Vishendran, Kruse, 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Amir, Shekar, Priya, Harden, Catherine, Hollands, Heidi, King, Angela, March, Linda, Minto, Gary, Patrick, Abigail, Waugh, Darren, Kumara, Paramesh, Simeson, Karen, Yarwood, Jamie, Browning, Julie, Hatton, Jonathan, Julian, Howes, Mitra, Atideb, Newton, Maria, Pernu, Pawan, Wilson, Alison, Commey, Thelma, Foot, Helen, Glover, Lyn, Gupta, Ajay, Lancaster, Nicola, Levin, Jill, Mackenzie, Felicity, Mestanza, Claire, Nofal, Emma, Pout, Lauren, Varden, Rosanna, Wild, Jonathan, Jones, Stephanie, Moreton, Sarah, Pulletz, Mark, Davies, Charlotte, Martin, Matthew, Thomas, Sian, Burns, Karen, McArthur, Carol, Patel, Panna, Lau, Gary, Rich, Natalie, Davis, Fiona, Self, James, Lyons, Rachel, Port, Beth, Prout, Rachel, Smith, Christopher, Adelaja, Yemi, Bennett, Victoria, Bidd, Heena, Dumitrescu, Alexandra, Murphy, Jacqui Fox, Keen, Abigail, Mguni, Nhlanhla, Ong, Cheng, Adams, George, Boshier, Piers, Brown, Richard, Butryn, Izabella, Chatterjee, Jayanta, Freethy, Alexander, Lockwood, Geoffrey, Tsakok, Maria, Tsiligiannis, Sophia, Peat, William, Stephenson, Lorraine, Bradburn, Mike, Pick, Sara, Cunha, Pedro, Olagbaiye, Olufemi, Tayeh, Salim, Abernethy, Caroline, Balasubramaniam, Madhu, Bennett, Rachael, Bolton, David, Martinson, Victoria, Naylor, Charde, Smith, Neil, Bell, Stephanie, Heather, Blaylock, Kushakovsky, Vlad, Alcock, Liam, Alexander, Hazel, Anderson, Colette, Baker, Paul, Brookes, Morag, Cawthorn, Louise, Cirstea, Emanuel, Colling, Kerry, Coulter, Ian, Das, Suparna, Haigh, Kathryn, Hamdan, Alhafidz, Hugill, Keith, Kottam, Lucksy, Lisseter, Emily, Mawdsley, Matthew, McGivern, Julie, Padala, Krishnaveni, Phelps, Victoria, kumar, Vineshykaa Ramesh, Stewart, Kirsten, Towse, Kayley, Tregonning, Julie, Vahedi, Ali, Walker, Alycon, Baines, Duncan, Bilolikar, Anjali, Chande, Shiv, Copley, Edward, Dunk, Nigel, Kulkarni, Raghavendra, Kumar, Pawan, Metodiev, Yavor, Ncomanzi, Dumisani, Raithatha, Bhavesh, Raymode, Parizade, Szafranski, Jan, Twohey, Linda, Watt, Philip, Weatherall, Lucie, Weatherill, J., Whitman, Zoe, Wighton, Elinor, Abayasinghe, Chamika, Chan, Alexander, Darwish, Sharif, Gill, James, Glasgow, Emma, Hadfield, Daniel, Harris, Clair, Kochhar, Arun, Mellis, Clare, Pool, Andrew, Riozzi, Paul, Selman, Andrew, Smith, Emma-Jane, Vele, Liana, Gercek, Yuksel, Guy, Kramer, Holden, Douglas, Watson, Nicholas, Whysall, Karen, Andreou, Prematie, Hales, Dawn, Thompson, Jonathan, Bowrey, Sarah, McDonald, Shara, Thompson, Jonathan, Gilmore, Jemma, Hills, Vicky, Kelly, Chan, Kelly, Sinead, Lloyd, Geraint, Abbott, Tom, Gall, Lewis, Torrance, Hew, Vivian, Mark, Berntsen, Emer, Nolan, Tracey, Turner, Angus, Vohra, Akbar, Brown, Andrew, Clark, Richard, Coughlan, Elaine, Daniel, Conway, Patvardhan, Chinmay, Pearson, Rachel, Predeep, Sheba, Saad, Hesham, Shanmugam, Mohanakrishnan, Varley, Simon, Vohra, Akbar, Wylie, Katharine, Cooper, Lucy, Makowski, Arystarch, Misztal, Beata, Moldovan, Eliza, Pegg, Claire, Donovan, Andrew, Foot, Jayne, Large, Simon, Claxton, Andrew, Netke, Bhagyashree, Armstrong, Richard, Calderwood, Claire, Kwok, Andy, Mohr, Otto, Oyeniyi, Peter, Patnaik, Lisa, Post, Benjamin, Ali, Sarah, Arshad, Homa, Baker, Gerard, Brenner, Laura, Brincat, Maximilian, Brunswicker, Annemarie, Cox, Hannah, Cozar, Octavian Ionut, Durst, Alexander, Fengas, Lior, Flatt, Jim, Glister, Georgina, Narwani, Vishal, Photi, Evangelos, Rankin, Adeline, Rosbergen, Melissa, Tan, Mark, Beaton, Ceri, Horn, Rachel, Hunt, Jane, Rousseau, Guy, Stancombe, Lucia, Absar, Mohammed, Allsop, Joanne, Drinkwater, Zoe, Hodgkiss, Tracey, Smith, Kirsty, Brown, Jamie, Pick, Sara, Alexander-Sefre, Farhad, Campey, Lorraine, Dudgeon, Lucy, Hall, Kathryn, Hitchcock, Rachael, James, Lynne, Smith, Kate, Winstone, Ulrika, Ahmad, Norfaizan, Bauchmuller, Kris, Harrison, Jonathan, Jeffery, Holly, Miller, Duncan, Pinder, Angela, Pothuneedi, Sailaja, Rosser, Jonathan, Sanghera, Sumayer, Swift, Diane, Walker, Rachel, Bester, Delia, Cavanagh, Sarah, Cripps, Heather, Daniel, Harvey, Lynch, Julie, Paton, Alison, Pyke, Shirley, Scholefield, John, Whitworth, Helen, Bottrill, Fiona, Ramalingam, Ganesh, Webb, Stephen, Akerman, Nik, Antill, Philip, Bourner, Lynsey, Buckley, Sarah, Castle, Gail, Charles, Rob, Eggleston, Christopher, Foster, Rebecca, Gill, Satwant, Lindley, Kate, Lklouk, Mohamed, Lowery, Tracey, Martin, Oliver, Milne, David, O’Connor, Patrick, Ratcliffe, Andrew, Rose, Alastair, Simeson, Karen, Smith, Annie, Varma, Sandeep, Ward, Jackie, Simeson, Karen, Barcraft-Barnes, Helena, Camsooksai, Julie, Colvin, Carolyn, Reschreiter, Henrik, Tbaily, Lee, Venner, Nicola, Hamilton, Caroline, Kelly, Lewis, Toth-Tarsoly, Piroska, Dodsworth, Kerry, Foord, Denise, Gordon, Paul, Hawes, Elizabeth, Lamb, Nikki, Mouland, Johanna, Nightingale, Jeremy, Rose, Steve, Schrieber, Joe, Al ‘Amri, Khalid, Aladin, Hafiz, Arshad, Mohammed Asif, Barraclough, James, Bentley, Conor, Bergin, Colin, Carrera, Ronald, Clarkson, Aisling, Collins, Michelle, Cooper, Lauren, Denham, Samuel, Griffiths, Ewen, Ip, Peter, Jeyanthan, Somasundaram, Joory, Kavita, Kaur, Satwant, Marriott, Paul, Mitchell, Natalie, Nagaiah, Sukumar, Nilsson, Annette, Parekh, Nilesh, Pope, Martin, Seager, Joseph, Serag, Hosam, Tameem, Alifia, Thomas, Anna, Thunder, Joanne, Torrance, Andrew, Vohra, Ravinder, whitehouse, Arlo, Wong, Tony, Blunt, Mark, Wong, Kate, Giles, Julian, Reed, Isabelle, Weller, Debbie, Bell, Gillian, Birch, Julie, Damant, Rose, Maiden, Jane, Mewies, Clare, Prince, Claire, Radford, Jane, Balain, Birender, Banerjee, Robin, Barnett, Andrew, Burston, Ben, Davies, Kirsty, Edwards, Jayne, Evans, Chris, Ford, David, Gallacher, Pete, Hill, Simon, Jaffray, David, Karlakki, Sudheer, Kelly, Cormac, Kennedy, Julia, Kiely, Nigel, Lewthwaite, Simon, Marquis, Chris, Ockendon, Matthew, Phillips, Stephen, Pickard, Simon, Richardson, James, Roach, Richard, Smith, Tony, Spencer-Jones, Richard, Steele, Niall, Steen, Julie, Van Liefland, Marck, White, Steve, Faulds, Matthew, Harris, Meredyth, Kelly, Carrie, Nicol, Scott, Pearson, Sally Anne, Chukkambotla, Srikanth, Andrew, Alyson, Attrill, Elizabeth, Campbell, Graham, Datson, Amanda, Fouracres, Anna, Graterol, Juan, Graves, Lynne, Hong, Bosun, Ishimaru, Alexander, Karthikeyan, Arvind, King, Helen, Lawson, Tom, Lee, Gregory, Lyons, Saoirse, Macalister Hall, Andrew, Mathoulin, Sophie, Mcintyre, Eilidh, Mclaughlin, Danny, Mulcahy, Kathleen, Ratcliffe, Anna, Robbins, James, Sung, Weilin, Tayo, Adeoluwa, Trembath, Lisa, Venugopal, Suneetha, Walker, Robert, Wigmore, Geoffrey, Boereboom, Catherine, Downes, Charlotte, Humphries, Ryan, Melbourne, Susan, Smith, Coral, Tou, Samson, Ullah, Shafa, Batchelor, Nick, Boxall, Leigh, Broomby, Rupert, Deen, Tariq, Hellewell, Alistair, Helliwell, Laurence, Hutchings, Melanie, Hutchins, David, Keenan, Samantha, Mackie, Donna, Donna, Alison, Smith, Frances, Stone, Lucy, Thorpe, Kevin, Wassall, Richard, Woodgate, Andrew, Baillie, Shelley, Campbell, Tara, James, Sarah, King, Chris, Marques de Araujo, Daniela, Martin, Daniel, Morkane, Clare, Neely, Julia, Rajendram, Rajkumar, Burton, Megan, James, Kathryn, Keevil, Edward, Minik, Orsolya, Morgan, Jenna, Musgrave, Anna, Rajanna, Harish, Roberts, Tracey, Szakmany, Tamas, Adamson, Michael, Jumbe, Sandra, Kendall, Jennie, Muthuswamy, Mohan Babu, Anderson, Charlotte, Cruikshanks, Andrew, Pothuneedi, Sailaja, Walker, Rachel, Wrench, Ian, Zeidan, Lisa, Ardern, Diane, Harris, Benjamin, Hellstrom, Johanna, Martin, Jane, Thomas, Richard, Varsani, Nimu, Wrey Brown, Caroline, Docherty, Philip, Gillies, Michael, McGregor, Euan, Usher, Helen, Craig, Jayne, Smith, Andrew, Ahmad, Tahania, Bodger, Phoebe, Creary, Thais, Everingham, Kirsty, Fowler, Alexander, Hewson, Russ, Ijuo, Eke, Januszewska, Marta, Jones, Timothy, Kantsedikas, Ilya, Lahiri, Sumitra, McLean, Aaron Lawson, Niebrzegowska, Edyta, Phull, Mandeep, Wang, Difei, Wickboldt, Nadine, Baldwin, Jacqueline, Doyle, Donna, Mcmullan, Sean, Oladapo, Michelle, Owen, Thomas, Tripathi, Shiva, Williams, Alexandra, Daniel, Hull, Gregory, Peter, Husain, Tauqeer, Kirk-Bayley, Justin, Mathers, Edward, Montague, Laura, White, Stuart, Avis, Joanne, Cook, Tim, Dali-Kemmery, Lola, Kerslake, Ian, Lambourne, Victoria, Pearson, Annabel, Boyd, Christine, Callaghan, Mark, Lawson, Cathy, McCrossan, Roopa, Nesbitt, Vanessa, O’connor, Laura, Scott, Julia, Sinclair, Rhona, Farid, Nahla, Morgese, Ciro, Bhatia, Kailash, Karmarkar, Swati, Vohra, Akbar, Ahmed, Jamil, Branagan, Graham, Hutton, Monica, Swain, Andrew, Brookes, Jamie, Cornell, Jonathan, Dolan, Rachael, Hulme, Jonathan, Jansen van Vuuren, Amanda, Jowitt, Tom, Kalashetty, Gunasheela, Lloyd, Fran, Patel, Kiran, Sherwood, Nicholas, Brown, Lynne, Chandler, Ben, Deighton, Kerry, Emma, Temlett, Haunch, Kirsty, Cheeseman, Michelle, Dent, Kathy, Garg, Sanjeev, Gray, Carol, Hood, Marion, Jones, Dawn, Juj, Joanne, Mitra, Atideb, Rao, Roshan, Walker, Tara, Al Anizi, Mashel, Cheah, Clarissa, Cheing, Yushio, Coutinho, Francisco, Gondo, Prisca, Hadebe, Bernard, Onie