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2. Modular, Resilient, and Scalable System Design Approaches -- Lessons learned in the years after DARPA Subterranean Challenge

Author

Sriganesh, Prasanna, Maier, James, Johnson, Adam, Shirose, Burhanuddin, Chandrasekar, Rohan, Noren, Charles, Spisak, Joshua, Darnley, Ryan, Vundurthy, Bhaskar, and Travers, Matthew

Subjects
Computer Science - Robotics
Abstract

Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate robot capabilities. In this work, we present a system architecture designed to optimize both robot autonomy and the operator experience in multi-robot scenarios. Drawing on lessons learned from our team's participation in the DARPA SubT Challenge, our architecture emphasizes modularity and interoperability. We empower the operator by allowing for adjustable levels of autonomy ("sliding mode autonomy"). We enhance the operator experience by using intuitive, adaptive interfaces that suggest context-aware actions to simplify control. Finally, we describe how the proposed architecture enables streamlined development of new capabilities for effective deployment of robot autonomy in the field., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Published
2024

7. The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

Author

Fusaroli, Michele, Salvo, Francesco, Begaud, Bernard, AlShammari, Thamir M., Bate, Andrew, Battini, Vera, Brueckner, Andreas, Candore, Gianmario, Carnovale, Carla, Crisafulli, Salvatore, Cutroneo, Paola Maria, Dolladille, Charles, Drici, Milou-Daniel, Faillie, Jean-Luc, Goldman, Adam, Hauben, Manfred, Herdeiro, Maria Teresa, Mahaux, Olivia, Manlik, Katrin, Montastruc, François, Noguchi, Yoshihiro, Norén, G. Niklas, Noseda, Roberta, Onakpoya, Igho J., Pariente, Antoine, Poluzzi, Elisabetta, Salem, Myriam, Sartori, Daniele, Trinh, Nhung T. H., Tuccori, Marco, van Hunsel, Florence, van Puijenbroek, Eugène, Raschi, Emanuel, and Khouri, Charles

Published
2024
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8. The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement

Author

Fusaroli, Michele, Salvo, Francesco, Begaud, Bernard, AlShammari, Thamir M., Bate, Andrew, Battini, Vera, Brueckner, Andreas, Candore, Gianmario, Carnovale, Carla, Crisafulli, Salvatore, Cutroneo, Paola Maria, Dolladille, Charles, Drici, Milou-Daniel, Faillie, Jean-Luc, Goldman, Adam, Hauben, Manfred, Herdeiro, Maria Teresa, Mahaux, Olivia, Manlik, Katrin, Montastruc, François, Noguchi, Yoshihiro, Norén, G. Niklas, Noseda, Roberta, Onakpoya, Igho J., Pariente, Antoine, Poluzzi, Elisabetta, Salem, Myriam, Sartori, Daniele, Trinh, Nhung T. H., Tuccori, Marco, van Hunsel, Florence, van Puijenbroek, Eugène, Raschi, Emanuel, and Khouri, Charles

Published
2024
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9. Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators

Author

Noren, Håkon, Eidnes, Sølve, and Celledoni, Elena

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

We introduce the mean inverse integrator (MII), a novel approach to increase the accuracy when training neural networks to approximate vector fields of dynamical systems from noisy data. This method can be used to average multiple trajectories obtained by numerical integrators such as Runge-Kutta methods. We show that the class of mono-implicit Runge-Kutta methods (MIRK) has particular advantages when used in connection with MII. When training vector field approximations, explicit expressions for the loss functions are obtained when inserting the training data in the MIRK formulae, unlocking symmetric and high-order integrators that would otherwise be implicit for initial value problems. The combined approach of applying MIRK within MII yields a significantly lower error compared to the plain use of the numerical integrator without averaging the trajectories. This is demonstrated with experiments using data from several (chaotic) Hamiltonian systems. Additionally, we perform a sensitivity analysis of the loss functions under normally distributed perturbations, supporting the favorable performance of MII., Comment: 23 pages, 10 figures

Published
2023

10. Bias mitigation techniques in image classification: fair machine learning in human heritage collections

Author

Pablo, Dalia Ortiz, Badri, Sushruth, Norén, Erik, and Nötzli, Christoph

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the application of these models in human heritage collections, where data sets usually consist of low-quality pictures of people with diverse ethnicity, gender, and age. In this work, we evaluate three bias mitigation techniques using two state-of-the-art neural networks, Xception and EfficientNet, for gender classification. Moreover, we explore the use of transfer learning using a fair data set to overcome the training data scarcity. We evaluated the effectiveness of the bias mitigation pipeline on a cultural heritage collection of photographs from the 19th and 20th centuries, and we used the FairFace data set for the transfer learning experiments. After the evaluation, we found that transfer learning is a good technique that allows better performance when working with a small data set. Moreover, the fairest classifier was found to be accomplished using transfer learning, threshold change, re-weighting and image augmentation as bias mitigation methods.

Published
2023

11. Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods

Author

Noren, Håkon

Subjects
Mathematics - Numerical Analysis, Computer Science - Machine Learning
Abstract

Numerical integrators could be used to form interpolation conditions when training neural networks to approximate the vector field of an ordinary differential equation (ODE) from data. When numerical one-step schemes such as the Runge-Kutta methods are used to approximate the temporal discretization of an ODE with a known vector field, properties such as symmetry and stability are much studied. Here, we show that using mono-implicit Runge-Kutta methods of high order allows for accurate training of Hamiltonian neural networks on small datasets. This is demonstrated by numerical experiments where the Hamiltonian of the chaotic double pendulum in addition to the Fermi-Pasta-Ulam-Tsingou system is learned from data., Comment: 8 pages, 4 figures

Published
2023

14. Shared Situational Awareness with V2X Communication and Set-membership Estimation

Author

Narri, Vandana, Alanwar, Amr, Mårtensson, Jonas, Norén, Christoffer, and Johansson, Karl Henrik

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle's field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper.

