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For the first time the transcriptional reprogramming of distinct root cortex cells during the arbuscular mycorrhizal (AM) symbiosis was investigated by combining Laser Capture Mirodissection and Affymetrix GeneChip® Medicago genome array hybridization. The establishment of cryosections facilitated the isolation of high quality RNA in sufficient amounts from three different cortical cell types. The transcript profiles of arbuscule-containing cells (arb cells), non-arbuscule-containing cells (nac cells) of Rhizophagus irregularis inoculated Medicago truncatula roots and cortex cells of non-inoculated roots (cor) were successfully explored. The data gave new insights in the symbiosis-related cellular reorganization processes and indicated that already nac cells seem to be prepared for the upcoming fungal colonization. The mycorrhizal- and phosphate-dependent transcription of a GRAS TF family member (MtGras8) was detected in arb cells and mycorrhizal roots. MtGRAS shares a high sequence similarity to a GRAS TF suggested to be involved in the fungal colonization processes (MtRAM1). The function of MtGras8 was unraveled upon RNA interference- (RNAi-) mediated gene silencing. An AM symbiosis-dependent expression of a RNAi construct (MtPt4pro::gras8-RNAi) revealed a successful gene silencing of MtGras8 leading to a reduced arbuscule abundance and a higher proportion of deformed arbuscules in root with reduced transcript levels. Accordingly, MtGras8 might control the arbuscule development and life-time. The targeting of MtGras8 by the phosphate-dependent regulated miRNA5204* was discovered previously (Devers et al., 2011). Since miRNA5204* is known to be affected by phosphate, the posttranscriptional regulation might represent a link between phosphate signaling and arbuscule development. In this work, the posttranscriptional regulation was confirmed by mis-expression of miRNA5204* in M. truncatula roots. The miRNA-mediated gene silencing affects the MtGras8 transcript abundance only in the first two weeks of the AM symbiosis and the mis-expression lines seem to mimic the phenotype of MtGras8-RNAi lines. Additionally, MtGRAS8 seems to form heterodimers with NSP2 and RAM1, which are known to be key regulators of the fungal colonization process (Hirsch et al., 2009; Gobbato et al., 2012). These data indicate that MtGras8 and miRNA5204* are linked to the sym pathway and regulate the arbuscule development in phosphate-dependent manner.
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.
Systems of Systems (SoS) have received a lot of attention recently. In this thesis we will focus on SoS that are built atop the techniques of Service-Oriented Architectures and thus combine the benefits and challenges of both paradigms. For this thesis we will understand SoS as ensembles of single autonomous systems that are integrated to a larger system, the SoS. The interesting fact about these systems is that the previously isolated systems are still maintained, improved and developed on their own. Structural dynamics is an issue in SoS, as at every point in time systems can join and leave the ensemble. This and the fact that the cooperation among the constituent systems is not necessarily observable means that we will consider these systems as open systems. Of course, the system has a clear boundary at each point in time, but this can only be identified by halting the complete SoS. However, halting a system of that size is practically impossible. Often SoS are combinations of software systems and physical systems. Hence a failure in the software system can have a serious physical impact what makes an SoS of this kind easily a safety-critical system. The contribution of this thesis is a modelling approach that extends OMG's SoaML and basically relies on collaborations and roles as an abstraction layer above the components. This will allow us to describe SoS at an architectural level. We will also give a formal semantics for our modelling approach which employs hybrid graph-transformation systems. The modelling approach is accompanied by a modular verification scheme that will be able to cope with the complexity constraints implied by the SoS' structural dynamics and size. Building such autonomous systems as SoS without evolution at the architectural level --- i. e. adding and removing of components and services --- is inadequate. Therefore our approach directly supports the modelling and verification of evolution.
Antarctic glacier forfields are extreme environments and pioneer sites for ecological succession. The Antarctic continent shows microbial community development as a natural laboratory because of its special environment, geographic isolation and little anthropogenic influence. Increasing temperatures due to global warming lead to enhanced deglaciation processes in cold-affected habitats and new terrain is becoming exposed to soil formation and accessible for microbial colonisation. This study aims to understand the structure and development of glacier forefield bacterial communities, especially how soil parameters impact the microorganisms and how those are adapted to the extreme conditions of the habitat. To this effect, a combination of cultivation experiments, molecular, geophysical and geochemical analysis was applied to examine two glacier forfields of the Larsemann Hills, East Antarctica. Culture-independent molecular tools such as terminal restriction length polymorphism (T-RFLP), clone libraries and quantitative real-time PCR (qPCR) were used to determine bacterial diversity and distribution. Cultivation of yet unknown species was carried out to get insights in the physiology and adaptation of the microorganisms. Adaptation strategies of the microorganisms were studied by determining changes of the cell membrane phospholipid fatty acid (PLFA) inventory of an isolated bacterium in response to temperature and pH fluctuations and by measuring enzyme activity at low temperature in environmental soil samples. The two studied glacier forefields are extreme habitats characterised by low temperatures, low water availability and small oligotrophic nutrient pools and represent sites of different bacterial succession in relation to soil parameters. The investigated sites showed microbial succession at an early step of soil formation near the ice tongue in comparison to closely located but rather older and more developed soil from the forefield. At the early step the succession is influenced by a deglaciation-dependent areal shift of soil parameters followed by a variable and prevalently depth-related distribution of the soil parameters that is driven by the extreme Antarctic conditions. The dominant taxa in the glacier forefields are Actinobacteria, Acidobacteria, Proteobacteria, Bacteroidetes, Cyanobacteria and Chloroflexi. The connection of soil characteristics with bacterial community structure showed that soil parameter and soil formation along the glacier forefield influence the distribution of certain phyla. In the early step of succession the relative undifferentiated bacterial diversity reflects the