Hove, Mazvangu, khader, Ahamed, Krishnachetty, Bobby, Rhodes, Karen, Sokhi, Jagdish, Baker, Katie-Anne, Bertram, Wendy, Looseley, Alex, Mouton, Ronelle, Arnold, Glenn, Arya, Shobhit, Balfoussia, Danai, Baxter, Linden, Harris, James, Jones, Craig, Knaggs, Alison, Markar, Sheraz, Perera, Anisha, Scott, Alasdair, Shida, Asako, Sirha, Ravneet, Wright, Sally, Frost, Victoria, Gray, Catherine, MacGregor, Mark, Andrews, Emma, Arrandale, Lindsay, Barrett, Stephen, Bidd, Heena, Cifra, Elna, Cooper, Mariese, Dragnea, Dragos, Elna, Cifra, Maclean, Jennifer, Meier, Sonja, Milliken, Donald, Munns, Christopher, Ratanshi, Nadir, Salvana, Abegail, Watson, Anthony, Ali, Hani, Campbell, Gill, Critchley, Rebecca, Hicks, Catherine, Liddle, Alison, Pass, Marc, Ritchie, Charlotte, Thomas, Charlotte, Too, Lingxi, Welsh, Sarah, Gill, Talvinder, Johnson, Joanne, Reed, Joanne, Davis, Edward, Papadopoullos, Sam, Attwood, Clare, Biffen, Andrew, Boulton, Kerenza, Gray, Sophie, Hay, David, Mills, Sarah, Montgomery, Jane, Riddell, Rory, Simpson, James, Bhardwaj, Neeraj, Paul, Elaine, Uwubamwen, Nosakhare, Vohra, Akbar, Berntsen, Emer, Nolan, Tracey, Turner, Angus, Alexander, Maini, Arrich, James, Arumugam, Swarna, Blackwood, Douglas, Boggiano, Victoria, Brown, Robyn, Lam Chan, Yik, Chatterjee, Devnandan, Chhabra, Ashok, Christian, Rachel, Costelloe, Hannah, CoxwellMatthewman, Madeline, Dalton, Emma, Darko, Julia, Davari, Maria, Dave, Tejal, Deacon, Matthew, Deepak, Shantal, Edmond, Holly, Ellis, Jessica, El-Sayed, Ahmed, Eneje, Philip, English, Rose, Ewe, Renee, Foers, William, Franklin, John, Gallego, Laura, Garrett, Emily, Goldberg, Olivia, Goss, Harry, Greaves, Rosanna, Harris, Rudy, Hennings, Charles, Jones, Eleanor, Kamali, Nelson, Kokkinos, Naomi, Lewis, Carys, Lignos, Leda, Malgapo, Evaleen Victoria, Malik, Rizwana, Milne, Andrew, Mulligan, John-Patrick, Nicklin, Philippa, Palipane, Natasha, Parsons, Thomas, Piper, Rebecca, Prakash, Rohan, Ramesh, Byron, Rasip, Sarah, Reading, Jacob, Rela, Mariam, Reyes, Anna, Robert, Stephens, Rooms, Martin, Shah, Karishma, Simons, Henry, Solanki, Shalil, Spowart, Emma, Stevens, Amy, Thomas, Christopher, Waggett, Helena, Yassaee, Arrash, Kennedy, Anthony, Scott, Sara, Somanath, Sameer, Berg, Andrew, Hernandez, Miguel, Nanda, Rajesh, Tank, Ghanshyambhai, Wilson, Natalie, Wilson, Debbie, Al-Soudaine, Yassr, Baldwin, Matthew, Cornish, Julie, Davies, Zoe, Davies, Leigh, Edwards, Marc, Frewer, Natasha, Gallard, Sian, Glasbey, James, Harries, Rhiannon, Hopkins, Luke, Kim, Taeyang, Koompirochana, Vilavan, Lawson, Simon, Lewis, Megan, Makzal, Zaid, Scourfield, Sarah, Ahmad, Yousra, Bates, Sarah, Blackwell, Clare, Bryant, Helen, Coulter, Suzanne, Cruickshank, Ross, Daniel, Sonya, Daubeny, Thomas, Edwards, Mark, Golder, Kim, Hawkins, Lesley, Helen, Bryant, Hinxman, Honor, Levett, Denny, Skinner, Ben, Walsgrove, Joseph, Bradburn, Mike, Dickson, Jane, Constantin, Kathryn, Karen, Markwell, O’Brien, Peter, O’Donohoe, Lynn, Payne, Hannah, Sundayi, Saul, Walker, Elaine, Brooke, Jenny, Cardy, Jon, Humphreys, Sally, Kessack, Laura, Kubitzek, Christiane, Kumar, Suhas, Cotterill, Donna, Hodzovic, Emil, Hosdurga, Gurunath, Miles, Edward, Saunders, Glenn, Campbell, Marta, Chan, Peter, Jemmett, Kim, Raj, Ashok, Naik, Aditi, Ramamoorthy, Rajarajan, Shah, Nimesh, Sylvan, Axel, Blyth, Katharine, Burtenshaw, Andrew, Freeman, David, Johnson, Emily, Lo, Philip, Martin, Terry, Plunkett, Emma, Wollaston, Julie, Allison, Joanna, Carroll, Christine, Craw, Nicholas, Craw, Sarah, Pitt-Kerby, Tressy, Rowland-Axe, Rebecca, Spurdle, Katie, McDonald, Andrew, Simon, Davies, Sinha, Vivek, Smith, Thomas, Banner-Goodspeed, Valerie, Boone, Myles, Campbell, Kathleen, Lu, Fengxin, Scannell, Joseph, Sobol, Julia, Balajonda, Naraida, Clemmons, Karen, Conde, Carlos, Funk, Bonita, Hall, Roger, Hopkins, Thomas, Olaleye, Omowunmi, Omer, Omer, Pender, Michelle, Porto, Angelo, Stevens, Alice, Waweru, Peter, Yeh, Erlinda, Bodansky, Daniella, Evans, Adam, Kleopoulos, Steven, Maril, Robert, Mathney, Edward, Sanchez, Angela, Tinuoye, Elizabeth, Bateman, Brian, Eng, Kristen, Jiang, Ning, Ladha, Karim, Needleman, Joseph, Chen, Lee-lynn, Chen, Lee-lynn, Lane, Rondall, Robinowitz, David, Ghushe, Neil, Irshad, Mariam, Patel, Samir, Takemoto, Steven, Wallace, Art, Mazzeffi, Michael, Rock, Peter, Wallace, Karin, Zhu, Xiaomao, Chua, Pandora, Fleisher, Lee, Mattera, Matthew, Sharar, Rebecca, Thilen, Stephan, Treggiari, Miriam, Morgan, Angela, Sofjan, Iwan, Subramaniam, Kathirvel, Avidan, Michael, Maybrier, Hannah, Muench, Maxwell, Wildes, Troy, and The International Surgical Outcomes Study (ISOS) group
Published: 2017
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23. Pathogenesis of Tobacco-Associated Lung Adenocarcinoma Is Closely Coupled with Changes in the Gut and Lung Microbiomes