Published
2023

16. Distributed Optimal Control Framework for High-Speed Convoys: Theory and Hardware Results

Author

Bagree, Namya, Noren, Charles, Singh, Damanpreet, Travers, Matthew, and Vundurthy, Bhaskar

Subjects
Computer Science - Robotics
Abstract

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle ``drafting'' or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots., Comment: 8 pages, IFAC Conference

Published
2022

18. The Swedish Parliament Corpus 1867 - 2022.

Author

Väinö Aleksi Yrjänäinen, Fredrik Mohammadi Norén, Robert Borges, Johan Jarlbrink, Lotta åberg Brorsson, Anders P. Olsson, Pelle Snickars, and Måns Magnusson

Published
2024

26. Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Author

Gauffin, Oskar, Brand, Judith S., Vidlin, Sara Hedfors, Sartori, Daniele, Asikainen, Suvi, Català, Martí, Chalabi, Etir, Dedman, Daniel, Danilovic, Ana, Duarte-Salles, Talita, García Morales, Maria Teresa, Hiltunen, Saara, Jödicke, Annika M., Lazarevic, Milan, Mayer, Miguel A., Miladinovic, Jelena, Mitchell, Joseph, Pistillo, Andrea, Ramírez-Anguita, Juan Manuel, Reyes, Carlen, Rudolph, Annette, Sandberg, Lovisa, Savage, Ruth, Schuemie, Martijn, Spasic, Dimitrije, Trinh, Nhung T. H., Veljkovic, Nevena, Vujovic, Ankica, de Wilde, Marcel, Zekarias, Alem, Rijnbeek, Peter, Ryan, Patrick, Prieto-Alhambra, Daniel, and Norén, G. Niklas

Published
2023
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27. Activities and Values in School-Age Educare Mathematics

Author

Wallin, Anna, Valero, Paola, and Norén, Eva

Abstract

Based on an empirical study of policy enactment and mathematical enculturation in Swedish school-age educare, a conceptualization of mathematics as the assemblage of "activities" and "values" is proposed. Observations were analysed using policy enactment theory and the lens of mathematical activities. The result shows that making creations, describing relationships and addressing problems are mathematical activities evident in the practices of school-age educare. Values of plausibility, critical stance and connectedness are evident. This alternative conceptualization of mathematics offers possibilities for respecting a balance between education and care in practice. The result opens for alternative ways of thinking about mathematics as a complementing and compensating area that resists the tendency towards "schoolification" in educare.

Published
2021

30. GÊNERO, RAÇA E ‘PROBLEMAS EXTERNALIZANTES’ EM UM SERVIÇO DE SAÚDE MENTAL INFANTIL

Author

Anna Norén Flynn and Vania Bustamante

Subjects
Saúde mental infantil, gênero, raça., Psychology, BF1-990
Abstract

Este estudo visa caracterizar o perfil de crianças que apresentaram queixas envolvendo ‘problemas externalizantes’ em um serviço de saúde mental infantil situado em Salvador, Bahia. Com base nas fichas de acompanhamento das 97 crianças que frequentaram o serviço em 2018, as seguintes variáveis foram levantadas: idade, sexo, raça/cor, motivo de procura, arranjo familiar, principal acompanhante, procedência de encaminhamento e queixa escolar. Com as variáveis levantadas, os dados foram organizados em uma planilha e submetidos à análise estatística simples incluindo a identificação de frequências e o cruzamento de algumas variáveis. Os resultados mostram diferenças na proporção de problemas externalizantes vinculadas às variáveis sexo, raça/cor e queixa escolar. A maioria das crianças é negra (71,14%), do sexo masculino (70,10%), com idade entre de 4-9 anos (61,85%). Foram trazidas queixas envolvendo problemas externalizantes para 54,42% dos meninos e 31,03% das meninas e para 56,41% das crianças pretas, 56,67% das crianças pardas e 40% das crianças brancas. Chama a atenção a alta frequência de queixas escolares relacionadas às crianças que apresentaram problemas externalizantes e a maior proporção de queixas escolares entre crianças pretas quando comparadas às crianças pardas e brancas. Os resultados reiteram alguns achados de estudos anteriores, como as diferenças de gênero, e inovam ao trazer algumas relações com raça/cor e queixa escolar. Destaca-se a necessidade de aprofundar este recorte em novos estudos visando o fortalecimento de práticas de saúde não sexistas e antirracistas.

Published
2024
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31. Properties of vertebrate predator–prey networks in the high Arctic

Author

Muzit Abrham, Karin Norén, Jordi Bartolomé Filella, Anders Angerbjörn, Nicolas Lecomte, Patrícia Pečnerová, Susana Freire, and Fredrik Dalerum

Subjects
Ellesmere Island, Greenland, modularity, nestedness, predation, trophic network, Ecology, QH540-549.5
Abstract

Abstract Predation is an important ecological process that can significantly impact the maintenance of ecosystem services. In arctic environments, the relative ecological importance of predation is thought to be increasing due to climate change, partly because of increased productivity with rising temperatures. Therefore, understanding predator–prey interactions in arctic ecosystems is vital for the sustainable management of these northern regions. Network theory provides a framework for quantifying the structures of ecological interactions. In this study, we use dietary observations on mammalian and avian predators in a high arctic region, including isolated peninsulas on Ellesmere Island and north Greenland, to construct bipartite trophic networks. We quantify the complexity, specialization, and nested as well as modular structures of these networks and also determine if these properties varied among the peninsulas. Mammal prey remains were the dominant diet item for all predators, but there was spatial variation in diet composition among peninsulas. The predator–prey networks were less complex, had more specialized interactions, and were more nested and more modular than random expectations. However, the networks displayed only moderate levels of modularity. Predator species had less specialized interactions with prey than prey had with predators. All network properties differed among the peninsulas, which highlights that ecosystems often show complex responses to environmental characteristics. We suggest that gaining knowledge about spatial variation in the characteristics of predator–prey interactions can enhance our ability to manage ecosystems exposed to environmental perturbations, particularly in high arctic environments subject to rapid environmental change.

Published
2024
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32. Interpretable Identification of Comorbidities Associated with Recurrent ED and Inpatient Visits

Author

Liu, Luoluo, Simhon, Eran, Kulkarni, Chaitanya, Noren, David, and Mans, Ronny

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource usage. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing reoccurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. Additionally, health care costs can be reduced significantly with fewer preventable recurrent visits. To address this, we developed a computationally efficient and interpretable framework that both identifies recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Specifically, we present a novel algorithm, called the minimum similarity association rules (MSAR), balancing confidence-support trade-off, to determine the conditions most associated with reoccurring Emergency department (ED) and inpatient visits. We validate MSAR on a large Electric Health Record (EHR) dataset.

