TY - GEN A1 - Ahlgrimm, Frederik A1 - Westphal, Andrea A1 - Heck, Sebastian T1 - Why students travel abroad (and so many others do not) BT - Exploring predictors and decision-making processes in study-related student travel N2 - Over the past few years, studying abroad and other educational international experiences have become increasingly highly regarded. Nevertheless, research shows that only a minority of students actually take part in academic mobility programs. But what is it that distinguishes those students who take up these international opportunities from those who do not? In this study we reviewed recent quantitative studies on why (primarily German) students choose to travel abroad or not. This revealed a pattern of predictive factors. These indicate the key role played by students’ personal and social background, as well as previous international travel and the course of studies they are enrolled in. The study then focuses on teaching students. Both facilitating and debilitating factors are discussed and included in a model illustrating the decision-making process these students use. Finally, we discuss the practical implications for ways in which international, studyrelated travel might be increased in the future. We suggest that higher education institutions analyze individual student characteristics, offering differentiated programs to better meet the needs of different groups, thus raising the likelihood of disadvantaged students participating in academic international travel. KW - internationalization KW - international academic mobility KW - study-related student travel KW - study abroad KW - teaching students KW - teacher education Y1 - 2018 SN - 978-84-9048-690-0 U6 - https://doi.org/10.4995/HEAd18.2018.8161 SP - 1135 EP - 1142 PB - Universitat Politecnica de Valencia CY - Valencia ER - TY - GEN A1 - Albers, Philip A1 - Uestuen, Suayib A1 - Witzel, Katja A1 - Bornke, Frederik T1 - Identification of a novel target of the bacterial effector HopZ1a T2 - Phytopathology N2 - The plant pathogen Pseudomonas syringae is a gram-negative bacterium which infects a wide range of plant species including important crops plants. To suppress plant immunity and cause disease P.syringae injects type-III effector proteins (T3Es) into the plant cell cytosol. In this study, we identified a novel target of the well characterized bacterial T3E HopZ1a. HopZ1a is an acetyltransferase that was shown to disrupt vesicle transport during innate immunity by acetylating tubulin. Using a yeast-two-hybrid screen approach, we identified a REMORIN (REM) protein from tobacco as a novel HopZ1a target. HopZ1a interacts with REM at the plasma membrane (PM) as shown by split-YFP experiments. Interestingly, we found that PBS1, a well-known kinase involved in plant immunity also interacts with REM in pull-down assays, and at the PM as shown by BiFC. Furthermore, we confirmed that REM is phosphorylated by PBS1 in vitro. Overexpression of REM provokes the upregulation of defense genes and leads to disease-like phenotypes pointing to a role of REM in plant immune signaling. Further protein-protein interaction studies reveal novel REM binding partners with a possible role in plant immune signaling. Thus, REM might act as an assembly hub for an immune signaling complex targeted by HopZ1a. Taken together, this is the first report describing that a REM protein is targeted by a bacterial effector. How HopZ1a might mechanistically manipulate the plant immune system through interfering with REM function will be discussed. Y1 - 2018 SN - 0031-949X SN - 1943-7684 VL - 108 IS - 10 PB - American Phytopathological Society CY - Saint Paul ER - TY - GEN A1 - Alviano, Mario A1 - Romero Davila, Javier A1 - Schaub, Torsten H. T1 - Preference Relations by Approximation T2 - Sixteenth International Conference on Principles of Knowledge Representation and Reasoning N2 - Declarative languages for knowledge representation and reasoning provide constructs to define preference relations over the set of possible interpretations, so that preferred models represent optimal solutions of the encoded problem. We introduce the notion of approximation for replacing preference relations with stronger preference relations, that is, relations comparing more pairs of interpretations. Our aim is to accelerate the computation of a non-empty subset of the optimal solutions by means of highly specialized algorithms. We implement our approach in Answer Set Programming (ASP), where problems involving quantitative and qualitative preference relations can be addressed by ASPRIN, implementing a generic optimization algorithm. Unlike this, chains of approximations allow us to reduce several preference relations to the preference relations associated with ASP’s native weak constraints and heuristic directives. In this way, ASPRIN can now take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions Y1 - 2018 SP - 2 EP - 11 PB - AAAI Conference on Artificial Intelligence CY - Palo Alto ER - TY - GEN A1 - Aranda, Juan A1 - Schölzel, Mario A1 - Mendez, Diego A1 - Carrillo, Henry T1 - An energy consumption model for multiModal wireless sensor networks based on wake-up radio receivers T2 - 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) N2 - Energy consumption is a major concern in Wireless Sensor Networks. A significant waste of energy occurs due to the idle listening and overhearing problems, which are typically avoided by turning off the radio, while no transmission is ongoing. The classical approach for allowing the reception of messages in such situations is to use a low-duty-cycle protocol, and to turn on the radio periodically, which reduces the idle listening problem, but requires timers and usually unnecessary wakeups. A better solution is to turn on the radio only on demand by using a Wake-up Radio Receiver (WuRx). In this paper, an energy model is presented to estimate the energy saving in various multi-hop network topologies under several use cases, when