Author: Finnicum, Casey, primary, Rahal, Zahraa, additional, Hassane, Maya, additional, Treekitkarnmongkol, Warapen, additional, Sinjab, Ansam, additional, Morris, Rhiannon, additional, Liu, Yuejiang, additional, Tang, Elizabeth, additional, Viet, Sarah, additional, Petersen, Jason, additional, Lorenzi, Philip, additional, Tan, Lin, additional, Petrosino, Joseph, additional, Hoffman, Kristi, additional, Fujimoto, Junya, additional, Moghaddam, Seyed, additional, and Kadara, Humam, additional
Published: 2022
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24. Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective

Author: Liu, Yuejiang, primary, Cadei, Riccardo, additional, Schweizer, Jonas, additional, Bahmani, Sherwin, additional, and Alahi, Alexandre, additional
Published: 2022
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25. Activation of UPR Signaling Pathway is Associated With the Malignant Progression and Poor Prognosis in Prostate Cancer

Author: Liu, Junnan, Xiao, Maolin, Li, Jianjun, Wang, Delin, He, Yunfeng, He, Jiang, Gao, Fei, Mai, Li, Li, Ying, Liang, Yong, Liu, Yuejiang, and Zhong, Xiaoni
Published: 2017
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26. Social NCE: Contrastive Learning of Socially-aware Motion Representations

Author: Liu, Yuejiang, primary, Yan, Qi, additional, and Alahi, Alexandre, additional
Published: 2021
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27. Learning Decoupled Representations for Human Pose Forecasting

Author: Parsaeifard, Behnam, primary, Saadatnejad, Saeed, additional, Liu, Yuejiang, additional, Mordan, Taylor, additional, and Alahi, Alexandre, additional
Published: 2021
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28. Effect of indwelling time of double J tube on infected ureteral calculi and the distribution of pathogenic characteristics in diabetics

Author: Xie, Wenxian, Zhang, Suchuan, Li, Xiaobin, Liu, Yuejiang, Yang, Jian, Liu, Ping, and Zeng, Ke
Subjects: Original Article
Abstract: Objective: To investigate the effect of double J tube indwelling time on infected ureteral calculi (UC) and distribution of pathogenic characteristics in diabetics. Methods: 132 diabetics with infected UC admitted to our hospital from April 2017 to April 2020 were selected. All patients were implanted with a double J tube, followed by percutaneous nephrolithotomy or ureteroscopic holmium laser lithotripsy. According to the indwelling time, they were divided into a research group (≤ 7 d, 60 cases) and a control group (> 7 d, 72 cases). We compared the baseline data, and surgical data of the two groups, and analyzed pathogenic bacteria. Results: None of the differences in the operation time, hospital stay, and stone diameter were statistically significant (P > 0.05). Before placement of the double J tube, no striking differences in urinary white blood cells and blood white blood cells were observed between the two groups (P > 0.05). 7 days after the placement of the double J tube, a significant decrease of the urinary white blood cells and blood white blood cells was recorded (P < 0.05), with no significant differences between the two groups (P > 0.05). Before and after placement of double J tube, no striking differences in body temperature > 38.5°C or positive blood culture were observed between the two groups (P > 0.05). Surgical methods, stone removal rate one month after the operation, or incidence of postoperative complications were not significantly different (P > 0.05). 49 pathogenic strains were detected, among which Gram-negative bacteria accounted for 63.27%. The main pathogens were Escherichia coli and Pseudomonas aeruginosa. Conclusion: The indwelling time of the double J tube has no significant effect on the effectiveness and safety in diabetic patients with infected UC. It is necessary to reduce the indwelling time and implement targeted stone surgery.
Published: 2021

29. In Situ Gelling Gelrite/Alginate Formulations as Vehicles for Ophthalmic Drug Delivery

Author: Liu, Yuejiang, Liu, Jinpeng, Zhang, Xiaolin, Zhang, Ruodan, Huang, Yongliang, and Wu, Chunjie
Published: 2010
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30. Design and implementation of a pipeline for fully autonomous driving systems

Author: Universitat Politècnica de Catalunya. Departament d'Organització d'Empreses, École polytechnique fédérale de Lausanne, Van Wunnik, Lucas Philippe, Liu, Yuejiang, Bahar, Mohammadhossein, Alahi, Alexandre, Conejo Barceló, Carlos, Universitat Politècnica de Catalunya. Departament d'Organització d'Empreses, École polytechnique fédérale de Lausanne, Van Wunnik, Lucas Philippe, Liu, Yuejiang, Bahar, Mohammadhossein, Alahi, Alexandre, and Conejo Barceló, Carlos
Abstract: Mobile robots are already playing a pivotal role in our society. New innovative systems are emerging, while autonomous driving’s software is evolving thanks to Deep Learning (DL). In our work, we propose a modular pipeline for autonomous systems, which can combine DL algorithms for Computer Vision (CV) with classic robust functions for Path Planning and Control tasks. We design our structure to be genericfor diverse mobile robots, flexible to changes on its modules, and robust in different scenarios. As we use the Robot Operating System (ROS) and socket connections between the pipeline and our system,we create a wireless network that goes from sensors’ outputs to actuators’ inputs. Finally, we implement the designed environment in a Loomo Segway robot with real scenarios, testing the proposed method’s robustness and flexibility.
Published: 2021