Published
2021

35. Garbage consumption by Arctic terrestrial predators in one of the most pristine land areas on Earth

Author

Araceli Gort-Esteve, Muzit Abrham, Christian Carøe, Johannes Måsviken, Susana Freire, Nicolas Lecomte, Patrícia Pečnerová, Anders Angerbjörn, Jordi Bartolomé Filella, Karin Norén, and Fredrik Dalerum

Subjects
litter, terrestrial, arctic fox, arctic wolf, greenland, canada, Environmental sciences, GE1-350, Oceanography, GC1-1581
Abstract

Garbage may cause substantial environmental perturbations, in part because of its consumption by wildlife. Such consumption may have direct health implications for animals and may also influence trophic relationships. Even in pristine Arctic ecosystems, wildlife feeding in marine environments consume garbage in the form of plastic debris transported by ocean currents. We show that Arctic wildlife in pristine terrestrial environments may also ingest garbage or food items derived from abandoned camp sites. We found the remains of a chocolate wrapper and a milk powder bag in two Arctic fox (Vulpes lagopus) scats and a piece of cloth in an Arctic wolf (Canis lupus arctos) scat collected near Nares Strait, northern Greenland, one of the most pristine terrestrial wilderness regions on Earth. Found on Washington Land and associated with long-abandoned camp sites, these three scats were among 657 Arctic fox scats and 92 wolf scats collected as part of a larger study. Our study demonstrates that these two highly opportunistic predators managed to consume garbage despite the almost complete lack of human activity in this High-Arctic region. Our results highlight that abandoned anthropogenic material in the High Arctic may function as a source of garbage for local terrestrial wildlife over extended time periods, and that garbage consumption may become a potential issue if human activity in remote Arctic regions increases.

Published
2024
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36. Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia

Author

Olga Krali, Yanara Marincevic-Zuniga, Gustav Arvidsson, Anna Pia Enblad, Anders Lundmark, Shumaila Sayyab, Vasilios Zachariadis, Merja Heinäniemi, Janne Suhonen, Laura Oksa, Kaisa Vepsäläinen, Ingegerd Öfverholm, Gisela Barbany, Ann Nordgren, Henrik Lilljebjörn, Thoas Fioretos, Hans O. Madsen, Hanne Vibeke Marquart, Trond Flaegstad, Erik Forestier, Ólafur G. Jónsson, Jukka Kanerva, Olli Lohi, Ulrika Norén-Nyström, Kjeld Schmiegelow, Arja Harila, Mats Heyman, Gudmar Lönnerholm, Ann-Christine Syvänen, and Jessica Nordlund

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.

Published
2023
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38. Optimized cytogenetic risk-group stratification of KMT2A-rearranged pediatric acute myeloid leukemia

Author

van Weelderen, Romy E., Harrison, Christine J., Klein, Kim, Jiang, Yilin, Abrahamsson, Jonas, Alonzo, Todd, Aplenc, Richard, Arad-Cohen, Nira, Bart-Delabesse, Emmanuelle, Buldini, Barbara, De Moerloose, Barbara, Dworzak, Michael N., Elitzur, Sarah, Fernández Navarro, José M., Gamis, Alan, Gerbing, Robert B., Goemans, Bianca F., de Groot-Kruseman, Hester A., Guest, Erin, Ha, Shau-Yin, Hasle, Henrik, Kelaidi, Charikleia, Lapillonne, Hélène, Leverger, Guy, Locatelli, Franco, Miyamura, Takako, Norén-Nyström, Ulrika, Polychronopoulou, Sophia, Rasche, Mareike, Rubnitz, Jeffrey E., Stary, Jan, Tierens, Anne, Tomizawa, Daisuke, Zwaan, C. Michel, and Kaspers, Gertjan J. L.

Published
2024
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40. Mathematics in Initial Teacher Education Programs in Sweden, Germany, and Australia

Author

Mathematics Education Research Group of Australasia, Cooke, Audrey, Jenßen, Lars, and Norén, Eva Norén

Abstract

International comparisons of student achievement in mathematics and their attitudes towards, confidence with and anxiety to mathematics have a long history. Likewise, detailed international comparisons of teacher education programs have also been conducted. However, the relationship between the teacher education programs and teacher anxiety for teaching mathematics have not been investigated. This paper is part of a larger research project investigating the relationship between teacher education programs and mathematical anxiety of its pre-service teachers. It reports on the initial comparison of the teacher education programs for primary teachers from a university in Sweden, Germany, and Australia, specifically the mathematics education addressed in the programs. The paper concludes with an outline of the future research.

Published
2019

41. Set-Membership Estimation in Shared Situational Awareness for Automated Vehicles in Occluded Scenarios

Author

Narri, Vandana, Alanwar, Amr, Mårtensson, Jonas, Norén, Christoffer, Col, Laura Dal, and Johansson, Karl Henrik

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Robotics
Abstract

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to appropriately adjust the ego-vehicle speed to maintain a proper safety margin. It is also argued that the framework is suitable for handling sensor failures and false detections in a systematic way. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety., Comment: This paper is submitted to IEEE Intelligent Vehicles Symposium 2021

Published
2021

43. Diagnostic yield and clinical impact of germline sequencing in children with CNS and extracranial solid tumors—a nationwide, prospective Swedish study

Author

Tesi, Bianca, Robinson, Kristina Lagerstedt, Abel, Frida, Díaz de Ståhl, Teresita, Orrsjö, Sara, Poluha, Anna, Hellberg, Maria, Wessman, Sandra, Samuelsson, Sofie, Frisk, Tony, Vogt, Hartmut, Henning, Karin, Sabel, Magnus, Ek, Torben, Pal, Niklas, Nyman, Per, Giraud, Geraldine, Wille, Joakim, Pronk, Cornelis Jan, Norén-Nyström, Ulrika, Borssén, Magnus, Fili, Maria, Stålhammar, Gustav, Herold, Nikolas, Tettamanti, Giorgio, Maya-Gonzalez, Carolina, Arvidsson, Linda, Rosén, Anna, Ekholm, Katja, Kuchinskaya, Ekaterina, Hallbeck, Anna-Lotta, Nordling, Margareta, Palmebäck, Pia, Kogner, Per, Smoler, Gunilla Kanter, Lähteenmäki, Päivi, Fransson, Susanne, Martinsson, Tommy, Shamik, Alia, Mertens, Fredrik, Rosenquist, Richard, Wirta, Valtteri, Tham, Emma, Grillner, Pernilla, Sandgren, Johanna, Ljungman, Gustaf, Gisselsson, David, Taylan, Fulya, and Nordgren, Ann

Published
2024
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44. Immunomodulators for immunocompromised patients hospitalized for COVID-19: a meta-analysis of randomized controlled trials