a WuRx is used instead of a classical low-duty-cycling protocol. The presented model also allows for estimating the benefit of various WuRx properties like using addressing or not. KW - Energy efficiency KW - multimodal wireless sensor network KW - low-duty-cycling KW - wake-up radio Y1 - 2018 SN - 978-1-5386-6820-7 U6 - https://doi.org/10.1109/ColComCon.2018.8466728 PB - IEEE CY - New York ER - TY - GEN A1 - Autenrieth, Marijke A1 - Ernst, Anja A1 - Deaville, Rob A1 - Demaret, Fabien A1 - Ijsseldijk, Lonneke L. A1 - Siebert, Ursula A1 - Tiedemann, Ralph T1 - Putative origin and maternal relatedness of male sperm whales (Physeter macrocephalus) recently stranded in the North Sea T2 - Mammalian biology = Zeitschrift für Säugetierkunde N2 - The globally distributed sperm whale (Physeter macrocephalus) has a partly matrilineal social structure with predominant male dispersal. At the beginning of 2016, a total of 30 male sperm whales stranded in five different countries bordering the southern North Sea. It has been postulated that these individuals were on a migration route from the north to warmer temperate and tropical waters where females live in social groups. By including samples from four countries (n = 27), this event provided a unique chance to genetically investigate the maternal relatedness and the putative origin of these temporally and spatially co-occuring male sperm whales. To utilize existing genetic resources, we sequenced 422 bp of the mitochondrial control region, a molecular marker for which sperm whale data are readily available from the entire distribution range. Based on four single nucleotide polymorphisms (SNPs) within the mitochondrial control region, five matrilines could be distinguished within the stranded specimens, four of which matched published haplotypes previously described in the Atlantic. Among these male sperm whales, multiple matrilineal lineages co-occur. We analyzed the population differentiation and could show that the genetic diversity of these male sperm whales is comparable to the genetic diversity in sperm whales from the entire Atlantic Ocean. We confirm that within this stranding event, males do not comprise maternally related individuals and apparently include assemblages of individuals from different geographic regions. (c) 2017 Deutsche Gesellschaft fur Saugetierkunde. Published by Elsevier GmbH. All rights reserved. KW - Mitochondrial DNA KW - Maternal relationships KW - Population genetics KW - Migration KW - Marine mammals Y1 - 2018 U6 - https://doi.org/10.1016/j.mambio.2017.09.003 SN - 1616-5047 SN - 1618-1476 VL - 88 SP - 156 EP - 160 PB - Elsevier CY - München ER - TY - GEN A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander ED - Xu, Z Peng T1 - Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea T2 - Innovative Water Resources Management in a Changing Environment – Understanding and Balancing Interactions between Humankind and Nature N2 - The Aral Sea desiccation and related changes in hydroclimatic conditions on a regional level is a hot topic for past decades. The key problem of scientific research projects devoted to an investigation of modern Aral Sea basin hydrological regime is its discontinuous nature - the only limited amount of papers takes into account the complex runoff formation system entirely. Addressing this challenge we have developed a continuous prediction system for assessing freshwater inflow into the Small Aral Sea based on coupling stack of hydrological and data-driven models. Results show a good prediction skill and approve the possibility to develop a valuable water assessment tool which utilizes the power of classical physically based and modern machine learning models both for territories with complex water management system and strong water-related data scarcity. The source code and data of the proposed system is available on a Github page (https://github.com/SMASHIproject/IWRM2018). Y1 - 2018 U6 - https://doi.org/10.5194/piahs-379-151-2018 SN - 2199-899X VL - 379 SP - 151 EP - 158 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Balazadeh, Salma A1 - Müller-Röber, Bernd T1 - A balance to death T2 - Nature plants N2 - Leaf senescence plays a crucial role in nutrient recovery in late-stage plant development and requires vast transcriptional reprogramming by transcription factors such as ORESARA1 (ORE1). A proteolytic mechanism is now found to control ORE1 degradation, and thus senescence, during nitrogen starvation. Y1 - 2018 U6 - https://doi.org/10.1038/s41477-018-0279-6 SN - 2055-026X SN - 2055-0278 VL - 4 IS - 11 SP - 863 EP - 864 PB - Nature Publ. Group CY - London ER - TY - GEN A1 - Barlow, Axel A1 - Sheng, Gui-Lian A1 - Lai, Xu-Long A1 - Hofreiter, Michael A1 - Paijmans, Johanna L. A. T1 - Once lost, twice found: Combined analysis of ancient giant panda sequences characterises extinct clade T2 - Journal of biogeography Y1 - 2018 U6 - https://doi.org/10.1111/jbi.13486 SN - 0305-0270 SN - 1365-2699 VL - 46 IS - 1 SP - 251 EP - 253 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Barrett, Lindsay A1 - Eckstein, Lars A1 - Hurley, Andrew Wright A1 - Schwarz, Anja T1 - Remembering German-Australian colonial entanglement BT - an introduction T2 - Postcolonial studies : culture, politics, economy Y1 - 2018 U6 - https://doi.org/10.1080/13688790.2018.1443671 SN - 1368-8790 SN - 1466-1888 VL - 21 IS - 1 SP - 1 EP - 5 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - GEN A1 - Bartz, Christian A1 - Yang, Haojin A1 - Meinel, Christoph T1 - SEE: Towards semi-supervised end-to-end scene text recognition T2 - Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Thirtieth Innovative Applications of Artificial Intelligence Conference, Eight Symposium on Educational Advances in Artificial Intelligence N2 - Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results. Y1 - 2018 SN - 978-1-57735-800-8 VL - 10 SP - 6674 EP - 6681 PB - ASSOC Association for the Advancement of Artificial Intelligence CY - Palo Alto ER -