31. Collaborative Sampling in Generative Adversarial Networks

Author: Liu, Yuejiang, primary, Kothari, Parth, additional, and Alahi, Alexandre, additional
Published: 2020
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32. Two Brain Pathways Initiate Distinct Forward Walking Programs in Drosophila

Author: Bidaye, Salil S., Laturney, Meghan, Chang, Amy K., Liu, Yuejiang, Bockemuehl, Till, Bueschges, Ansgar, Scott, Kristin, Bidaye, Salil S., Laturney, Meghan, Chang, Amy K., Liu, Yuejiang, Bockemuehl, Till, Bueschges, Ansgar, and Scott, Kristin
Abstract: An animal at rest or engaged in stationary behaviors can instantaneously initiate goal-directed walking. How descending brain inputs trigger rapid transitions from a non-walking state to an appropriate walking state is unclear. Here, we identify two neuronal types, P9 and BPN, in the Drosophila brain that, upon activation, initiate and maintain two distinct coordinated walking patterns. P9 drives forward walking with ipsilateral turning, receives inputs from central courtship-promoting neurons and visual projection neurons, and is necessary for a male to pursue a female during courtship. In contrast, BPN drives straight, forward walking and is not required during courtship. BPN is instead recruited during and required for fast, straight, forward walking bouts. Thus, this study reveals separate brain pathways for object-directed walking and fast, straight, forward walking, providing insight into how the brain initiates context-appropriate walking programs.
Published: 2020

33. High expression of citron kinase predicts poor prognosis of prostate cancer

Author: Liu, Junnan, primary, Dou, Jianguo, additional, Wang, Wujiao, additional, Liu, Hengchuan, additional, Qin, Yunlang, additional, Yang, Qixin, additional, Jiang, Wencheng, additional, Liang, Yong, additional, Liu, Yuejiang, additional, He, Jiang, additional, Mai, Li, additional, Li, Ying, additional, and Wang, Delin, additional
Published: 2020
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34. Two brain pathways initiate distinct forward walking programs inDrosophila

Author: Bidaye, Salil S., primary, Laturney, Meghan, additional, Chang, Amy K., additional, Liu, Yuejiang, additional, Bockemühl, Till, additional, Büschges, Ansgar, additional, and Scott, Kristin, additional
Published: 2019
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35. Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning

Author: Chen, Changan, primary, Liu, Yuejiang, additional, Kreiss, Sven, additional, and Alahi, Alexandre, additional
Published: 2019
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36. Map-based Deep Imitation Learning for Obstacle Avoidance

Author: Liu, Yuejiang, primary, Xu, An, additional, and Chen, Zichong, additional
Published: 2018
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37. Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries The International Surgical Outcomes Study group