Author

Hermine, Olivier, Mariette, Xavier, Ravaud, Philippe, Bureau, Serge, Dougados, Maxime, Resche-Rigon, Matthieu, Tharaux, Pierre-Louis, Tibi, Annick, Azoulay, Elie, Cadranel, Jacques, Emmerich, Joseph, Fartoukh, Muriel, Guidet, Bertrand, Humbert, Marc, Lacombe, Karine, Mahevas, Matthieu, Pene, Frédéric, Porcher, Raphaël, Pourchet-Martinez, Valerie, Schlemmer, Frédéric, Yazdanpanah, Yazdan, Baron, Gabriel, Perrodeau, Elodie, Vanhoye, Damien, Kedzia, Cécile, Demerville, Lauren, Gysembergh-Houal, Anne, Bourgoin, Alexandre, Raked, Nabil, Mameri, Lakhdar, Montlahuc, Claire, Biard, Lucie, Alary, St.phanie, Hamiria, Samir, Bariz, Thinhinane, Semri, Hala, Hai, Dhiaa Meriem, Benafla, Moustafa, Belloul, Mohamed, Vauboin, Pernelle, Flamand, Saskia, Pacheco, Claire, Walter-Petrich, Anouk, Stan, Emilia, Benarab, Souad, Nyanou, Corine, Charreteur, Robin, Dupre, Céline, Cardet, Kévin, Lehmann, Blandine, Baghli, Kamyl, Madelaine, Claire, D'Ortenzio, Eric, Puéchal, Oriane, Semaille, Caroline, Savale, Laurent, Harrois, Anatole, Figueiredo, Samy, Duranteau, Jacques, Anguel, Nadia, Pavot, Arthur, Monnet, Xavier, Richard, Christian, Teboul, Jean-Louis, Durand, Philippe, Tissieres, Pierre, Jevnikar, Mitja, Montani, David, Pavy, Stephan, Nocturne, Gaétane, Bitoun, Samuel, Noel, Nicolas, Lambotte, Olivier, Escaut, Lelia, Jauréguiberry, Stephane, Baudry, Elodie, Verny, Christiane, Lefevre, Edouard, Zaidan, Mohamad, Molinari, Domitille, Leprun, Gaël, Fourreau, Alain, Cylly, Laurent, Grimaldi, Lamiae, Virlouvet, Myriam, Meftali, Ramdane, Fabre, Soléne, Licois, Marion, Mamoune, Asmaa, Boudali, Yacine, Le Tiec, Clotilde, Verstuyft, Céline, Roques, Anne-Marie, Georgin-Lavialle, Sophie, Senet, Patricia, Pialoux, Gilles, Soria, Angele, Parrot, Antoine, François, Helene, Rozensztajn, Nathalie, Blin, Emmanuelle, Choinier, Pascaline, Camuset, Juliette, Rech, Jean-Simon, Canellas, Antony, Rolland-Debord, Camille, Lemarié, Nadege, Belaube, Nicolas, Nadal, Marine, Siguier, Martin, Petit-Hoang, Camille, Chas, Julie, Drouet, Elodie, Lemoine, Matthieu, Phibel, Audrey, Aunay, Lucie, Bertrand, Eliane, Ravato, Sylviane, Vayssettes, Marie, Adda, Anne, Wilpotte, Celine, Thibaut, Pélagie, Fillon, Julie, Debrix, Isabelle, Fellahi, Soraya, Bastard, Jean-Philippe, Lefévre, Guillaume, Gottenberg, Jacques-Eric, Hansmann, Yves, Blanc, Frédéric, Ohlmann-Caillard, Sophie, Castelain, Vincent, Chatelus, Emmanuel, Chatron, Eva, Collange, Olivier, Danion, François, De Blay, Frédéric, Diemunsch, Pierre, Diemunsch, Sophie, Felten, Renaud, Goichot, Bernard, Greigert, Valentin, Guffroy, Aurelien, Heger, Bob, Kaeuffer, Charlotte, Kassegne, Loic, Korganow, Anne Sophie, Le Borgne, Pierrick, Lefebvre, Nicolas, Mertes, Paul-Michel, Noll, Eric, Oberlin, Mathieu, Poindron, Vincent, Pottecher, Julien, Ruch, Yvon, Weill, François, Meyer, Nicolas, Andres, Emmanuel, Demonsant, Eric, Tayebi, Hakim, Nisand, Gabriel, Brin, Stéphane, Sublon, Cédric, Becker, Guillaume, Hutt, Anne, Martin, Tristan, Bayer, Sophie, Metzger, Catherine, Mekinian, Arsene, Abisror, Noémie, Adedjouma, Amir, Bollens, Diane, Bonneton, Marion, Bourcicaux, Nathalie, Bourrier, Anne, Thibault Chiarabiani, Maria Chauchard, Chopin, Doroth.e, Cohen, Jonathan, Devred, Ines, Donadille, Bruno, Fain, Olivier, Hariri, Geoffrey, Jachiet, Vincent, Ingliz, Patrick, Garnier, Marc, Gatfosse, Marc, Ghrenassia, Etienne, Gobert, Delphine, Krause le Garrec, Jessica, Landman, Cecilia, Lavillegrand, Jean Remy, Lefebvre, Benedicte, Mahevas, Thibault, Mazerand, Sandie, Meynard, Jean Luc, Morgand, Marjolaine, Ouaz.ne, Zineb, Pacanowski, Jerome, Riviere, S.bastien, Seksik, Philippe, Sokol, Harry, Soliman, Heithem, Valin, Nadia, Urbina, Thomas, McAvoy, Chloé, Miranda, Maria Pereira, Aratus, Gladys, Berard, Laurence, Simon, Tabassome, Nguyen, Anne Daguenel, Girault, Elise, Mayala-Kanda, Cl.mentine, Antignac, Marie, Leplay, Céline, Arlet, Jean-Benoit, Diehl, Jean-Luc, Bellenfant, Florence, Blanchard, Anne, Buffet, Alexandre, Cholley, Bernard, Fayol, Antoine, Flamarion, Edouard, Godier, Anne, Gorget, Thomas, Hamada, Sophie-Rym, Hauw-Berlemont, Caroline, Hulot, Jean-Sébastien, Lebeaux, David, Livrozet, Marine, Michon, Adrien, Neuschwander, Arthur, Pennet, Marie-Aude, Planquette, Benjamin, Ranque, Brigitte, Sanchez, Olivier, Volle, Geoffroy, Briois, Sandrine, Cornic, Mathias, Elisee, Virginie, Denis, Jesuthasan, Djadi-Prat, Juliette, Jouany, Pauline, Junquera, Ramon, Henriques, Mickael, Kebir, Amina, Lehir, Isabelle, Meunier, Jeanne, Patin, Florence, Paquet, Val.rie, Tréhan, Anne, Vigna, Véronique, Sabatier, Brigitte, Bergerot, Damien, Jouve, Charléne, Knosp, Camille, Lenoir, Olivia, Mahtal, Nassim, Resmini, Léa, Lescure, Xavier, Ghosn, Jade, Bachelard, Antoine, Rachline, Anne, Isernia, Valentina, Bao-chau, Phung, Vallois, Dorothée, Sautereau, Aurelie, Neukrich, Catherine, Dossier, Antoine, Borie, Raphaël, Crestani, Bruno, Ducrocq, Gregory, Steg, Philippe