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Transcription factor OsHsfC1b regulates salt tolerance and development in Oryza sativa ssp japonica
(2012)
Background and aims Salt stress leads to attenuated growth and productivity in rice. Transcription factors like heat shock factors (HSFs) represent central regulators of stress adaptation. Heat shock factors of the classes A and B are well established as regulators of thermal and non-thermal stress responses in plants; however, the role of class C HSFs is unknown. Here we characterized the function of the OsHsfC1b (Os01g53220) transcription factor from rice.
Methodology We analysed the expression of OsHsfC1b in the rice japonica cultivars Dongjin and Nipponbare exposed to salt stress as well as after mannitol, abscisic acid (ABA) and H2O2 treatment. For functional characterization of OsHsfC1b, we analysed the physiological response of a T-DNA insertion line (hsfc1b) and two artificial micro-RNA (amiRNA) knock-down lines to salt, mannitol and ABA treatment. In addition, we quantified the expression of small Heat Shock Protein (sHSP) genes and those related to signalling and ion homeostasis by quantitative real-time polymerase chain reaction in roots exposed to salt. The subcellular localization of OsHsfC1b protein fused to green fluorescent protein (GFP) was determined in Arabidopsis mesophyll cell protoplasts.
Principal results Expression of OsHsfC1b was induced by salt, mannitol and ABA, but not by H2O2. Impaired function of OsHsfC1b in the hsfc1b mutant and the amiRNA lines led to decreased salt and osmotic stress tolerance, increased sensitivity to ABA, and temporal misregulation of salt-responsive genes involved in signalling and ion homeostasis. Furthermore, sHSP genes showed enhanced expression in knock-down plants under salt stress. We observed retarded growth of hsfc1b and knock-down lines in comparison with control plants under non-stress conditions. Transient expression of OsHsfC1b fused to GFP in protoplasts revealed nuclear localization of the transcription factor.
Conclusions OsHsfC1b plays a role in ABA-mediated salt stress tolerance in rice. Furthermore, OsHsfC1b is involved in the response to osmotic stress and is required for plant growth under non-stress conditions.
During natural reading, a parafoveal preview of the upcoming word facilitates its subsequent recognition (e.g., shorter fixation durations compared to masked preview) but nothing is known about the neural correlates of this so-called preview benefit. Furthermore, while the evidence is strong that readers preprocess orthographic features of upcoming words, it is controversial whether word meaning can also be accessed parafoveally. We investigated the timing, scope, and electrophysiological correlates of parafoveal information use in reading by simultaneously recording eye movements and fixation-related brain potentials (FRPs) while participants read word lists fluently from left to right. For one word the target (e.g., "blade") parafoveal information was manipulated by showing an identical ("blade"), semantically related ("knife"), or unrelated ("sugar") word as preview. In boundary trials, the preview was shown parafoveally but changed to the correct target word during the incoming saccade. Replicating classic findings, target words were fixated shorter after identical previews. In the EEG, this benefit was reflected in an occipitotemporal preview positivity between 200 and 280 ms. In contrast, there was no facilitation from related previews. In parafoveal-on-foveal trials, preview and target were embedded at neighboring list positions without a display change. Consecutive fixation of two related words produced N400 priming effects, but only shortly (160 ms) after the second word was directly fixated. Results demonstrate that neural responses to words are substantially altered by parafoveal preprocessing under normal reading conditions. We found no evidence that word meaning contributes to these effects. Saccade-contingent display manipulations can be combined with EEG recordings to study extrafoveal perception in vision.