Author: Ahmad, T, Bouwman, RA, Grigoras, I, Aldecoa, C, Hofer, C, Hoeft, A, Holt, P, Fleisher, LA, Buhre, W, Pearse, RM, Ferguson, Marissa, MacMahon, Michael, Shulman, Mark, Cherian, Ritchie, Currow, Helen, Kanathiban, Kathirgamanathan, Gillespie, David, Pathmanathan, Edward, Phillips, Katherine, Reynolds, Jenifer, Rowley, Joanne, Douglas, Jeanene, Kerridge, Ross, Garg, Sameer, Bennett, Michael, Jain, Megha, Alcock, David, Terblanche, Nico, Cotter, Rochelle, Leslie, Kate, Stewart, Marcelle, Zingerle, Nicolette, Clyde, Antony, Hambidge, Oliver, Rehak, Adam, Cotterell, Sharon, Huynh, Wilson Binh Quan, McCulloch, Timothy, Ben-Menachem, Erez, Egan, Thomas, Cope, Jennifer, Halliwell, Richard, Fellinger, Paul, Haisjackl, Markus, Haselberger, Simone, Holaubek, Caroline, Lichtenegger, Paul, Scherz, Florian, Schmid, Werner, Hoffer, Franz, Cakova, Veronika, Eichwalder, Andreas, Fischbach, Norbert, Klug, Reinhold, Schneider, Elisabeth, Vesely, Martin, Wickenhauser, Reinhart, Grubmueller, Karl Gernot, Leitgeb, Marion, Lang, Friedrich, Toro, Nancy, Bauer, Marlene, Laengle, Friedrich, Haberl, Claudia, Mayrhofer, Thomas, Trybus, Christoph, Buerkle, Christian, Forstner, Karin, Germann, Reinhard, Rinoesl, Harald, Schindler, Elke, Trampitsch, Ernst, Bogner, Gerhard, Dankl, Daniel, Duenser, Martin, Fritsch, Gerhard, Gradwohl-Matis, Ilse, Hartmann, Andreas, Hoelzenbein, Thomas, Jaeger, Tarkan, Landauer, Franz, Lindl, Gregor, Lux, Michael, Steindl, Johannes, Stundner, Ottokar, Szabo, Christian, Bidgoli, Jawad, Verdoodt, Hans, Forget, Patrice, Kahn, David, Lois, Fernande, Momeni, Mona, Pregardien, Caroline, Pospiech, Audrey, Steyaert, Arnaud, Veevaete, Laurent, De Kegel, Dirk, De Jongh, Karen, Foubert, Luc, Smitz, Carine, Vercauteren, Marcel, Poelaert, Jan, Van Mossevelde, Veerle, Abeloos, Jacques, Bouchez, Stefaan, Coppens, Marc, De Baerdemaeker, Luc, Deblaere, Isabel, De Bruyne, Ann, De Hert, Stefan, Fonck, Kristine, Heyse, Bjorn, Jacobs, Tom, Lapage, Koen, Moerman, Anneliese, Neckebroek, Martine, Parashchanka, Aliaksandra, Roels, Nathalie, Van Den Eynde, Nancy, Vandenheuvel, Michaël, van Limmen, Jurgen, Vanluchene, Ann, Vanpeteghem, Caroline, Wouters, Patrick, WYFFELS, PIET, Huygens, Christel, Vandenbempt, Punitha, Van de Velde, Marc, Dylst, Dimitri, Janssen, Bruno, Schreurs, Evelien, Aleixo, Fabia Berganton, Candido, Keulle, Batista, Hugo Dias, Guimaraes, Mario, Guizeline, Jaqueline, Hoffmann, Joao, Lobo, Suzana, Marques Lobo, Francisco Ricardo, Nascimento, Vinicius, Nishiyama, Katia, Pazetto, Lucas, Souza, Daniela, Rodrigues, Rodrigo Souza, Vilela dos Santos, Ana Maria, Jardim, Jaquelline, Sa Malbouisson, Luiz Marcelo, Silva, Joao, do Nascimento Junior, Paulo, Baio, Thalissa Herminia, Pereira de Castro, Gabriel Isaac, Watanabe Oliveira, Henri Roger, Amendola, Cristina Prata, Cardoso, Gutemberg, Ortega, Daniela, Brotto, Ana Flavia, De Oliveira, Mirella Cristine, Rea-Neto, Alvaro, Dias, Fernando, Travi, Maria Eduarda, Zerman, Luiza, Azambuja, Pedro, Knibel, 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Shelley, Campbell, Tara, James, Sarah, King, Chris, de Araujo, Daniela Marques, Martin, Daniel, Morkane, Clare, Neely, Julia, Rajendram, Rajkumar, Burton, Megan, James, Kathryn, Keevil, Edward, Minik, Orsolya, Morgan, Jenna, Musgrave, Anna, Rajanna, Harish, Roberts, Tracey, Szakmany, Tamas, Adamson, Michael, Jumbe, Sandra, Kendall, Jennie, Muthuswamy, Mohan Babu, Anderson, Charlotte, Cruikshanks, Andrew, Wrench, Ian, Zeidan, Lisa, Ardern, Diane, Harris, Benjamin, Hellstrom, Johanna, Martin, Jane, Thomas, Richard, Varsani, Nimu, Brown, Caroline Wrey, Docherty, Philip, Gillies, Michael, McGregor, Euan, Usher, Helen, Craig, Jayne, Smith, Andrew, Ahmad, Tahania, Bodger, Phoebe, Creary, Thais, Everingham, Kirsty, Fowler, Alexander, Hewson, Russ, Ijuo, Eke, Januszewska, Marta, Jones, Timothy, Kantsedikas, Ilya, Lahiri, Sumitra, McLean, Aaron Lawson, Niebrzegowska, Edyta, Phull, Mandeep, Wang, Difei, Wickboldt, Nadine, Baldwin, Jacqueline, Doyle, Donna, Mcmullan, Sean, Oladapo, Michelle, Owen, Thomas, Williams, Alexandra, Daniel, Hull, Gregory, Peter, Husain, Tauqeer, Kirk-Bayley, Justin, Mathers, Edward, Montague, Laura, Harper, Mark, White, Stuart, Jack, James, Ridley, Carrie, Avis, Joanne, Cook, Tim, Dali-Kemmery, Lola, Kerslake, Ian, Lambourne, Victoria, Pearson, Annabel, Boyd, Christine, Callaghan, Mark, Lawson, Cathy, McCrossan, Roopa, Nesbitt, Vanessa, O'connor, Laura, Scott, Julia, Sinclair, Rhona, Farid, Nahla, Morgese, Ciro, Bhatia, Kailash, Karmarkar, Swati, Ahmed, Jamil, Branagan, Graham, Hutton, Monica, Swain, Andrew, Brookes, Jamie, Cornell, Jonathan, Dolan, Rachael, Hulme, Jonathan, van Vuuren, Amanda Jansen, Jowitt, Tom, Kalashetty, Gunasheela, Lloyd, Fran, Patel, Kiran, Sherwood, Nicholas, Brown, Lynne, Chandler, Ben, Deighton, Kerry, Emma, Temlett, Haunch, Kirsty, Cheeseman, Michelle, Dent, Kathy, Garg, Sanjeev, Gray, Carol, Hood, Marion, Jones, Dawn, Juj, Joanne, Rao, Roshan, Walker, Tara, Al Anizi, Mashel, Cheah, Clarissa, Cheing, Yushio, Coutinho, Francisco, Gondo, Prisca, Hadebe, Bernard, Hove, Mazvangu Onie, khader, Ahamed, Krishnachetty, Bobby, Rhodes, Karen, Sokhi, Jagdish, Baker, Katie-Anne, Bertram, Wendy, Looseley, Alex, Mouton, Ronelle, Hanna, George, Arnold, Glenn, Arya, Shobhit, Balfoussia, Danai, Baxter, Linden, Harris, James, Jones, Craig, Knaggs, Alison, Markar, Sheraz, Perera, Anisha, Scott, Alasdair, Shida, Asako, Sirha, Ravneet, Wright, Sally, Frost, Victoria, Gray, Catherine, Andrews, Emma, Arrandale, Lindsay, Barrett, Stephen, Cifra, Elna, Cooper, Mariese, Dragnea, Dragos, Elna, Cifra, Maclean, Jennifer, Meier, Sonja, Milliken, Donald, Munns, Christopher, Ratanshi, Nadir, Ramessur, Suneil, Salvana, Abegail, Watson, Anthony, Ali, Hani, Campbell, Gill, Critchley, Rebecca, Endersby, Simon, Hicks, Catherine, Liddle, Alison, Pass, Marc, Ritchie, Charlotte, Thomas, Charlotte, Too, Lingxi, Welsh, Sarah, Gill, Talvinder, Johnson, Joanne, Reed, Joanne, Davis, Edward, Papadopoullos, Sam, Attwood, Clare, Biffen, Andrew, 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Kathryn, Karen, Markwell, O'Brien, Peter, O'Donohoe, Lynn, Payne, Hannah, Sundayi, Saul, Walker, Elaine, Brooke, Jenny, Cardy, Jon, Humphreys, Sally, Kessack, Laura, Kubitzek, Christiane, Kumar, Suhas, Cotterill, Donna, Hodzovic, Emil, Hosdurga, Gurunath, Miles, Edward, Saunders, Glenn, Campbell, Marta, Chan, Peter, Jemmett, Kim, Raj, Ashok, Naik, Aditi, Oshowo, Ayo, Ramamoorthy, Rajarajan, Shah, Nimesh, Sylvan, Axel, Blyth, Katharine, Burtenshaw, Andrew, Freeman, David, Johnson, Emily, Lo, Philip, Martin, Terry, Plunkett, Emma, Wollaston, Julie, Allison, Joanna, Carroll, Christine, Craw, Nicholas, Craw, Sarah, Pitt-Kerby, Tressy, Rowland-Axe, Rebecca, Spurdle, Katie, McDonald, Andrew, Simon, Davies, Sinha, Vivek, Smith, Thomas, Banner-Goodspeed, Valerie, Boone, Myles, Campbell, Kathleen, Lu, Fengxin, Scannell, Joseph, Sobol, Julia, Balajonda, Naraida, Clemmons, Karen, Conde, Carlos, Elgasim, Magdi, Funk, Bonita, Hall, Roger, Hopkins, Thomas, Olaleye, Omowunmi, Omer, Omer, Pender, Michelle, Porto, Angelo, Stevens, Alice, Waweru, Peter, Yeh, Erlinda, Bodansky, Daniella, Evans, Adam, Kleopoulos, Steven, Maril, Robert, Mathney, Edward, Sanchez, Angela, Tinuoye, Elizabeth, Bateman, Brian, Eng, Kristen, Jiang, Ning, Ladha, Karim, Needleman, Joseph, Chen, Lee-lynn, Lane, Rondall, Robinowitz, David, Ghushe, Neil, Irshad, Mariam, O'Connor, John, Patel, Samir, Takemoto, Steven, Wallace, Art, Mazzeffi, Michael, Rock, Peter, Wallace, Karin, Zhu, Xiaomao, Chua, Pandora, Fleisher, Lee, Mattera, Matthew, Sharar, Rebecca, Thilen, Stephan, Treggiari, Miriam, Morgan, Angela, Sofjan, Iwan, Subramaniam, Kathirvel, Avidan, Michael, Maybrier, Hannah, Muench, Maxwell, and Wildes, Troy
Subjects: surgery, postoperative care/methods, cohort studies, operative/mortality, data, postoperative care/statistics and numerical, Medicine and Health Sciences, critical care/utilisation, surgical procedures
Abstract: Background: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. Methods: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. Results: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with amedian hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. Conclusions: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care.
Published: 2016