Gabriel, Dieude, Philippe, Papo, Thomas, Marcault, Estelle, Chaudhry, Marhaba, Da Silveira, Charléne, Metois, Annabelle, Mahenni, Ismahan, Meziani, Meriam, Nilusmas, Cyndie, Le Gac, Sylvie, Ndiaye, Awa, Louni, Fran.oise, Chansombat, Malikhone, Julia, Zelie, Chalal, Solaya, Chalal, Lynda, Kramer, Laura, Le Grand, Jeniffer, Ouifiya, Kafif, Piquard, Valentine, Tubiana, Sarah, Nguyen, Yann, Honsel, Vasco, Weiss, Emmanuel, Codorniu, Anais, Zarrouk, Virginie, de Lastours, Victoire, Uzzan, Matthieu, Gamany, Naura, Claveirole, Agathe, Navid, Alexandre, Fouque, Tiffanie, Cohen, Yonathan, Lupo, Maya, Gilles, Constance, Rahli, Roza, Louis, Zeina, Boutboul, David, Galicier, Lionel, Amara, Yaël, Archer, Gabrielle, Benattia, Amira, Bergeron, Anne, Bondeelle, Louise, de Castro, Nathalie, Clément, Melissa, Darmon, Michaël, Denis, Blandine, Dupin, Clairelyne, Feredj, Elsa, Feyeux, Delphine, Joseph, Adrien, Lenglin, Etienne, Le Guen, Pierre, Liégeon, Geoffroy, Lorillon, Gwenaël, Mabrouki, Asma, Mariotte, Eric, Martin de Frémont, Grégoire, Mirouse, Adrien, Molina, Jean-Michel, Peffault de Latour, Régis, Oksenhendler, Eric, Saussereau, Julien, Tazi, Abdellatif, Tudesq, Jean-Jacques, Zafrani, Lara, Brindele, Isabelle, Bugnet, Emmanuelle, Lebras, Karine Celli, Chabert, Julien, Djaghout, Lamia, Fauvaux, Catherine, Jegu, Anne Lise, Kozakiewicz, Ewa, Meunier, Martine, Tremorin, Marie-Thérèse, Davoine, Claire, Madelaine, Isabelle, Caillat-Zucman, Sophie, Delaugerre, Constance, Morin, Florence, Sène, Damien, Burlacu, Ruxandra, Chousterman, Benjamin, Mégarbanne, Bruno, Richette, Pascal, Riveline, Jean-Pierre, Frazier, Aline, Vicaut, Eric, Berton, Laure, Hadjam, Tassadit, Vazquez-Ibarra, Miguel Alejandro, Jourdaine, Clément, Tran, Olivia, Jouis, Véronique, Jacob, Aude, Smati, Julie, Renaud, Stéphane, Pernin, Claire, Suarez, Lydia, Semerano, Luca, Abad, Sébastien, nainous, Ruben B., Bonnet, Nicolas, Comparon, Celine, Cohen, Yves, Cordel, Hugues, Dhote, Robin, Dournon, Nathalie, Duchemann, Boris, Ebstein, Nathan, Gille, Thomas, Giroux-Leprieur, Benedicte, Goupil de Bouille, Jeanne, Nunes, Hilario, Oziel, Johanna, Roulot, Dominique, Sese, Lucile, ClaireTantet, Uzunhan, Yurdagul, Bloch-Queyrat, Coralie, Levy, Vincent, Messani, Fadhila, Rahaoui, Mohammed, Petit, Myléne, Brahmi, Sabrina, Rathoin, Vanessa, Rigal, Marthe, Costedoat-Chalumeau, Nathalie, Luong, Liem Binh, Hamou, Zakaria Ait, Benghanem, Sarah, Blanche, Philippe, Carlier, Nicolas, Chaigne, Benjamin, Gauzit, Remy, Joumaa, Hassan, Jozwiak, Mathieu, Lachétre, Marie, Lafoeste, Hélène, Launay, Odie, Legendre, Paul, Marey, Jonathan, Morbieu, Caroline, Palmieri, Lola-Jade, Szwebel, Tali-Anne, Abdoul, Hendy, Bruneau, Alexandra, Beclin-Clabaux, Audrey, Larrieu, Charly, Montanari, Pierre, Dufour, Eric, Clarke, Ada, Le Bourlout, Catherine, Marin, Nathalie, Menage, Nathalie, Saleh-Mghir, Samira, Cisse, Mamadou Salif, Cheref, Kahina, Guerin, Corinne, Zerbit, Jérémie, Michel, Marc, Gallien, Sébastien, Crickx, Etienne, Le Vavasseur, Benjamin, Kempf, Emmanuelle, Jaffal, Karim, Vindrios, William, Oniszczuk, Julie, Guillaud, Constance, Lim, Pascal, Fois, Elena, Melica, Giovanna, Matignon, Marie, Jalabert, Maud, Lelièvre, Jean-Daniel, Schmitz, David, Bourhis, Marion, Belazouz, Sylia, Languille, Laetitia, Boucle, Caroline, Cita, Nelly, Didier, Agnés, Froura, Fahem, Ledudal, Katia, Sadaoui, Thiziri, Thiemele, Alaki, Le Febvre De Bailly, Delphine, Verlinde, Muriel Carvhalo, Mayaux, Julien, Cacoub, Patrice, Saadoun, David, Vautier, Mathieu, Bugaut, Héléne, Benveniste, Olivier, Allenbach, Yves, Leroux, Gaëlle, Rigolet, Aude, Guillaume-Jugnot, Perrine, Domont, Fanny, Desbois, Anne Claire, Comarmond, Chloé, Champtiaux, Nicolas, Toquet, Segolene, Ghembaza, Amine, Vieira, Matheus, Maalouf, Georgina, Boleto, Goncalo, Ferfar, Yasmina, Corvol, Jean-Christophe, Louapre, C.line, Sambin, Sara, Mariani, Louise-Laure, Karachi, Carine, Tubach, Florence, Estellat, Candice, Gimeno, Linda, Martin, Karine, Bah, Aicha, Keo, Vixra, Ouamri, Sabrine, Messaoudi, Yasmine, Yelles, Nessima, Faye, Pierre, Cavelot, Sebastien, Larcheveque, Cecile, Annonay, Laurence, Benhida, Jaouad, Zahrate-Ghoul, Aida, Hammal, Soumeya, Belilita, Ridha, Charbonnier, Fanny, Aguilar, Claire, Alby-Laurent, Fanny, Burger, Carole, Campos-Vega, Clara, Chavarot, Nathalie, Fournier, Benjamin, Rouzaud, Claire, Vimpére, Damien, Elie, Caroline, Bakouboula, Prissile, Choupeaux, Laure, Granville, Sophie, Issorat, Elodie, Broissand, Christine, Alyanakian, Marie-Alexandra, Geri, Guillaume, Derridj, Nawal, Sguiouar, Naima, Meddah, Hakim, Djadel, Mourad, Chambrin-Lauvray, Héléne, Duclos-vallée, Jean-Charles, Saliba, Faouzi, Sacleux, Sophie-Caroline, Kounis, Ilias, Tamazirt, Sonia, Rudant, Eric, Michot, Jean-Marie, Stoclin, Annabelle, Colomba, Emeline, Pommeret, Fanny, Willekens, Christophe, Da