Silvicultural practices lead to changes in forest composition and structure and may impact species diversity from the overall regional species pool to stand-level species occurrence. We explored to what extent fine-scale occupancy patterns in differently managed forest stands are driven by environment and ecological traits in three regions in Germany using a multi-species hierarchical model. We tested for the possible impact of environmental variables and ecological traits on occupancy dynamics in a joint modelling exercise while taking possible variation in coefficient estimates over years and plots into account. Bird species richness differed across regions and years, and trends in species richness across years were different in the three regions. On the species level, forest management affected occupancy of species in all regions, but only 35% of the total assemblage-level variation in occurrence probability was explained by either forest type and successional stage and <?1% by forest edge. On the assemblage level, bird occurrence decreased with body mass in all regions. Species with smaller breeding ranges had lower occurrence probabilities in one region, while later spring arrival decreased occurrence probabilities in the two other regions. Spatial variation in the effect size of trait covariates such as species phylogeny and breeding strata showed that variation in patch occupancy due to fine-scale differences in forest management is, to some extent, predictable from ecological traits. Our results show that environmental factors and ecological traits jointly predict variation in bird occupancy patterns and their response to forest management. Observations at the fine scale of forest stands, at which conservation efforts can be arranged along with forest management practices in heterogeneous environments, have been shown to provide meaningful insights despite the difficulties involved in monitoring mobile organisms such as birds at the plot level.
Nowadays, model-driven engineering (MDE) promises to ease software development by decreasing the inherent complexity of classical software development. In order to deliver on this promise, MDE increases the level of abstraction and automation, through a consideration of domain-specific models (DSMs) and model operations (e.g. model transformations or code generations). DSMs conform to domain-specific modeling languages (DSMLs), which increase the level of abstraction, and model operations are first-class entities of software development because they increase the level of automation. Nevertheless, MDE has to deal with at least two new dimensions of complexity, which are basically caused by the increased linguistic and technological heterogeneity. The first dimension of complexity is setting up an MDE environment, an activity comprised of the implementation or selection of DSMLs and model operations. Setting up an MDE environment is both time-consuming and error-prone because of the implementation or adaptation of model operations. The second dimension of complexity is concerned with applying MDE for actual software development. Applying MDE is challenging because a collection of DSMs, which conform to potentially heterogeneous DSMLs, are required to completely specify a complex software system. A single DSML can only be used to describe a specific aspect of a software system at a certain level of abstraction and from a certain perspective. Additionally, DSMs are usually not independent but instead have inherent interdependencies, reflecting (partial) similar aspects of a software system at different levels of abstraction or from different perspectives. A subset of these dependencies are applications of various model operations, which are necessary to keep the degree of automation high. This becomes even worse when addressing the first dimension of complexity. Due to continuous changes, all kinds of dependencies, including the applications of model operations, must also be managed continuously. This comprises maintaining the existence of these dependencies and the appropriate (re-)application of model operations. The contribution of this thesis is an approach that combines traceability and model management to address the aforementioned challenges of configuring and applying MDE for software development. The approach is considered as a traceability approach because it supports capturing and automatically maintaining dependencies between DSMs. The approach is considered as a model management approach because it supports managing the automated (re-)application of heterogeneous model operations. In addition, the approach is considered as a comprehensive model management. Since the decomposition of model operations is encouraged to alleviate the first dimension of complexity, the subsequent composition of model operations is required to counteract their fragmentation. A significant portion of this thesis concerns itself with providing a method for the specification of decoupled yet still highly cohesive complex compositions of heterogeneous model operations. The approach supports two different kinds of compositions - data-flow compositions and context compositions. Data-flow composition is used to define a network of heterogeneous model operations coupled by sharing input and output DSMs alone. Context composition is related to a concept used in declarative model transformation approaches to compose individual model transformation rules (units) at any level of detail. In this thesis, context composition provides the ability to use a collection of dependencies as context for the composition of other dependencies, including model operations. In addition, the actual implementation of model operations, which are going to be composed, do not need to implement any composition concerns. The approach is realized by means of a formalism called an executable and dynamic hierarchical megamodel, based on the original idea of megamodels. This formalism supports specifying compositions of dependencies (traceability and model operations). On top of this formalism, traceability is realized by means of a localization concept, and model management by means of an execution concept.
Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.