38. Activation of UPR Signaling Pathway is Associated With the Malignant Progression and Poor Prognosis in Prostate Cancer

Author: Liu, Junnan, primary, Xiao, Maolin, additional, Li, Jianjun, additional, Wang, Delin, additional, He, Yunfeng, additional, He, Jiang, additional, Gao, Fei, additional, Mai, Li, additional, Li, Ying, additional, Liang, Yong, additional, Liu, Yuejiang, additional, and Zhong, Xiaoni, additional
Published: 2016
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39. Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Author: Kothari, Parth, Li, Danya, Liu, Yuejiang, and Alahi, Alexandre
Subjects: FOS: Computer and information sciences, Computer Science - Robotics, Distribution Shifts, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Motion Forecasting, Transfer Learning, Robotics (cs.RO)
Abstract: Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts. Unlike the conventional fine-tuning approach that updates the whole encoder, our main idea is to reduce the amount of tunable parameters that can precisely account for the target domain-specific motion style. To this end, we introduce two components that exploit our prior knowledge of motion style shifts: (i) a low-rank motion style adapter that projects and adjusts the style features at a low-dimensional bottleneck; and (ii) a modular adapter strategy that disentangles the features of scene context and motion history to facilitate a fine-grained choice of adaptation layers. Through extensive experimentation, we show that our proposed adapter design, coined MoSA, outperforms prior methods on several forecasting benchmarks., CoRL 2022

40. On Pitfalls of Test-time Adaptation

Author: Zhao, Hao, Liu, Yuejiang, Alahi, Alexandre, and Lin, Tao
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract: Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts. However, the lack of consistent settings and systematic studies in prior literature hinders thorough assessments of existing methods. To address this issue, we present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols. Through extensive experiments, our benchmark reveals three common pitfalls in prior efforts. First, selecting appropriate hyper-parameters, especially for model selection, is exceedingly difficult due to online batch dependency. Second, the effectiveness of TTA varies greatly depending on the quality and properties of the model being adapted. Third, even under optimal algorithmic conditions, none of the existing methods are capable of addressing all common types of distribution shifts. Our findings underscore the need for future research in the field to conduct rigorous evaluations on a broader set of models and shifts, and to re-examine the assumptions behind the empirical success of TTA. Our code is available at \url{https://github.com/lins-lab/ttab}., Accepted at ICML 2023

41. TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Author: Liu, Yuejiang, Kothari, Parth, Van Delft, Bastien, Bellot-Gurlet, Baptiste, Mordan, Taylor, and Alahi, Alexandre
Subjects: Distribution Shifts, Source-free Domain Adaptation, Test-time Adaptation, Self-supervised Learning
Abstract: Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at its limitations and show that TTT can possibly deteriorate, instead of improving, the test-time performance in the presence of severe distribution shifts. To address this issue, we introduce a test-time feature alignment strategy utilizing offline feature summarization and online moment matching, which regularizes adaptation without revisiting training data. We further scale this strategy in the online setting through batch-queue decoupling to enable robust moment estimates even with limited batch size. Given aligned feature distributions, we then shed light on the strong potential of TTT by theoretically analyzing its performance post adaptation. This analysis motivates our use of more informative self-supervision in the form of contrastive learning for visual recognition problems. We empirically demonstrate that our modified version of test-time training, termed TTT++, outperforms state-of-the-art methods by significant margins on several benchmarks. Our result indicates that storing and exploiting extra information, in addition to model parameters, can be a promising direction towards robust test-time adaptation.

42. Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow

Author: Liu, Yuejiang, Hours, Jean-Hubert, Stathopoulos, Georgios, and Jones, Colin
Abstract: The optimal power flow (OPF) problem, a fundamental problem in power systems, is generally nonconvex and computationally challenging for networks with an increasing number of smart devices and real-time control requirements. In this paper, we first investigate a fully distributed approach by means of the augmented Lagrangian and proximal alternating minimization method to solve the nonconvex OPF problem with a convergence guarantee. Given time-critical requirements, we then extend the algorithm to a distributed parametric tracking scheme with practical warm-starting and termination strategies, which aims to provide a closed-loop sub-optimal control policy while taking into account the grid information updated at the time of decision making. The effectiveness of the proposed algorithm for real-time nonconvex OPF problems is demonstrated in numerical simulations.