Silva, Rosa, Dejean, Valérie, Mekid, Yasmina, Ben-Mabrouk, Ines, Netzer, Florence, Pradon, Caroline, Drouard, Laurence, Camara-Clayette, Valérie, Morel, Alexandre, Garcia, Gilles, Mohebbi, Abolfazl, Berbour, Férial, Dehais, Mélanie, Pouliquen, Anne-Lise, Klasen, Alison, Soyez-Herkert, Loren, London, Jonathan, Keroumi, Younes, Guillot, Emmanuelle, Grailles, Guillaume, El amine, Younes, Defrancq, Fanny, Fodil, Hanane, Bouras, Chaouki, Dautel, Dominique, Gambier, Nicolas, Dieye, Thierno, Bienvenu, Boris, Lancon, Victor, Lecomte, Laurence, Beziriganyan, Kristina, Asselate, Belkacem, Allanic, Laure, Kiouris, Elena, Legros, Marie-Héléne, Lemagner, Christine, Martel, Pascal, Provitolo, Vincent, Ackermann, Félix, Le Marchand, Mathilde, Chan Hew Wai, Aurélie, Fremont, Dimitri, Coupez, Elisabeth, Adda, Mireille, Duée, Frédéric, Bernard, Lise, Gros, Antoine, Henry, Estelle, Courtin, Claire, Pattyn, Anne, Guinot, Pierre-Grégoire, Bardou, Marc, Maurer, Agnes, Jambon, Julie, Cransac, Amélie, Pernot, Corinne, Mourvillier, Bruno, Marquis, Eric, Benoit, Philippe, Roux, Damien, Gernez, Coralie, Yelnik, Cécile, Poissy, Julien, Nizard, Mandy, Denies, Fanette, Gros, Helene, Mourad, Jean-Jacques, Sacco, Emmanuelle, Renet, Sophie, Ader, F., Yazdanpanah, Y., Mentre, F., Peiffer-Smadja, N., Lescure, F.X., Poissy, J., Bouadma, L., Timsit, J.F., Lina, B., Morfin-Sherpa, F., Bouscambert, M., Gaymard, A., Peytavin, G., Abel, L., Guedj, J., Andrejak, C., Burdet, C., Laouenan, C., Belhadi, D., Dupont, A., Alfaiate, T., Basli, B., Chair, A., Laribi, S., Level, J., Schneider, M., Tellier, M.C., Dechanet, A., Costagliola, D., Terrier, B., Ohana, M., Couffin-Cadiergues, S., Esperou, H., Delmas, C., Saillard, J., Fougerou, C., Moinot, L., Wittkop, L., Cagnot, C., Le Mestre, S., Lebrasseur-Longuet, D., Petrov-Sanchez, V., Diallo, A., Mercier, N., Icard, V., Leveau, B., Tubiana, S., Hamze, B., Gelley, A., Noret, M., D’Ortenzio, E., Puechal, O., Semaille, C., Welte, T., Paiva, J.A., Halanova, M., Kieny, M.P., Balssa, E., Birkle, C., Gibowski, S., Landry, E., Le Goff, A., Moachon, L., Moins, C., Wadouachi, L., Paul, C., Levier, A., Bougon, D., Djossou, F., Epelboin, L., Dellamonica, J., Marquette, C.H., Robert, C., Gibot, S., Senneville, E., Jean-Michel, V., Zerbib, Y., Chirouze, C., Boyer, A., Cazanave, C., Gruson, D., Malvy, D., Andreu, P., Quenot, J.P., Terzi, N., Faure, K., Chabartier, C., Le Moing, V., Klouche, K., Ferry, T., F, Valour, Gaborit, B., Canet, E., Le Turnier, P., Boutoille, D., Bani-Sadr, F., Benezit, F., Revest, M., Cameli, C., Caro, A., Um Tegue, MJ Ngo, Le Tulzo, Y., Laviolle, B., Laine, F., Thiery, G., Meziani, F., Hansmann, Y., Oulehri, W., Tacquard, C., Vardon-Bounes, F., Riu-Poulenc, B., Murris-Espin, M., Bernard, L., Garot, D., Hinschberger, O., Martinot, M., Bruel, C., Pilmis, B., Bouchaud, O., Loubet, P., Roger, C., Monnet, X., Figueiredo, S., Godard, V., Mira, J.P., Lachatre, M., Kerneis, S., Aboab, J., Sayre, N., Crockett, F., Lebeaux, D., Buffet, A., Diehl, J.L., Fayol, A., Hulot, J.S., Livrozet, M., Dessap, A Mekontso, Ficko, C., Stefan, F., Le Pavec, J., Mayaux, J., Ait-Oufella, H., Molina, J.M., Pialoux, G., Fartoukh, M., Textoris, J., Brossard, M., Essat, A., Netzer, E., Riault, Y., Ghislain, M., Beniguel, L., Genin, M., Gouichiche, L., Betard, C., Belkhir, L., Altdorfer, A., Centro, V Fraipont, Braz, S., Ribeiro, JM Ferreira, Alburqueque, R Roncon, Berna, M., Alexandre, M., Lamprecht, B., Egle, A., Greil, R., Joannidis, M., Patterson, Thomas F., Ponce, Philip O., Taylor, Barbara S., Patterson, Jan E., Bowling, Jason E., Javeri, Heta, Kalil, Andre C., Larson, LuAnn, Hewlett, Angela, Mehta, Aneesh K., Rouphael, Nadine G., Saklawi, Youssef, Scanlon, Nicholas, Traenkner, Jessica J., Trible, Ronald P., Jr., Walter, Emmanuel B., Ivey, Noel, Holland, Thomas L., Ruiz-Palacios, Guillermo M., Ponce de León, Alfredo, Rajme, Sandra, Hsieh, Lanny, Amin, Alpesh N., Watanabe, Miki, Lee, Helen S., Kline, Susan, Billings, Joanne, Noren, Brooke, Kim, Hyun, Bold, Tyler D., Tapson, Victor, Grein, Jonathan, Sutterwala, Fayyaz, Iovine, Nicole, Beattie, Lars K., Wakeman, Rebecca Murray, Shaw, Matthew, Jain, Mamta K., Mocherla, Satish, Meisner, Jessica, Luque, Amneris, Sweeney, Daniel A., Benson, Constance A., Ali, Farhana, Atmar, Robert L., El Sahly, Hana M., Whitaker, Jennifer, Falsey, Ann