undifferentiated soil development and has a high potential to shift according to past and present environmental conditions. With progressing development environmental constraints such as water or carbon limitation have a greater influence. Adapting the culturing conditions to the cold and oligotrophic environment, the number of culturable heterotrophic bacteria reached up to 108 colony forming units per gram soil and 148 isolates were obtained. Two new psychrotolerant bacteria, Herbaspirillum psychrotolerans PB1T and Chryseobacterium frigidisoli PB4T, were characterised in detail and described as novel species in the family of Oxalobacteraceae and Flavobacteriaceae, respectively. The isolates are able to grow at low temperatures tolerating temperature fluctuations and they are not specialised to a certain substrate, therefore they are well-adapted to the cold and oligotrophic environment. The adaptation strategies of the microorganisms were analysed in environmental samples and cultures focussing on extracellular enzyme activity at low temperature and PLFA analyses. Extracellular phosphatases (pH 11 and pH 6.5), β-glucosidase, invertase and urease activity were detected in the glacier forefield soils at low temperature (14°C) catalysing the conversion of various compounds providing necessary substrates and may further play a role in the soil formation and total carbon turnover of the habitat. The PLFA analysis of the newly isolated species C. frigidisoli showed that the cold-adapted strain develops different strategies to maintain the cell membrane function under changing environmental conditions by altering the PLFA inventory at different temperatures and pH values. A newly discovered fatty acid, which was not found in any other microorganism so far, significantly increased at decreasing temperature and low pH and thus plays an important role in the adaption of C. frigidisoli. This work gives insights into the diversity, distribution and adaptation mechanisms of microbial communities in oligotrophic cold-affected soils and shows that Antarctic glacier forefields are suitable model systems to study bacterial colonisation in connection to soil formation.
In the presence of a solid-liquid or liquid-air interface, bacteria can choose between a planktonic and a sessile lifestyle. Depending on environmental conditions, cells swimming in close proximity to the interface can irreversibly attach to the surface and grow into three-dimensional aggregates where the majority of cells is sessile and embedded in an extracellular polymer matrix (biofilm). We used microfluidic tools and time lapse microscopy to perform experiments with the polarly flagellated soil bacterium Pseudomonas putida (P. putida), a bacterial species that is able to form biofilms. We analyzed individual trajectories of swimming cells, both in the bulk fluid and in close proximity to a glass-liquid interface. Additionally, surface related growth during the early phase of biofilm formation was investigated. In the bulk fluid, P.putida shows a typical bacterial swimming pattern of alternating periods of persistent displacement along a line (runs) and fast reorientation events (turns) and cells swim with an average speed around 24 micrometer per second. We found that the distribution of turning angles is bimodal with a dominating peak around 180 degrees. In approximately six out of ten turning events, the cell reverses its swimming direction. In addition, our analysis revealed that upon a reversal, the cell systematically changes its swimming speed by a factor of two on average. Based on the experimentally observed values of mean runtime and rotational diffusion, we presented a model to describe the spreading of a population of cells by a run-reverse random walker with alternating speeds. We successfully recover the mean square displacement and, by an extended version of the model, also the negative dip in the directional autocorrelation function as observed in the experiments. The analytical solution of the model demonstrates that alternating speeds enhance a cells ability to explore its environment as compared to a bacterium moving at a constant intermediate speed. As compared to the bulk fluid, for cells swimming near a solid boundary we observed an increase in swimming speed at distances below d= 5 micrometer and an increase in average angular velocity at distances below d= 4 micrometer. While the average speed was maximal with an increase around 15% at a distance of d= 3 micrometer, the angular velocity was highest in closest proximity to the boundary at d=1 micrometer with an increase around 90% as compared to the bulk fluid. To investigate the swimming behavior in a confinement between two solid boundaries, we developed an experimental setup to acquire three-dimensional trajectories using a piezo driven objective mount coupled to a high speed camera. Results on speed and angular velocity were consistent with motility statistics in the presence of a single boundary. Additionally, an analysis of the probability density revealed that a majority of cells accumulated near the upper and lower boundaries of the microchannel. The increase in angular velocity is consistent with previous studies, where bacteria near a solid boundary were shown to swim on circular trajectories, an effect which can be attributed to a wall induced torque. The increase in speed at a distance of several times the size of the cell body, however, cannot be explained by existing theories which either consider the drag increase on cell body and flagellum near a boundary (resistive force theory) or model the swimming microorganism by a multipole expansion to account for the flow field interaction between cell and boundary. An accumulation of swimming bacteria near solid boundaries has been observed in similar experiments. Our results confirm that collisions with the surface play an important role and hydrodynamic interactions alone cannot explain the steady-state accumulation of cells near the channel walls. Furthermore, we monitored the number growth of cells in the microchannel under medium rich conditions. We observed that, after a lag time, initially isolated cells at the surface started to grow by division into colonies of increasing size, while coexisting with a comparable smaller number of swimming cells. After 5:50 hours, we observed a sudden jump in the number of swimming cells, which was accompanied by a breakup of bigger clusters on the surface. After approximately 30 minutes where planktonic cells dominated in the microchannel, individual swimming cells reattached to the surface. We interpret this process as an emigration and recolonization event. A number of complementary experiments were performed to investigate the influence of collective effects or a depletion of the growth medium on the transition. Similar to earlier observations on another bacterium from the same family we found that the release of cells to the swimming phase is most likely the result of an individual adaption process, where syntheses of proteins for flagellar motility are upregulated after a number of division cycles at the surface.