43. Design and implementation of a pipeline for fully autonomous driving systems

Author: Conejo Barcelo, Carlos, Universitat Politècnica de Catalunya. Departament d'Organització d'Empreses, École polytechnique fédérale de Lausanne, Van Wunnik, Lucas Philippe, Liu, Yuejiang, Bahar, Mohammadhossein, and Alahi, Alexandre
Subjects: Artificial intelligence -- Engineering applications, Vehicles autònoms, Autonomous vehicles, Intel·ligència artificial -- Aplicacions a l'enginyeria, Deep learning, Enginyeria de la telecomunicació [Àrees temàtiques de la UPC], Aprenentatge profund
Abstract: Mobile robots are already playing a pivotal role in our society. New innovative systems are emerging, while autonomous driving’s software is evolving thanks to Deep Learning (DL). In our work, we propose a modular pipeline for autonomous systems, which can combine DL algorithms for Computer Vision (CV) with classic robust functions for Path Planning and Control tasks. We design our structure to be genericfor diverse mobile robots, flexible to changes on its modules, and robust in different scenarios. As we use the Robot Operating System (ROS) and socket connections between the pipeline and our system,we create a wireless network that goes from sensors’ outputs to actuators’ inputs. Finally, we implement the designed environment in a Loomo Segway robot with real scenarios, testing the proposed method’s robustness and flexibility.
Published: 2021

44. Inflammation Mediated by Gut Microbiome Alterations Promotes Lung Cancer Development and an Immunosuppressed Tumor Microenvironment.

Author: Rahal Z, Liu Y, Peng F, Yang S, Jamal MA, Sharma M, Moreno H, Damania AV, Wong MC, Ross MC, Sinjab A, Zhou T, Chen M, Tarifa Reischle I, Feng J, Chukwuocha C, Tang E, Abaya C, Lim JK, Leung CH, Lin HY, Deboever N, Lee JJ, Sepesi B, Gibbons DL, Wargo JA, Fujimoto J, Wang L, Petrosino JF, Ajami NJ, Jenq RR, Moghaddam SJ, Cascone T, Hoffman K, and Kadara H
Subjects: Animals, Mice, Humans, Adenocarcinoma of Lung immunology, Adenocarcinoma of Lung microbiology, Adenocarcinoma of Lung pathology, Lipocalin-2 metabolism, Mice, Inbred C57BL, Disease Models, Animal, Fecal Microbiota Transplantation, Mice, Knockout, Gastrointestinal Microbiome immunology, Tumor Microenvironment immunology, Lung Neoplasms immunology, Lung Neoplasms microbiology, Lung Neoplasms pathology, Inflammation immunology
Abstract: Accumulating evidence indicates that the gut microbiome influences cancer progression and therapy. We recently showed that progressive changes in gut microbial diversity and composition are closely coupled with tobacco-associated lung adenocarcinoma in a human-relevant mouse model. Furthermore, we demonstrated that the loss of the antimicrobial protein Lcn2 in these mice exacerbates protumor inflammatory phenotypes while further reducing microbial diversity. Yet, how gut microbiome alterations impinge on lung adenocarcinoma development remains poorly understood. In this study, we investigated the role of gut microbiome changes in lung adenocarcinoma development using fecal microbiota transfer and delineated a pathway by which gut microbiome alterations incurred by loss of Lcn2 fostered the proliferation of proinflammatory bacteria of the genus Alistipes, triggering gut inflammation. This inflammation propagated systemically, exerting immunosuppression within the tumor microenvironment, augmenting tumor growth through an IL6-dependent mechanism and dampening response to immunotherapy. Corroborating our preclinical findings, we found that patients with lung adenocarcinoma with a higher relative abundance of Alistipes species in the gut showed diminished response to neoadjuvant immunotherapy. These insights reveal the role of microbiome-induced inflammation in lung adenocarcinoma and present new potential targets for interception and therapy., (©2024 American Association for Cancer Research.)
Published: 2024
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45. Paraneoplastic renal dysfunction in fly cancer models driven by inflammatory activation of stem cells.

Author: Kwok SH, Liu Y, Bilder D, and Kim J
Abstract: Tumors can induce systemic disturbances in distant organs, leading to physiological changes that enhance host morbidity. In Drosophila cancer models, tumors have been known for decades to cause hypervolemic 'bloating' of the abdominal cavity. Here we use allograft and transgenic tumors to show that hosts display fluid retention associated with autonomously defective secretory capacity of fly renal tubules, which function analogous to those of the human kidney. Excretion from these organs is blocked by abnormal cells that originate from inappropriate activation of normally quiescent renal stem cells (RSCs). Blockage is initiated by IL-6-like oncokines that perturb renal water-transporting cells, and trigger a damage response in RSCs that proceeds pathologically. Thus, a chronic inflammatory state produced by the tumor causes paraneoplastic fluid dysregulation by altering cellular homeostasis of host renal units.
Published: 2024
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46. Effect of indwelling time of double J tube on infected ureteral calculi and the distribution of pathogenic characteristics in diabetics.

Author: Xie W, Zhang S, Li X, Liu Y, Yang J, Liu P, and Zeng K
Abstract: Objective: To investigate the effect of double J tube indwelling time on infected ureteral calculi (UC) and distribution of pathogenic characteristics in diabetics., Methods: 132 diabetics with infected UC admitted to our hospital from April 2017 to April 2020 were selected. All patients were implanted with a double J tube, followed by percutaneous nephrolithotomy or ureteroscopic holmium laser lithotripsy. According to the indwelling time, they were divided into a research group (≤ 7 d, 60 cases) and a control group (> 7 d, 72 cases). We compared the baseline data, and surgical data of the two groups, and analyzed pathogenic bacteria., Results: None of the differences in the operation time, hospital stay, and stone diameter were statistically significant (P > 0.05). Before placement of the double J tube, no striking differences in urinary white blood cells and blood white blood cells were observed between the two groups (P > 0.05). 7 days after the placement of the double J tube, a significant decrease of the urinary white blood cells and blood white blood cells was recorded (P < 0.05), with no significant differences between the two groups (P > 0.05). Before and after placement of double J tube, no striking differences in body temperature > 38.5°C or positive blood culture were observed between the two groups (P > 0.05). Surgical methods, stone removal rate one month after the operation, or incidence of postoperative complications were not significantly different (P > 0.05). 49 pathogenic strains were detected, among which Gram-negative bacteria accounted for 63.27%. The main pathogens were Escherichia coli and Pseudomonas aeruginosa., Conclusion: The indwelling time of the double J tube has no significant effect on the effectiveness and safety in diabetic patients with infected UC. It is necessary to reduce the indwelling time and implement targeted stone surgery., Competing Interests: None., (AJTR Copyright © 2021.)
Published: 2021

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