R., Branche, Angela R., Rozario, Cheryl, Pineda, Justino Regalado, Martinez-Orozco, José Arturo, Lye, David Chien, Ong, Sean WX., Chia, Po Ying, Young, Barnaby E., Sandkovsky, Uriel, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Cantos, Valeria D., Kelley, Colleen F., Rebolledo Esteinou, Paulina A., Kandiah, Sheetal, Doernberg, Sarah B., Crouch, Pierre-Cedric B., Jang, Hannah, Luetkemeyer, Anne F., Dwyer, Jay, Cohen, Stuart H., Thompson, George R., 3rd, Nguyen, Hien H., Finberg, Robert W., Wang, Jennifer P., Perez-Velazquez, Juan, Wessolossky, Mireya, Jackson, Patrick E.H., Bell, Taison D., West, Miranda J., Taiwo, Babafemi, Krueger, Karen, Perez, Johnny, Pearson, Triniece, Paules, Catharine I., Julian, Kathleen G., Ahmad, Danish, Hajduczok, Alexander G., Arguinchona, Henry, Arguinchona, Christa, Erdmann, Nathaniel, Goepfert, Paul, Ahuja, Neera, Frank, Maria G., Wyles, David, Young, Heather, Oh, Myoung-don, Park, Wan Beom, Kang, Chang Kyung, Marconi, Vincent, Moanna, Abeer, Cribbs, Sushma, Harrison, Telisha, Kim, Eu Suk, Jung, Jongtak, Song, Kyoung-Ho, Kim, Hong Bin, Tan, Seow Yen, Shafi, Humaira, Chien, Jaime, Fong, Raymond KC., Murray, Daniel D., Lundgren, Jens, Nielsen, Henrik, Jensen, Tomas, Zingman, Barry S., Grossberg, Robert, Riska, Paul F., Yang, Otto O., Ahn, Jenny, Arias, Rubi, Rapaka, Rekha R., Hauser, Naomi, Campbell, James D., Short, William R., Tebas, Pablo, Baron, Jillian T., McLellan, Susan L.F., Blanton, Lucas S., Seashore, Justin B., Creech, C. Buddy, Rice, Todd W., Walker, Shannon, Thomsen, Isaac P., Lopez de Castilla, Diego, Van Winkle, Jason W., Riedo, Francis X., Pada, Surinder Kaur, Wang, Alvin DY., Lin, Li, Harkins, Michelle, Mertz, Gregory, Sosa, Nestor, Ann Chai, Louis Yi, Tambyah, Paul Anantharajah, Tham, Sai Meng, Archuleta, Sophia, Yan, Gabriel, Lindholm, David A., Markelz, Ana Elizabeth, Mende, Katrin, Mularski, Richard, Hohmann, Elizabeth, Torres-Soto, Mariam, Jilg, Nikolaus, Maves, Ryan C., Utz, Gregory C., George, Sarah L., Hoft, Daniel F., Brien, James D., Paredes, Roger, Mateu, Lourdes, Loste, Cora, Kumar, Princy, Thornton, Sarah, Mohanraj, Sharmila, Hynes, Noreen A., Sauer, Lauren M., Colombo, Christopher J., Schofield, Christina, Colombo, Rhonda E., Chambers, Susan E., Novak, Richard M., Wendrow, Andrea, Gupta, Samir K., Lee, Tida, Lalani, Tahaniyat, Holodniy, Mark, Chary, Aarthi, Huprikar, Nikhil, Ganesan, Anuradha, Ohmagari, Norio, Mikami, Ayako, Price, D. Ashley, Duncan, Christopher J.A., Dierberg, Kerry, Neumann, Henry J., Taylor, Stephanie N., Lacour, Alisha, Masri, Najy, Swiatlo, Edwin, Widmer, Kyle, Neaton, James D., Bessesen, Mary, Stephens, David S., Burgess, Timothy H., Uyeki, Timothy M., Walker, Robert, Marks, G. Lynn, Osinusi, Anu, Cao, Huyen, Cardoso, Anabela, de Bono, Stephanie, Schlichting, Douglas E., Chung, Kevin K., Ferreira, Jennifer L., Green, Michelle, Makowski, Mat, Wierzbicki, Michael R., Conrad, Tom M., El-Khorazaty, Jill Ann, Hill, Heather, Bonnett, Tyler, Gettinger, Nikki, Engel, Theresa, Lewis, Teri, Wang, Jing, Beigel, John H., Tomashek, Kay M., Ghazaryan, Varduhi, Beresnev, Tatiana, Nayak, Seema, Dodd, Lori E., Dempsey, Walla, Nomicos, Effie, Lee, Marina, Pikaart-Tautges, Rhonda, Elsafy, Mohamed, Jurao, Robert, Koo, Hyung, Proschan, Michael, Yokum, Tammy, Arega, Janice, Florese, Ruth, Voell, Jocelyn D., Davey, Richard, Serrano, Ruth C., Wiley, Zanthia, Phadke, Varun K., Goepfert, Paul A., Gomez, Carlos A., Sofarelli, Theresa A., Certain, Laura, Imlay, Hannah N., Wolfe, Cameron R., Ko, Emily R., Engemann, John J., Felix, Nora Bautista, Wan, Claire R., Elmor, Sammy T., Bristow, Laurel R., Harkins, Michelle S., Iovine, Nicole M., Elie-Turenne, Marie-Carmelle, Tapson, Victor F., Choe, Pyoeng Gyun, Mularski, Richard A., Rhie, Kevin S., Hussein, Rezhan H., Ince, Dilek, Winokur, Patricia L., Takasaki, Jin, Saito, Sho, McConnell, Kimberly, Wyles, David L., Sarcone, Ellen, Grimes, Kevin A., Perez, Katherine, Janak, Charles, Whitaker, Jennifer A., Rebolledo, Paulina A., Gharbin, John, Lambert, Allison A., Zea, Diego F., Bainbridge, Emma, Hostler, David C., Hostler, Jordanna M., Shahan, Brian T., Ling, Evelyn, Go, Minjoung, Hubbard, Fleesie A., Chakrabarty, Melony, Laguio-Vila, Maryrose, Walsh, Edward E., Guirgis, Faheem, Marconi, Vincent C., Madar, Christian, Borgetti, Scott A., Levine, Corri, Nock, Joy, Candiotti, Keith, Rozman, Julia, Dangond, Fernando, Hyvert, Yann, Seitzinger, Andrea, Cross, Kaitlyn, Pettibone, Stephanie, Nayak, Seema U., Deye, Gregory A., Siempos, Ilias I., Belhadi, Drifa, Veiga, Viviane Cordeiro, Cavalcanti, Alexandre Biasi, Branch-Elliman, Westyn, Papoutsi, Eleni, Gkirgkiris, Konstantinos, Xixi, Nikoleta A., and Kotanidou, Anastasia

Published
2024
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47. Multi-omics for studying and understanding polar life

Author

M. S. Clark, J. I. Hoffman, L. S. Peck, L. Bargelloni, D. Gande, C. Havermans, B. Meyer, T. Patarnello, T. Phillips, K. R. Stoof-Leichsenring, D. L. J. Vendrami, A. Beck, G. Collins, M. W. Friedrich, K. M. Halanych, J. F. Masello, R. Nagel, K. Norén, C. Printzen, M. B. Ruiz, S. Wohlrab, B. Becker, K. Dumack, F. Ghaderiardakani, K. Glaser, S. Heesch, C. Held, U. John, U. Karsten, S. Kempf, M. Lucassen, A. Paijmans, K. Schimani, A. Wallberg, L. C. Wunder, and T. Mock

Subjects
Science
Abstract

Abstract Polar ecosystems are experiencing amongst the most rapid rates of regional warming on Earth. Here, we discuss ‘omics’ approaches to investigate polar biodiversity, including the current state of the art, future perspectives and recommendations. We propose a community road map to generate and more fully exploit multi-omics data from polar organisms. These data are needed for the comprehensive evaluation of polar biodiversity and to reveal how life evolved and adapted to permanently cold environments with extreme seasonality. We argue that concerted action is required to mitigate the impact of warming on polar ecosystems via conservation efforts, to sustainably manage these unique habitats and their ecosystem services, and for the sustainable bioprospecting of novel genes and compounds for societal gain.

Published
2023
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48. Combined clarification and affinity capture using magnetic resin enables efficient separation of rAAV5 from cell lysate

Author

Federico Turco, Adam Wegelius, Ola Lind, Nils Norrman, Ann-Christin Magnusson, Christine Sund-Lundström, Björn Norén, Jesper Hedberg, and Ronnie Palmgren

Subjects
AAV, gene therapy, affinity chromatography, magnetic separation, bioprocessing, process development, Genetics, QH426-470, Cytology, QH573-671
Abstract

Recombinant adeno-associated virus (rAAV) vectors have displayed enormous potential as a platform for delivery of gene therapies. Purification of rAAV at industrial scale involves a series of elaborate, material, and time-consuming midstream steps, such as clarification by depth filtration and concentration/buffer exchange by tangential flow filtration. In this study, we developed a filter-less flow capture method for purification of rAAV serotype 5, using a high-gradient magnetic separator and magnetic Mag Sepharose beads coupled to an AVB affinity ligand. In under 2 h, we captured and eluted rAAV5 directly from ∼5 L of cell lysate with a recovery yield of 63% (±5%, n = 3). Compared to cell lysate, the eluate showed a 3-log reduction of host cell DNA and host cell proteins. The process developed eliminates the need for filtration and column chromatography in the early steps of industrial rAAV purification. This will be of high value for industrial-scale manufacturing of rAAVs by reducing time and material in the purification process, without compromising product recovery and purity.

Published
2023
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49. Parenting children with disabilities in Sweden: a cluster-analysis of parenting stress and sufficiency of informal and formal support

Author

Torun Täljedal, Mats Granlund, Fatumo Osman, Eva Norén Selinus, and Karin Fängström

Subjects
disabled children, parenting stress, informal support, formal support, person-oriented analysis, Psychology, BF1-990
Abstract

ObjectiveTo investigate patterns of parenting stress and access to sufficient informal and formal support among parents of children with disabilities. To explore whether child cognitive level, conduct problems and the need of language interpretation in contacts between parents and professionals are associated with patterns of parenting stress and support.MethodParents (N = 140) of children with disabilities in Sweden completed a questionnaire about parenting stress and support. Patterns of three variables—parenting stress and access to sufficient informal and formal support—were investigated using cluster analysis. The relationship of child cognitive level, level of conduct problems and of language interpretation needs between parents and professionals to cluster membership was explored using multinomial logistic regression.ResultsFive different clusters of parenting stress and support emerged. Parents in cluster 1 had lower than sample mean ratings on all three variables. Cluster 2 had elevated parenting stress, cluster 3 had elevated insufficient informal support and cluster 4 had elevated insufficient formal support. Cluster 5 had elevated ratings on all three variables. Greater child cognitive difficulties increased the likelihood of parent membership in cluster 2 (elevated stress), cluster 3 (elevated insufficient informal support), or cluster 5 (elevated ratings on all variables). Child conduct problems increased the likelihood of membership in cluster 2 (elevated stress) or cluster 5 (elevated ratings on all variables). No relationship between language interpretation needs and cluster membership was found.ConclusionsPatterns of parenting stress and sufficiency of support, and their associations with child characteristics, vary substantially. However, families of children with conduct problems experiencing elevated parenting stress in combination with insufficient informal and formal support, may be particularly vulnerable. The results of the current study highlight the clinical importance of exploring and identifying individual parenting stressors and perceived levels of support, to be able to adapt services to better suit a variety of needs, and thus promote equitable care.

Published
2024
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50. Pedestrian Trajectory Prediction with Convolutional Neural Networks

Author

Zamboni, Simone, Kefato, Zekarias Tilahun, Girdzijauskas, Sarunas, Christoffer, Noren, and Col, Laura Dal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.6, I.5.1
Abstract

Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction., Comment: 35 pages, 8 figures. This article is a the accepted manuscript submitted to Pattern Recognition Journal. Article DOI: https://doi.org/10.1016/j.patcog.2021.108252. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Published
2020

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